Office of Solid Waste EPA530-R-05-006
and Emergency Response September 2005
(5305W) www.epa.gov/osw
v>EPA Human Health Risk
Assessment Protocol for
Hazardous Waste
Combustion
United States
Environmental Protection
Agency
Final
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EPA530-R-05-006
September 2005
Human Health Risk Assessment Protocol for
Hazardous Waste Combustion Facilities
U.S. EPA, OFFICE OF SOLID WASTE
U.S. ENVIRONMENTAL PROTECTION AGENCY
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DISCLAIMER
This document provides guidance to EPA regional and state RCRA hazardous waste programs, as well as
to facilities subject to RCRA requirements and the general public. More specifically, this guidance
document conveys how EPA generally intends to exercise its discretion in implementing RCRA statutory
and regulatory provisions concerning combustion facilities subject to RCRA. EPA designed this
guidance to explain and clarify national policy on issues related to EPA's obligation to ensure that
operating permits granted to combustion facilities contain conditions necessary to protect human health
and the environment.
The statutory provisions and EPA regulations discussed in this handbook contain legally binding
requirements. This guidance itself does not substitute for those provisions, nor is it a regulation itself.
Thus, this guidance does not impose legally binding requirements on EPA, states, or the regulated
community, and may not apply to a particular situation based on the specific circumstances of the
combustion facility. EPA and state regulators base their permitting decisions on the statute and
regulations as applied to the specific combustion facility and retain their discretion to use approaches on a
case-by-case basis that differ from those recommended in this guidance where appropriate. Therefore,
interested parties are free to raise questions and concerns about the substance of this guidance document
and the appropriateness of the application of recommendations to a particular situation. Because this
guidance is not a regulation, EPA and state regulators will consider such questions and concerns when
implementing the recommendations (for example, during the comment period provided on draft
combustion permits). Whether the recommendations in this Handbook are appropriate in a given
situation will depend on facility-specific circumstances.
11
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ACKNOWLEDGMENTS
This document was developed by the U.S. Environmental Protection Agency (EPA) Region 6 and the
Office of Solid Waste. Jeff Yurk from EPA Region 6 was the primary author of this document with
significant technical support and background research being provided by Jeff Ayers of PGM Professional
Services, Jeff Secrest of The Air Group, Tetra Tech EM, me, under Contract No. 68-W-99-018, and
Research Triangle Institute, under Contract No. 68-W-03-042. Karen Pollard and Timothy Taylor of the
Office of Solid Waste provided authorship, editorial input, and overall coordination with experts in other
EPA Regions, Headquarters, and the Office of Research and Development. Special thanks are extended
to the following individuals for their valuable comments and contributions.
U.S. EPA REVIEWERS
Office of Solid Waste
Economics, Methods, and Risk Analysis Division
David Cozzie
Virginia Colten-Bradley
Becky Daiss
Stephen Kroner
David Layland
Alec McBride
Permits and State Programs Division
Sasha Gerhardt
Val De LaFuente
Sonya Sasseville
Rosemary Workman
Municipal and Industrial Solid Waste Division
Bill Schoenborn
Hazardous Waste Minimization and Management
Division
Fred Chanania
Office of Solid Waste and Emergency
Response
Office of the Assistant Administrator
Dorothy Canter
Office of Research & Development
National Center for Environmental
Assessment/Cincinnati
Eletha Brady-Roberts
Randy Bruins
David Reisman
Glenn Rice
Sue Schock
Jeff Swartout
Office of Research & Development (contd.)
National Center for Environmental
Assessment/DC
David Cleverly
Jim Cogliano
Matthew Lorber
National Center for Environmental
Assessment/RTF
Judy Strickland
National Exposure Research Laboratory
Robert Ambrose
Larry Johnson
Donna Schwede
National Health and Environmental Effects
Research Laboratory/RTF
John Nichols
National Risk Management Research Laboratory
Paul Lemieux
Jeffrey Ryan
Office of Air Quality Planning and Standards
Air Quality Monitoring Group
Joe Touma
Risk and Exposure Assessment Group
Dave Guinnup
Deirdre Murphy
Office of General Council
Solid Waste and Emergency Response Law Office
Laurel Celeste
Karen Kraus
ill
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EPA Region 1
Office of Ecosystem Protection Division
Jui-Yu Hsieh
EPA Region 2
Division of Environmental Planning and
Protection
John Brogard
EPA Region 3
Waste and Chemicals Management Division
Gary Gross
EPA Region 4
Waste Management Division
Beth Antley
Rick Gillam
Ken Mitchell
EPA Region 5
Waste, Pesticide and Toxic Division
Gary Victorine
Mario Mangino
EPA Region 6
Multimedia Planning and Permitting Division
Cynthia Kaleri
Steve Thompson
David Weeks
Superfund Group
Ghassan Khoury
Jon Rauscher
Susan Roddy
EPA Region 7
Air, RCRA and Toxics Division
Ken Herstowski
John Smith
EPA Region 8
Hazardous Waste Program
Bob Benson
Carl Daly
Tala Henry
EPA Region 9
Waste Management Division
Stacy Braye
EPA Region 10
Office of Environmental Assessment
Rick Poeton
Marcia Bailey
Roseanne Lorenzana
Office of Waste and Chemicals
Catherine Massimino
STATE REVIEWERS
Texas Natural Resource Conservation
Commission
Toxicology and Risk Assessment Section
Larry Champagne
Lucy Frasier
Roberta Grant
Laurie Haws
Loren Lund
Torin McCoy
Robert Opiela
Mark Rudolph
Dom Ruggeri
Andrew Tachovsky
Arkansas Department of Pollution Control
and Ecology
Phillip Murphy
Tammi Hynum
Colorado Department of Health
Hazardous Materials and Waste Management
Division
Joe Schieffelin
R. David Waltz
Utah Department of Environmental Quality
Christopher Bittner
Alabama Department of Environmental
Management
Air Division
John Rogers
Nathan Hartman
Division of Epidemiology
Brian Hughes
IV
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In addition, during the public review period of the guidance document, EPA received a number of
insightful and valuable comments from the members of the general public and industry.
SCIENTIFIC PEER REVIEWERS
On May 25 and 26, 2000, an external peer review workshop was held. The peer review and workshop
were organized, convened and conducted by Tech Law, an EPA contractor under the EPA contract
number: 68-W-99-017. Panel members and reviewers at this workshop included:
Combustion Engineering
Dr. William Schofield
Focus Environmental
Atmospheric Modeling
Dr. Walter Dabberdt
National Center of Atmospheric Research
Human Health Toxicology
Dr. Thomas A. Gasiewicz
University of Rochester
Dr. Mary Davis
University of West Virginia
Human Health Exposure
Dr. James P. Butler
Argonne National Laboratory/University of Chicago
Dr. Richard L. DeGrandchamp
University of Colorado
Mr. Steven Washburn
Environ Corporation
Chemical Fate and Transport
Dr. George F. Fries
U.S. Department of Agriculture (retired)
Dr. Douglas Smith
ENSR International
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Human Health Risk Assessment Protocol
Contents September 2005
CONTENTS
CONTENTS vi
FIGURES xii
TABLES xiii
LIST OF ACRONYMS xv
INDEXED LIST OF VARIABLES xx
1 INTRODUCTION 1-1
1.1 OBJECTIVE AND DOCUMENT ORGANIZATION 1-1
1.2 BACKGROUND 1-3
1.3 USING THIS DOCUMENT 1-6
1.4 PRIMARY REFERENCE DOCUMENTS 1-13
1.5 RISK NOMENCLATURE 1-17
2 CHARACTERIZING FACILITY EMISSIONS 2-1
2.1 COMPILING BASIC FACILITY INFORMATION 2-1
2.2 IDENTIFYING EMISSION SOURCES & ESTIMATING EMISSION RATES 2-2
2.2.1 Estimating Stack Emission Rates for Existing Facilities 2-3
2.2.2 Estimating Emission Rates for Facilities with Multiple Stacks 2-13
2.2.3 Estimating Stack Emission Rates for Facilities Not Yet Operational 2-13
2.2.4 Estimating Stack Emission Rates for Facilities Previously Operated 2-14
2.2.5 Emissions From Process Upsets 2-15
2.2.6 RCRA Fugitive Emissions 2-17
2.2.7 RCRA Fugitive Ash Emissions 2-27
2.2.8 Cement Kiln Dust (CKD) Fugitive Emissions 2-28
2.3 IDENTIFYING COMPOUNDS OF POTENTIAL CONCERN 2-31
2.3.1 Criteria Pollutants 2-39
2.3.2 Endocrine Disrupters 2-41
2.3.3 Hexachlorobenzene and Pentachlorophenol 2-42
2.3.4 Hydrogen Chloride/Chorine Gas 2-43
2.3.5 Metals 2-44
2.3.6 Nitroaromatics 2-57
2.3.7 Particulate Matter 2-58
2.3.8 Phthalates 2-59
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering VI
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CONTENTS (contd.)
Chapter
2.3.9 Polychlorinated Biphenyls 2-61
2.3.10 Polychlorinated Dibenzo(p)dioxins and Dibenzofurans 2-66
2.3.11 Polynuclear Aromatic Hydrocarbons 2-71
2.3.12 Radionuclides 2-74
2.3.13 Volatile Organic Compounds 2-78
2.4 ESTIMATING COPC CONCENTRATIONS FORNON-DETECTS 2-79
2.4.1 Definitions of Commonly Reported Detection Limits 2-79
2.4.2 Using Non-Detect Data In the Risk Assessment 2-82
2.4.3 Statistical Distribution Techniques 2-85
2.4.4 Our Recommendations on Quantifying Non-Detects 2-85
2.4.5 Estimated Maximum Possible Concentration (EMPC) 2-86
2.5 EVALUATING CONTAMINATION IN BLANKS 2-87
3 AIR DISPERSION AND DEPOSITION MODELING 3-1
3.1 DESCRIPTION OF AIR MODELS 3-3
3.1.1 Background on Air Dispersion Models for Risk Assessment 3-3
3.1.2 Preprocessing Programs 3-7
3.2 PARTITIONING OF EMISSIONS 3-8
3.2.1 Vapor Phase Modeling 3-8
3.2.2 Particle Phase Modeling (Mass Weighting) 3-8
3.2.3 Particle-Bound Modeling (Surface Area Weighting) 3-12
3.3 SITE-SPECIFIC INFORMATION NEEDED FOR AIR MODELING 3-14
3.3.1 Surrounding Terrain Information 3-15
3.3.2 Surrounding Land Use Information 3-16
3.3.3 Information on Facility Building Characteristics 3-20
3.4 METEOROLOGICAL DATA PRIMER 3-22
3.4.1 Wind Direction and Wind Speed 3-23
3.4.2 Dry Bulb Temperature 3-24
3.4.3 Opaque Cloud Cover 3-25
3.4.4 Cloud Ceiling Height 3-25
3.4.5 Surface Pressure 3-26
3.4.6 Incoming Short-wave RadiationVLeaf Area Index 3-26
3.4.7 Precipitation Amount and Type 3-26
3.4.8 Upper Air Data (Mixing Height) 3-27
3.4.9 Potential Data Sources 3-27
3.5 METEOROLOGICAL PREPROCESSOR DATA NEEDS 3-31
3.5.1 Monin-Obukhov Length 3-32
3.5.2 Anemometer Height 3-33
3.5.3 Surface Roughness Length at Measurement Site 3-33
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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CONTENTS (contd.)
Chapter Page
3.5.4 Surface Roughness Length at Application Site 3-33
3.5.5 Noon-Time Albedo 3-34
3.5.6 Bowen Ratio 3-35
3.5.7 Anthropogenic Heat Flux 3-37
3.5.8 Fraction of Net Radiation Absorbed at the Ground 3-38
3.6 ISCST3 MODEL INPUT FILES 3-39
3.6.1 COntrol Pathway 3-42
3.6.2 SOurce Pathway 3-47
3.6.3 REceptor Pathway 3-52
3.6.4 MEteorological Pathway 3-55
3.6.5 Terrain Grid (TG) Pathway 3-56
3.6.6 OUtput Pathway 3-57
3.7 ISCST3 MODEL EXECUTION 3-58
3.8 USING MODEL OUTPUT 3-58
3.8.1 Unit Rate Output vs. COPC-Specific Output 3-60
3.8.2 ISCST3 Model Output 3-61
3.8.3 Using Model Output to Estimate Media Concentrations 3-62
3.9 MODELING FUGITIVE EMISSIONS 3-64
3.10 MODELING ACUTE RISK 3-67
4 EXPOSURE SCENARIO IDENTIFICATION 4-1
4.1 CHARACTERIZING THE EXPOSURE SETTING 4-3
4.1.1 Current and Reasonable Potential Future Land Use 4-4
4.1.2 Water Bodies and their Associated Watersheds 4-7
4.1.3 Special Population Characteristics 4-10
4.2 RECOMMENDED EXPOSURE SCENARIOS 4-11
4.2.1 Farmer 4-15
4.2.2 Farmer Child 4-18
4.2.3 Resident 4-18
4.2.4 Resident Child 4-19
4.2.5 Fisher 4-19
4.2.6 Fisher Child 4-20
4.2.7 Acute Receptor Scenario 4-21
4.3 SELECTING EXPOSURE SCENARIO LOCATIONS 4-21
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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CONTENTS (contd.)
Chapter Page
5 ESTIMATING MEDIA CONCENTRATIONS 5-1
5.1 CALCULATING COPC CONCENTRATIONS IN AIR FOR DIRECT
INHALATION 5-2
5.2 CALCULATING COPC CONCENTRATIONS IN SOIL 5-3
5.2.1 Calculating Cumulative Soil Concentration (Cs) 5-4
5.2.2 Calculating the COPC Soil Loss Constant (ks) 5-7
5.2.3 Calculating the Deposition Term (Ds) 5-19
5.2.4 Site-Specific Parameters for Calculating Cumulative Soil Concentration . . . 5-19
5.3 CALCULATING COPC CONCENTRATIONS IN PRODUCE 5-22
5.3.1 Aboveground Produce Concentration Due to Direct Deposition (Pd) 5-24
5.3.2 Aboveground Produce Concentration Due to Air-to-Plant Transfer (Pv) . . . 5-32
5.3.3 Produce Concentration Due to Root Uptake (Pr) 5-35
5.4 CALCULATING COPC CONCENTRATIONS IN BEEF AND DAIRY
PRODUCTS 5-37
5.4.1 Forage and Silage Concentrations Due to Direct Deposition (Pd) 5-39
5.4.2 Forage and Silage Concentrations Due to Air-to-Plant Transfer (Pv) 5-42
5.4.3 Forage, Silage, and Grain Concentrations Due to Root Uptake (Pr) 5-43
5.4.4 Beef Concentration Resulting from Plant and Soil Ingestion (Abeef) 5-43
5.4.5 COPC Concentration In Milk Due to Plant and Soil Ingestion (Amilk) 5-49
5.5 CALCULATING COPC CONCENTRATIONS IN PORK 5-53
5.5.1 Fraction of Plant Type i Grown on Contaminated Soil and Eaten
by the Animal (Swine) (Fi) 5-54
5.5.2 Quantity of Plant Type i Eaten by the Animal (Swine) Each Day (Qpi) .... 5-55
5.5.3 Concentration of COPC in Plant Type i Eaten by the Animal (Swine) (Pi) . 5-56
5.5.4 Quantity of Soil Eaten by the Animal (Swine) Each Day (Qs) 5-56
5.5.5 Average Soil Concentration Over Exposure Duration (Cs) 5-57
5.5.6 Soil Bio availability Factor (Bs) 5-57
5.5.7 Metabolism Factor (MF) 5-57
5.6 CALCULATING COPC CONCENTRATIONS IN CHICKEN AND EGGS 5-57
5.6.1 Fraction of Plant Type i Grown on Contaminated Soil and Eaten
by the Animal (Chicken) (Fi) 5-59
5.6.2 Quantity of Plant Type i Eaten by the Animal (Chicken) Each Day (Qpi) . . 5-59
5.6.3 Concentration of COPC in Plant Type i Eaten by the Animal (Chicken) (Pi) 5-60
5.6.4 Quantity of Soil Eaten by the Animal (Chicken) Each Day (Qs) 5-60
5.6.5 Average Soil Concentration Over Exposure Duration (Cs) 5-61
5.6.6 Soil Bio availability Factor (Bs) 5-61
5.7 CALCULATING COPC CONCENTRATIONS IN DRINKING WATER
AND FISH 5-61
5.7.1 Total COPC Load to the Water Body (LT) 5-64
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering IX
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CONTENTS (contd.)
Chapter Page
5.7.2 Universal Soil Loss Equation - USLE 5-69
5.7.3 Sediment Delivery Ratio (SD) 5-70
5.7.4 Total Water Body COPC Concentration (Cwtot) 5-71
5.7.5 Concentration of COPC in Fish (Cfish) 5-85
5.8 USING SITE-SPECIFIC vs. DEFAULT PARAMETER VALUES 5-89
6 QUANTIFYING EXPOSURE 6-1
6.1 INHALATION EXPOSURE PATHWAYS 6-2
6.1.1 Soil Inhalation Resulting from Dust Resuspension 6-3
6.2 INGESTION EXPOSURE PATHWAYS 6-4
6.2.1 Body Weight 6-5
6.2.2 Food (Ingestion) Exposure Pathways 6-6
6.2.3 Soil (Ingestion) Exposure Pathway 6-13
6.2.4 Water (Ingestion) Exposure Pathways 6-14
6.3 DERMAL EXPOSURE PATHWAYS 6-17
6.3.1 Dermal Exposure to Soil 6-17
6.3.2 Dermal Exposure to Water 6-18
6.4 EXPOSURE FREQUENCY 6-19
6.5 EXPOSURE DURATION 6-19
6.6 AVERAGING TIME 6-21
7 CHARACTERIZING RISK AND HAZARD 7-1
7.1 QUANTITATIVELY ESTIMATING CANCER RISK 7-3
7.2 QUANTITATIVELY ESTIMATING NONCANCER HAZARD 7-5
7.3 TARGET LEVELS 7-10
7.4 ESTIMATING ACUTE EXPOSURE FROM DIRECT INHALATION 7-10
7.4.1 Existing Hierarchical Approaches for Acute Inhalation Exposure 7-10
7.4.2 Our Recommended Hierarchal Approach 7-12
7.4.3 Characterizing Potential Health Effects from Acute Exposure 7-14
8 INTERPRETING UNCERTAINTY FOR HUMAN HEALTH RISK ASSESSMENT 8-1
8.1 UNCERTAINTY AND LIMITATIONS OF THE RISK ASSESSMENT PROCESS . 8-1
8.2 TYPES OF UNCERTAINTY 8-2
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering X
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CONTENTS (contd.)
Chapter Page
8.3 QUALITATIVE ESTIMATES OF UNCERTAINTY 8-5
8.4 QUANTITATIVE ESTIMATES OF UNCERTAINTY 8-5
8.5 RISK ASSESSMENT UNCERTAINTY DISCUSSION 8-7
9 COMPLETING THE RISK ASSESSMENT REPORT AND FOLLOW-ON ACTIVITIES . . 9-1
9.1 CONCLUSIONS 9-1
9.2 ACTIVITIES FOLLOWING RISK ASSESSMENT COMPLETION 9-2
REFERENCES R-l
APPENDICES
A CHEMICAL-SPECIFIC DATA
A-1 CHEMICALS OF POTENTIAL INTEREST
A-2 CHEMICAL-SPECIFIC PARAMETER VALUES
B ESTIMATING MEDIA CONCENTRATION EQUATIONS AND VARIABLE VALUES
B-l SOIL INGESTION EQUATIONS
B-2 PRODUCE INGESTION EQUATIONS
B-3 ANIMAL PRODUCTS INGESTION EQUATIONS
B-4 DRINKING WATER AND FISH INGESTION EQUATIONS
B-5 DIRECT INHALATION EQUATION
B-6 ACUTE EQUATION
C RISK CHARACTERIZATION EQUATIONS
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering XI
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Contents September 2005
FIGURES
Figure Page
1-1 HUMAN HEALTH RISK ASSESSMENT PROCESS 1-10
2-1 EXAMPLE FACILITY PLOT MAP 2-18
2-2 EXAMPLE EMISSIONS INVENTORY 2-19
2-3 COPC IDENTIFICATION 2-32
2-4 PHASE ALLOCATION AND SPECIATION OF MERCURY IN AIR 2-47
3-1 SOURCES OF METEOROLOGICAL DATA 3-28
3-2 EXAMPLE INPUT FILE FOR "PARTICLE PHASE" 3-40
4-1 ISCST3 GRID NODES AND LAND USE DESIGNATIONS 4-23
5-1 COPC CONCENTRATION IN AIR FOR DIRECT INHALATION 5-2
5-2 COPC CONCENTRATION IN SOIL 5-3
5-3 COPC CONCENTRATION IN PRODUCE 5-24
5-4 COPC CONCENTRATION IN BEEF AND DAIRY PRODUCTS 5-38
5-5 COPC CONCENTRATION IN PORK 5-53
5-6 COPC CONCENTRATION IN CHICKEN AND EGGS 5-58
5-7 COPC LOADING TO THE WATER BODY 5-63
5-8 COPC CONCENTRATION IN FISH 5-86
U.S. EPA Region 6 U.S. EPA
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TABLES
Table Page
2-1 EXAMPLE CALCULATION TOTAL FUGITIVE EMISSION RATES FOR
EQUIPMENT IN WASTE FEED STORAGE AREA 2-22
2-2 EXAMPLE CALCULATION SPECIATED FUGITIVE EMISSIONS FOR
EQUIPMENT IN WASTE FEED STORAGE AREA 2-24
2-3 SOCMI AVERAGE EMISSION FACTORS 2-25
2-4 QUALITATIVE EFFECTS OF PHYSICAL & CHEMICAL CONDITIONS ON
METHYLATION 2-54
2-5 TOXICITY EQUIVALENCY FACTORS FOR COPLANAR PCBs 2-64
2-6 SLOPE FACTORS FOR PCBs 2-65
2-7 PCDD/PCDF TOXICITY EQUIVALENCY FACTOR VALUES 2-69
2-8 RELATIVE POTENCY FACTORS FOR CLASS B2 CARCINOGEN PAHs 2-70
3-1 HYPOTHETICAL PARTICLE SIZE DISTRIBUTION DATA TO SUPPORT
EXAMPLE CALCULATIONS 3-10
3-2 URBAN LAND USE TYPES 3-17
3-3 SURFACE ROUGHNESS HEIGHTS 3-19
3-4 L VALUES FOR VARIOUS LAND USES 3-32
3-5 ALBEDO OF NATURAL GROUND COVERS FOR LAND USE TYPES AND
SEASONS 3-34
3-6 DAYTIME BOWEN RATIOS BY LAND USE, SEASON, AND PRECIPITATION
CONDITIONS 3-36
3-7 ANTHROPOGENIC HEAT FLUX (Qf) AND NET RADIATION (Q.)
FOR SEVERAL URBAN AREAS 3-38
3-8 ISCST3 INPUT FILE SECTIONS 3-41
3-9 DRY DEPOSITION VELOCITY ESTIMATES AVAILABLE IN LITERATURE 3-43
3-10 ISCST3 AIR PARAMETER OUTPUT 3-59
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering Xlll
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TABLES (contd.)
4-1 RECOMMENDED EXPOSURE SCENARIOS FOR A HUMAN HEALTH RISK
ASSESSMENT 4-13
6-1 MEAN CONSUMPTION RATES FOR RECOMMENDED EXPOSURE SCENARIOS .... 6-4
6-2 COOKING-RELATED WEIGHT LOSSES FOR VARIOUS HOME-PRODUCED
FOODS 6-11
6-3 EXPOSURE DURATION VALUES 6-21
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering XIV
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LIST OF ACRONYMS
•§
• m
Microgram
Micrometer
ACGIH
ADD
AEFA
AEGL
AERMOD
Ah
AHH
AIEC
AIHA
APCD
APCS
ARE
Acute REL
ASTM
atm
ATSDR
AWFCO
American Conference of Governmental Industrial Hygienists
Average daily dose
Average emission factor approach
Acute inhalation exposure guidelines
American Meteorological Society/EPA Regulatory Model
Aryl hydrocarbon
Aryl hydrocarbon hydroxylase
Acute inhalation exposure criteria
American Industrial Hygiene Association
Air pollution control device
Air pollution control system
Acute reference exposure
Acute reference exposure level
American Society for Testing and Materials
Atmosphere
Agency for Toxic Substances and Disease Registry
Automatic waste feed cutoff
BaP
BAF
BBS
BCF
BEHP
BIF
BPIP
BSAF
Btu
BW
CAA
CALPUFF
CARB
CAS
CFR
CKD
CLP
cm
COPC
CRQL
CSV
CWA
Benzo(a)pyrene
Bioaccumulation factor
Bulletin board service
Bioconcentration factor
Bis(2-ethylhexyl) phthalate
Boiler and industrial furnace
Building profile input program check
Sediment bioaccumulation factor
British thermal unit
Body weight
Clean Air Act
California Puff Model
California Air Resources Board
Chemical Abstracts Service
Code of Federal Regulations
Cement kiln dust
Contract Laboratory Program
Centimeters
Compound of potential concern
Contract required quantitation limit
Unspeciated chromatographical semivolatiles
Clean Water Act
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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LIST OF ACRONYMS (contd.)
DEHP
dL
DNA
DNOP
DOE
DRE
DW
EPACA
EQL
ERPG
ESP
FW
GAQM
GC
GEP
GRAY
H3TD
HEAST
HI
HQ
IARC
IDE
IEU/BK
IPM
IUPAC
IRIS
ISC-PRIME
ISCSTDFT
ISCST3
K
kg
LADD
L
Ib
LCD
Diethylhexylphthalate
Decaliter
Deoxyribonucleic acid
Di(n)octyl phthalate
Department of Energy
Destruction and removal efficiency
Dry weight of soil or plant/animal tissue
U.S. Environmental Protection Agency Correlation Approach
Estimated quantitation limit
Emergency response planning guidelines
Electrostatic precipitator
Fresh weight (or whole/wet weight) of plant or animal tissue
Grams
Guideline to Air Quality Models
Gas chromatography
Good engineering practice
Unspeciated gravimetric compounds
Hierarchy of Human Health Toxicity Data
Health Effects Assessment Summary Tables
Hazard index
Hazard quotient
International Agency for Research on Cancer
Instrument detection limit
Integrated exposure uptake/biokinetic
Insoluble polystyrene microspheres
International Union of Pure and Applied Chemistry
Integrated Risk Information System
Industrial Source Complex-Plume Rise Model Enhancements
Industrial Source Complex Short Term Draft
Industrial Source Complex Short Term 3
Kelvin
Kilogram
Lifetime average daily dose
Liter
Pound
Local climatological data annual summary with comparative data
m
MACT
Meters
Maximum achievable control technology
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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Contents
September 2005
LIST OF ACRONYMS (contd.)
MDL
MEHP
mg
Mg
MIR
MJ
mL
MPRM
MPTER
MRL
Method detection limit
Monoethylhexyl phthalate
Milligram
Megagram
Maximum individual risk
Megajoule
Milliliter
Meteorological processor for regulatory models
Air quality model for multiple point source gaussian dispersion algorithm with
terrain adjustments
Minimum risk level
NCDC
NC DEHNR
NCEA
NCP
NIOSH
NRC
NRC COT
NTP
NWS
National Climatic Data Center
North Carolina Department of Environment, Health, and Natural Resources
National Center for Environmental Assessment
National Oil and Hazardous Substances Pollution Contingency Plan
National Institute of Occupational Safety and Health
Nuclear Regulatory Commission
National Research Council Committee on Toxicology
National Toxicology Program
National Weather Service
OAQPS
OEHHA
ORD
OSHA
OSW
OSWER
PAH
PCB
PCDD
PCDF
PCRAMMET
PDF
Pg
PIC
PM
PMD
PM10
POHC
ppb
ppm
ppmv
ppt
Office of Air Quality Planning and Standards
Office of Environmental Health Hazard Assessment
Office of Research and Development
U.S. Occupational Safety and Health Administration
Office of Solid Waste
Office of Solid Waste and Emergency Response
Polynuclear aromatic hydrocarbon
Poly chlorinated biphenyl
Polychlorinated dibenzo(p)dioxin
Polychlorinated dibenzofuran
Personal computer version of the meteorological preprocessor for the old RAM
program
Probability density function
Picogram
Product of incomplete combustion
Particulate matter
Portable monitoring device
Particulate matter less than 10 micrometers in diameter
Principal organic hazardous constituent
Parts per billion
Parts per million
Parts per million by volume
Parts per trillion
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LIST OF ACRONYMS (contd.)
PQL
PU
QA
QAPjP
QC
RCRA
RfC
RfD
RME
RPF
RTDM
RTDMDEP
SAMSON
SCAPA
SCRAM
SF
SLERA
SOCMI
SQL
SRA
SVOC
SW-846
TCDD
TDA
TDI
TEELs
TEF
TEQ
TG
TIC
TLV
TOC
TOE
TSD
TTN
TWA
U/BK
USCA
USDA
Practical quantitation limit
Polyurethane
Quality assurance
Quality assurance project plan
Quality control
Resource Conservation and Recovery Act
Reference concentration
Reference dose
Reasonable maximum exposure
Relative potency factor
Rough terrain diffusion model
Rough terrain diffusion model deposition
Second
Solar and Meteorological Surface Observational Network
Subcommittee on Consequence Assessment and Protective Actions
Support Center for Regulatory Air Models
Slope factor
Screening level ecological risk assessment
Synthetic Organic Chemical Manufacturing Industries
Sample quantitation limit
Screening ranges approach
Semivolatile organic compound
U.S. Environmental Protection Agency Test Methods for Evaluating Solid Waste
Tetrachlorodibenzo(p)dioxin
Toluenediamine
Toluene diisocyanate
Temporary emergency exposure limits
Toxicity equivalent factor
Toxicity equivalent quotient
Terrain grid
Tentatively identified compound
Threshold limit value
Total organic carbon
Total organic emissions
Treatment, storage, and disposal
Technology transfer network
Time-weighted average
Uptake/biokinetic
Unit-Specific Correlation Approach
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LIST OF ACRONYMS (contd.)
U.S. EPA U.S. Environmental Protection Agency
USGS U.S. Geological Survey
USLE Universal soil loss equation
UTM Universal transverse mercator
VOC Volatile organic compound
WHO World Health Organization
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Human Health Risk Assessment Protocol
Contents
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Variable
Y
^z
M-a
M-w
Pa
PS
Psoil
Pw
e
efcs
6™
6v
a
beef
A
•"•chicken
ADD
ADDmfant
ADDmat
AEF
^ess
Ah
Ahi
A,
Units
unitless
unitless
g/cm-s
g/cm-s
g/cm or g/m
kg/L
g/cm3
g/cm3
unitless
unitless
mL water/cm3
soil
cm3/cm3
unitless
mg COPC/kg FW
tissue
mg COPC/kg FW
tissue
mg COPC/kg
BW-day
pg COPC/kg BW
infant/day
pg COPC/kg BW
mother/day
kg/hr- source
mg COPC/kg FW
tissue
m
m
m2
INDEXED LIST OF VARIABLES
Definition
Empirical constant
Dimensionless viscous sublayer thickness
Viscosity of air
Viscosity of water corresponding to water
temperature
Density of air
Bed sediment density
Solids particle density
Density of water corresponding to water
temperature
Temperature correction factor
Bed sediment porosity
Soil volumetric water content
Soil void fraction
Empirical intercept coefficient
Concentration of COPC in beef
Concentration of COPC in chicken meat
Average daily dose
Average daily dose for infant exposed to
contaminated breast milk
Average daily dose (mother)
Applicable average emission factor for the
equipment type
Concentration of COPC in eggs
Area planted
Area planted to z'th crop
Impervious watershed area receiving COPC
deposition
[Sections]/
Equations
used to generate Rp
5-41B; 5-42B; B-4-
20; B-4-21
5-42B; B-4-21
5-41B; B-4-20
5-18;5-41-B; 5-
42B; B-2-8; B-3-8;
B-4-20; B-4-21
used to generate 6fa
5-7a;B-l-6;B-2-6;
B-3-6;B-4-6
5-41B; B-4-20
5_40;B-4-19
5-36B; 5-37; 5-47;
B-4-16;B-4-25
[5.2.4.4]; 5-4; 5-5A;
5-7A;5-7C;5-32;5-
33;B-1-3;B-1-4;B-
l-5;B-l-6;B-2-3;
B-2-4;B-2-5;B-2-6;
B-3-3;B-3-4;B-3-5;
B-3-6;B-4-3;B-4-4;
B-4-5;B-4-6;B-4-
10; B-4-11
5-7B; 5-7C
5-34;B-4-14
[5.4.4]; 5-22; B-3-
10
[5.6.1]; 5-26; B-3-
14
6-1
C-3-2
[5.6.1]; C-l-3
used to estimate Yp
see Ah
5-31; 5-32; 5-33; 5-
36C; B-4-9; B-4-10
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INDEXED LIST OF VARIABLES (contd.)
Variable
AL
•"•milk
A
pork
AT
Aw
b
Babeef
Bachicken
Baegg
BAFfish
Bamilk
Bapork
BCF
D l-"r fish
BD
Units
m2
mg COPC/kg FW
tissue
mg COPC/kg FW
tissue
days
2
m
unitless
day /kg FW tissue
day /kg FW tissue
day/kg FW tissue
L/kg FW tissue
day/kg FW tissue
day /kg FW tissue
unitless
(mg COPC/kg
FW tissue)/(mg
COPC/kg
dissolved water)
g soil/cm3 soil
Definition
Total watershed area receiving COPC deposition
Concentration of COPC in milk
Concentration of COPC in pork
Averaging time
Water body surface area
Empirical slope coefficient
Biotransfer factor for beef
Biotransfer factor for chicken
Biotransfer factor for eggs
Bioaccumulation factor for fish
Biotransfer factor for milk
Biotransfer factor for pork
Bioconcentration factor for fish
Soil bulk density
[Sections]/
Equations
5-32; 5-33; 5-34; 5-
36C; 5-43; B-4-10;
B-4-ll;B-4-14;B-4-
22
[5.4.5]; 5-24; B-3-
11
[5.5.1]; 5-25; B-3-
12
[6.5]; 6-1; C-1-7;C-
[4.1.2]; 5-29; 5-30;
5-35; 5-36C; 5-43;
B-4-8;B-4-12;B-4-
15; B-4-22
5-34;B-4-14
5-22; [A2.5.1]; A-2-
16; B-3-10
5-26; [A2.5.3]; B-3-
14
5-26; [A2.5.3]; A-2-
18; B-3-13
5-49; [A2.5.4]; B-4-
27
5-24; [A2.5.1]; A-2-
17; B-3-11
5-25; [A2.5.2]; B-3-
12
5-48; [A2.5.4]; B-4-
26
[5.2.4.2]; 5-4; 5-5A;
Brn
Br
forage
unitless Plant-soil bioconcentration factor for
aboveground produce
unitless Plant-soil bioconcentration factor for forage
(Hg COPC/g DW
plant)/(fig
COPC/g soil)
5-7A;5-ll;5-32;5-
33;B-1-1;B-1-3;B-
l-4;B-l-5;B-l-6;
B-2-l;B-2-3;B-2-4;
B-2-5;B-2-6;B-3-l;
B-3-3;B-3-4;B-3-5;
B-3-6;B-4-l;B-4-3;
B-4-4;B-4-5;B-4-6;
B-4-10; B-4-11
5-20A; [A2.4.3]; A-
2-14A;B-2-9
5-20A; [A2.4.3]; A-
2-14B;B-3-9
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INDEXED LIST OF VARIABLES (contd.)
Variable
grain
rootveg
Bs
BSAF
Bvag
^forage/silage
C
Ca
r
^ acute
Cancer Riskt
Cancer
RiSkinh(i)
CBS
cd
cdw
(-'fish
Chv
Chp
Chpb
Units
unitless
(Hg COPC/g DW
plant)/(ng
COPC/g soil)
unitless
(Hg COPC/g FW
plant)/(ng
COPC/g soil)
unitless
unitless
unitless
(mg COPC/kg
lipid tissue)/(mg
COPC/kg
sediment)
unitless
(Hg COPC/g DW
plant )/(ng
COPC/g air)
unitless
Hg/m3
unitless
unitless
g sediment/cm
water
unitless
mg COPC/L
water
mg COPC/kg FW
tissue
ug-s/g-m3
ug-s/g-m3
ug-s/g-m3
Definition
Plant-soil bioconcentration factor for COPC in
grain
Plant-soil bioconcentration factor for COPC in
belowground produce
Soil bioavailability factor
Biota-to-sediment accumulation factor
COPC air-to-plant biotransfer factor for
aboveground produce (|ig COPC/g DW
plant)/(|ig COPC/g air)— unitless
Air-to-plant biotransfer factor for forage and
silage
USLE cover management factor
Total COPC air concentration
Acute air concentration (|o,g/m3)
Individual lifetime risk through indirect exposure
to COPC carcinogen i
Individual lifetime cancer risk through direct
inhalation of COPC carcinogen i
Bed sediment concentration (or sediment bulk
density)
Drag coefficient
Dissolved phase water concentration
Concentration of COPC in fish
Unitized hourly air concentration from vapor
phase
Unitized hourly air concentration from particle
phase
Unitized hourly air concentration from particle-
bound phase
[Sections]/
Equations
5-20A; [A2.4.3]; A-
2-14B;B-3-9
5-20B; [A2.4.2]; A-
2-13;B-2-10
[5.4.4.6]; 5-22; 5-
24; 5-25; 5-26; B-3-
10; B-3-11; B-3-12;
B-3-13;B-3-14
[5.75]; 5-50;
[A2.5.4.3]; B-4-28
5-18; [A2.4.4]; A-2-
15A&B;B-2-8
5-18; [A2.4.4]; A-2-
15A&B;B-3-8
5-33A;B-4-13
[6.1]; 7-1; 7-5; B-5-
1; C-2-l;C-2-2; C-
3-1
7-9; B-6-1; C-4-1
7-3; C-l-7
C-2-1
5-36A; 5-37; 5-43;
5_47;B-4-16;B-4-
22; B-4-25
5-41B; 5-42B; B-4-
20;
[5.7.4.9]; 5-46; 5-
48; 5-49;B-4-24;B-
4-26; B-4-27; C-l-5
[5.7.5]; 5-48; 5-49;
5-50; B-4-28; C-l-4
B-6-1
B-6-1
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Variable
Units
INDEXED LIST OF VARIABLES (contd.)
Definition
[Sections]/
Equations
Cs
C,,
Dme
Ds
D,.,
mg COPC/kg soil Average soil concentration over exposure
duration
mg COPC/kg
sediment
Concentration sorbed to bed sediment
[5.2.1]; 5-1C&D; 5-
20A&B; 5-22; 5-24;
5-25; 5-26; 5-32; 5-
33;B-l-l;B-2-l;B-
2-9;B-2-10;B-3-l;
B-3-9;B-3-10;B-3-
11; B-3-12; B-3-13;
B-3-14;B-4-l;B-4-
10; B-4-11
[5.7.4.10]; 5-47; 5-
50; B-4-25; B-4-28
CSF
CstD
^wctot
cwtot
Cyp
Cyv
Cywv
Da
(mg/kg-day)'1
mg COPC/kg soil
mg COPC/L
water column
gCOPC/m3 water
body (or mg/L)
Hg-s/g-m3
Hg-s/g-m3
Hg-s/g-m3
cm /s
Cancer slope factor
Soil concentration at time tD
Total COPC concentration in water column
Total water body COPC concentration including
water column and bed sediment
Unitized yearly average air concentration from
particle phase
Unitized yearly average air concentration from
vapor phase
Unitized yearly (water body and watershed)
average air concentration from vapor phase
Diffusivity of COPC in air
7-2; [A2.6.2];C-l-7
[5.2.1]; 5-1E; B-l-1;
C-3-1
[5.7.4.8]; 5-45; 5-
46; B-4-23; B-4-24
[5.7.4]; 5-35; 5-45;
5-47; B-4-15; B-4-
23; B-4-25
[3. 8.3.2]; B-5-1
[3.8.3.1]; 5-18; B-2-
8;B-3-8; B-5-1
[3.8.3.1]; 5-30; B-4-
12
5-7A;5-42B;
mg COPC/kg
soil-yr
cm /s
Depth of upper benthic sediment layer
Mean particle size density for a particular filter
cut size
Deposition term
Depth of water column
Diffusivity of COPC in water
[A2.3.5]; A-2-2A;
B-l-6;B-2-6;B-3-6;
B-4-6;B-4-21
5-35; 5-36A; 5-43;
5-45; 5-47; B-4-15;
B-4-16;B-4-18;B-
4-22; B-4-23; B-4-
25
3-1
[5.2.3]; 5-1C, D&E;
5-36C;B-l-l;B-2-
l;B-3-l; B-4-1
[4.1.2]; 5-35; 5-
36A; 5-45; 5-47; B-
4-15; B-4-16; B-4-
18; B-4-23; B-4-25
5-41A&B; [A2.3.5];
A-2-2B; B-4-20
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Variable
Dydp
Dydv
Dytwp
Dywp
Dywv
Dytwv
dz
ED
EF
ER
Ev
L
Ft
Jlipid
Fw
fwc
Units
s/m -yr
s/m2-yr
s/m -yr
s/m -yr
s/m -yr
s/m -yr
m
yr
days/yr
unitless
cm/yr
unitless
unitless
unitless
unitless
unitless
INDEXED LIST OF VARIABLES (contd.)
Definition
Unitized yearly average dry deposition from
particle phase
Unitized yearly average dry deposition from
vapor phase
Unitized yearly (water body or watershed)
average total (wet and dry) deposition from
particle phase
Unitized yearly average wet deposition from
particle phase
Unitized yearly average wet deposition from
vapor phase
Unitized yearly (water body or watershed)
average total (wet and dry) deposition from
vapor phase
Total water body depth
Exposure duration
Exposure frequency
Soil enrichment ratio
Average annual evapotranspiration
Fraction of total water body COPC concentration
in benthic sediment
Fraction of plant type i grown on contaminated
soil and eaten by the animal
Fish lipid content
Fraction of COPC wet deposition that adheres to
plant surfaces
Fraction of total water body COPC concentration
in the water column
[Sections]/
Equations
[3.8.3.2]; 5-11; 5-
14; B-l-1; B-2-1; B-
2-7; B-3-1; B-3-7
[3.8.3.2]; 5-11; 5-
14; B-l-1; B-2-1; B-
2-7; B-3-1; B-3-7
[3.8.3.2]; 5-29; 5-
31;B-4-l;B-4-8;B-
4-9
[3.8.3.2]; 5-11; 5-
14;B-1-1; B-2-1; B-
2-7; B-3-1; B-3-7
[3.8.3.2]; 5-11; 5-
14; B-l-1; B-2-1; B-
2-7; B-3-1; B-3-7
[3.8.3.2]; 5-29; 5-
31;B-4-l;B-4-8;B-
4-9
5-36A; 5-39; 5-41A;
B-4-16;B-4-18;B-
4-20
6-l;C-l-7; C-l-8;
C-3-l;C-3-2
6-l;C-l-7; C-l-8;
C-3-1
5-33;B-l-3;B-2-3;
B-3-3;B-4-3;B-4-
11
5-5A;B-l-5;B-2-5;
B-3-5; B-4-5
[5.7.4.1]; 5-36B; 5-
38;5-47;B-4-16;B-
4-17;B-4-25
5-22; 5-24; 5-25; 5-
26; B-3-10; B-3-11;
B-3-12;B-3-13B-3-
14
5-50;B-4-28
5-14;B-2-7; B-3-7
[5.7.4.1]; 5-35; 5-
36A; 5-38; 5-45; B-
4-15; B-4-16; B-4-
17; B-4-23
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Variable
Units
INDEXED LIST OF VARIABLES (contd )
Definition
[Sections]/
Equations
unitless
Fraction of COPC air concentration in vapor
phase
[3.2]; 5-11; 5-14; 5-
18; 5-29; 5-30; 5-31;
B-2-8; B-3-1; B-3-7;
B-3-8; B-4-1; B-4-8;
B-4-9; B-4-12; B-5-
H
HI
HIj
HQ
TJ/^I
z^i
HQ^m
/
I
k
K
kb
Kdbs
Kdl}
Kds
atm-m3/mol
unitless
unitless
unitless
unitless
unitless
cm/yr
mg/day
unitless
ton/acre
yi"
cm3 water/g
bottom sediment
unitless
cm3 water/g soil
Henry's Law constant
Hazard index
Hazard index for exposure pathway/
Hazard quotient
Hazard quotient for COPC /
Hazard quotient for direct inhalation of COPC
Average annual irrigation
Daily intake of COPC (/') from animal tissue
von Karman's constant
USLE erodibility factor
Benthic burial rate constant
Bed sediment/sediment pore water partition
coefficient
Partition coefficient for COPC / associated with
sorbing material/
Soil-water partition coefficient
5-7 A; 5-30; 5-40;
[A2.3.4];A-2-l;B-
1-6; B-2-6; B-3-6;
B-4-6; B-4-12; B-4-
19
7-6;7-7;C-l-ll
C-l-10
7-5; C-l-8
7-6
C-2-2; C-2-4
5-5A; B-l-5; B-2-5;
B-3-5; B-4-5
[6.2.2]; C-l-3
5-41B;5-42B;B-4-
20;B-4-21
5-33A; B-4-13
[5.7.4.7]; 5-38; 5-
43; 5-44; B-4-17
5-36B; 5-47;
[A2.3.8]; A-2-8C;
B-4-16; B-4-25;
5-4; 5-5A; 5-7 A; 5-
L water/kg
suspended
sediment
m/y
Suspended sediments/surface water partition
coefficient
Gas phase transfer coefficient
20B; 5-32; 5-33;
[A2.3.8]; A-2-8A;
B-l-3;B-l-4; B-l-5;
B-l-6; B-2-3; B-2-4;
B-2-5; B-2-6; B-2-
10;B-3-3;B-3-4;B-
3-5; B-3-6; B-4-3;
B-4-4; B-4-5; B-4-6;
B-4-10; B-4-11
5-36A; 5-39; 5-46;
[A2.3.8]; A-2-8B;
B-4-16; B-4-18;B-
4-24
[5.7.4.6]; 5-40; 5-
42A&B; B-4-19; B-
4-21
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Variable
Units
INDEXED LIST OF VARIABLES (contd.)
Definition
[Sections]/
Equations
kp
ks
kse
ksg
ksl
ksr
ksv
m/yr
Liquid phase transfer coefficient
mL water/g soil Soil organic carbon-water partition coefficient
unitless Octanol-water partition coefficient
(mg COPC/L
octanol)/(mg
COPC/L octanol)
yr
yr
yr
yr
yr
yr
m/yr
Plant surface loss coefficient
COPC soil loss constant due to all processes
COPC loss constant due to soil erosion
COPC loss constant due to biotic and abiotic
degradation
COPC loss constant due to leaching
COPC loss constant due to surface runoff
COPC loss constant due to volatilization
Water column volatilization rate constant
Overall COPC transfer rate coefficient
Overall total water body dissipation rate constant
[5.7.4.5]; 5-40; 5-
41A&B; B-4-19;B-
4-20
[A2.3.7]; A-2-4;A-
2-5; A-2-6; A-2-7;
[A2.3.6]; A-2-4;A-
2-5; A-2-6; A-2-7;
A-2-12A&B; A-2-
14A&B; A-2-15A;
A-2-16; A-2-17; A-
2-19
[5.3.1.2]; 5-14; B-2-
7; B-3-7
[5.2.2]; 5-1C, D&E;
B-l-l;B-l-2; B-2-
l;B-2-2; B-3-l;B-
3-2; B-4-l;B-4-2;
[5.2.2.2]; 5-2A;B-
l-2;B-l-3;B-2-2;
B-2-3;B-3-2;B-3-3;
B-4-2;B-4-3
[5.2.2.1]; 5-2A;
[A2.3.9]; A-2-9; B-
1-2; ; B-2-2; B-3-2;
B-4-2
[5.2.2.4]; 5-2A; 5-
5A; B-l-2; B-l-5;
B-2-2; B-2-5; B-3-
2;B-3-5; B-4-2; B-
4-5
[5.2.2.3]; 5-2A; 5-4;
B-l-2; B-l-4; B-2-2;
B-2-4; B-3-2; B-3-4;
B-4-2; B-4-4
[5.2.2.5]; 5-2A; 5-
7A; B-l-2; B-l-6;
B-2-2; B-2-6; B-3-2;
B-3-6; B-4-2; B-4-6
[5.7.4.3]; 5-38; 5-
39; B-4-17; B-4-18
[5.7.4.4]; 5-30; 5-
39;5-40;B-4-12;B-
4-18;B-4-19
[5.7.4.2]; 5-35; 5-
38; B-4-15;B-4-17
L
LADD
mg CO PC/kg
BW-day
Monin-Obukhov Length
Lifetime average daily dose
[3.5.1]
7-2
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INDEXED LIST OF VARIABLES (contd.)
Variable
LDEP
L«
leak rate
LE
LR
LRI
LT
LS
Mstin
^vegetable
MF
MW
ocsed
P°L
P°S
P
PF
Pd
P.
Pr
Prbg
Pv
Units
g/yr
g/yr
kg/hr
g/yr
g/yr
g/yr
g/yr
unities s
CT
C>
g
unities s
g/mo le
unitless
atm
atm
cm/yr
unitless
mg CO PC/kg
DW
mg/kg DW
mg CO PC/kg
DW
mg CO PC/kg
DW
mg CO PC/kg
DW
Definition
Total (wet and dry) particle phase and vapor
phase COPC direct deposition load to water body
Vapor phase COPC diffusion load to water body
Emission rate from the individual item of
equipment
Soil erosion load
Runoff load from pervious surfaces
Runoff load from impervious surfaces
Total COPC load to the water body including
deposition, runoff, and erosion
USLE length-slope factor
Mass of a thin (skin) layer of below ground
vegetable
Mass of the entire vegetable
Metabolism factor
Molecular weight
Fraction of organic carbon in bottom sediment
Liquid phase vapor pressure of chemical
Solid phase vapor pressure of chemical
Average annual precipitation
USLE supporting practice factor
Aboveground exposed produce concentration
due to direct (wet and dry) deposition onto plant
surfaces
Total COPC concentration in plant type i
ingested by the animal
Aboveground exposed and protected produce
concentration due to root uptake
Belowground produce concentration due to root
uptake
Concentration of COPC in plant due to air-to-
plant transfer
[Sections]/
Equations
[5.7.1.1]; 5-28; 5-
29;B-4-7; B-4-8
[5.7.1.2]; 5-28; 5-
30; B-4-7; B-4-12
[2.2.6.1]
[5.7.1.5]; 5-28; 5-
33; B-4-7; B-4-11
[5.7.1.4]; 5-28; 5-
32; B-4-7; B-4-10
[5.7.1.3]; 5-28; 5-
31; B-4-7; B-4-9
[5.7.1]; 5-28; B-4-7;
B-4-15
5-33A;B-4-13
5-19
5-19
[5.4.4.7]; 5-22; 5-
24;5-25;B-3-10;B-
3-ll;B-3-12
[A2.3.1]; A-2-1
5-50;B-4-28
A-2-1 1
A-2-1 1
5-5A;B-l-5;B-2-5;
B-3-5;B-4-5
5-33A;B-4-13
[5.3.1]; 5-14; 5-23;
B-2-7; B-3-7;C-l-2
[5.4.4.3]; 5-22; 5-
23; 5-24; 5-25; 5-
26;5-27;B-3-10;B-
3-ll;B-3-12;B-3-
13; B-3-14
[5.3.3]; 5-20A&B;
5-23; 5-27; B-2-9;
B-3-9;C-l-2
B-2-10; C-l-2
[5.3.2]; 5-18; 5-23;
B-2-8; B-3-8; C-l-2
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INDEXED LIST OF VARIABLES (contd.)
Variable
Q
Q,
^li(adj)
Qcpi(adj)
Qcpi
Qf
Qpi
Q*
Q,
r
R
RCF
RO
Units
g/s
g/s
g/s
g/s
g/s
W/m2
kg DW/day
kg/day
W/m2
unitless
atm-m3/mol-K
(|ig COPC/g DW
plant)/(|ig
COPC/mL soil
water)
cm/yr
Definition
COPC emission rate
Emission rate of COPC ft)
Adjusted emission rate of COPC ft)
Adjusted emission rate of Table A-l
carcinogenic COPC ft)
Emission rate of Table A-l carcinogenic COPC
ft)
Anthropogenic heat flux
Quantity of plant type i ingested by the animal
each day
Quantity of soil ingested by the animal each day
Net radiation absorbed
Interception fraction — the fraction of material in
rain intercepted by vegetation and initially
retained
Universal gas constant
Root concentration factor
Average annual surface runoff from pervious
[Sections]/
Equations
5-ll;5-14;5-18;5-
29; 5-30; 5-31; B-l-
l;B-2-l;B-2-7;B-
2-8;B-3-l;B-3-7;
B-3-8;B-4-l;B-4-8;
B-4-9;B-4-12;B-5-
1,6-6-1
[3.5.7]
[5.4.4.2]; 5-22; 5-
24; 5-25; 5-26; B-3-
10; B-3-11; B-3-12;
B-3-13;B-3-14
[3.5.8]
5-7A;5-30; 5-40; A-
2-ll;B-l-6;B-2-6;
B-3-6;B-4-6;B-4-
12; B-4-19
5-20B; [A2.4.1]; A-
2-12A&B; A-2-13;
B-2-10
5-4; 5-5A; 5-32; B-
surfaces
l-4;B-l-5;B-2-4;
B-2-5;B-3-4;B-3-5;
B-4-4;B-4-5;B-4-
10
REL
RF
RfC
RfD
Rp
S
yr-'
mg CO PC/kg
body weight/day
unitless
mg COPC/L
water
California EPA Air Toxics Hot Spots Program
acute reference exposure levels
USLE rainfall (or erosivity) factor
Inhalation reference dose
Oral reference dose
Interception fraction of the edible portion of
plant
Solubility of COPC in water
[7.4.2]
5-33A;B-4-13
7-5; [A2.6.1]; C-2-2
7-5; [A2.6.1]; C-l-8
[5.3.11]; 5-14; B-2-
7;B-3-7
[A2.3.3]; A-2-1
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Variable
SD
'Sf
SF
INDEXED LIST OF
Units Definition
unitless
unitless
(mg/kg-day)"1
cmVcm3 air
Sediment delivery
Entropy of fusion
Slope factor
Whitby's average
(aerosols)
VARIABLES (contd )
ratio
[•£/# = 6.79]
surface area of particulates
[Sections]/
Equations
[5.7.3]; 5-33; 5-34;
5-36C;5-43;B-l-3;
B-2-3;B-3-3;B-4-3
A-2-11
A-2-11
tD
Tp
K
yr
yr
yr
K
yr
Ambient air temperature
Time period at the beginning of combustion
Length of exposure duration
Time period over which deposition occurs (time
period of combustion)
Melting point of chemical
Length of plant exposure to deposition per
harvest of edible portion of plant
[3.4.2]; 5-7A; A-2-
11; B-l-6; B-2-6; B-
3-6; B-4-6
B-3-1; B-4-1
5-lC&D;B-l-l;B-
5-lC,D&E;B-l-l;
B-2-1; B-3-1; B-4-1
[A2.3.2]
[5.3.1.3]; 5-14; 5-
16;5-21;B-2-7;B-
3-7
tyi
Total Cancer
Risk
Total Cancer
Riskinh
TSS
Twk
t,n
u
URF
Vfx
VGag
VG
rootveg
Vp
w
wh
yr
unitless
unitless)
mg/L
K
days
m/s
•g/m3
m3/yr
unitless
unitless
atm
m/s
m/yr
Length of plant's exposure to deposition per
harvest of the edible portion of the /' th plant
group
Individual lifetime cancer risk through indirect
exposure to all COPC carcinogens
Total individual lifetime cancer risk through
direct inhalation of all COPC carcinogens
Total suspended solids concentration
Water body temperature
Half-time of COPC
Current velocity
Unit risk factor
Average volumetric flow rate through water body
Empirical correction factor for aboveground
produce (forage and silage)
Empirical correction factor for below ground
produce
Vapor pressure of COPC
Average annual wind speed
Rate of burial
5-13
7-3; 7-4; C-l-9
C-2-3
5-36A; 5-36C; 5-39;
5.43; 5-46; B-4-16;
B-4-18; B-4-22; B-
4-24
5-30; 5-40; B-4-12;
B-4-19;
5-15
5-41A;B-4-20
7-1; C-2-1
5-35; 5-36C; 5-43;
B-4-15; B-4-22;
[5.3.2.1; 5.4.2.1]; 5-
18; B-2-8; B-3-8
5-19; 5-20B; B-2-10
[A2.3.3]; A-2-1
[3.4.1]; 5-41B; 5-
42B; B-4-20; B-4-
21
5-44
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Variable
Units
INDEXED LIST OF VARIABLES (contd.)
Definition
[Sections]/
Equations
kg/m -yr
Unit soil loss
[5.7.2]; 5-33; 5-
33A; 5-36C; 5-43;
B-l-3;B-2-3;B-3-3;
B-4-3; B-4-ll;B-4-
13; B-4-22
Yh
YPi
kg DW Dry harvest yield
kg DW Harvest yield of z'th crop
kg DW/m2 Yield or standing crop biomass of edible portion
of plant (productivity)
kg DW/m2 Yield or standing crop biomass of the edible
portion of the plant (productivity)
[5.3.1.4; 5.4.1.4]; 5-
14; B-2-7;B-3-7
5-13
Soil mixing zone depth
[5.2.4.1]; 5-4; 5-5A;
5-7A&B; 5-11; B-l-
B-2-3;B-2-4;B-2-5;
B-2-6;B-3-l;B-3-3;
B-3-4;B-3-5;B-3-6;
B-4-1; B-4-3; B-4-4;
B-4-5;B-4-6
0.01
io-6
io-6
0.31536
365
907.18
0.1
0.001
100
1000
4047
1 x IO3
3.1536 x IO7
kg cm /mg-m
g/^g
kg/mg
m-g-s/cm-fig-yr
days/yr
kg/ton
g-kg/cm -m
g/mg
mg-cm /kg-cm
mg/g
m2/acre
g/kg
s/yr
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
Units conversion factor
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Chapter 1: INTRODUCTION
What's Covered in Chapter 1:
1. 1 Objective and Document Organization
1. 2 Background
1. 3 Using this Document
1. 4 Primary Reference Documents
1. 5 Risk Nomenclature
1.1 OBJECTIVE AND DOCUMENT ORGANIZATION
The U.S. Environmental Protection Agency's ("U.S. EPA" or "the Agency") Office of Solid Waste
(OSW) has developed an approach for conducting multi-pathway, site-specific human health risk
assessments on Resource Conservation and Recovery Act (RCRA) hazardous waste combustors. The
approach, also known as the Human Health Risk Assessment Protocol ("HHRAP" or "protocol") can be
used where the permitting authority determines such risk assessments are necessary. The HHRAP
replaces an earlier Peer Review Draft published in July 1998.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak is the performer of a risk assessment: the person (or persons) who will
actually put the recommended methods into practice.
Our primary objective in developing the protocol was to offer a user-friendly approach to performing site-
specific combustion risk assessments. We wanted to develop a guidance document that would:
• be useful to a diverse group of users: risk assessors, permit writers, risk managers, and
community relations personnel;
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provide a logical method for doing risk assessments of facilities that burn hazardous waste;
completely explain the reason for each recommended procedure and parameter value;
provide a comprehensive enough collection of default input parameters to conduct a risk
assessment. The collection would also be flexible enough to accommodate regional or
site-specific information; and finally
make sufficient tools available to produce transparent, defensible, and realistic results. When
coupled with an ecological assessment (U.S. EPA 1999a), these tools would provide critical
information often needed by risk managers when faced with the decision of permitting a
hazardous waste combustion facility.
The HHRAP brings together information from other risk assessment guidance and method documents
prepared by U.S. EPA and state environmental agencies. It also contains the latest advancements in risk
assessment science, as well as experience EPA has gained through conducting and reviewing combustion
risk assessments. This version of the protocol also addresses the comments put forward by the public and
external scientific peer reviewers regarding earlier drafts of the HHRAP.
The first volume of the HHRAP contains the main body of the document, providing a detailed
explanation of a risk assessment approach that we recommend you consider when conducting a risk
assessment. The second volume contains the appendices, including:
Appendix A: a collection of chemical-specific information which might be of interest -
• A-l - a compilation (from various EPA sources) of compounds of potential
interest;
• A-2 - Compound-specific parameter value information. Details the sources of, or
equations used to calculate, parameter values used in fate & transport-,
biotransfer-, exposure-, and toxicity-related equations. Parameter values
themselves are found in a companion database to the HHRAP, which is also
available for download.
Appendix B: Equations and recommended default variable values for estimating media
concentrations; and
Appendix C: Equations and recommended default variable values for estimating potential cancer
risk and non-cancer health effects.
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Please Note. Although these guidelines address many types of situations encountered in
the field, they cannot encompass every potential situation. You should ensure that the
recommendations in this guidance are appropriate for the facility, based on site-specific
information and/or circumstances.
This protocol is a "snapshot" of current risk assessment science, and we encourage you to evaluate
updates and alternatives to the recommended parameters (e.g., toxicological benchmarks; exposure
factors) when they become available. If you use alternative values, however, keep in mind how changes
in one parameter may affect other parameter values and/or calculations. We may revise the protocol in
the future if any of the following become available:
• new research in risk assessment science and/or the combustion field;
new information gathered while conducting site-specific risk assessments; and
• new initiatives introduced by the Agency.
These types of changes are inevitable in this evolving and highly technical field.
1.2 BACKGROUND1
Hazardous waste combustors are required to meet RCRA national performance standards and obtain a
permit under 40 CFR Part 264, Subpart O; Part 266, Subpart H; and Part 270.2 In addition,
Section 3005(c)(3) of RCRA [codified at 270.32(b)(2)] requires that each permit contain the terms and
conditions that the permitting authority considers necessary to protect human health and the environment.
This is commonly referred to as the "omnibus authority." The omnibus authority gives the Agency both
1 This section summarizes the historical context of regulatory authority and associated policy regarding hazardous
waste combustion site-specific risk assessment under the RCRA program. This discussion is not intended to update, revise or
articulate new guidance or policy. Nor is it intended to update, revise or provide any new interpretations of any statutory or
regulatory authority, including those relevant to the RCRA 3005(c)(3) "omnibus authority". In addition, it is not intended to
reopen for consideration any statutory or regulatory interpretations of other related guidance documents, or the MACT rule (64
FR 52828).
When combustion sources demonstrate compliance with the Part 63, Subpart EEE MACT standards, they may
request that certain RCRA permit conditions (e.g., those based on the national performance standards) be removed from their
RCRA permit, because they are no longer applicable, through a class 1 permit modification request with prior agency approval.
In some cases, RCRA performance standards may be retained in the RCRA permit if they are more stringent than the relevant
MACT standard.
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the authority and the responsibility to set up site-specific RCRA permit conditions as necessary to "be
protective of human health and the environment." These permit conditions are intended to supplement,
not replace those conditions that are already required under the national performance standards [See
Federal Register 1999 (MACT Rule)].
The RCRA national performance standards for incinerators were published in 1981 (40 CFR Part 264,
Subpart O) and for boilers and industrial furnaces in 1991 (40 CFR Part 266, Subpart H). Since then,
however, new information on indirect exposure pathways and non-dioxin products of incomplete
combustion (PICs) suggests that these standards may not fully address all potentially significant risks.
For example the RCRA national standards were based on estimates of risk only from direct exposure to
(i.e. inhaling) stack emissions. New information suggests risks from indirect exposures (e.g. ingesting
contaminated soil, food, or water) are also important (Federal Register 1999). Bioaccumulation tends to
concentrate some chemicals as they migrate through the environment. These higher concentrations can
lead to exposures and risks of concern. For example, Fradkin et al. (1988) linked elevated levels of
chemical pollutants in soils, lake sediments, and cow's milk to atmospheric transport and deposition of
pollutants from combustion sources. Also, the 1994 Draft Health Reassessment ofDioxin-Like
Compounds (U.S. EPA 1994a), and the 1991 Mercury Study Report to Congress (U.S. EPA 1997c),
indicate that indirect exposure pathways can lead to significant risks.
Indirect exposure pathways weren't directly taken into account by the 1981 and 1991 hazardous waste
combustion standards. The regulations also didn't take into account the uncertainty surrounding the types
and quantities of non-dioxin products of incomplete combustion (non-dioxin PICs) or any potential risks
posed by these compounds.
To address these concerns, the Agency issued the Hazardous Waste Minimization and Combustion
Strategy in 1994. This strategy recommended conducting a site-specific risk assessment for each
combustion facility seeking a RCRA permit. Permitting authorities could use the results of an assessment
to determine, on a case-by-case basis, if a combustor operating in accordance with the performance
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standards is protective of human health and the environment. If the permitting authority finds that the
combustor operating in accordance with the performance standards is not protective of human health and
the environment, the permitting authority would invoke the "omnibus authority" and either add additional
conditions to the RCRA permit, or deny the RCRA permit. The permitting authority must explain the
reasons for any additional permit conditions in the administrative record of the facility (Federal Register
1999).
In 1999 the Agency updated its earlier site-specific risk assessment recommendation, to account for the
Phase 1 Maximum Achievable Control Technology (MACT) standards for hazardous waste incinerators,
cement kilns, and lightweight aggregate kilns (see 64 FR 52828). While the Phase 1 MACT standards
provide additional protection, we recognize that there may continue to be circumstances for which site-
specific risk assessments are appropriate. For example, a site-specific risk assessment might be
appropriate if there is reason to believe that operating in accordance with Phase 1 MACT standards alone
may not be protective of human health and the environment. So, in the MACT standards rulemaking, we
recommend that the permitting authority evaluate the need for a site-specific risk assessment on a case-
by-case basis. For hazardous waste combustors not subject to the Phase 1 MACT standards, such as
boilers and industrial furnaces, we continue to recommend that site-specific risk assessments generally be
conducted as part of the RCRA permitting process (see Federal Register 1999).
As part of the September 2005 rule finalizing MACT standards for hazardous waste-burning incinerators,
cement kilns, lightweight aggregate kilns, boilers, and industrial furnaces, we maintain virtually the same
the site-specific risk assessment policy recommendation as conveyed in the 1999 final rule preamble (see
previous paragraph)3. That policy, which establishes that the need for an SSRA should be determined on
a case-by-case basis, now applies equally to both Phase 1 and Phase 2 sources.
3 The standards for Phase 1 sources (incinerators, cement kilns, and lightweight aggregate kilns) are
referred to as Replacement Standards. The Replacement Standards replace the February 13, 2002 Interim Standards
that were developed in response to a court decision to vacate challenged portions of the 1999 Phase 1 MACT final
standards. Thus, the 2005 final rule establishes MACT standards for both Phase 1 and Phase 2 (boilers and
industrial furnaces) sources.
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In addition, the 2005 final rule codifies additional regulatory language that provides the authority for
SSRAs. Although a comparative risk analysis conducted for the 2005 final rule concluded that the
MACT standards for both Phase 1 and Phase 2 sources are generally protective, there may be instances
where we cannot be assured that emissions from each source will be protective of human health and the
environment. Because we believe that SSRAs are likely to continue to be necessary at some facilities
(i.e., mainly those that have not previously conducted an SSRA), we have codified language in
§§270.10(1) and 270.32(b)(3) that explicitly provides for the permit authority to require SSRAs on a
case-by-case basis and add conditions to RCRA permits based on SSRA results, respectively. The
language also reminds permit authorities that the determination that the MACT standards may not be
sufficiently protective is to be based only on factors relevant to the potential risk from the hazardous
waste combustion unit at the site. Additionally, guiding factors have been identified for permitting
authorities to consider in determining whether the MACT will be sufficiently protective at an individual
site. In summary, the 2005 final rule only modifies the statutory authority under which we implement the
SSRA policy, while maintaining the same SSRA policy from a substantive standpoint.
1.3 USING THIS DOCUMENT
This document contains our generally recommended approach for conducting multi-pathway, site-
specific human health risk assessments of RCRA hazardous waste combustors. This document does not
provide recommendations on how to:
• Determine if a site-specific risk assessment should be performed,
You can find U.S. EPA's most recent recommendations for when, or if, a site-specific risk
assessment should be performed, in documentation of the MACT rule, published on September
30, 1999 (Federal Register 1999).
• Conduct stack emissions testing for a site-specific assessment,
A separate guidance document entitled Risk Burn Guidance for Hazardous Waste Combustion
Facilities EPA 530-R-01-001, July 2001 (U.S. EPA 2001c) contains approaches for collecting
emissions data to support site-specific risk assessments. This document is on the U.S. EPA OSW
website at: http://www.epa. gov/epaoswer/hazwaste/combust/pdfs/burn.pdf.
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Develop a site-specific ecological risk assessment,
We'd previously published our recommendations for conducting screening level ecological
combustion risk assessments in a separate, companion document to the HHRAP. This companion
document, the Screening Level Ecological Risk Assessment Protocol for Hazardous Waste
Combustion Facilities (SLERAP) Peer Review Draft (U.S. EPA 1999a), is currently undergoing
substantial revision. Until revisions are complete, we can't recommend using the SLERAP.
Use risk assessment results in risk management decisions, such as setting RCRA permit
conditions.
Because this protocol is a technical risk assessment tool, it does not discuss risk management
issues, such as how risk managers are to use the provided information (including uncertainty
information), the potential for cumulative risks, or target risk levels. U.S. EPA's generally
recommended risk and hazard targets can be found in Draft Exposure Assessment Guidance for
RCRA Hazardous Waste Combustion Facilities (U.S. EPA 1994f). Additionally, EPA Region 6's
region-specific risk target recommendations, Region 6 Risk Management Addendum (U.S. EPA
1998b), are available on their website at: www.epa.gov/earthlr6/6pd/rcra c/protocol/r6add.pdf
Please Note: the ultimate decision for how to incorporate risk assessment estimates in risk
management decisions rests with the permitting authority.
For your convenience, the HHRAP many of the recommendations found in the above-referenced
documents. However, unless we say so explicitly, the HHRAP does not intend to update, revise, or
replace any of the information contained in the above-referenced documents.
The HHRAP does update and replace the following guidance documents:
• U.S. EPA, Guidance for Performing Screening Level Risk Analyses at Combustion
Facilities Burning Hazardous Wastes, April 15, 1994 Draft and the October 4, 1994
Errata;
• U.S. EPA, Protocol For Screening Level Human Health Risk Assessment at Hazardous
Waste Combustion Facilities - Volumes 1 & 2, Internal Review Draft, EPA-R6-096-002
February 28, 1997;
• U.S. EPA, Human Health Risk Assessment Protocol, Draft Interim Final, April 1998
(CD-Rom version);
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• U.S. EPA, Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities - Volume 1-3, Peer Review Draft, EPA 530-D-98-001A, B & C, July 1998 and
the August 2, 1999, Errata.
We anticipate that risk assessments will be completed for new and interim-status facilities, where
necessary, when they apply for their RCRA permit. The process we recommend evaluates risks to
receptors posed by potential emissions from RCRA-regulated units. We encourage you to use existing
and site-specific information throughout the risk assessment process in order to properly evaluate actual
regulated operations for any particular combustor. We generally recommend conservative default
assumptions only when they will provide confidence that ensuing permit limits will be health protective.
Throughout the HHRAP we offer parameter values for you to consider. These values are based on a
number of elements, such as the best science available and professional judgement. Since this is a
national level guidance, the recommended values typically reflect national average conditions. The
values will be more appropriate for some sites, and less so for others. For example, the type of waterbody
near a facility (i.e. lake, river, wetland) may affect the methylation rate of mercury in the waterbody, or
the type offish consumed may affect percent lipid content used in the assessment. So, a value that is
reasonable for one facility may be over (or under) protective at a different facility.
In all cases, though, we give the reason for the suggested value. We encourage you to consider our
reasoning, in deciding if a more representative estimate of a site-specific value (or range of values) is
available and appropriate. If you use values other than those we recommend, you should explore how, or
if, those changes may affect other parameter values and calculations used in the assessment. As with
values recommended in this guidance, using values other than those recommended here should always be
clearly identified and discussed in the risk plan and/or risk assessment (as appropriate). This will ensure
clarity and transparency of the final risk assessment results.
You would need considerable time, effort, and funding to investigate the representativeness of all the
values (or ranges of values) available in the HHRAP. As a result, you might choose to use only readily
available site-specific information in an initial assessment. You could then use the results of that
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assessment to determine where (or if) more site-specific risk information should be collected (see Figure
1-1). This allows you to use resources most efficiently and effectively, by focusing resources on areas
that are considered "risk drivers", rather than areas that do not appreciably affect the risk outcome. For
example, if the assessment shows that the primary pollutant and exposure pathway is mercury in fish, then
you could target site-specific data gathering efforts on values related to mercury emissions, surface water
concentrations and/or fish consumption. You would not have to spend resources collecting site-specific
information that may not affect the final results of the assessment (for example, Manganese exposure
through ingestion of produce).
You can also use the FiHRAP as a screening tool. For example, a facility with a highly variable waste
stream might choose to provide the permitting authority with historical data and assume that all
compounds will be retained in the risk analysis. Or you might choose to use more conservative
assumptions throughout, to make the assessment fit a more classic "screening level" approach. For
example, you could choose not to initially investigate the actual land use surrounding a facility, but
instead locate all the selected receptors at the area of greatest contaminant deposition. If estimates don't
exceed the selected risk target, additional iterations of the assessment may not be necessary.
Regardless, every risk assessment is limited by the quantity and quality of:
• Site-specific environmental data;
• Emission rate information; and
Other assumptions made during the risk estimation process (e.g., fate and transport
variables, exposure assumptions, and receptor characteristics).
These limitations and uncertainties are described extensively throughout the main document and the
appendices, and are summarized in Chapter 8. You should generally make every effort to reduce
limitations and uncertainties in the risk assessment process, since they can affect the confidence in the
risk assessment results.
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FIGURE 1-1
HUMAN HEALTH RISK ASSESSMENT PROCESS
Facility Characterization
- Compiling Basic Facility Information (Section 2.1)
- Identifying Emission Sources & Estimating Emission Rates (Section 2.2)
- Identifying Compounds of Potential Concern (COPC) (Section 2.3)
- Estimating COPC Concentrations for Non-Detects (Section 2.4)
- Evaluating Contamination in Blanks (Section 2.5)
Air Dispersion and Deposition Modeling
- Partitioning Emissions (Section 3.2)
- Site-Specific Characteristics Required for Air Modeling (Section 3.3;
- Meteorological Preprocessor Data Needs (Section 3.5)
- ISCST3 Model Input Files (Sections 3.6)
-ISCST3 Model Execution (Section 3. 7)
- Using Model Output (Section 3.8)
- Modeling Fugitive Emissions (Section 3.9)
- Modeling Acute Risk (Section 3.10)
1
Exposure Scenario Selection
- Exposure Setting Characterization (Section 4.1)
- Recommended Exposure Scenarios (Section 4.2)
- Selecting Exposure Scenario Locations (Section 4.3)
Estimating Media Concentrations
- Calculating Air Concentrations in Air for Direct Inhalation (Section 5.1)
- Calculating Concentrations in Soil (Section 5.2)
- Calculating Concentrations in Produce (Section 5.3)
- Calculating Concentrations in Meats, Milk, and Eggs (Sections 5.4 through 5.6)
- Calculating Concentrations in Drinking Water and Fish (Section 5.7)
Quantifying Exposure
- Inhalation Exposure Pathways (Section 6.1)
- Ingestion Exposure Pathways (Section 6.2)
- Dermal Exposure Pathways (Section 6.3)
- Exposure Frequency (Section 6.4)
- Exposure Duration (Section 6.5)
- Averaging Time (Section 6.6)
Characterizing Risk and Hazard
- Quantitatively Estimating Cancer Risk (Section 7.1)
- Quantitatively Estimating Noncancer Hazard (Section 7.2)
- Target Levels (Section 7.3)
- Estimating Acute Exposure from Direct Inhalation (Section 7.4)
Risk Assessment
Interpreting Uncertainty
- Understanding Uncertainty and Limitations of the Human Health Risk Assessment Process (Section 8.1)
- Identifying Types of Uncertainty (Section 8.2)
- Determining Qualitative Uncertainty (Section 8.3)
- Determining Quantitative Uncertainty (Section 8.4)
- Discussing Human Health Risk Assessment Uncertainty (Section 8.5)
Collect Additional
Site-Specific Information
Develop Protective
Operating Permit
Risk Management
Use Additional
Site-Specific Information
To Reevaluate Risk
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The EPA Information Quality Guidelines (U.S. EPA 2002c) recommend ensuring the objectivity of
information found in risk assessments by applying, to the extent practicable and consistent with Agency
statutes and existing legislative regulations, the following adaptation of the quality principles found in the
Safe Drinking Water Act (SDWA) Amendments of 1996:
(A) The substance of the information is accurate, reliable and unbiased. This involves the use of:
(i) the best available science and supporting studies conducted in accordance with sound
and objective scientific practices, including, when available, peer reviewed science and
supporting studies; and
(ii) data collected by accepted methods or best available methods (if the reliability of the
method and the nature of the decision justifies the use of the data).
(B) The presentation of information on human health, safety, consistent with the purpose of the
information, is comprehensive, informative, and understandable. In a document made available to
the public, EPA specifies:
(i) each population addressed by any estimate of applicable human health risk;
(ii) the expected risk for the specific populations affected;
(iii) each appropriate upper-bound or lower-bound estimate of risk;
(iv) each significant uncertainty identified in the process of the assessment of risk; and
(v) peer-reviewed studies known to the Administrator that support, are directly relevant
to, or fail to support any estimate of risk and the methodology used to reconcile
inconsistencies in the scientific data.
How risk results are viewed by the risk manager and other stakeholders is complex and can involve other
factors besides those included in this document (e.g. public concern). Consequently, interpreting risk
assessment results warrants careful consideration. Risk management decisions are beyond the scope of
the HHRAP, and we don't provide any guidance on interpreting risk results. It should be noted, though,
that identifying potentially unacceptable risks does not necessarily signify the end of the risk assessment
process. You can view risk assessments as an iterative process4, with a number of available options once
risk estimates are produced. The iterative nature of the risk assessment/risk management interface is
graphically represented in Figure 1-1, and the various available options are briefly described below:
4As stated in the U.S. EPA (2002c) "Risk assessments may be performed iteratively, with the first
iteration employing protective (conservative) assumptions to identify possible risks. Only if potential risks are
identified in a screening level assessment is it necessary to pursue a more refined, data-intensive risk assessment.
The screening level assessments may not result in "central estimates" of risk or upper and lower-bounds of risks.
Nevertheless, such assessments may be useful in making regulatory decisions..."
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Example 1: If the initial risk estimates (coupled with any other related factors) indicate that risks are not
expected to pose a concern to human health or the environment, the risk manager and/or permit
writer will likely end the site-specific risk assessment process and the facility will likely receive a
permit.
Example 2: If the initial risk estimates (coupled with any other related factors) indicate that the risks are
at or above a level that may pose a risk to human health or the environment, then additional
information might be added to the risk assessment (e.g. site-specific information that's more
representative of the actual exposure settings). Additional iterations of the risk assessment could
then be performed. This iterative process enables you to determine if the risks identified in the
earlier assessment accurately represent the situation at a given combustion facility.
Example 3: If the initial risk estimates (coupled with any other related factors) or subsequent iterations
(as detailed in Example 2 above), indicate potentially unacceptable risk, risk managers and/or
permit writers might use the results of the risk assessment to propose revised or additional permit
conditions (such as waste feed limits and/or process operating conditions) to lower the potential
risk to acceptable levels. Another risk assessment could verify that the proposed permit
conditions will enable the combustor to operate in a manner that's protective of human health and
the environment.
In some situations, target risk levels might be selected and back-calculations conducted to
determine what emission and/or waste feed rate would allow the facility to operate in a protective
manor. In any case, the acceptable waste feed rate and other appropriate conditions could be
incorporated into the RCRA permit.
Example 4: If the initial risk estimates or subsequent iterations (coupled with any other related factors)
indicate potentially unacceptable risk, risk managers and/or permit writers might also choose,
where appropriate, to deny the permit.
The HHRAP may also be useful when a facility or regulatory agency decides to perform a pre-trial burn
risk assessment. A pre-trial burn risk assessment can evaluate pre-existing permit limits (e.g. regulatory
limits such as MACT or BIF) to determine if more extensive or refined risk-based testing is necessary as
part of the trial burn testing program. Also, the pre-trial burn risk assessment can test the parameters used
in the initial trial burn sampling and analysis plan. Testing trial burn parameters minimizes trial burn
contributions to risk assessment uncertainty, and avoids the expense of multiple trial burn iterations. For
example, if the initial detection or quantitation limit for a specific compound (such as a dioxin, furan, or
bioaccumulative metal) is too high during trial burn sampling and analysis, then the final risk estimate
may be artificially inflated, especially for indirect exposure pathways. If trial burn sampling and analysis
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uses a lower detection or quantitation limit, the compound might be found not to add appreciably to the
risk results. The pre-trial burn risk assessment can also determine whether modifications to the sampling
collection (such as increased sample volumes) are needed to achieve lower detection or quantitation
limits. Please see Chapter 2 for more detailed information on how risk assessments relate to trial burns.
1.4 PRIMARY REFERENCE DOCUMENTS
One of the main benefits of the HHRAP is that it assembles in one place more than a decade of research
and experience regarding practices for conducting risk assessments of hazardous waste combustion
facilities. This section describes, in chronological order, the primary guidance documents we used to
prepare the HHRAP. Many other important reference materials were also needed to produce a document
of this magnitude. We have listed all reference materials used in preparing this document in the
Reference chapter.
Some of the documents we used were themselves developed over a period of several years, including
revisions. In some cases, revisions to the original document address only specific issues rather than a
complete revision of the original document. The following discussion lists and briefly describes each
document. Overall, the guidance documents listed below reflect a continual refining and enhancing of the
risk assessment method.
The following was the first U.S. EPA guidance document for conducting risk assessments at combustion
units:
U.S. EPA. 1990e. Methodology for Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions, Interim Final. Environmental Criteria and Assessment Office. ORD.
EPA-600-90-003. January.
Referred to as the "IEM" document, EPA (1990e) outlines and explains a set of general procedures for
conducting risk assessments that includes both the direct inhalation pathway and indirect food chain
pathways. The IEM document was subsequently supplemented by the following:
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U.S. EPA. 1993f. Addendum to the Methodology for Assessing Health Risks Associated with
Indirect Exposure to Combustor Emissions, Review Draft. Office of Health and Environmental
Assessment. ORD. EPA-600-AP-93-003. November 10.
Referred to as the "Addendum", EPA (1993f) outlines recommended revisions and added new exposure
pathways to the previous U.S. EPA guidance (1990e), and has been used by the risk assessment
community since its release.
In 1994, we issued several additional hazardous waste combustion risk assessment documents, including:
U.S. EPA. 1994f. Draft Exposure Assessment Guidance for RCRA Hazardous Waste
Combustion Facilities. OSWER. EPA-530-R-94-021. April.
This document (1994f) is made up of a series of four attachments, all issued around the same time frame
(April/May 1994) as separate documents:
U.S. EPA. 1994g. Draft Guidance for Performing Screening Level Risk Analyses at Combustion
Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment Guidance for
RCRA Hazardous Waste Combustion Facilities. April 15.
U.S. EPA. 1994h. Table 1, "Chemicals Recommended for Identification," and Table 2,
"Chemicals for Potential Identification." Attachment A, Draft Exposure Assessment Guidance for
RCRA Hazardous Waste Combustion Facilities. April 15.
U.S. EPA. 1994i. Draft Revision, Implementation Guidance for Conducting Indirect Exposure
Analysis at RCRA Combustion Units. Attachment, Draft Exposure Assessment Guidance for
RCRA Hazardous Waste Combustion Facilities. April 22.
U.S. EPA. 1994J. Draft Guidance on Trial Burns. Attachment B, Draft Exposure Assessment
Guidance for RCRA Hazardous Waste Combustion Facilities. May 2
Combined, these four documents present a generally recommended procedure for sampling the combustor
emissions, identifying the compounds of concern, conducting both a direct and indirect risk assessment,
and implementing the results of the risk assessment for hazardous waste combustion facilities. We used
the methodologies identified in both the ORD "IEM" and "Addendum" documents as the foundation of
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our hazardous waste combustion risk assessment methodology. The "IBM" and the "Addendum" were
broader in scope than our document. We used many, but not all, of the methods, models and exposure
scenarios that are described in the two ORD documents. Because the ORD documents contain much of
the background information necessary to complete a risk assessment, that information was not repeated in
our guidance documents. Shortly after the release of our documents, the trial burn portion and the risk
assessment portion were further revised with the following releases:
U.S. EPA. 1994n. Draft Revision of Guidance on Trial Burns. Attachment B, Draft Exposure
Assessment Guidance for RCRA Hazardous Waste Combustion Facilities. OSWER. June 2.
U.S. EPA. 1994p. Errata, Draft Guidance for Performing Screening Level Risk Analyses at
Combustion Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment
Guidance for RCRA Hazardous Waste Combustion Facilities. October 4.
As a follow-up to these documents, we prepared another draft guidance. The following was released for
internal review, but never formally or officially released as a program-supported document:
U.S. EPA. 1994r. Revised Draft Guidance for Performing Screening Level Risk Analyses at
Combustion Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment
Guidance for RCRA Hazardous Waste Combustion Facilities. Office of Emergency and
Remedial Response. OSW. December 14.
In 1997, the state of North Carolina's Department of Environment, Health, and Natural Resources
(DEHNR) developed the following guidance document for conducting risk assessments in their state:
NC DEHNR. 1997. North Carolina Protocol for Performing Indirect Exposure Risk
Assessments for Hazardous Waste Combustion Units. January.
The NC DEHNR document reiterates U.S. EPA procedures (1994r), with the addition of a tiered approach
that can help the regulatory agency or facility to choose approaches that reflect the investment they want
to make in conducting risk assessments. For instance, a small, on-site unit with limited waste stream
variability may find the first tier assessment (worst-case) in the North Carolina protocol appropriate,
whereas a larger facility with a diverse waste feed mixture may decide to complete a Tier 2 or Tier 3
assessment, which are progressively more site-specific.
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In 1998, the ORD revised, updated and combined the "IBM" and the "Addendum" documents into one
document, entitled:
U.S. EPA 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of
Exposure to Combustor Emissions (U.S. EPA 1998c).
This document is referred to as the "MPE" document. It includes information which was gained from
cross-Agency review, EPA's Science Advisory Board (SAB) and the public on the "IEM" and the
"Addendum" documents. It also includes information from the draft dioxin reassessment "Estimating
Exposure to Dioxin-Like Compounds " (U.S. EPA 1994a) and the "Mercury Study Report to Congress "
(December 1997). As with the MPE's predecessor documents, it is considered the foundation of our
hazardous waste combustion risk assessment methodology and is frequently referenced in the HHRAP.
In 1999 we released a technical document that detailed the risk assessment conducted to support the
hazardous waste combustion Maximum Achievable Control Technology (MACT) standards:
RTI1999. The Background Information Document to the Risk Assessment Support to the
Development of Technical Standards for Emissions from Combustion Units Burning Hazardous
Wastes Final Report. EPA Contract Number 68-W6-0053.
To ensure consistency, we considered EPA(1999) throughout the development of the HHRAP.
Finally, in 2001 we updated and finalized the document entitled:
U.S. EPA 2001c. Risk Burn Guidance for Hazardous Waste Combustion Facilities, July.
Referred to as the "risk burn guidance," EPA(2001c) was prepared by U.S. EPA Region 4 and U.S. EPA
OSW. It details recommendations regarding stack emissions tests which may be performed at hazardous
waste combustion facilities to support site-specific risk assessments.
As previously stated, our primary objective in developing the HHRAP was to suggest a user-friendly
approach to performing site-specific combustion risk assessments. The HHRAP achieves this goal by
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offering a comprehensive set of tools. You will no longer need to search through a long list of guidance
documents to find an appropriate method and/or value when conducting a site-specific risk assessment of
a hazardous waste combustor. Instead, you have one self-contained document with the majority of all the
available information needed to complete a risk assessment. With the HHRAP's extensive reference list,
you also have the original source of a method and/or value. This simplifies the process of deciding if the
reference is appropriate to use for your specific situation.
1.5
RISK NOMENCLATURE
Unless otherwise stated, the following definitions for risk-related terms are from the National Academy of
Sciences 1983, Risk Assessment in the Federal Government, and used throughout this guidance:
Risk Assessment
Hazard
Risk
Dose
Exposure
Indirect Exposure
Direct Exposure
The scientific evaluation of potential health impacts that may result from
exposure to a particular substance or mixture of substances under
specified conditions.
An impact to human health by chemicals of potential concern.
An estimation of the probability that an adverse health impact may occur
as a result of exposure to chemicals in the amount and by the pathways
identified.
The amount of a substance available for interaction with metabolic
processes or biologically significant receptors after crossing the
exchange boundary of an organism (U.S. EPA 1998c).
The condition of a chemical contacting the exchange boundary of an
organism (U.S. EPA 1998c).
Resulting from contact of human and ecological receptors with soil,
plants, or waterbodies on which emitted chemical has been deposited.
For screening level purposes, indirect exposure includes ingestion of
above ground fruits and vegetables, beef and milk, chicken and eggs,
freshwater fish and soil.
Exposure via inhalation.
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Chapter 2:
Characterizing Facility Emissions
What's Covered in Chapter 2:
2.1 Compiling Basic Facility Information
2.2 Identifying Emission Sources & Estimating Emission Rates
2.3 Identifying Compounds of Potential Concern (COPCs)
2.4 Estimating COPC Concentrations for Non-Detects
2.5 Evaluating Contamination In Blanks
This chapter provides guidance on characterizing the nature and magnitude of facility emissions.
Characterizing includes (1) compiling basic facility information, (2) identifying emission sources, (3)
estimating emission rates, (4) identifying COPCs, (5) estimating COPC concentrations for non-detects,
and (6) evaluating contamination in blanks. You can consider the information listed in the highlighted
box at the end of each section the minimum that we recommend to ensure a risk assessment is
scientifically sound. However, you may want to consult up front the more detailed discussions found in
each section. A more complete understanding of the relevant issues will make sure that all appropriate
information is collected simultaneously. This will help minimize the time and effort expended collecting
site-specific information.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak is the performer of a risk assessment: the person (or persons) who will
actually put the recommended methods into practice.
2.1 COMPILING BASIC FACILITY INFORMATION
If you are a risk assessor, there is basic facility information you should consider while conducting the risk
assessment, and include in the risk assessment report. Including this basic facility information in the
report will enable reviewers to establish a contextual sense of how the facility relates to other facilities
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and other hazardous waste combustors. It's also very important to thoroughly understand (and document)
any regulatory limits evaluated in the risk assessment, because the risk assessment report informs the
setting of risk-based permit limits. For example, specific emissions data might not be collected for a
particular unit where waste feeds are controlled in lieu of demonstrating compliance with an emissions
limit under the regulations (e.g., Tier I under the BIF rule for certain metals, MTEC under the FIWC
MACT rule). For transparency, we therefore recommend clearly identifying the basis for the assumptions
and/or data to be used in the risk assessment, along with the rationale for how the information will be
used.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Description of physical setting
Principal business and primary production processes
Normal and maximum production rates
Types of waste storage and treatment facilities
Type and quantity of wastes stored and treated
Process flow diagrams showing both mass and energy inputs and outputs
• • Relevant information from an existing or proposed permit and/or compliance documents (e.g.,
Waste Analysis Plan or Feedstream Analysis Plan; Startup, Shutdown, and Malfunction Plan;
Certifications of Compliance; etc.)
2.2 IDENTIFYING EMISSION SOURCES & ESTIMATING EMISSION RATES
Burning hazardous waste typically emits combustion by-products from a stack. In addition to emissions
from a combustion stack, types of emissions associated with the combustion of hazardous waste may
include (1) process upsets emissions, (2) accidental releases, (3) general RCRA fugitive emissions, and if
the facility is a cement kiln (4) cement kiln dust (CKD) fugitive emissions. Each of these emission source
types is defined below with regards to the context and scope of this guidance.
Stack Emissions - Release of compounds or pollutants from a hazardous waste combustor into
the ambient air while the unit is operated as intended and in compliance with a permit and/or
regulation (for interim status).
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Process Upset Emissions - Release of compounds or pollutants from a hazardous waste
combustor into the ambient air while the unit is not being operated as intended, or during periods
of startup or shutdown. Upset emissions usually occur during events and times when the unit is
not operating within the limits specified in a permit or regulation. Conditions within the
combustion system during the process upset result in incomplete destruction of the wastes, or
otherwise promote the formation and/or release of hazardous compounds from combustion stacks.
Upset emissions are generally expected to be greater than stack emissions.
Accidental Releases - an accidental release is defined in Section 112(r) of the Clean Air Act as an
unanticipated emission of a regulated substance or other extremely hazardous substance into the
ambient air from a stationary source. Accidental releases are typically associated with non-routine
emissions from RCRA facilities, such as the failure of tanks or other material storage and
handling equipment, complete failure of combustion and air pollution control systems (e.g.
resulting from explosions, and fires), or transportation accidents.
RCRA Fugitive Emissions - Release of compounds or pollutants into the ambient air from
RCRA-regulated sources other than hazardous waste combustion stacks. RCRA fugitive
emissions are typically associated with the release of pollutants from leaks in the combustion
chamber (e.g., "puffs"); tanks, valves, flanges, and other material-handling equipment used in the
storage and handling of RCRA hazardous wastes; residues from the combustion process such as
ash or quench water; and other RCRA treatment, storage, or disposal units (e.g., landfills).
CKD Fugitive Emissions - Release of pollutants into the ambient air caused by the handling,
storage, and disposal of cement kiln dust.
We generally recommend that as applicable, all of these emission source types except accidental releases
be addressed in the risk assessment. Accidental releases aren't within the scope of this guidance. We
generally recommend evaluating accidental releases per Section 112(r) of the CAA and current Agency
guidance (U.S. EPA 1996f) or the RMP Offsite Consequence Analysis Guidance, dated May 24, 1996.
Despite this general guidance, it is for the permitting authority to decide on a site-specific basis whether
the risk assessment will consider accidental releases.
The following subsections contain guidance for estimating emissions of the source types to be included in
the risk assessment. Guidance on air dispersion modeling of stack and fugitive emissions is presented in
Chapter 3.
2.2.1 Estimating Stack Emission Rates for Existing Facilities
We generally consider it important to determine stack emission rates (in grams per second) of every
COPC identified using the procedures outlined in Section 2.3. We anticipate that emission rates for
existing facilities (i.e. already built and operational) will be based on direct stack measurements from
regulatory performance tests, because permitting authorities generally require performance tests before
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granting a permit to burn hazardous wastes, or in order to demonstrate compliance with emission
standards.
As mentioned in the Special Note "How This Document Relates to Trial Burns" and elsewhere, we
suggest incorporating data collection for a risk assessment into the regulatory performance testing
program whenever possible. This will optimize the use of both facility and permitting agency resources,
Test Design: Performance vs. Health Criteria
Performance testing involves demonstrating compliance with
emission standards or performance criteria under any
operational circumstance. Facilities typically wish to
maximize the flexibility of operating conditions allowed by
their permit. This leads to performance tests designed to
demonstrate compliance even while operating under extreme
conditions (i.e., conditions that may only seldom occur or
may only occur for brief durations over the course of a year).
Regulatory performance requirements are generally
national-scale, technology-based instantaneous limits.
Such performance requirements might not consider various
health impacts to receptors located near a particular
facility. Therefore, data needs to assess potential health
risks are typically more comprehensive than those of a
performance test. For example, a performance test will
provide information to calculate destruction and removal
efficiency (DRE) for a principal organic hazardous
constituent (POHC) and perhaps measure total paniculate
matter, but will not typically quantify the various products of
incomplete combustion (PICs) in the stack gases, nor
establish particle size distribution for gases exiting the stack.
Situations have occurred in which some facilities assume
that the only way to maximize permit flexibility and satisfy
risk assessment data needs is to conduct separate tests. We
encourage another interpretation. As mentioned elsewhere,
we suggest modifying performance-based test protocols to
include risk assessment data requirements as appropriate to
evaluate both acute and chronic exposures posed by
facility-specific operations that may occur under the terms of
the permit.
and minimize expenditures associated with stack
testing and subsequent data review, evaluation, and
permitting. Incorporating risk assessment data
collection into the performance test program can
ensure that proper evaluation across test conditions
is achieved for optimal data usability and
versatility- both engineering considerations relating
to unit operations flexibility, and streamlined data
collection considerations for characterizing
potential emissions.
Experience has shown us that in order to evaluate
both acute and chronic reasonable maximum
exposure estimates, the potential emissions
evaluated in the risk assessment need to be based on
actual operating scenarios that may occur under the
terms of the permit. We acknowledge that proper
design of a regulatory test program that includes
risk assessment data collection is challenging for
facilities that burn highly variable waste and/or
have multiple operating conditions. At the same
time, inadvertently omitting potential risk drivers
from the risk assessment due to improper or
oversimplified test design is to be avoided.
Therefore, if feed streams differ between the various test conditions in the regulatory test program, we
generally recommend providing appropriate rationale for this difference in the test plan, and discussing in
the risk assessment report any impacts the differences may have had upon the risk analysis.
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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In some cases, a facility may elect to focus data collection efforts for a risk assessment into one test
condition in the regulatory testing program that demonstrates "normal operations" using feedstreams
considered "worst-case" from an operational and potential emissions standpoint.
Please Note. We suggest such a test condition only for facilities that can identify and
substantiate via historical operating records the typical (or day-to-day) operating mode
for the hazardous waste combustion unit and ancillary equipment, regardless of the type
of waste fed.
If combining multiple and variable feedstreams into a single "worst-case" feed, the mixture needs to
represent those actual waste matrices and constituents that are the most difficult to burn (ensuring that the
combustion unit is fairly challenged). A "worst-case" feed also needs to contain the most toxic
substances managed in the unit, on a mass basis proportional to that fed at any time (ensuring that all
potentially toxic emissions are quantified). Multiple and/or highly variable feedstreams that require
different extremes of a wide operating envelope, or feedstreams that are combined on a disproportional
mass basis to that fed at any time, may not be
representative of actual operations. We're
Test Conditions Relation to Permit Conditions
concerned that they might even result in emission The feed and operating conditions demonstrated during the
„ . . testing will define an operating envelope that not only
estimates that are not sufficiently conservative for establishes the working assumptions for the risk assessment,
the risk assessment. Please see the Risk Burn but also me final Permit terms- If a facility collects
emissions data for the risk assessment under "normal"
Guidance for Hazardous Waste Combustion operating conditions, additional permit limitations may be
_, .... /TTOT^A,-,™,^ r- i • r- • appropriate to ensure that conditions represented as normal
Facilities (U.S. EPA 2001c) for further information ^^ ^ ^ ^^ normal overthe long.term
on designing a testing program that integrates risk operation of the facility.
assessment data collection into the regulatory
testing of hazardous waste combustors.
We generally recommend including the risk assessor in the early planning efforts of the test program
development, so that the test program can be more effectively streamlined while meeting multiple and
complex data collection goals. At least three valid runs at steady state are needed to characterize a test
condition. Since steady state conditions are typically outlined in the test plan for each test condition and
verified by field observations during the test program, resulting emission rates for any particular COPC
are likely to be fairly consistent between runs for each test condition. Test reports document any
abnormalities in steady state operations for a particular run or test condition. If any one run experiences
significant issues that may impact the data quality or comparability with the other runs, the facility and
permitting authority might decide during the test to discontinue the problem run and initiate a new run to
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ensure a valid test condition. Test reports also document this occurrence and identify those valid runs of
the test condition.
For risk assessments, we generally recommend using the maximum of the three emission rates identified
for each COPC during a particular test condition, adjusted for process upsets. This approach is consistent
with implementing a steady state test designed for collecting risk data where the combustor burns
representative, yet worst-case or challenging feeds, at operating conditions that are allowable and/or
typical under the permit. This approach will also allow consistency in refinements to the final risk
analysis that may involve several risk evaluations for different operating scenarios (i.e., across the various
test conditions) in order to afford operational flexibility while maintaining permit provisions that ensure
protection of human health.
Please Note. The recommendation to use the highest of the three emission rates is an
update to Section 8.1.2 of U.S. EPA (200Ic).
An alternative to a regulatory performance test is the use of data "in lieu of testing. Permitting
authorities generally consider this type of data on a case-by-case basis. Prior to accepting such data as a
surrogate for use in the risk assessment, we recommend evaluating the data from both an engineering
perspective and a data usability perspective. To evaluate the similarities between combustors, consider
the design and construction of the combustor and associated air pollution control devices, along with the
basic operating conditions of the process equipment as tested (e.g., capacity, flow rates, supplemental
fuels used, etc.) to ensure comparable emissions. Stack test measurements from a similar combustor are
useful if the combustor burns similar waste feed(s) in terms of constituents, type of waste matrix, and
amount of waste fed on a mass basis. In addition, we recommend evaluating the methods used to quantify
COPCs and associated detection limits achieved during the test, as well as verifying that the data quality
documentation is acceptable for risk assessment purposes.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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SPECIAL NOTE: HOW THIS DOCUMENT RELATES TO TRIAL BURNS
We believe that generating defensible emission rates for compounds of potential concern (COPCs) is
one of the most important parts of the risk assessment process. This requires special consideration
when planning a risk assessment. Therefore, we consider emissions testing, risk assessment planning,
and implementation as interdependent aspects of the hazardous waste combustion site-specific risk
assessment process.
As described elsewhere in this chapter, traditional regulatory performance tests (e.g., RCRA trial
burns designed solely to measure DRE) do not sufficiently characterize COPC emissions for
performing site-specific risk assessments. We therefore generally recommend that collecting
emissions data for a site-specific risk assessment include a thorough understanding of the operating
limits to be established in the regulatory permit and the possible emissions that may result under the
permitted operations of the unit. Regardless of whether the emissions data for the risk assessment is
collected in a separate test condition or in multiple test conditions that are part of a regulatory
performance test program, we recommend that to the extent possible, the planning, regulatory agency
review, and collecting of emissions data be conducted simultaneously, to ensure consistency in data
evaluation across test conditions is achieved and actual operations allowable under the permit are
appropriately evaluated. This approach also eliminates redundancy or the need to repeat activities and
minimizes cost expenditures overall.
The guidance documents below relate to the RCRA hazardous waste combustion program. You may
find the listed documents useful for developing and conducting trial burns:
U.S. EPA. 1989f. Handbook: Guidance on Setting Permit Conditions and Reporting Trial
Burn Results. Volume II of the Hazardous Waste Incineration Guidance Series. Office of
Research and Development (ORD). EPA/625/6-89/019. January.
U.S. EPA. 1989g. Handbook: Hazardous Waste Incineration Measurement Guidance
Manual. Volume III of the Hazardous Waste Incineration Guidance Series. Office of Solid
Waste and Emergency Response. EPA/625/6-89/021. June.
U.S. EPA 1990i. Handbook: Quality Assurance/Quality Control (QA/QC) Procedures for
Hazardous Waste Incineration. Office of Research and Development. EPA/625/6-89/023.
January.
U.S. EPA. 1992c. Technical Implementation Document for EPA 's Boiler and Industrial
Furnace Regulations. Office of Solid Waste and Emergency Response. EPA-530-R-92-011.
March.
U.S. EPA. 2001c. Risk Burn Guidance for Hazardous Waste Combustion Facilities. U.S.
EPA Region 4 and OSW. EPA 530/R/01/001. July.
Generic Trial Burn Plans and/or Quality Assurance Plans and Procedures (QAPP) developed
by individual EPA regional offices or authorized states.
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2.2.1.1 Additional Emissions Testing Considerations
COPC emission rates demonstrated in a traditional regulatory performance test (such as a RCRA trial
burn) are expected to be greater than normal emission rates because a facility "challenges" its combustor
during a trial burn. These challenges introduce a wide range of conditions for automatic waste feed cutoff
(AWFCO) systems. Regulatory performance tests are usually conducted under two conditions:
1. a high-temperature test, in which the emission rate of metals is maximized, and
2. a low-temperature test, in which the ability of the combustor to destroy principal organic
hazardous constituents (POHCs) in the waste feed is challenged.
The combination of high POHC feed rates and extreme operating conditions tested during a
low-temperature trial burn typically produce higher PIC emission rates. However, this is not true in all
cases. For example, the formation of Poly chlorinated dibenzo(p)dioxins (PCDDs) and poly chlorinated
dibenzofurans (PCDFs) doesn't depend on "POHC incinerability" low temperature conditions. PCDDs
and PCDFs can be formed catalytically in the low-temperature regions of the combustion unit or APCS.
We recommend basing the decision to test under low, high, or both temperature conditions on the
characteristics of the facility as discussed in the preceding section, considering facility-specific unit
operation information for the particular types of wastes burned in the combustion unit as well as the
particular APCS.
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RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
All stack sampling information (current and historical data relevant to the risk assessment) on
emission rates from the combustor during normal or day-to-day operating conditions and/or
regulatory performance test conditions.
A description of the waste feed streams burned during the stack sampling. This includes
chemical composition and physical properties, which demonstrate that the waste feeds are
representative of worst-case wastes relative to producing emissions that may pose a potential
risk concern.
•• Description of the operating conditions under which each set of emission rate data being used
was developed.
* * * NOTICE * * *
The permitting authority might not request a risk assessment for every possible metal or PIC
from a combustor. This doesn't imply, however, that it will only ask for targeted sampling for
COPCs during regulatory performance tests. Based on permitting experience and discussions
with analytical laboratories, we maintain that complete target analyte list analyses conducted
when using U.S. EPA standard sampling methods (e.g., 0010 or 0030) don't subject facilities to
significant additional costs or burdens during the trial burn process. We recommend that stack
emission samplers strive to collect as much information as possible to characterize the stack
gases generated from the combustion of hazardous waste. Facilities should, then, generally
expect that data collected for the risk assessment may include the following tests: Method
0010, Method 0030 or 0031 (as appropriate), total organic compounds (using the Guidance for
Total Organics, including Method 0040), Method 0023 A, Method 26A or 26 (as appropriate),
the multiple metals train, and method for particle size distribution (e.g., CARB 501 or Method
5 Modified with analysis by Scanning Electron Microscopy [SEM]). The permitting authority
might also determine that using other test methods is appropriate for the performance test, to
address detection limit or other site-specific issues. See Table B.I-4 of the Risk Bum Guidance
for Hazardous Waste Combustion Facilities (U.S. EPA 200Ic) for a complete list of methods.
2.2.1.2 Estimating the Total Organic Emission (TOE) Rate
We recognize that despite all efforts, it might not be possible to identify all the compounds in the
emissions from a facility. This data gap has the potential to underestimate risks and represents a
non-conservative uncertainty. Organic compounds that can't be identified by laboratory analysis can't be
defensibly treated as COPC's in the risk calculations. However, these compounds might still contribute
significantly to the overall risk, and so it's reasonable to consider them qualitatively in the risk assessment
(DeCicco 1995; U.S. EPA 19941).
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U.S. EPA developed the total organic emissions (TOE) test as one approach to account for unidentified
organic compounds because pre-existing methods, such as total hydrocarbon analyzers, don't fully
determine the total mass of organics present in stack gas emissions (Johnson 1996). We anticipate that
trial/risk burns will generally include the TOE test, in order to provide sufficient information to address
concerns about the unknown fraction of organic emissions. The TOE test is used in conjunction with the
identified organic compounds to calculate a TOE factor. We recommend using the TOE factor to
qualitatively evaluate potential risks from the unidentified fraction of organic compounds in the stack gas.
The TOE test is the subject of other guidance, such as the Guidance for Total Organics (U.S. EPA 1996d)
with additional clarification provided in Section B.7 of U.S. EPA (2001c). Proper use of TOE data
depends on a good understanding of the test method and how the data is reported. The TOE method
defines total organics as the sum of three fractions:
Fraction 1: Total Volatile Organic Compounds (TOVOC) (referred to as Field GC
Component in the TO Guidance) - The fraction of organic compounds with boiling points less
than 100°C. This VOC fraction is collected using U.S. EPA Method 0040. U.S. EPA Method
0040 allows for quantification of the total mass of organic compounds with boiling points less
than 100°C, determined by summing the gas chromatograph/flame ionization detector results as
described in the TO Guidance.
Fraction 2: Total Chromatographical Semivolatiles (TOSVOC) (referred to as Total
Chromatographical Organics Component in the TO Guidance) - The fraction of organic
compounds with boiling points from 100°C to and including 300°C. This VOC fraction is
collected using modified U.S. EPA Method 0010 procedures as defined by U.S. EPA (1996d).
The total mass of organic compounds with boiling points 100°C up to and including 300°C is
determined by summing the total gas chromatograph/flame ionization detector results as
described in the TO Guidance.
Fraction 3: Total Gravimetric Compounds (TOGRAV) (referred to as Gravimetric
component in the TO Guidance) - The fraction of organic compounds with boiling points
greater than 300°C. This fraction is determined by using modified U.S. EPA Method 0010
procedures defined by U.S. EPA (1996d), which quantify the mass, above this fractions boiling
point, by measuring the total mass by evaporation and gravimetry (weighing) for nonvolatile total
organics.
Please note that the TO total (TOTOTAL) is the sum of the sums of each fraction. The sum of the TO
fractions is described as follows:
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TOTAL
= TOvoc + T®svoc + T®GRAV Equation 2-1
where:
TOTOTAL = stack concentration of TO, including identified and unidentified
compounds (mg/m3)
TOmc = stack concentration of volatile TO, including identified and
unidentified compounds (mg/m3)
TOSVOC = stack concentration of SVOC TO, including identified and
unidentified compounds (mg/m3)
TOGRAV = stack concentration of GRAY TO, including identified and
unidentified compounds (mg/m3)
Use the TOE data in conjunction with the identified data to compute a TOE factor. Previously-computed
TOE factors range from 2 to 40. The HHRAP defines the TOE factor as the ratio of the TOTOTAL mass to
the mass of identified organic compounds, as calculated by the following equation:
TO
_ 2^
r TOE ~ - , — Equation 2-2
where
FTOE = TOE factor (unitless)
TOTOTAL = total organic emission (mg/m3)
Ct = stack concentration of the rth identified COPC (mg/m3)
Identifying the organic compounds in the denominator of Equation 2-2 is one of the most critical
components of the TOE factor. Although the permitting authority may not request that you analyze the
organic compounds with all possible analytical methods, you may wish to consider the effects that gaps in
compound-specific identification may have on the computation of the TOE factor. For example,
hazardous waste-burning cement kilns have expressed concern about the amount of light hydrocarbons
that may evolve from the raw materials processed in the cement kilns, because these light hydrocarbons
have not typically been identified in trial burns. If such concerns are significant, you and the permitting
authority might choose to use additional test methods in the trial burn in order to speciate the maximum
number of organic compounds.
We also generally recommend including tentatively identified compounds (TICs) in the denominator
when computing the TOE factor, so that appropriate credit is given to defensible efforts at identifying the
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maximum number of organic compounds. Finally, we generally recommend that non-detect COPCs be
treated consistently between the risk assessment and TOE evaluation. That is, if a non-detected
constituent is deleted as a COPC (See Section 2.3), then it would not be included in the identified fraction
of the TOE equation. COPCs identified per Section 2.3, but not detected, might be included in the TOE
factor equation at the reliable detection limit (non-isotope dilution methods) or the estimated detection
limit (isotope dilution methods).
It's important to carefully evaluate the results of the gravimetric fraction when using the TOE factor.
Both regulated industry and U.S. EPA have expressed some concern that the gravimetric fraction may
over-report the organic fraction. It's been suggested that the gravimetric fraction may consist of organic
and/or inorganic mass not directly attributable to organic incinerator emissions (U.S. EPA 1997a). The
U.S. EPA Office of Research and Development (ORD) National Risk Management Research Laboratory
(NRMRL) recently conducted a series of experiments to investigate this issue. The results indicate that it
is indeed possible for inorganic mass to become soluble and retained in the TOE train methylene chloride
extract. More importantly, the ORD/NRMRL research identified and demonstrated techniques for
successfully mitigating this problem. Details of the experiments, results, and procedures for mitigating
the GRAY bias will be made available in a forthcoming ORD report. Ultimately, these procedures will be
contained in the forthcoming TOE guidance currently being revised by ORD. Further information on this
topic is also available in U.S. EPA (200Ic).
We recommend using the TOE factor in the uncertainty section of the risk assessment report to evaluate
the risks from the unknown fraction of organics. The permitting authority can then evaluate the TOE
factor and assess to what extent actual risks may be greater than estimated risks. For example, if the risk
from the known portion of the emissions show that risks may be borderline and/or the TOE method shows
that the unknowns are a significant portion of the emission profile, the permitting authority has several
options, including:
1. Describe in a narrative form what is known of the unknown portion of the emissions.
2. As a bounding estimate, attribute a risk to the unknown portion of the emissions.
An example is presented as a preferred option in U.S. EPA (1994f), which assumes that
the unknown compounds are similar in toxicity and chemical properties to the known
compounds taken as a whole. The referenced equation is as follows:
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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TO
n - n TOTAL
zf i,adj ~ *£ i ' - ~^, — Equation 2-2A
where
Qiiadj = adjusted emission rate of compound/ (g/s)
Qt = emission rate of compound /' (g/s)
TOTOTAL = total organic emission (mg/m3)
Ci = stack concentration of the rth identified COPC (mg/m3)
3. Recommend additional testing to identify a greater fraction of the organic compounds.
4. Specify permit conditions that further control total organic emissions or that further
control the risks associated with known emissions.
Variations of the TOE factor can be useful to address site-specific concerns. For example, compute three
separate TOE factors based on the apportioning provided by the TOE test (i.e., TOVOC, TOSVOC, and
TOGRAV). Then evaluate the unknowns associated with each fraction of unidentified organic compounds
separately.
2.2.2 Estimating Emission Rates for Facilities with Multiple Stacks
We generally recommend that the risk assessment consider emissions from all combustors burning
hazardous waste at a facility, not just the unit currently undergoing the permitting process. As discussed
further in Chapter 3, air dispersion modeling for each combustor (source) is frequently conducted
separately, to evaluate risk on a stack- or source -specific basis. An example case is a chemical
manufacturing facility which operates both an on-site incinerator and several hazardous waste-burning
boilers. Whether it is the incinerator or the boilers being permitted, the risk assessment considers the
emissions from all the combustors in the estimate of facility risk. In addition to RCRA combustors,
emissions from other RCRA treatment, storage, or disposal units (e.g., open burning/open detonation and
thermal desorption) might also be included in the risk evaluation in some cases.
2.2.3 Estimating Stack Emission Rates for Facilities Not Yet Operational
The permitting process for new hazardous waste combustion facilities includes submitting information of
sufficient detail for the regulatory authority to evaluate compliance with existing regulations, guidance,
and standards of protect veness. Stack (or other source) locations and dimensions, design flow and
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emission rate estimates, waste feed characteristics, surrounding building dimension data, facility plot
plans, and terrain data are frequently reviewed and used in a pre-operation risk assessment. This assists
decision-making and designing permit requirements.
We generally recommend reviewing design emission rates, waste feed characteristics, and other design
data, along with supplementary documentation, to make sure they are representative, accurate, and
comprehensive. Good engineering practice dictates a check of, and comparison with, data from similar
existing units. Stack test reports for facilities of similar technology, design, operation, capacity, auxiliary
fuels, waste feed types, and APCSs can be useful in estimating COPC emission rates for new facilities
that have not been constructed. In addition to design data, particle size distribution data from a similar
type unit that is operational may be useful. Estimated emission rates used to complete pretrial burn risk
assessments are frequently compared to the measured emission rates from actual performance tests
completed after the new facility receives a permit, is constructed and operational.
If surrogate data from similar facilities aren't available, some state environmental agencies enforce
emission rate limits based on state laws. Since these limits cannot be exceeded, you could use them to
develop emission rate estimates for the risk assessment. A trial or risk burn could then demonstrate that
facility emissions are less than those considered in the permit and risk assessment.
2.2.4 Estimating Stack Emission Rates for Facilities Previously Operated
We generally recommend that the risk assessment also consider emissions from the historical operation of
other combustors burning hazardous waste at the facility, not just the unit currently undergoing the
permitting process. The permitting authority will determine the appropriateness of this on a case-by-case
basis. An example case might be when the emissions from historical operation of a source or sources
have already resulted in potential risk concerns at or near the facility. You could model emissions from
historical operations as a separate source or, if applicable, include them in the fate and transport equations
by adding the previous years of operation to the anticipated time period of combustion for an existing or
newly operating source. In some cases, you might also include historical emissions from other RCPvA
treatment, storage, or disposal units at the facility (e.g., open burning/open detonation and thermal
desorption) in the risk assessment, in addition to RCRA combustors.
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RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
All stack test reports for combustors used to develop emission rate estimates
If using surrogate data to assess a new facility, descriptions of how the combustion data used
represent similar technology, design, operation, capacity, auxiliary fuels, waste feed types,
APCSs, and particle size distributions
Demonstration that the data used to develop the emission rate estimates were collected using
appropriate U.S. EPA sampling and analysis procedures
The range of data obtained, and values used, in completing the risk assessment
2.2.5 Emissions From Process Upsets
It is possible for unburned hazardous waste to be emitted through the stack as a result of various process
upsets, such as start-ups, shutdowns, and malfunctions of the combustion unit or APCS. Emissions can
also be caused by operating upsets in other areas of the facility (e.g., an upset in a reactor which vents
gases to a boiler burning hazardous waste could trigger a process upset in the boiler, resulting in increased
emissions). U.S. EPA (1994i) indicates that upsets aren't generally expected to significantly increase
stack emissions over the lifetime of the facility.
To account for the increased emissions associated with process upsets, we generally recommend that the
stack emission rates estimated from trial burn data be multiplied by an upset factor. The upset factor is
not applied to non-PIC emission rate estimates where the total mass of a constituent in the waste feed is
assumed to be emitted. When available, site-specific emissions or process data can be useful to estimate
the upset factor. You may also want to consider and evaluate the following types of data to derive the
upset factor:
Data from continuous emissions monitoring systems that measure stack carbon
monoxide, oxygen, total hydrocarbon (if requested), or opacity (if appropriate)
•• Data on combustion chamber, APCS, or stack gas temperature
•• Data on hazardous waste residence time
•• Frequency and causes of automatic waste feed cutoffs (AWFCO)
•• Frequency of start-up and shut-down events
Ratio of AWFCO frequency and duration to operating time
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•• APCS operating variables like baghouse pressure drop, liquid scrubber flow rate, or
electrostatic precipitator voltage
Stack test data collected while the combustor was operated under upset conditions
You might use this information to estimate the magnitude of the increase in emissions and the percentage
of time, on an annual basis, that the unit operates at upset conditions. Additional information regarding
upset factors for liquid-burning BIFs is available in the Louisiana Chemical Association (LCA) Letter
Report on Upset Factors, dated October 27, 1999, available on the U.S. EPA Region 6 web site
(www.epa.gov/region06/).
If you don't have site-specific data, or they are inappropriate for deriving an upset factor, we generally
recommend estimating upset emission rates using a procedure based on work by the California Air
Resources Board (CARB) (1990).
Estimating Emissions from Process Upsets: To represent stack emission rates during process
upsets, multiply the emission rate developed from the trial burn data by 2.8 for organics and
1.45 for metals. These factors are derived by assuming that emissions during process upsets are
10 times greater than emissions measured during the trial burn. Since the unit doesn't operate
under upset conditions continually, the factor is adjusted to account for only the period of time,
on an annual basis, that the unit operates under upset conditions. For organic compounds, the
facility is assumed to operate as measured during the trial burn 80 percent of the year and operate
under upset conditions 20 percent of the year [(0.80)(1)+(0.20)(10)=2.8]. For metals, the
combustor is assumed to operate as measured during the trial burn 95 percent of the year and
operate under upset conditions the remaining 5 percent of the year [(0.95)(1)+(0.05)(10)=1.45].
Catastrophic process upsets brought about by complete failure of combustion and air pollution control
systems (e.g. resulting from non-routine events such as explosions, fires, and power failures) are typically
considered accidental releases and consequently aren't addressed by this guidance.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Historical operating data demonstrating the frequency and duration of process upsets
A discussion of the potential cause(s) of the process upsets
Estimates of upset magnitude or emissions
Calculations which describe the derivation of the upset factor.
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2.2.6 RCRA Fugitive Emissions
RCRA fugitive emission sources frequently evaluated in site-specific risk assessments include waste
storage tanks; process equipment ancillary to the combustor; and the handling and disposal of combustion
system residues such as ash. Fugitive emissions from other RCRA treatment, storage, or disposal units
(e.g., landfills) may also warrant evaluation in some cases.
This section contains guidance for quantitatively estimating fugitive emissions using procedures outlined
in other U.S. EPA guidance. Guidance regarding air dispersion modeling of fugitive emissions is
presented in Chapter 3.
2.2.6.1 Quantitative Estimation of RCRA Fugitive Emissions from Process Equipment
We generally recommend the following series of steps to quantitatively estimate RCRA fugitive
emissions: (1) identify equipment to evaluate as a fugitive emission source(s); (2) group equipment, as
appropriate, into a combined source; and (3) estimate compound-specific emission rates for each resulting
source. We illustrate an example in Figures 2-1 and 2-2 and Tables 2-1 and 2-2, to help explain the
recommended steps. Figure 2-1 presents the plot plan of a hypothetical facility that includes one RCRA
combustion unit (CU-1), two hazardous waste feed storage tanks (WST-1 and WST-2), and ancillary
equipment identified in a RCRA Part B permit application.
Step 1: Identify Fugitive Emission Sources - Generally, identify RCRA fugitive emission sources such
as waste storage tanks and process equipment that comes in contact with a RCRA hazardous
waste. Such equipment is specified in Title 40, Code of Federal Regulations (40 CFR) Part 265,
Subpart BB. Equipment covered under Subpart BB includes:
•• Pumps
Valves
Connectors (flanges, unions, tees, etc.)
•• Compressors
•• Pressure-relief devices
Open-ended lines
Product accumulator vessels
•• Sampling connecting systems
•• Closed vent systems
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Agitators
Note each fugitive emission source on a facility plot map with a descriptor and the location
denoted with Universal Transverse Mercator (UTM) coordinates (specify if North American
Datum [NAD] of 27 orNAD83).
Step 2: Group Equipment Into a Combined Source - To significantly reduce the effort required to
complete air dispersion modeling and the subsequent risk assessment, group equipment in close
proximity, and evaluate as a single combined source. The speciated emission rates for the group
are the summation of the emissions from the grouped individuals. Clearly denote on a facility
plot plan or map the area extent of the grouped or combined source, as defined by UTM
coordinates (specify if NAD27 or NAD83). Define the area extent of the combined source using
the actual locations of the equipment being grouped, without exaggeration to cover areas without
fugitive sources. It may also be useful to consider how fugitive emission sources are to be
defined when conducting the air dispersion modeling (see Chapter 3).
Equipment in two areas of the hypothetical facility shown in Figure 2-1 are grouped into combined
sources; these consist of the Waste Feed Storage Area and the RCRA Combustor Area.
Step 3: Estimate Fugitive Emissions from Tanks - Obtain fugitive emission rates for waste storage tanks
from the facility's emission inventory or Title V air permit application prepared in compliance
with Clean Air Act Amendments of 1990 (see example provided as Figure 2-2). If that
information is not available, fugitive emissions from storage tanks can be calculated using U.S.
EPA's TANKS Program or by following the procedures outlined in U.S. EPA (1995a),
"Compilation of Air Pollution Emission Factors, January 1995. "
The information needed to accurately estimate fugitive emission rates from storage tanks
includes, but is not limited to:
•• Dimensions of the tanks
Shell height and diameter
•• Characteristics of the tank roof
Color and shade
Condition (e.g., poor, good)
Type (e.g., cone, dome)
Height
Radius or slope
Fixed or floating
•• Characteristics of the shell
Color and shade
Condition (e.g., poor, good)
Heated
Settings on breathe vents
Vacuum setting
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Pressure setting
Characteristics of the stored liquids
Maximum and annual average liquid height
Working volume
Turnovers per year
Net throughput
Average annual temperature
Vapor pressures of speciated constituents (at annual average temperature)
Step 4: Estimate Fugitive Emissions from Process Equipment - Estimate fugitive emissions for each
type of equipment listed under 40 CFR Part 265, Subpart BB by using the following four
approaches, listed in order of increasing refinement and data requirements:
•• Average Emission Factor Approach (AEFA)
Screening Ranges Approach (SRA)
U.S. EPA Correlation Approach (EPACA)
•• Unit-Specific Correlation Approach (USCA)
These four approaches would generally be applicable to estimate fugitive emission rates of volatile
organic compounds (VOCs) from equipment on any facility. Except for the AEFA method, all of the
approaches need screening data collected using a portable monitoring device (PMD). Because data on
fugitive emissions at a facility is typically limited, the AEFA method is expected to be used in most cases,
and therefore has been selected for use in the example illustrated in Figure 2-1, and Tables 2-1 and 2-2.
However, we recommend using more refined approaches such as SRA, EPACA, or USCA, if sufficient
data is available. U.S. EPA (1995k) provides a detailed discussion on these three approaches. Additional
information on estimating fugitive emission rates is available in U.S. EPA (1995k), "Protocolfor
Equipment Leak Emission Estimates, EPA-453/R-95-017."
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FIGURE 2-1
EXAMPLE FACILITY PLOT MAP
FACILITY BOUNDARY
\
\ '
\l
WASTE FEED
i STORAGEAREA
r
COMBUSTION UNIT AREA
CU-1
AREA EXTENT OF
WASTE FEED STORAGE
LL X=585873 ¥=3617184
LR X=585896 Y=36171S4
UR X=585896 Y=3617208
UL X=585873 ¥=3617208
AREA EXTENT OF
COA^USTION UNIT AREA
LL X-585952 ¥=3617114
LR X=585962 ¥=3617114
UR X=585962 ¥=3617124
UL X=585952 ¥=3617124
3617500
3617400
3617300
3617200
3617100
3617000
3616900
3616800
NOTE: UTM COORDINATE GRID
IS 100-METER NAD83
200 4QO
SCALE IN FEET
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FIGURE 2-2
EXAMPLE EMISSIONS INVENTORY
Department of Environmental Quality
Air Quality Division
P. O. Box 82135
Baton Rouge, LA 70884-2135
(504) 765-0219
LOUISIANA
SINGLE POINT SOURCE / AREA SOURCE
Emission Inventory Questionnaire (EIQ)
for Air Pollutants
LADEQ
Company Name
Hypothetical Chemical Company
Plant location and name (if any)
Baton Rouge, LA Plant
Date of submittal
February 1996
Source ID Number
WST-1
Descriptive name of the equipment served by this stack or vent
Waste Feed Tank
Location of stack or vent (see instructions on how to determine
location of area sources)
Horizontal Coordinate 589100 mE
UTM zone no. 15 Vertical coordinate 3616200 m N
STACK and DISCHARGE
PHYSICAL
CHARACTERISTICS
Change [ ] yes [x] no
Height of stack
above grade [ft]
8
Diameter or stack
discharge area
0.167ft
Stack gas exit
temperature (*F)
125
Stack gas flow at process
conditions, not at standard (cfm)
24.27
Stack gas exit velocity
(ft/sec)
18.32
For tanks, list volume
(gals)
800
Date of construction
Fuel
Type of fuel used and heat input (see instructions)
Type of Fuel
Heat input (MMBtu/hr)
Operating
Characteristics
Percent of annual throughout of
pollutants through this emission point
Dec-Feb
25
Mar-May
25
Jun-Aug
25
Sep-Nov
25
Normal operating time
of this point
hrs/ days/ weeks/
day week year
24.00 7 52.0
Normal
operating rate
100%
Air Pollutant Specific Information
Pollutant
Control
equipment
code
Control
equipment
efficiency
Emission Rate
Average
(Ibs/hr)
Maximum
(Ibs/hr)
Annual
(tons/yr)
Emission
estimation
method
Add,
change,
delete
code
Concentration in gases
exiting at stack
2-Nitropropane
Acetaldehyde
Acetanitrite
Methanol
Non-Toxic Voc
000
000
000
000
000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0023
0.0041
0.0023
0.0023
0.0062
0.3463
125.00
21.1266
4.502
195.3347
0.01
0.081
0.01
0.01
0.028
N/A ppmbyvol
N/A ppmbyvol
N/A ppmbyvol
N/A ppmbyvol
N/A ppmbyvol
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TABLE 2-1
EXAMPLE CALCULATION
TOTAL FUGITIVE EMISSION RATES FOR EQUIPMENT IN WASTE FEED STORAGE AREA
1
Fugitive
Emission
Source
Waste
Feed
Storage
Area
2
Waste
Stream
Process
A
Wastes
Process
B
Wastes
3
Type of Waste
Stream In
Service
Light Liquid
Light Liquid
Light Liquid
Light Liquid
Light Liquid
Heavy Liquid
Heavy Liquid
Heavy Liquid
Heavy Liquid
Heavy Liquid
4
Equipment
Type
Pumps
Valves
Connectors
Tank WST-1
Tank WST-2
Pumps
Valves
Connector
Tank WST-1
Tank WST-2
5
Number of Each
Equipment
Type Per Waste
Stream
3
70
30
1
1
2
75
50
1
1
6
Equipment Emission
Factors
(kg/hr)
0.01990
0.00403
0.00183
~
~
0.00862
0.00023
0.00183
~
~
(g/sec)
0.00553
0.00112
0.00051
~
~
0.00239
0.00112
0.00051
-
~
7
Total VOC
Weight
Fraction
0.9
0.9
0.9
0.9
0.9
0.6
0.6
0.6
0.6
0.6
8
Operational
Time Period of
Equipment
(days)
180
180
180
180
180
180
180
180
0
0
9
Total VOC
Emissions Rate by
Equipment (g/sec)
0.01493
0.07056
0.01377
0.02
0.03
0.00287
0.0504
0.0153
0
0
10
Total Fugitive
Emission Rate
(g/sec)
0.14926
0.06857
Notes:
Column 1 Equipment in the Waste Feed Storage Area was identified and grouped as a combined RCRA fugitive emission source with an area extent defined by UTM
coordinates (NAD83).
Column 2 The waste streams serviced by equipment in the Waste Feed Storage Area can be determined through review of the facility's RCRA Part B Permit Application, Air
Emission Standards.
Column 3 The type of waste stream in service, defined as light or heavy for determination of equipment-specific emission factors, can be determined from review of waste
stream vapor pressure.
Column 4 Similar types of equipment can be grouped according to the most applicable equipment-specific emission factor and type of waste stream service (light or heavy)
provided in U.S. EPA (1995k).
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Column 5 The number of equipment type (per waste stream) identified in column 3.
Column 6 Emission factors specific to each type of equipment can be obtained from U.S. EPA (1995k), with the exception of storage tanks.
Column 7 Weight fraction of total volatile organic compounds was obtained from dividing the concentration of VOCs (mg/L) by the density of the waste stream (mg/L).
Column 8 Assumed the equipment is operational for 180 days a year.
Column 9 Equipment-specific fugitive emission rates were determined by multiplying Columns 5, 6, and 7. Emission rates for tanks were obtained from Title V air permit
application. In the absence of such data, emission rates for tanks can be calculated using U.S. EPA's TANKS Program or by following the procedures outlined in
U.S. EPA(1995a).
Column 10 The total fugitive emission rate for each waste stream is determined by summing emission rates for all the equipment. Table 2-2 presents calculations for estimating
speciated fugitive emissions.
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TABLE 2-2
EXAMPLE CALCULATION
SPECIATED FUGITIVE EMISSIONS
FOR EQUIPMENT IN WASTE FEED STORAGE AREA
1
Fugitive
Emission
Source
Waste Feed
Storage Area
2
Waste Stream
Process A Wastes
Process B Wastes
3
Waste Stream
Composition
Acetaldehyde
Acetonitrile
2-Nitropropane
Nitromethane
Acetaldehyde
Acetonitrile
Methanol
Propionitrile
4
Weight Fraction
ofEachVOCIn
Waste Stream
(%)
0.20
0.25
0.25
0.20
0.20
0.10
0.20
0.05
5
Total
Fugitive
Emission
Rate
(g/sec)
0.14926
0.06857
6
Speciated
Fugitive
Emissions (g/sec)
0.0030
0.0037
0.0037
0.0030
0.0137
0.0069
0.0137
0.0034
Notes:
Column 1 Equipment in the Waste Feed Storage Area was identified and grouped as a combined RCRA
fugitive emission source with an aerial extent defined by UTM coordinates (NAD83).
Column 2 The waste streams serviced by equipment in the Waste Feed Storage Area can be determined
through review of the facility's RCRA Part B Permit Application, Air Emission Standards.
Column 3 The waste stream composition can be determined from analytical data
Column 4 Weight fraction of compounds in the waste stream can be determined from analytical data or review
of the facility's Title V Air Permit Application, Emissions Inventory Questionnaire (EIQ) for Air
Pollutants (see example in Figure 2-2).
Column 5 The total fugitive emission rate for each waste stream was obtained from Column 10, Table 2-1.
Column 6 Speciated fugitive emissions were obtained by multiplying Column 4 and 5.
An Example Calculation Using theAEFA Method
Information needed to estimate fugitive emission rates using the AEFA method includes:
Type of waste stream associated with each equipment type (Columns 2 and 3, Table 2-1)
light liquids are those in which the sum of the concentration of individual
constituents with a vapor pressure over 0.3 kilopascals (kPa) at 20°C is greater
than or equal to 20 weight percent
heavy liquids are all others liquids not meeting the definition of light liquids as
specified above
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Number of each equipment type associated with each waste stream (Columns 4 and 5,
Table 2-1)
•• Total VOC weight fraction of each waste stream (Column 7, Table 2-1)
•• Weight fraction of each VOC in each waste stream (Columns 3 and 4, Table 2-2)
•• Operational time period of equipment (Column 8, Table 2-1)
In the AEFA method, equipment is grouped by waste streams of similar characteristics and VOC
composition (Columns 1 and 2, Table 2-1). However, the AEFA method doesn't account for different
site-specific conditions such as temperature, vapor pressure, or screening values, among process units
within a source category. Site-specific factors can significantly influence fugitive emission rates of leaks
from equipment.
U.S. EPA (1995k) presents the average emission factors (Column 6, Table 2-1) for synthetic organic
chemicals manufacturing industry process units, refineries, and natural gas plants. The following table is
an excerpt from this guidance document. These emission factors are most valid for estimating rates of
emissions from a grouping of equipment over a long time period.
TABLE 2-3
SOCMI AVERAGE EMISSION FACTORS
Equipment type
Valves
Pump seals
Compressor seals
Pressure relief valves
Connectors
Open-ended lines
Sampling connectors
Service
Gas
Light liquid
Heavy liquid
Light liquid
Heavy liquid
Gas
Gas
All
All
All
Emission factor
(kg/hr/source)
0.00597
0.00403
0.00023
0.0199
0.00862
0.228
0.104
0.00183
0.0017
0.0150
Source: U.S. EPA (1995k)
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To calculate the total VOC emissions rate for a specified equipment type, multiply the equipment
emission factor by the total VOC weight fraction and the number of each equipment type per waste
stream (Column 9, Table 2-1 = Column 6 x Column 7 x Column 5).
Generate the total fugitive emission rate for the waste stream (Column 10, Table 2-1) by summing the
total VOC emission rates for each equipment type. Speciated fugitive emissions are then calculated by
multiplying the weight fraction of each VOC in the waste stream and the total fugitive emission rate for
the waste stream (Column 6, Table 2-2 = Column 4 x Column 5). This speciated emission rate is the
emission rate used in the risk assessment.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Summary of the step-by-step process conducted to evaluate fugitive emissions
Facility plot map clearly identifying each fugitive emission source with a descriptor and the
location denoted with UTM coordinates (specify if NAD27 or NAD83).
Speciated emission rate estimates for each waste stream serviced by each source, with
supporting documentation
Applicable discussion of monitoring and control measures used to mitigate fugitive emissions
2.2.6.2 Fugitive Emissions from Combustor Leaks
We recommend that when appropriate, the risk assessment evaluate fugitive emissions resulting from the
construction, design, or operation of a hazardous waste combustor. Examples of fugitive emissions from
combustor leaks include:
Combustors operating under negative pressure may experience temporary positive pressures
("puffing") that cause fugitive emissions. This condition can occur when a slug of high BTU
waste is combusted, causing a rapid expansion in the volume of combustion gases that exceeds
the volume of the combustion chamber.
•• Fugitive emissions resulting from the day-to-day operation of the combustor and APCS. These
emissions will typically include (1) leaks that occur due to a positive pressure in the APCS, and
(2) routine maintenance activities such as replacement of baghouse collection bags.
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Currently, we don't offer any specific guidance on how to quantitatively estimate fugitive emissions from
hazardous waste combustors. However, if no site-specific quantitative methods are available, one option
is to address risks associated with leaks in the uncertainty section of the risk assessment. Under such an
approach, the permitting authority could review facility-specific data to determine whether or not the
design addresses equipment leaks and whether the operational data indicate that equipment leaks may be
a problem.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
•• Process design information and drawings (if necessary)
•• Past operating data indicating the frequency, duration, and magnitude of combustor leaks
•• Information regarding the probable cause of combustor leaks
•• Summary of procedures in place to monitor or minimize fugitive emissions resulting from
combustor leaks
2.2.7 RCRA Fugitive Ash Emissions
Burning hazardous waste may produce flyash. Fugitive particle emissions may result from the associated
collection, handling, and disposal of the flyash. Typically, fugitive emissions of flyash collected from an
air pollution control device (APCD) will occur during transfer into covered trucks or other conveyance
mechanisms prior to disposal. Emissions generated during the loading process can be controlled by
APCDs or other types of equipment. However, some of the flyash may still escape into the atmosphere as
fugitive emissions.
We generally recommend the following steps to quantitatively estimate RCRA fugitive ash emissions:
(1) determine an empirical emission factor, (2) estimate the flyash generation rate, and (3) account for air
pollution control equipment, if applicable. As demonstrated in the example calculation below, it is then
possible to estimate the fugitive ash emission rate by multiplying the empirical emission factor by the
flyash generation rate and, if applicable, the control deficiency of the air pollution control equipment.
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Step 1: Determine an Empirical Emission Factor - One approach to estimate particle emissions
associated with flyash loading and unloading is to use an empirical emission factor of 1.07 Ib per
ton flyash. This factor is based on a field testing program conducted at a coal fired power plant
equipped with an electrostatic precipitator (ESP) (Muleski and Pendleton 1986). Because
burning coal and hazardous wastes are similar activities, flyash generated from similar control
devices is expected to behave similarly under the same conditions, with respect to fugitive
emissions. In general, particle behavior is dependent more on the physical form of the flyash than
on the feed (or waste) stream being burned. The emission factor determined during the empirical
study (0.107 Ib per ton flyash) can be adjusted by a factor (e.g., 10) to account for the fact that the
flyash from burning coal (in the study) was wetted. Depending on the facility, the flyash from the
hazardous waste combustion facility may or may not be wetted.
Step 2: Estimate the Flyash Generation Rate - Obtain the APCD flyash generation rate from the Part B
Permit Application. Obtain the total ash content of the "generic" waste streams created from the
waste profile. Both values should be approximately the same. Since a major portion of ash fed to
the combustor is converted to bottom ash, it is likely that this value can be assumed to be a high
estimate of the actual flyash generation rate.
Step 3: Account for Air Pollution Control Equipment - If an APCD is used for controlling emissions
during flyash handling operations, you can generally apply an efficiency factor (e.g., 99.5
percent) to the emission rate. An efficiency factor of 99.5 percent is based on typical collection
efficiencies of particulate matter control devices, for the particle sizes in the range of 2.5 to 10 um
(U.S. EPA 1995a).
Example Calculation
Multiply the empirical emission factor (Step 1) times the estimated flyash generation rate (Step 2):
[(1.07 Ib per ton) * (5,000 tons per year) = 5,350 Ibs per year].
To account for the air pollution control equipment, multiply the product of Steps 1 and 2 times one minus
the fabric filter efficiency (Step 3) to calculate the final RCRA fugitive ash emission rate for use in the
risk assessment:
[(5,350 Ibs per year) * (1 - 0.995) = 26.75 Ibs per year].
2.2.8 Cement Kiln Dust (CKD) Fugitive Emissions
CKD is the particulate matter (PM) that is removed from combustion gas leaving a cement kiln. This PM
is typically collected by an APCS—such as a cyclone, baghouse, ESP—or a combination of APCSs.
Many facilities recycle a part of the CKD back into the kiln. Current and applicable guidance on
evaluating CKD includes (1) the Technical Background Document for the Report to Congress (U.S. EPA
1993g), and (2) the regulatory determination of CKD (60 FR 7366, February 7, 1995).
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Most CKD constituents (for example, metals) aren't volatile but could be released to air through fugitive
dust emissions as volatile or semivolatile organics. These emissions can be in gaseous form and present
in relatively low concentrations, if at all (U.S. EPA 1993a). Dust particles may be suspended in the air by
either wind erosion or mechanical disturbances. The extent to which dust is blown into the air by wind
erosion depends on several site-specific characteristics, including (1) the texture (particle size
distribution) and moisture content of the CKD on the surface of piles, (2) non-erodible elements, such as
clumps of grass or stones on the pile, (3) presence of a surface crust, and (4) wind speeds. Mechanical
disturbances that can suspend CKD constituents in the air include (1) vehicular traffic on and around
CKD piles, (2) CKD dumping and loading operations, and (3) transportation of CKD around a plant site
in uncovered trucks. Cement plants may use various control measures to limit the release of CKD to the
air. For example, CKD may be pelletized in a pug mill, compacted, wetted, and covered to make the
material less susceptible to wind erosion.
To keep the dust down, many facilities add water to CKD before disposal, to agglomerate individual
particles. In addition, as CKD sits in a pile exposed to the elements, occasional wetting by rainfall may
form a thin surface crust in inactive areas of the pile. This acts to mitigate air entrainment of particles.
However, based on field observations by U.S. EPA (1993g), neither surface wetting nor natural surface
crusting eliminates the potential for CKD to be blown into the air. Wetting the dust before disposal
provides incomplete and temporary control because water is infrequently applied, and the dust ultimately
dries and returns to a fine particulate that is available for suspension and transport. Similarly, a surface
crust may develop, but (1) the crust breaks when vehicles or people move on the pile, and (2) fresh dust is
regularly added to the pile, providing a continual, exposed reservoir of fine particles. Please note that a
crust doesn't always form, for a variety of reasons such as weather and CKD chemistry.
CKD constituents that are released to the air are transported and dispersed by the winds, and are
ultimately deposited onto land or water, either by settling in a dry form or by being entrained in
precipitation.
2.2.8.1 Composition and Characteristics of CKD
We evaluated the potential direct and indirect risks resulting from on-site and off-site management of
CKD (U.S. EPA 1993g; 1993h). These studies highlight the limited amount of available information
regarding variation in the chemical constituents of CKD generated by facilities burning hazardous waste
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as fuel, and by facilities burning only fossil or nonhazardous waste fuels. There may also be differences
in composition between the "as-generated" CKD ~ a portion of which is recycled back into the system -
and the "as-managed" CKD that is disposed of on or offsite.
The air exposure pathway is generally of concern for CKD, because the dust is a fine PM that is readily
suspendable, transportable, and respirable in air. In general, particles that are • 400 micrometers can be
suspended in the wind and transported. Within this range, particles that are • 30 micrometers can be
transported for considerable distances downwind. However, particles that are • 40 micrometers are of
primary concern for respiration by humans (U.S. EPA, 1993g). Virtually all of the dust generated at the
15 facilities evaluated by U.S. EPA (1993g) in the Cement Kiln Dust Report to Congress may be
suspended and transported in the wind (that is, the vast majority of particles are • 400 micrometers), and
over two-thirds of all CKD particles generated may be transported over long distances. Additionally, a
significant percentage of the total dust generated (from 22 to 95 percent, depending on kiln type)
comprises respirable particles that are • 40 micrometers.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
•• Physical data, including particle size distribution and density
•• Chemical data, including organic and inorganic analytical tests similar to those used for
sampling combustion gases
Plant net CKD generation rate (how much CKD per year that is available for disposal)
Ambient air monitoring data
CKD management, transportation, storage, and disposal methods
Containment procedures, including fugitive dust prevention measures and the area of exposed
CKD
•• Meteorological data, including wind speed and precipitation
2.2.8.2 Estimating CKD Fugitive Emissions
In general, the HHRAP doesn't address quantitative estimation of risk from fugitive CKD emissions.
However, risk assessments of cement manufacturing facilities are still able to evaluate the fugitive CKD
emissions qualitatively. The Technical Background Document for the Report to Congress (U.S. EPA
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1993g), includes methods to estimate the magnitude of fugitive emissions from the handling, storage, and
disposal of CKD. Sampling data of CKD collected during maximum waste metal feed rate conditions
from the trial burn, risk burn and/or certification of compliance tests may also be useful in evaluating
CKD fugitive emissions. You can then evaluate it qualitatively by comparing the risks estimated for the
kiln stack emissions, to the high end national screening level estimated by U.S. EPA for CKD in U.S.
EPA (1993g) and the regulatory determination of CKD (60 FR 7366, February 7, 1995). If the risks are
equivalent, the combined risks appear significant, or the risks attributed to the CKD are greater than the
risks estimated for the kiln stack emissions, it might be appropriate to evaluate the risk from CKD
emissions in a more quantitative fashion. We generally recommend that the permitting authority make
sure that any qualitative evaluation includes a comparison of the conditions at the facility to the
conditions at the model facilities we evaluated in U.S. EPA (1993g; 1993h). In addition, an analysis of a
specific facility's compliance with other risk-based environmental statutes and regulations is often an
appropriate method to qualitatively evaluate risks associated with the handling, storage, and disposal of
CKD.
2.3 IDENTIFYING COMPOUNDS OF POTENTIAL CONCERN
Compounds of potential concern (COPCs) are those compounds evaluated throughout the risk
assessment. There is no universal list of COPCs, because a compound that's a COPC for one combustor
may not be a COPC for another combustor. COPCs in the emissions from hazardous waste combustors
vary widely, depending on the type of
• • combustor,
fuel and hazardous waste feed being burned, and
APCS used.
COPCs include metals, products of incomplete combustion (PICs), and/or reformation products. PICs are
any organic compounds emitted from a source that are present in the feed stream (even in trace amounts)
and not completely destroyed in the combustion process. Reformation products are organic compounds
that are formed immediately after combustion, due to interaction of specific constituents in the
combustion gasses and specific unit operating conditions relative to a particular combustion process and
associated air pollution control equipment.
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PICs can be formed by trace toxic organic compounds in the waste feed stream. Therefore, we generally
recommend evaluating these trace compounds as PIC precursors, in addition to those compounds more
prevalent in the hazardous waste feed. Don't confuse PICs with principal organic hazardous
constituents (POHCs). POHCs are compounds in the waste feed stream used during a performance test
burn to measure combustor DRE. Unburned POHCs and partially destroyed or reacted POHCs are PICs,
but PICs are not necessarily POHCs. We've typically subdivided COPCs into seven different constituent
categories (U.S. EPA 1994g; 1994i; 1994J; 1994n):
•• Poly chlorinated dibenzo(p)dioxins (PCDDs) and poly chlorinated dibenzofurans (PCDFs)
•• Polynuclear aromatic hydrocarbons (PAHs)
Poly chlorinated biphenyls (PCBs)
Nitroaromatics
•• Phthalates
•• Other organics
Metals
Table A-l (Appendix A) presents a comprehensive list of compounds typically found (1) in hazardous
waste, and (2) in hazardous waste combustion stack gas emissions. Table A-l identifies the Chemical
Abstracts Service (CAS) number for each compound, and states whether the compound has been
identified as a carcinogen. Table A-l also indicates whether a compound has been identified as a
potential COPC by
U.S. EPA and state risk assessment reference documents,
• emission test results that have identified the compound in the emissions from hazardous
waste combustion facilities, or
• other literature that suggests that the risks from the compound may be significant.
We provide Table A-l to help you make sure that the performance test program considers the full range
of compounds potentially emitted from a combustor, and the appropriate analytical method. A risk
assessment won't necessarily evaluate every metal, potential PIC, and reformation product listed in
Table A-l. Once the performance tests are completed, we recommend selecting the risk assessment
COPCs from the stack test data and available facility-specific process information, rather than Table
A-l.
Identify COPCs from the trial/risk burn data based on their potential to pose increased risk or hazard via
one or more of the direct or indirect exposure pathways. We recommend focusing on compounds that
• are likely to be emitted because they (or their precursors) are present in the waste feed,
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are likely to be emitted because they are likely reformation products,
are potentially toxic to humans, and/or
• have a tendency to bioaccumulate or bioconcentrate in food chains.
Appendix A discusses carcinogenic and noncarcinogenic toxicity of specific compounds. The toxicity
information provided in the HHRAP Companion Database is for informational purposes, to help you
explain the basis for selecting COPCs. Please keep in mind that toxicity benchmarks and slope factors
might change as additional toxicity research is conducted. We recommend consulting the hierarchy of
human health toxicity data (see Appendix A, Section A.2.6) before completing the risk assessment, to
make sure that you use the most current toxicity data.
We generally recommend the following six-step approach (illustrated in Figure 2-3) for identifying the
COPCs to evaluate in a site-specific risk assessment (U.S. EPA 1994i).
Step 1: Evaluate analytical data from the stack tests performed during the regulatory test burn program,
and compounds associated with fugitive emissions (see Section 2.2.6). Prepare a list that includes
all the compounds specified in the analytical methods performed in the stack tests, and all
compounds found in the fugitive emissions evaluation. Also include compounds of concern due
to site-specific factors (e.g., community and regulatory concern, high background
concentrations), as well as PCDD/PCDFs, PAHs, and PCBs if not otherwise included. Notate
whether each compound was detected or not detected.
In the recommended approach, a detection in any one of the sampling components (e.g., front half rinse,
XAD resin, condensate, Tenaxtube), in any run constitutes a detection for that specific compound.
Evaluating blank contamination results [included in the quality assurance (QA) data section of the trial
burn report] may be relevant when determining the non-detect status of the compounds (see Section 2.5).
Regardless of the analytical methods performed in the regulatory test burn program, we recommend that
risk assessments consider PCDD/PCDFs, PAHs, and PCBs (the rationale for including these compounds
is discussed in greater detail under Step 3 and in Sections 2.3.1 through 2.3.3).
Steps 2 through 4 are unnecessary for compounds detected in the stack test data analysis or identified in
the fugitive emissions evaluation; they may jump to Step 5. All other compounds continue to Step 2.
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FIGURE 2-3
COPC IDENTIFICATION
STEP1
Trial/risk burn analytes and
fugitive emissions,
PLUS compounds:
* With related site-specific factors;
* Otherwise recommended
List of Tentatively
Identified Compounds
(TICs)
with peaks >= 10%
of full scale
STEP 2
STEPS
STEP 4
Was compound detected?
s non-detect
compound present in:
Waste being burned; OR
other materials fed to
unit?
Does non-detect
have a high potential
To be emitted as a
1C /Ref. product?
Are there:
Related site-specific factors
AND is it
possibly emitted?
Yes
Yes
Yes
Yes
I STEPS
DELETE FROM LIST
List of COPCs
for
QUANTITATIVE
human health
risk assessment
STEP 6
List of COPCs
for
QUALITATIVE
assessment, using
surrogate toxicity data
from a similar
compound
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Step 2: Evaluate all wastes that the unit will be permitted to burn. Retain for evaluation any non-detected
compound present in the waste (Section 2.4 discusses estimating concentrations for non-detects).
For example, if a facility is permitted to burn explosives which characteristically include nitroaromatic
compounds, yet the stack test didn't detect any nitroaromatic compounds, it may be appropriate for
nitroaromatic compounds to still be evaluated in the risk assessment. It is prudent to also consider other
materials fed to the combustor (e.g. raw materials, or coal in a cement kiln).
Steps 3 and 4 are unnecessary for constituents retained as part of the Step 2 evaluation; they may jump to
Step 5. All other compounds, i.e. non-detected compounds that did not satisfy Step 2, continue to Step 3.
Step 3: Retain for evaluation any non-detect with a high potential to be emitted as a Product of
Incomplete Combustion (PIC).
As defined earlier, PICs are either present in the feed stream and not completely destroyed, or formed
during the combustion process. It's therefore important to consider combustion chemistry in identifying
COPCs. For example, PCDDs and PCDFs may not themselves be found in any feed stream yet still be
emitted, because they can form when chlorine-containing chemicals react with organic matter in the low-
temperature regions of the combustion unit or APCS. We therefore generally recommend that PCDDs
and PCDFs be assessed. The potential for various PICs to be found in combustor emissions is dealt with
in more detail in Sections 2.3.1 through 2.3.13, as well as EPA (2001c).
Identifying/including some compounds (nitroaromatics, phthalates, hexachlorobenzene, and
pentachlorophenol) as PICs in the risk assessment may be warranted, considering waste feed composition
and their potential to be emitted (e.g., nitrogenated wastes, plastics, or highly chlorinated organic waste
streams) (see Sections 2.3.4 through 2.3.6).
Step 4 is unnecessary for PCDDs/PCDFs, PAH's, PCB's, and other compounds with high potential to be
emitted as PICs; they may jump to Step 5. All other compounds, i.e. non-detected compounds that did
not satisfy Steps 2 or 3, continue to Step 4.
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Step 4: Retain for evaluation those compounds that (1) are a concern due to site-specific factors, and
(2) may be emitted by the combustor.
As mentioned in Step 1, site-specific factors may contribute COPCs. For example, if there is
community/regulatory concern about high background concentrations of a substance which would not
have otherwise been assessed (i.e. it was neither a risk/trial burn analyte, nor found in the fugitive
emissions evaluation), and there is reasonable potential for it to be emitted, it may be appropriate to
include the compound. Also, if a compound found in the trial/risk burn analysis or fugitive emissions
evaluation (and therefore included in the COPC list) doesn't satisfy Steps 2 or 3, yet is of concern for site-
specific factors and has reasonable potential to be emitted, it may be appropriate for it to continue to Step
5.
If a compound doesn't have a reasonable potential of being present in the stack emissions, we generally
recommend that the risk assessment report justify this assertion. This information will generally provide
the risk manager with sufficient information to conclude that the facility has not overlooked a serious risk.
Compounds of concern due to site-specific factors with reasonable potential to be emitted continue to
Step 5. Delete all other compounds (i.e. non-detected compounds that did not satisfy Steps 2 through 4)
from consideration in the risk assessment.
Step 5: Research the recommended hierarchy of human health toxicity data (see Appendix A2.6) for
available compound-specific health benchmarks. Add compounds with available toxicity data to
the COPC list for quantitative assessment. Retain compounds that have no toxicity data on the
COPC list for qualitative assessment, and use surrogate toxicity data from a lexicologically
similar compound.
As detailed in Appendix A, we recommend a hierarchy of sources for toxicity data appropriate to use in
the risk assessment. The tox hierarchy represents a library of sources for scientifically defensible,
compound-specific human health benchmarks.
We generally recommend that the assessment of COPCs using surrogate toxicity data not be quantitative
but rather qualitative, and be reported in the Uncertainty section of the risk assessment. The definition of
a "toxicologically similar compound" will depend on the original compound, which in turn changes from
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assessment to assessment. We recognize
the uncertainties involved in even defining
what constitutes "toxicologically similar."
We therefore recommend consulting with
the permitting authority when identifying
toxicologically similar compounds. It's
also within the permitting authority's
purview to determine that it's technically
appropriate (on a compound-specific basis)
to use surrogate toxicity data quantitatively.
Previous guidance on how to qualitatively
assess risk is inconsistent. Common
practice is also highly variable. One option
is to generate quantitative estimates for
compounds using surrogate toxicity data.
These results, however, aren't typically
reported with the rest of the COPCs, nor do
they contribute to risk totals. Instead, the
surrogate-based risk results are typically
reported in the Uncertainty section of the
risk assessment report, to inform risk
management decision-makers.
Please Note: The above is
only one option. We
recommend consulting with
the permitting authority
regarding the appropriate
level of effort in acquiring
surrogate toxicity data, and
other methods and
processes to use in
qualitative assessment.
SPECIAL NOTE:
REGARDING FATE & TRANSPORT DATA
Step 5 in Identifying COPCs focuses on availability of toxicity data
because it tends to be the controlling factor: without toxicity parameter
values, quantitative assessment is not possible. Depending on the
compound, though, availability of fate & transport parameter values
could also be a limiting factor.
If these parameter values are available, then it is scientifically
reasonable, and in the interest of protecting human health and the
environment, to evaluate exposure of receptors to the COPC via various
direct and indirect pathways. However, if the necessary fate & transport
properties for a particular exposure pathway aren't available, then it
seems reasonable to exclude that COPC from consideration for the
affected pathway (or pathways). For example, if a biotransfer value for
milk (see Chapter 5) is not available for a COPC then it can be assumed
that, based on current information, the COPC won't be assessed via the
ingestion of milk exposure pathway. This principle holds true for other
variables as well. Please note, though, that the lack of fate and transport
data doesn't automatically equate to an absence of potential exposure
and risk. We generally recommend that as long as sufficient fate &
transport properties are available, the calculations for each exposure
pathway be completed, and any uncertainties introduced into the risk
assessment described in the uncertainty discussion of the risk assessment
report (see Chapter 8).
Fate & transport parameter data may be quite limited for some
compounds, and acquiring that data can be a labor-intensive and time-
consuming process. In an effort to streamline the risk assessment
process, the fate & transport parameter values needed to follow this
Protocol to assess the 200+ compounds most commonly found in
hazardous waste combustor risk assessments are made available in a
database companion to the HHRAP (available for download from the
HHRAP web site). For those compounds not found in the database, the
Superfund Chemical Data Matrix (SCDM) is a good first source to
acquire the necessary values. When actual values aren't directly
available, HHRAP Appendix A also lists our recommended methods for
estimating parameter values.
We generally recommend consulting with the permitting authority on the
appropriate level of effort to expend acquiring/estimating fate &
transport parameter values.
Step 6. Evaluate the tentatively identified compound (TIC) peaks obtained during gas chromatography
(GC) analysis, to determine whether any of the TICs have toxicities similar to the detected
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compounds. If they do, qualitatively assess using surrogate toxicity data, as recommended for
identified compounds in Step 5.
All organic compounds that are identified and quantified are ultimately subtracted from the total organic
emissions mass value. It is therefore beneficial for the laboratory to identify and quantify the maximum
number of compounds, including TICs. Although it's in your interest to characterize as many TICs as
possible, extensive characterization of TICs involves a significant commitment of time and expertise and
can reach a point of diminishing returns. We therefore generally recommend characterizing TICs when
the peak intensity is 10 percent or more of the full chromatographic scale, and obtaining a quantitative
estimate of the value using the nearest eluting internal standard and a response factor of 1. Unless the
identification of the TIC is confirmed by the analysis of an authentic standard, it may be appropriate to
qualify the quantitative value as "estimated."
We recognize that for many compounds, only limited information on potential health effects is available.
Also, for those chemicals with identified health effects, the relationship between dose and response may
be poorly understood. We suggest that the risk assessment use the sum of the available toxicological
information and evaluate the uncertainty associated with these issues. As stated previously, toxicity
benchmarks and slope factors may change as additional toxicity research is conducted. You may wish to
consult with the most current versions of the resources found in the tox hierarchy (see Appendix A,
Section A2.6) before completing the risk assessment, to make sure that the toxicity data used in the risk
assessment is the most current available.
Previous Agency guidance (1989e; 1994J; 1994n; 1998) recommended that the COPC list for indirect
exposure analysis consist of only those constituents considered to present the most significant risks.
These constituents were selected based on the
1. quantity of the hazardous waste to be burned,
2. toxicity of the hazardous waste to be burned, and
3. potential for the hazardous waste to bioaccumulate.
For direct exposure analysis, however, previous guidance recommended including all constituents for
which stack emission data and inhalation health benchmarks exist. We now recommend that a single
COPC list apply to both indirect and direct exposure analysis. We believe that, through the use of
computer-based calculations, you can efficiently assess all identified COPCs via both direct and indirect
exposure pathways. Savings gained through computer-based calculations will provide for an efficient use
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of facility and regulatory resources. Assessing the entire list of COPCs - rather than a subset as
previously recommended - may help minimize public concern over the exclusion of some COPCs and
reduce confusion for those interested in reviewing the results of the risk assessment.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Complete evaluation of hazardous wastes to be burned in the combustor
Complete evaluation of any raw materials or primary fuels burned in the combustor
Waste analysis procedures used to monitor the composition of hazardous waste feed streams
Analytical data and calculations used to complete the COPC identification process
The following subsections provide specific information and guidance on identifying COPCs for each
facility—with discussions for specific classes of compounds—that we typically recommend including in
risk assessments. Emerging issues surrounding the class of compounds referred to as "endocrine
disrupters" are also discussed.
The following subsections also focus on compounds that past experience has shown can drive risk
assessments. These compounds include PCDDs/PCDFs, PAHs, PCBs, nitroaromatics, phthalates,
hexachlorobenzene and pentachlorophenol, and metals. Volatile organic compounds are also discussed.
We also discuss specific issues that affect the COPC identification process, and evaluating these
compounds in the risk assessment.
2.3.1 Criteria Pollutants
Under the Clean Air Act, EPA establishes air quality standards to protect public health, including the
health of "sensitive" populations such as people with asthma, children, and older adults. EPA has set
national air quality standards (40 C.F.R. Part 40) for six principal air pollutants (also called the criteria
pollutants): nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), participate matter (PM), carbon
monoxide (CO), and lead (Pb). We discuss lead in Section 2.3.5.2, and PM in Section 2.3.7.
Nitrogen dioxide is a reddish brown, highly reactive gas that is formed in the ambient air through the
oxidation of nitric oxide (NO). Nitrogen oxides (NOx), the generic term for a group of highly reactive
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gases that contain nitrogen and oxygen in varying amounts, play a major role in the formation of ozone,
PM, haze, and acid rain. The major sources of man-made NOx emissions are high-temperature
combustion processes such as those that occur in automobiles and power plants. Short-term exposures
(e.g., less than 3 hours) to low levels of NO2 may lead to changes in airway responsiveness and lung
function in individuals with preexisting respiratory illnesses. These exposures may also increase
respiratory illnesses in children. Long-term exposures to NO2 may lead to increased susceptibility to
respiratory infection and may cause irreversible alterations in lung structure. NOx react in the air to form
ground-level ozone and fine particle pollution, which are associated with adverse health effects (U.S.
EPA 2005).
Ozone occurs naturally in the stratosphere approximately 10 to 30 miles above the earth's surface and
forms a layer that protects life on earth from the sun's harmful rays. Ozone is also formed at ground level
by a chemical reaction of various air pollutants combined with sunlight. The pollutants that contribute to
ozone formation are oxides of nitrogen (NOx) and volatile organic compounds (VOCs). "Ground-level"
ozone is an air pollutant that damages human health and the environment. Even at relatively low levels,
ozone may cause inflammation and irritation of the respiratory tract, particularly during physical activity.
The resulting symptoms can include breathing difficulty, coughing, and throat irritation. Breathing ozone
can affect lung function and worsen asthma attacks. Ozone can increase the susceptibility of the lungs to
infections, allergens, and other air pollutants. Medical studies have shown that ozone damages lung tissue
and complete recovery may take several days after exposure has ended (U.S. EPA 2004a).
Sulfur dioxide (SO2) belongs to the family of SOx gases. These gases are formed when fuel containing
sulfur (mainly coal and oil) is burned at power plants and during metal smelting and other industrial
processes. High concentrations of SO2 can result in temporary breathing impairment for asthmatic
children and adults who are active outdoors. Short-term exposures of asthmatic individuals to elevated
SO2 levels during moderate activity may result in breathing difficulties that can be accompanied by
symptoms such as wheezing, chest tightness, or shortness of breath. Other effects that have been
associated with longer-term exposures to high concentrations of SO2, in conjunction with high levels of
PM, include aggravation of existing cardiovascular disease, respiratory illness, and alterations in the
lungs' defenses. The subgroups of the population that may be affected under these conditions include
individuals with heart or lung disease, as well as the elderly and children (U.S. EPA 1986d; 2005).
Carbon monoxide is a colorless and odorless gas, formed when carbon in fuel is not burned completely. It
is a component of motor vehicle exhaust, which contributes about 60 percent of all CO emissions
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nationwide. Other sources of CO emissions include industrial processes, non-transportation fuel
combustion, and natural sources such as wildfires. CO enters the bloodstream through the lungs and
reduces oxygen delivery to the body's organs and tissues. The health threat from levels of CO sometimes
found in the ambient air is most serious for those who suffer from cardiovascular disease such as angina
pectoris. At much higher levels of exposure not commonly found in ambient air, CO can be poisonous,
and even healthy individuals may be affected. Visual impairment, reduced work capacity, reduced manual
dexterity, poor learning ability, and difficulty in performing complex tasks are all associated with
exposure to elevated CO levels (U.S. EPA 2000d).
The permitting authority decides whether to include criteria pollutants in the quantitative risk assessment.
For example, as noted in the November 14, 1997, decision of the Environmental Appeals Board in
reference to the Ash Grove Cement Company Permit No. KSD031203318 and risks associated with
exposure to cement kiln dust controlled through the state solid waste permit, compliance with other
environmental statutes (e.g., CAA, CWA) may be an appropriate method to consider and control risks
from non-RCRA related pollutants (Environmental Appeals Board 1997).
2.3.2 Endocrine Disruptors
Endocrine disrupters are chemicals are thought to mimic natural hormones, inhibit the action of
hormones, or alter the normal regulatory function of the immune, nervous, and endocrine systems.
Possible human health endpoints affected by these agents include breast cancer and endometriosis in
women, testicular and prostate cancers in men, abnormal sexual development, reduced male fertility,
alteration in pituitary and thyroid gland functions, immune suppression, and neurobehavioral effects (U.S.
EPA 1997g).
Problems were encountered while attempting to classify chemical compounds as endocrine disrupters.
Only limited empirical data are available to support the designation of specific chemicals as endocrine
disrupters, and some of the data are conflicting. There is a lack of clear structure-activity relationship, as
well as a lack of unifying dose-response relationship, among the diverse groups of chemicals considered
endocrine disrupters. Also, there are multiple modes of action for chemicals currently considered
endocrine disrupters.
Because the information currently available on endocrine disrupters is inconsistent and limited, U.S. EPA
has not yet developed a methodology for quantitative assessments of human health risk resulting from
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exposure to potential endocrine disrupters (U.S. EPA 1996i). However, the methods for addressing
endocrine disrupters are developing at a rapid pace. We therefore generally recommend contacting the
Economics, Methods and Risk Analysis Division (EMRAD) of the Office of Solid Waste for the latest
guidance on how to address endocrine disrupters in site-specific risk assessments. Additional information
(e.g., U.S. EPA 1997g) is available for review at the web site http://epa.gov/endocrine/pubs.html.
2.3.3 Hexachlorobenzene and Pentachlorophenol
In past guidance (U.S. EPA 1994g; 1994i; 1994J; 1994r) we recommended always including
hexachlorobenzene and pentachlorophenol in risk assessments of hazardous waste combustors. However,
we no longer recommend automatically including them. Rather, we generally recommend carefully
considering the information and issues presented below before deciding whether to include
hexachlorobenzene and pentachlorophenol as COPCs for quantitative assessment.
Hexachlorobenzene is an impurity in pentachlorophenol, while pentachlorophenol is formed from
hexachlorobenzene in the body as well as in some factories (ATSDR 1994a; ATSDR 1994b).
Hexachlorobenzene and pentachlorophenol, like all chlorinated aromatics, are synthesized by the reaction
of elemental chlorine with a parent aromatic (Deichmann and Keplinger 1981; Gray son 1985). The
addition of the first chlorine atom to the benzene or phenol molecule is rapid, but further chlorination
becomes progressively more difficult, requiring ferric chloride or another Lewis acid catalyst to complete
the reaction (March 1985). Therefore, these chlorinated compounds are difficult to make even under
controlled conditions. Hexachlorobenzene, but not pentachlorophenol, has been reported in emissions
from the combustion of municipal solid waste and from other processes (such as the chlorination of wood
pulp) that also produce PCDDs and PCDFs (ATSDR 1994a; ATSDR 1994b). The combustion properties
of these chlorinated compounds indicate that they aren't likely to be formed as PICs if they aren't present
in the waste feed stream.
We consider it prudent to include hexachlorobenzene and pentachlorophenol as COPCs for combustors
that burn waste feeds containing hexachlorobenzene and pentachlorophenol, wood preservatives,
pesticides, or highly variable waste streams such as municipal solid waste. However, we don't
recommend precluding these compounds from analytical testing during the trial burn based only on
process knowledge and waste feed characteristics. Because PCDDs and PCDFs can be formed from fly
ash-catalyzed reactions between halogens and undestroyed organic material from the furnace, other
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Agency guidance (U.S. EPA 1994i; 1998c) recommends including potential precursor compounds in the
risk assessment and trial burn (see Section 2.3). These precursor compounds might include chlorinated
phenols (such as pentachlorophenol) and chlorinated aromatics (such as hexachlorobenzene). Also, the
toxicity and uncertainties associated with combustion chemistry suggest that stack gas testing always
confirm the absence of these compounds from stack emissions.
2.3.4 Hydrogen Chloride/Chlorine Gas
Hydrogen chloride (which becomes hydrochloric acid when dissolved in water) and chlorine are major
products of the chemical industry, with uses too numerous to list. When chlorine gas dissolves in water
(whether during drinking water treatment or when someone inhales chlorine), it hydrolyzes to form equal
amounts of hydrochloric acid and hypochlorous acid; the adverse effects of which are similar but not
identical (Stokinger 1981; ACGIH 1991).
Hydrochloric acid has many uses. It is used in the production of chlorides, fertilizers, and dyes, in
electroplating, and in the photographic, textile, and rubber industries. Hydrochloric acid is corrosive to
the eyes, skin, and mucous membranes. Acute (short-term) inhalation exposure may cause eye, nose, and
respiratory tract irritation and inflammation and pulmonary edema in humans. Acute oral exposure may
cause corrosion of the mucous membranes, esophagus, and stomach and dermal contact may produce
severe burns, ulceration, and scarring in humans. Chronic (long-term) occupational exposure to
hydrochloric acid has been reported to cause gastritis, chronic bronchitis, dermatitis, and
photosensitization in workers. Prolonged exposure to low concentrations may also cause dental
discoloration and erosion (U.S. DHHS 1993).
Chlorine is a potent irritant to the eyes, the upper respiratory tract, and lungs. Chronic (long-term)
exposure to chlorine gas in workers has resulted in respiratory effects, including eye and throat irritation
and airflow obstruction (Cal EPA 2000). Depending on the exposure concentration, acute (short-term)
exposure to chlorine elicits reactions ranging from tickling of the nose and throat (Calabrese and Kenyon
1991) to chest pain, vomiting, dyspnea, and cough (U.S. DHHS 1993). Chlorine is also extremely
irritating to the skin and can cause severe burns in humans (U.S. DHHS 1993).
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2.3.5 Metals
Previous guidance (U.S. EPA 1994g; 1994i; 1998c; NC DEHNR 1997) recommends including the
following inorganic substances in the risk assessment: antimony, arsenic, barium, beryllium, cadmium,
hexavalent chromium, lead, mercury (elemental and divalent), nickel, selenium, silver, thallium, and zinc.
All of these substances, except nickel, selenium, and zinc, are regulated by 40 CFR Part 266, Subpart H
(the BIF regulations). We recommend evaluating nickel and selenium, to determine whether additional
terms and conditions may need to be incorporated into the permit, pursuant to 42 USC § 6925(c)(3) and
40 CFR Part 270.32(b)(2)-i.e., U.S. EPA's "omnibus" authority. In addition, U.S. EPA (200Ic)
recommends also characterizing the metals aluminum, copper, manganese, and vanadium. Another
potential option is applying the BIF regulation Tier I or MACT MTEC assumptions, which assume that
all metals in the waste feed pass through the combustion unit and APCS to the emission stream (U.S. EPA
1992c).
Please Note: It may be appropriate to include metals in the risk assessment even if they
aren't present in the combustor's feed streams. Although metals cannot be formed as
PICs, we are aware of combustors with metal emissions resulting from leaching from
stainless steel feed piping.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
•• Waste feed, raw material, and secondary fuel stream analytical data
•• Metal emission rate sampling data or assumptions based on waste feed data
•• Explanations for excluding specific metals from evaluation during the risk assessment
The following subsections provide additional information on our recommended procedures for evaluating
four metals—chromium, lead, mercury, and nickel. When evaluating stack emissions for the risk
assessment, we highly recommend considering how each of these metals may be affected by the
combustion process, including possible interactions with other constituents.
2.3.5.1 Chromium
The oxidation state of chromium is a crucial issue in evaluating the toxicity of this metal, and the risks
associated with exposure. Hexavalent chromium (Cr+6) is the most toxic valence state of chromium and
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has been shown to be a human carcinogen through inhalation exposure (U.S. EPA 2005i). Trivalent
chromium (Cr+3) is a commonly found, less-oxidized form of chromium. Trivalent chromium has not
been shown to be carcinogenic in either humans or laboratory animals (U.S. EPA 2005i). U.S. EPA
(1990a; 1990b) has indicated that chromium emitted from a combustor is not likely to be in the
hexavalent form. However, there is not sufficient evidence to reliably estimate the partitioning of
chromium emissions into these two valence states. In addition, we recognize that chromium may exist
partially or in some cases entirely as trivalent chromium in various media. For example, Amdur et al.
(1991) states that:
"Trivalent chromium is the most common form found in nature, and chromium in
biological materials is probably always trivalent. There is no evidence that trivalent
chromium is converted to hexavalent forms in biological systems. However, hexavalent
chromium readily crosses cell membranes and is reduced intracellularly to trivalent
chromium.."
We generally consider it best to use measured, speciated emissions data in the risk assessment. If site-
specific speciated emissions data is unavailable, you may generate a default speciation. We generally
recommend using the following method (developed by us through interpretation of data available in the
MACT database, as documented in Appendix D) to generate a default speciation:
• When the measured amount of total chromium is <10 • g/dscm, we recommend a default
of 5 • g/dscm hexavalent chromium.
• When the measured amount of total chromium is in the range of 10 • g/dscm to 100
• g/dscm, we recommend assuming 45 percent is hexavalent chromium.
• When the measured amount of total chromium is >100 • g/dscm, we recommend
assuming 30 percent is hexavalent chromium.
2.3.5.2 Lead
We generally recommend that risk assessments evaluating lead as a COPC use the IEUBK model when
soil concentrations are calculated to be above the benchmark.
The Integrated Risk Information System (IRIS) doesn't currently list an RfD or RfC for lead, because a
threshold level for exposure to lead has not been established. While the Agency has characterized lead as
a probable human carcinogen, it has not developed a quantitative estimate of cancer risk due to a number
of uncertainties, some of which may be unique to lead (U.S. EPA 2005b). The Agency has typically
relied on the neurological effects observed in children as the sensitive endpoint for evaluating lead
toxicity. Consequently, the Agency developed the integrated Exposure Uptake Biokinetic (IEUBK)
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Model for Lead in Children. Developed through the efforts of U.S. EPA (1990c) and Kneip, et al. (1983),
this model evaluates potential risks based on predicted blood lead levels associated with exposure to lead
(U.S. EPA 1994e). The IEUBK model integrates several assumptions about the complex exposure
patterns and physiological handling of lead by the body, and it has been validated at several sites at which
lead exposure data and human blood lead levels are available (U.S. EPA 1990c). The U.S. EPA Science
Advisory Board (U.S. EPA 1992b) and the U.S. EPA's Technical Review Workgroup for Lead have both
reviewed and recommended the IEUBK model.
The Agency has developed a computerized version of the IEUBK model that predicts blood lead levels
and distributions for children 0 to 7 years of age (U.S. EPA 1994e). The IEUBK model is available for
download at http://www.epa.gov/superfund/programs/lead/products.htm. The IEUBK computer model
cannot predict potential blood lead levels in adults. The Agency has developed an Interim Approach to
Assessing Risks Associated with Adult Exposures to Lead in Soil (U.S. EPA 1996r). This interim model is
intended for "assessing adult lead risks associated with nonresidential [industrial] exposure scenarios."
However, in general, children are more susceptible to lead exposures than adults because of higher soil
ingestion rates and greater absorption by the gut, in addition to nutritional variables and lower body
weight. In fact, The Agency's interim approach for assessing adult exposures to lead is based not on
limiting adult toxicity, but rather on limiting fetal toxicity by limiting indirect fetal exposure through
direct maternal exposures to lead (U.S. EPA 1996r).
As stated before, we generally recommend that risk assessments evaluating lead as a COPC use the
IEUBK model when soil concentrations are calculated to be above the benchmark. We don't generally
recommend evaluating carcinogenic risks or noncarcinogenic hazards of lead. When run with standard
recommended default values (these generally represent national averages, or "typical" values), the
Agency's IEUBK model predicts that no more than 5 percent of children exposed to a lead concentration
in soil of 400 mg/kg will have lead concentrations in blood exceeding 10 (ig/dL (U.S. EPA 1994e and
1994o).
2.3.5.3 Mercury
We generally recommend that the risk assessment evaluate exposure to three mercury species via varied
pathways:
1. Assess elemental mercury only through direct inhalation of the vapor phase;
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2. Assess divalent mercury through both direct inhalation and indirect exposure to vapor
and particle-bound mercuric chloride; and.
3. Assess methyl mercury only through indirect exposure.
Air emissions of mercury contribute to local, regional, and global deposition. The U.S. Congress
explicitly found this to be the case and required the Agency to prioritize maximum achievable control
technology (MACT) controls for mercury (U.S. Congress 1989).
The Mercury Study Report to Congress (U.S. EPA 1997c) found that anthropogenic mercury releases are
thought to be dominated on the national scale by industrial processes and combustion sources that release
mercury into the atmosphere. A portion of these anthropogenic releases is in the form of elemental
mercury, and a portion in the form of mercuric chloride. Coal combustion is responsible for more than
half of all mercury emissions from U.S. anthropogenic sources. The fraction of coal combustion
emissions in oxidized form (i.e. mercuric chloride) is thought to be less than the fraction in oxidized form
from other combustion emission sources (including waste incineration).
Stack emissions include both vapor and particulate forms of mercury. Most of the total mercury emitted
from the stack is in the vapor phase, although exit streams containing soot or particulates can bind up
some fraction of the mercury. Vapor mercury emissions are thought to include both elemental (Hg°) and
oxidized (e.g., Hg+2) chemical species. Particulate mercury emissions are thought to be composed
primarily of oxidized compounds, due to the relatively high vapor pressure of elemental mercury (U.S.
EPA 1997c).
The methods for analyzing mercury speciation in emission plumes are being refined, and there is still
controversy in this field. The speciation of mercury emissions is thought to depend on the fuel used, flue
gas cleaning, and operating temperatures. True speciation of mercury emissions from the various source
types is still uncertain and thought to vary not only among source types, but also between individual
plants (U.S. EPA 1997c). Total mercury exiting the stack is assumed to consist entirely of elemental and
divalent species, with no emissions of methyl mercury. The exit stream is thought to range from almost
all elemental mercury to nearly all divalent mercury. Much of the divalent mercury is thought to be
mercuric chloride (HgCl2) (U.S. EPA 1997c), particularly in the combustion of wastes containing
chlorine. The divalent fraction is split between vapor and particle-bound phases (Lindqvist et al. 1991).
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Please note that data on mercury speciation in stack emissions is very limited. Also, the behavior of
mercury emissions close to the point of release has not been extensively studied. It is possible for
chemical reactions to occur in the emission plume. This results in a significant degree of uncertainty
implicit in modeling mercury emissions. Additional examples of uncertainties include the precision of
measurement techniques, estimates of pollution control efficiency, limited data specific to source class
and activity level. U.S. EPA (1997c) discusses uncertainty, and sensitivity analyses of several of the
assumptions used in the modeling of mercury emissions. Additional discussions and examples of
mercury modeling can be found in the proceedings of the ITS conference (Kaleri 2000).
Site-Specific Mercury Sampling
If site-specific mercury sampling information is available, one option is to estimate the oxidation state and
phase distributions. We recommend basing the estimates on the concentration of mercury in various
components of the Agency's multiple metals sampling train (i.e., U.S. EPA Method 29 or Method 0060)
using the following guidelines:
• Mercury found in the acidic potassium permanganate impingers would be expected to be
the elemental form of mercury (Hg°).
• Divalent mercury (HgCl2) is soluble in water and would be expected to be found in the
dilute nitric acid/hydrogen peroxide impinges. This is also referred to as the ionic portion.
Mercury found in the probe and filter can be assumed to be vapor-phase mercury
adsorbed onto particulate matter or a solid-phase compound. This fraction is referred to
as Hg(PM).
We understand that these methods can be biased by high SO2 and trace C12 in the feed. This bias results
in the over-reporting of ionic vapor (or divalent form) and the under-reporting of elemental vapor
mercury. For risk assessment purposes, we consider this protective.
Default Phase Allocation and Speciation of Mercury Exiting the Stack
As discussed above, stack emissions are thought to be speciated into both divalent and elemental mercury,
and include both vapor and particle-bound forms. Vapor-phase divalent mercury is thought to be more
rapidly and effectively removed by both dry and wet deposition than particle-bound divalent mercury.
This is a result of the reactivity and water solubility of vapor-phase divalent mercury (Lindberg et al.
1992; Peterson et al. 1995; Shannon and Voldner 1994). Also, divalent mercury emitted either in the
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vapor phase or particle-bound, is thought to be subject to much faster atmospheric removal than elemental
mercury (Lindberg et al. 1992; Peterson et al. 1995; Shannon and Voldner 1994). A small fraction (about
one percent) of vapor-phase elemental mercury may be atmospherically transformed into divalent
mercury by tropospheric ozone and adsorbed to particulate soot in the air and subsequently deposited in
rainfall and snowfall (U.S. EPA 1997c).
Based on review of mercury emissions data presented for combustion sources in U.S. EPA (1997c) and
published literature (Peterson et al. 1995), estimates for the percentage of vapor and particle-bound
mercury emissions range widely from 20 to 80 percent. Therefore, unless site-specific mercury sampling
information is available, we generally recommend a protective approach that assumes phase allocation of
mercury emissions from hazardous waste combustion of 80 percent of total mercury in the vapor phase
and 20 percent of total mercury in the particle-bound phase. As illustrated in Figure 2-4, of the 80 percent
total mercury in the vapor phase, 20 percent of the total is in the elemental form and 60 percent of the
total is in the divalent form (Peterson et al. 1995). Of the 20 percent of the total mercury that is
particle-bound, 99 percent (assumed to be 100 percent in Figure 2-4) is in the divalent form. This
allocation is:
Consistent with mercury emissions speciation data for hazardous waste combustion
sources reported in literature (Peterson et al. 1995); and
Believed to be reasonably protective, since it results in the highest percentage of total
mercury being deposited in proximity to the source, and therefore, indicative of the
maximum indirect risk.
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FIGURE 2-4
PHASE ALLOCATION AND SPECIATION OF MERCURY IN AIR
Total Mercury Emissions Exiting Stack Into Air
[lO.Og]
r
80% is in Vapor Phase
[S.OOg]
V
20% is in Particle-Bound Phase J
[2.00g]
20%
is HgQ vapor
[2.00g]
60%
is Hg2+ vapor
[6.00g]
20% is H,
g2+ particle-bound
[2.00g]
[0.02g]
[0.72g]
-*• 99% Enters Global Cycle as Hg° vapor
32% Enters Global Cycle as Hg2+ vapor
64% Enters Global Cycle as Hg2+ particle-bound
Fv (Total Mercury) = 0.8
Without Considering Global Cycle:
• 20% of Total Mercury Emitted
is deposited as Hg° [2g / lOg]
• 80% of Total Mercury Emitted
is deposited as Hg2+ [(6g + 2g) / lOg]
Calculated F.,:
•Fv(Hg°)= [2g/2g] = 1.0
LEGEND
Hg° - Elemental Mercury
Hg2+ - Divalent Mercury
[ ] - Example Mass Allocation
v (Hg2+) = [6g / (6g+2g)] = 0.75
Considering Global Cycle:
• 0.2% of Total Mercury Emitted
is deposited as Hg° [0.02g / lOg]
• 48% of Total Mercury Emitted
is deposited as Hg2+ [(4.08g + 0.72g) / lOg]
Calculated F.,:
•Fv (Hg°) = [0.027(0.02 + 0)] = 1.0
•Fv (Hg2+) = [4.087(4.08 + 0.72)] = 0.85
Compound-Specific Emission Rate (Q)
•Actual Q (Hg°) = 0.2% * Q (Total Mercury)
•Actual Q (Hg2+) = 48% * Q (Total Mercury)
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The Mercury Global Cycle
According to information in U.S. EPA (1997c), a vast majority of mercury exiting the stack doesn't
readily deposit, but is vertically diffused to the free atmosphere, transported outside the study area and
into the global cycle. Regardless of the source of phase and speciation distribution values (i.e. either site-
specific sampling data or default values), we generally recommend using the following fractions from
U.S. EPA (1997c):
A vast majority of the vapor-phase elemental mercury (over 99 percent) doesn't readily
deposit, but becomes part of the global cycle;
Of the mercury emitted as vapor-phase divalent mercury, about 68 percent deposits and
about 32 percent diffuses vertically to the global cycle; and
36 percent of the particle-bound divalent mercury deposits, and the rest diffuses vertically
to the global cycle.
Deposition and Modeling of Mercury
Based on information in U.S. EPA (1997c) and as shown in Figure 2-4, we generally assume that
deposition to the various environmental media is almost entirely divalent mercury in either the vapor or
particle-bound form. Without considering the global cycle, 80 percent of total mercury would be
deposited as divalent mercury and the remaining 20 percent would be deposited as elemental mercury.
We generally recommend using the percentages provided in U.S. EPA (1997c) to account for the global
cycle. Using these figures, the percentage of total mercury deposited would be reduced to a total of
48.2 percent (40.8 percent as divalent vapor, 7.2 percent as divalent particle-bound, and 0.2 percent as
elemental vapor). As discussed in Appendix A-2, these speciation splits result in fraction in vapor phase
(Fv) values of 0.85 (40.8/48.0) for divalent mercury, and 1.0 (0.2/0.2) for elemental mercury. Also, to
account for the remaining 51.8 percent of the total mercury mass that is not deposited, the deposition and
media concentration equations (presented in Appendix B), multiply the compound-specific emission rate
(0 for elemental mercury by a default value of 0.002; and divalent mercury by a default value of 0.48.
Methylation of Mercury
The net mercury methylation rate (the net result of methylation and demethylation) for most soils appears
to be quite low; with much of the measured methyl mercury in soils potentially resulting from wet
deposition (U.S. EPA 1997c). Based on the information in U.S. EPA (1997c), We assume that 98 percent
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of the deposited mercury remains divalent mercury, and two percent speciates to organic mercury (methyl
mercury) in soil. A significant and important exception to mercury methylation rate being low in soils
appears to be wetland soils. Wetlands appear to convert a small but significant fraction of the deposited
mercury into methyl mercury; which can migrate to nearby water bodies and potentially bioaccumulate in
the aquatic food chain (U.S. EPA 1997c). Therefore, we assume the percentage of methyl mercury in
wetland soils is higher than the 2 percent assumed for non-wetland soils. However, wetlands soils aren't
specifically considered in any of the exposure pathways represented in the recommended human health
exposure scenarios (see Chapter 4).
Both watershed erosion and direct atmospheric deposition can be important sources of mercury to a water
body (U.S. EPA 1997c). There appears to be a great deal of variability in the processing of mercury
among water bodies. As a result, you can generally expect different water body types to have different
ranges of methylation, with wetlands generally expected to have higher percentages of methyl mercury
than lakes, and lakes subsequently more than rivers or streams (Driscoll et al. 1994; Hurley et al. 1995;
Krabbenhoft et al. 1999; Watras et al. 1995). Studies have also shown that rivers or lakes with wetland
components (particularly riparian wetlands) have an increased methyl mercury content (Hurley et al.
1995; Krabbenhoft et al. 1999; St. Louis et al. 1996). The percentage of the water body that constitutes a
riparian wetland also contributes (i.e., the higher percentage - the higher the methyl mercury
concentration). The watershed is also an important factor in determining the methyl mercury
concentration of the water body. Waterbodies that are surrounded by agricultural or forested land tend to
have higher methylation fractions. Waterbodies that are surrounded by mining activities have high
amounts of inorganic mercury in the water, and therefore have a lower methylation efficiency
(Krabbenhoft et al. 1999). As briefly discussed later in this section, this variability in methylated mercury
concentrations is primarily due to the characteristically wide range of chemical and physical properties of
water bodies. Additionally, mercury entering the water body can be methylated predominantly through
biotic processes (U.S. EPA 1997c).
In the absence of site-specific measurements to support evaluation of water body properties and biotic
conditions relevant to mercury methylation, we generally recommend assuming that 85 percent of total
mercury in surface water is divalent mercury, and the remaining mass is methyl mercury. This percentage
(i.e., 15 percent as methyl mercury) is based on the average of reported values for the fraction of total
mercury that is methyl mercury in surface water (Akagi et al. 1979; Bloom and Effler 1990; Bloom et al.
1991; Gill and Bruland 1990; Kudo et al. 1982; Lee and Hultberg 1990; Parks et al. 1989; Watras and
Bloom 1992). These literature sources were originally presented in the SAB Review Draft of the
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Mercury Study Report to Congress (U.S. EPA 1996s). The final Mercury Study Report to Congress (U.S.
EPA 1997c, Volume III; Appendix D) also presents literature values for the fraction of methyl mercury in
the water column. However, the data are specific to the epilimnion and hypolimnion. For the epilimnion,
reported values range from 4.6 percent to 15 percent, with a point estimate of 7.8 percent. For the
hypolimnion, reported values range from 27 percent to 44 percent, with a point estimate of 36 percent.
We are modifying our previous recommendation for applying the default mercury speciation (85 percent
divalent mercury and 15 percent methyl mercury) to each calculated water body loading. Instead, we
recommend that a dissolved water concentration first be calculated for total mercury using the fate and
transport parameters specified for mercuric chloride. Then, the dissolved total mercury concentration
should be apportioned based on an 85 percent divalent and 15 percent methyl mercury speciation split in
the water body. Appendix B (Table B-4-24) presents the equations we recommend for applying the
speciation assumptions.
For most environmental systems, the literature suggests that various physical and chemical conditions
may influence the methylation of mercury. In some cases you might need to consider these conditions,
and the magnitude of their potential impact, to assess the potential for over- or under-predicting mercury
methylation in media and subsequent biotransfer up the food chain. There is extreme variation between
modeled environmental systems, and at times disagreement in the literature regarding the quantitative
influence of specific conditions on methylation. Table 2-4 summarizes the qualitative effects that some
physical and chemical conditions, as reported in literature, may have on methylation. We therefore
generally recommend conducting extensive research of literature specific to the conditions prevalent at
the site, before deviating from the protective assumptions recommended above.
More recent advances in scientific understanding of the physical, chemical, and biological processes
controlling mercury speciation and partitioning in water bodies are summarized in U.S. EPA (2005g).
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TABLE 2-4
QUALITATIVE EFFECTS OF
PHYSICAL & CHEMICAL CONDITIONS ON METHYLATION
Physical or Chemical Condition
Low dissolved oxygen
Decreased pH
Decreased pH
Increased dissolved organic carbon
(DOC)
Increased dissolved organic carbon
(DOC)
Increased salinity
Increased nutrient concentrations
Increased selenium concentrations
Increased temperature
Increased sulfate concentrations
Increased sulfide concentrations
Qualitative Influence on Methylation
Enhanced methylation
Enhanced methylation in water column
Decreased methylation in sediment
Enhanced methylation in sediment
Decreased methylation in water column
Decreased methylation
Enhanced methylation
Decreased methylation
Enhanced methylation
Enhanced methylation
Enhanced methylation
Referenced Literature
Rudd et al. 1983; Parks et al. 1989
Xun 1987; Gilmour and Henry 1991;
Miskimmin et al. 1992
Ramlal et al. 1985; Steffan et al. 1988
Chois and Bartha 1994
Miskimmin et al. 1992
Blum and Bartha 1980
Wright and Hamilton 1982;
Jackson 1986; Regnell 1994;
Beckvaretal. 1996
Beckvaretal. 1996
Wright and Hamilton 1982; Parks et al.
1989
Gilmour and Henry 1991; Gilmour et
al. 1992
Beckvaretal. 1996
The deposition and media concentration equations that we generally recommend (presented in Chapter 5
and Appendix B) have been modified specifically to account for the methylation and subsequent
biotransfer of mercury, assuming steady-state conditions. The HHRAP companion database provides the
parameter values specific for methyl mercury, and Appendix A-2 includes additional discussion and
reference on their origin.
As noted above, methylation can be highly variable between environmental systems. This results in a
significant degree of uncertainty implicit in the modeling of mercury methylation. To expand on the
qualitative information presented in Table 2-4, and to better understand conditions that may influence
mercury methylation specific to a site, we recommend reviewing the related information presented in U.S.
EPA (1997c; 2005).
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We generally recommend using the equations and protective assumptions presented in this guidance to
estimate risks associated with mercury. If estimated risks exceed target levels, it may be appropriate to
use more extensive site-specific data (if available) and subsequently a more rigorous modeling effort, to
further evaluate points of potential exposure. For example, if sufficient site-specific data is available, it
could be used in a model that predicts transformation of chemical forms and biotransfer of mercury. One
such model is the SERAFM (Spreadsheet Ecological Risk Assessment for the Fate of Mercury) model
developed by EPA's Office of Research and Development, National Exposure Research Laboratory,
Ecosystems Research Division. SERAFM updates the IEM-2M mercury fate and transport algorithms
described in detail in U.S. EPA (1997c) by incorporating more recent advances in scientific
understanding of the physical, chemical, and biological processes controlling mercury speciation and
partitioning in water bodies. The SERAFM enhancements to IEM-2M are summarized in U.S. EPA
(2005g).
SERAFM is written in an easy-to-implement Microsoft Excel format so that all manipulations, parameters
and equations are readily available to the user. By specifying only a few additional water body
parameters beyond those already utilized in this guidance (i.e., water body pH, dissolved organic carbon
and color), the user is able to model specific water body mercury transformation processes instead of
using the default speciation assumptions (i.e., 85 percent divalent/15 percent methyl). In the SERAFM
model, mercury species are subject to several transformation reactions including photo-oxidation and dark
oxidation of elemental mercury in the water column, photo-reduction and methylation of divalent
mercury in the water column and sediment layers, and photo-degradation and demethylation of methyl
mercury in the water column and sediment layers. For hazardous waste combustion sources, it is
recommended that, watershed soil concentrations and water body loadings due to source deposition be
calculated externally to SERAFM using the equations presented in Chapter 5 (Equations 5-1E and 5-28)
and then linked to the appropriate SERAFM worksheets for calculation of speciated mercury
concentrations in the water body.
The decision to use more complex mercury models in a risk assessment is not precluded just because they
are different from the model we recommend in the HHRAP. It is for the permitting authority to decide
whether the assessment will use more complex mercury models. If you use more complex mercury
models, we recommend ensuring that sufficient and reliable site-specific data is readily available, and
then carefully identifying and evaluating the models' associated limitations, and clearly documenting the
evaluation in the Uncertainty section of the risk assessment report.
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Conclusion
We encourage all facilities to implement a combination of waste minimization and control technology
options to reduce mercury emission rates on an ongoing basis. Realistic expectations for mercury
emission reduction efforts may be established by considering various technology-based mercury emission
limits that apply to waste combustors (for example, standards for European combustors, the MACT
standards for hazardous waste combustors, or the MACT standards for municipal waste combustors). We
acknowledge that site-specific risk assessments as currently conducted may not identify the entire
potential risk from mercury emissions. Mercury that doesn't deposit locally will ultimately enter the
global mercury cycle for potential deposition elsewhere.
2.3.5.4 Nickel
We generally recommend evaluating nickel as an inhalation carcinogen using the inhalation unit risk
factor for nickel refinery dust. We generally recommend evaluating nickel for other effects using the oral
RfD for nickel soluble salts, the only available nickel-related RfD (see Appendix A-2 and the HHRAP
companion database).
Nickel refinery dust is identified as a potential human inhalation carcinogen (U.S. EPA 2005i). Major
components of nickel refinery dust include nickel subsulfide, nickel oxide and nickel sulfate. IRIS
classifies nickel subsulfide - the primary component (roughly 50%) of nickel refinery dust - a Class A
human carcinogen (U.S. EPA 2005e). However, all components responsible for the carcinogenicity of
nickel refinery dust have not been conclusively established (U.S. EPA 2005c). Because the component
(or components) of nickel refinery dust causing it to be carcinogenic have not been conclusively
established, we consider it appropriate to evaluate nickel emissions as a potential carcinogen via the
inhalation pathway. In addition, nickel oxides can be reduced to nickel sulfates (some of which are
carcinogenic) in the presence of sulfuric acid (Weast 1986). Hazardous waste combustors which burn wet
wastes containing significant amounts of nickel and sulfur may need to be especially concerned with
nickel emissions.
We generally recommend evaluating nickel as an inhalation carcinogen because some forms of
nickel—including nickel carbonyl, nickel subsulfide, and nickel refinery dust—are considered
carcinogens (U.S. EPA 2005c,d,e). This is contrary to the Agency's previous analysis of the toxicity of
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nickel emissions from hazardous waste combustors. These forms of nickel were not considered in
developing the BIF regulations, because the BIF regulations assumed that nickel can only be emitted as
nickel oxide, which by itself is not considered to be a carcinogen (U.S. EPA 1991a).
If the permitting authority has information at points of potential inhalation exposure that demonstrate the
absence of nickel refinery dust components, or the presence only of noncarcinogenic nickel species, it
may be appropriate to use this information as the basis for supplemental noncarcinogenic calculations.
For exposure pathways other than inhalation, nickel has not been shown to be carcinogenic (U.S. EPA
2005i).
2.3.6 Nitroaromatics
We generally recommend carefully considering the information in the following paragraphs before
deciding the appropriateness of including nitroaromatic organic compounds in the risk assessment. It is
reasonable to include nitroaromatics as COPCs if the combustor feed streams include nitroaromatic
compounds or close relatives (TDA and TDI).
Please Note. In earlier guidance (U.S. EPA 1994g; 1994i; 1994J; 1994r) we
recommended that risk assessments always include nitroaromatic organic compounds,
including 1,3-dinitrobenzene; 2,4-dinitrotoluene; 2,6-dinitrotoluene; nitrobenzene; and
pentachloronitrobenzene. We no longer recommend automatically including
nitroaromatic organic compounds in risk assessments.
Nitroaromatic organic compounds such as 1,3-dinitrobenzene; 2,4-dinitrotoluene; 2,6-dinitrotoluene;
nitrobenzene; and pentachloronitrobenzene (or close relatives such as toluenediamine [TDA] and toluene
diisocyanate [TDI]—derivatives of dinitrotoluene) are typically associated with explosives and other
highly nitrogenated hazardous wastes. Dinitrotoluene is used to make two products: trinitrotoluene and
TDA. TDA is, in turn, used to make TDI. TDI readily reacts with water, and is therefore very unstable at
ambient conditions. TDI is typically reacted with a polyol to form polyurethane (PU) plastics.
Combustion properties of these nitroaromatic compounds indicate that they won't be formed as PICs if
they aren't present in the waste feed stream, mainly because of the thermodynamic and chemical
difficulty of adding a nitro group to an aromatic. The process requires that (1) nitronium ions be
generated, and (2) an aromatic ring react with the nitronium ion, with the nitronium ion attaching to the
ring. This reaction process is not likely to occur in a hazardous waste combustor because (1) the reaction
is typically carried out by using a "nitrating acid" solution consisting of three parts concentrated nitric
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acid to one part sulfuric acid, and (2) nitronium ions aren't usually formed in a combustor environment (if
they are, a thermodynamically more favorable reaction will occur, thereby eliminating the nitronium ion)
(Hoggett, et al 1971; Schofield 1980; March 1985).
Combustion conditions most likely to result in nitrogenated PICs are associated with premature
quenching of the primary flame—resulting from low temperature or excess air in the primary combustion
chamber of the unit (U.S. EPA 1994J). Under such conditions, sampling for hydrogen cyanide is also
recommended (U.S. EPA 1994J).
2.3.7 Particulate Matter
We don't recommend evaluating PM as a separate COPC in the risk assessment. However, PM is
generally quite useful as an indicator variable, because it can be measured in real time and is sensitive to
changes in combustion conditions.
Particle pollution is a mixture of solid particles and liquid droplets found in the air. Some particles are
emitted directly from a source, while others form in complicated chemical reactions in the atmosphere. In
general, particle pollution consists of a mixture of larger materials, called "coarse particles," and smaller
particles, called "fine particles." Coarse particles have diameters ranging from about 2.5 micrometers
(• m) to more than 40 • m, while fine particles, also known as known as PM2.5, include particles with
diameters equal to or smaller than 2.5 • m. EPA also monitors and regulates PM10, which refers to
particles less than or equal to 10 • m in diameter. PM10 includes coarse particles that are "inhalable" -
particles ranging in size from 2.5 to 10 • m that can penetrate the upper regions of the body's respiratory
defense mechanisms (U.S. EPA 2004b).
Exposure to particles can lead to a variety of serious health effects. Scientific studies show links between
these small particles and numerous adverse health effects. Long-term exposures to PM, such as those
experienced by people living for many years in areas with high particle levels, are associated with
problems such as decreased lung function, development of chronic bronchitis, and premature death.
Short-term exposures to particle pollution (hours or days) are associated with a range of effects, including
decreased lung function, increased respiratory symptoms, cardiac arrythmias (heartbeat irregularities),
heart attacks, hospital admissions or emergency room visits for heart or lung disease, and premature
death. (U.S. EPA 1982c; 2004c).
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Ambient PM is a complex mix of constituents derived from many sources, both natural and
anthropogenic. Hence, the physicochemical composition of PM generally reflects the major contributing
local and regional sources arising locally as well as regionally. It stands to reason that the contribution of
any given component within the mix may not be equivalent in value or potency, but may well be highly
dependent on other physicochemical attributes (e.g., co- constituents, specific bioavailability, or chelates),
as well as the health status of the exposed individual. Evidence collected to date indicates that the
discovery of a uniquely responsible physicochemical attribute of PM is not likely to occur (U.S. EPA
2004c).
2.3.8 Phthalates
We generally recommend carefully considering the information in the following paragraphs before
deciding the appropriateness of including phthalates in combustor risk assessments. At the same time,
due to their toxicity and bioaccumulative potential, don't automatically discount the evaluation of
phthalates in the risk assessment. If phthalates are included as COPCs in the risk assessment, we
generally recommend using a metabolism factor (MF) of 0.01 for BEHP, and 1.0 for all other COPCs.
Please Note: In earlier guidance (U.S. EPA 1994g; 1994i; 1994J; 1994r) we
recommended always including BEHP and DNOP in every risk assessment. We no
longer recommend automatically including phthalates in risk assessments.
Phthalates such as bis(2-ethylhexyl)phthalate (BEHP) and di(n)octyl phthalate (DNOP) are synthesized
by reacting alcohol with phthalic anhydride in the presence of an acidic catalyst in a nonaqueous solvent
(ATSDR 1993; ATSDR 1995b). Among all phthalate plasticizers, BEHP—also referred to as
di(2-ethylhexyl)phthalate or dioctyl phthalate—is produced in the largest volume; it is used in the
manufacturing of polyvinyl chloride, the most widely produced plastic. DNOP is a plasticizer that is
produced in large volumes, and is used in the manufacture of plastics and rubber materials. Because
plastics have become so widely used in society, phthalate plasticizers such as BEHP and DNOP have
become widely distributed in food, water, and the atmosphere (Howard 1990). The general public's
exposure to phthalate-contaminated food averages 0.3 (ig/day/individual, with an estimated maximum
exposure of 2 mg/day/individual (ATSDR 1992). Phthalate plasticizers are commonly found in the
environment and are practically impossible to avoid, especially at the trace concentrations that modern
analyses can detect.
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Phthalates and their predecessors are readily burned, as indicated by their flash points of 150 to 225 °C
(NIOSH 1994). There is no apparent mechanism for phthalate PICs to be formed by burning other
chemical compounds. Therefore, phthalates are very unlikely to be emitted from a combustor, although
some degradation products, such as PAHs, are likely to be emitted when phthalates are included in the
waste feed. However, facilities that burn plastics or materials with phthalate plasticizers should carefully
consider the potential for phthalate plasticizers to exist in the stack gas emissions due to incomplete
combustion. Also, the uncertainties associated with combustion chemistry suggest that stack gas testing
confirm the absence of these compounds from stack emissions, rather than relying on process knowledge
or waste feed characterization data.
Based on the findings of long-term animal carcinogenicity studies, the Agency has classified BEHP as a
"probable human carcinogen" (class B2) (NTP 1982). Because of its octanol-water coefficient (Kow)
value, BEHP has been presumed to have a high tendency to bioaccumulate (Mackay, Shiu, and Ma 1992;
Karickoff and Long 1995). Considering its ubiquity, B2 classification, and high tendency to
bioaccumulate, BEHP is on most Agency lists of target chemicals (see Table A-l), including the Contract
Laboratory Program (CLP) semivolatile organics analysis list; the Groundwater Monitoring List (40 CFR
Part 264, Appendix IX); and the Hazardous Substances and Reportable Quantities List (40 CFR Part
302.4).
Evidence indicates BEHP is more readily metabolized and excreted by mammalian species than other
contaminants (ATSDR 1987). As stated above, we generally recommend using anMFof 0.01 for BEHP,
and 1.0 for all other COPCs. AnMF represents the estimated amount of COPC that remains in fat and
muscle. Based on a study by Ikeda et al. (1980), the Agency (EPA 1995h) used a COPC-specific MFto
account for metabolism in animals and humans. Considering the recommended values for this variable,
MFhas a quantitative effect on animal and human concentrations only for BEHP. No information could
be found on the metabolism or disposition of DNOP in the peer-reviewed literature. However,
disposition data were found for an isomer of DNOP, diisooctyl phthalate (DIOP), a branched-chain
phthalate (Ikeda et al., 1978). Based upon its similarity in structure, it may be assumed that DNOP would
behave comparatively to DIOP and BEHP, and therefore, may be over estimated by approximately a
factor of 100.
The MF applies only to mammalian species, including beef cattle, dairy cattle, and pigs. It does not relate
to metabolism in produce, chicken, or fish. In addition, since exposures evaluated using this guidance are
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intake driven, only apply a metabolism factor to evaluating indirect human exposure via ingestion of beef,
milk, and pork. In summary, using an MF doesn't apply for direct exposures to air, soil, or water, or to
ingestion of produce, chicken, or fish. Using anMFis further discussed in Section 5.4.4.7 and
Appendix B, Tables B-3-10, B-3-11, andB-3-12.
2.3.9 Polychlorinated Biphenyls
Because of evidence that PCBs can be emitted from combustion sources regardless of feed characteristics,
and considering the significant toxicity of PCBs, we recommend conducting stack testing for PCBs to
support the risk assessment. We also recommend automatically including PCBs as COPCs for
combustors that burn PCB-contaminated wastes or waste oils, highly variable waste streams such as
municipal and commercial wastes (for which PCB contamination is a reasonable assumption), and highly
chlorinated waste streams. Due to the toxicity and uncertainties associated with combustion chemistries,
we generally recommend that stack gas testing confirm the absence of these compounds from stack
emissions.
The most commercially useful property of PCBs is that they are chemically stable in relatively adverse
conditions, such as temperatures of several hundred degrees in an oxygen-containing atmosphere. The
more chlorinated congeners are more resistant to reaction. PCBs were produced commercially by the
reaction of the aromatic hydrocarbon biphenyl with chlorine gas in the presence of a suitable catalyst,
generally ferric chloride or another Lewis acid (ATSDR 1995d). The degree of chlorination was
controlled by manipulating the reaction conditions, including temperature, pressure, and the ratio of the
reactants (Erickson 1992; Grayson 1985). The uses and distribution of poly chlorinated biphenyls (PCBs)
were severely restricted in the United States in the late 1970s—with additional bans and restrictions
taking effect over the next decade (ATSDR 1995d).
Due to their stability in adverse conditions, destruction of PCBs by burning generally requires contact
with high temperatures (at least 1,200 °C) for an extended period of time (more than 2 seconds), under
conditions with adequate oxygen (Erickson 1992). Waste combustors can contribute significantly to total
emission inventories of PCBs (Alcock et al. 1999; U.S. EPA 1997e). An increasing body of information
supports the likelihood that PCBs may be emitted as by-products of burning, regardless of PCB
contamination in the combustor feed.
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It is possible that PCBs can be formed by the same types of reactions that produce dioxins and furans,
including gas-phase formation, heterogeneous formation from organic precursors, and de novo synthesis
from flyash-bound carbon. Lemieux et al. (1999) hypothesized that if PCBs and dioxins and furans are
formed by similar mechanisms, then emissions of PCBs should correlate with emissions of dioxins and
furans. This hypothesis was tested by reviewing data where both PCBs and dioxins and furans were
measured. An apparent trend was indeed found showing increased PCB emissions with increased
emissions of dioxins and furans. In most cases, PCBs were found in the stack even when there were no
PCBs in the combustor feed. Overall, PCB emissions exceeded dioxin and furan emissions by
approximately a factor of 20, and this trend appeared to hold over five orders of magnitude in dioxin and
furan emissions.
In addition, there is some limited data, from both laboratory and field studies, showing that PCBs may be
formed from burning hazardous waste. Stack tests performed in U.S. EPA Region 10 on a boiler and an
incinerator burning waste with 0.07 and 1.4 percent chlorine, respectively, confirmed the presence of
PCBs in the stack gases (Kalama Chemical, Inc. 1996; Idaho National Engineering Laboratory 1997).
The concentration of detected coplanar PCBs (as defined in Section 2.3.9.1) found in the boiler stack gas
was 0.55 ng/dscm @ 7% O2 at low temperature conditions (1,357° F) and 1.12 ng/dscm @ 7% O2 at high
temperature conditions (1,908° F). The concentration of total PCBs detected in the incinerator stack gas
was 211 ng/dscm @ 7% O2 at low temperature conditions (1,750 °F) and 205 ng/dscm @ 7% O2 at high
temperature conditions (2,075° F). PCBs with more than four chlorines comprised 51 percent of the total
PCBs in the low temperature test and 59 percent of the total PCBs in the high temperature test.
Other laboratory studies suggest the possible formation of PCBs as PICs from burning hazardous waste
with a high chlorine content. Bergman et al. (1984) heated samples of two chlorinated paraffins (CP) in
conditions similar to incinerator conditions. A CP containing 70 percent chlorine did produce PCB (up to
0.3 percent of the amount of CP), as well as chlorinated benzenes (up to 0.5 percent), chlorinated toluenes
(up to 0.6 percent), and chlorinated naphthalenes (up to 0.2 percent). Similar treatment of a CP
containing 59 percent chlorine produced only chlorinated benzenes (up to 0.1 percent of the amount of
CP, based on a detection limit of 0.0005 percent for each individual compound) and almost all of those
(about 90 percent) were monochlorobenzene (Bergman 1984). This study indicates that burning highly
chlorinated wastes (60 percent or greater chlorine) can produce PCBs. Also, in an experiment involving
4 percent charcoal, 7 percent chlorine, and 1 percent copper catalyst heated to 300° C, PCBs were formed
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at levels approaching 300 ppb for the penta homologue, 200 ppb for the hexa homologue, 150 ppb for the
hepta homologue, and less than 50 ppb for the tetra homologue (Stieglitz et al. 1989).
2.3.9.1 PCB Carcinogenic Risks
In earlier guidance (1994g; 1994i; 1994J; 1994r) we recommended that risk assessments treat all 209 PCB
congeners as a mixture having a single carcinogenic potency. This recommendation was based on the
Agency drinking water criteria for PCBs (U.S. EPA 1988a), which used available toxicological
information with the following limitations:
Aroclor 1260 was the only PCB for which a cancer SF had been developed; there was no
agreed upon procedure for applying this SF for similar mixtures with less chlorine
content.
Available physical, chemical, fate-and-transport, and toxicological information on
individual congeners was limited (primarily because separation and synthesis of pure
congeners can be technically difficult).
The number of tests conducted with various PCB mixtures and specific congeners to
demonstrate similar toxicological effects was very limited.
Research on PCBs has continued since the compilation of U.S. EPA (1988a),. The most important
finding of this research is that some of the moderately chlorinated PCB congeners can have dioxin-like
effects (U.S. EPA 1992e; 1994a; 1996q; ATSDR 1995d). This sub-category includes PCB congeners
with four or more chlorine atoms and few substitutions in the ortho positions (positions designated 2, 2',
6, or 6'). They are sometimes referred to as "coplanar" PCBs, because the rings can rotate into the same
plane if not blocked from rotation by ortho-substituted chlorine atoms. In this configuration, the shape of
the PCB molecule is very similar to that of a PCDF molecule. Studies have shown that these dioxin-like
congeners can react with the aryl hydrocarbon receptor; the same reaction believed to initiate the adverse
effects of PCDDs and PCDFs. The World Health Organization (WHO) used various test results to derive
interim toxicity equivalency factors (TEFs) ranging from 0.1 to 0.00001 for the dioxin-like coplanar PCB
congeners (WHO 1998).
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TABLE 2-5
TOXICITY EQUIVALENCY FACTORS
FOR COPLANAR PCBs
CAS Number
32598-13-3
70362-50-4
32598-14-4
74472-37-0
31508-00-6
65510-44-3
157465-28-8
38380-08-4
69782-90-7
52663-72-6
32774-16-6
39635-31-9
Chemical Structure
3 , 3 ' ,4 ,4' -tetrachlorobipheny 1
3,4,4',5-tetrachlorobiphenyl
2,3,3',4,4'-pentachlorobiphenyl
2 ,3 ,4 ,4' , 5 -pentachlorobipheny 1
2 ,3 ' ,4 ,4' , 5 -pentachlorobipheny 1
2',3,4,4',5-pentachlorobiphenyl
3 ,3 ' ,4 ,4' , 5 -pentachlorobipheny 1
2,3,3',4,4',5-hexachlorobiphenyl
2,3,3',4,4',5'-hexachlorobiphenyl
2,3',4,4',5,5'-hexachlorobiphenyl
3,3',4,4',5,5'-hexachlorobiphenyl
2,3,3',4,4',5,5'-heptachlorobiphenyl
WHO 1998 TEFs
(unitless)
0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
Source: World Health Organization (1998)
Additional congeners are suspected of producing similar reactions, but there is not yet enough data to
derive TEF values for them. Since available analytical methods can now quantify most if not all
individual PCB congeners, we generally consider it reasonable for the permitting authority to request that
additional congeners be reported. For instance, 2,2',4,4',5,5'-Hexachlorobiphenyl (CAS No. 35065-27-1)
is the most prevalent PCB congener found in human milk and fat (McFarland and Clark 1989). Work is
currently underway to develop a separate slope factor for this particular compound (not a coplanar
congener). However, until that work is complete, this compound can only be qualitatively assessed in the
risk assessment.
We generally recommend estimating risks from coplanar PCBs by computing a toxicity equivalency
quotient (TEQ) for PCBs, and then applying a slope factor for dioxin. High resolution gas chromatograph
test methods, available at most commercial laboratories with dioxin/furan analytical capabilities, are able
to identify the specific concentration of individual coplanar PCBs in stack gas.
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In addition to the coplanar (dioxin-like) PCB congeners, We also generally recommend evaluating the
remaining PCBs in the risk assessment. After considering the accumulated research on PCBs, especially
a recent carcinogenesis study of Aroclors 1016, 1242, 1254, and 1260 and a number of studies of the
transport and bioaccumulation of various congeners, USEPA (1996q) derived three new SFs to replace
the former single SF for PCBs. These new SFs became effective in IRIS on October 1, 1996. These SFs
are subject to revision as additional information from continuing research becomes available. The SFs
and the criteria for their use are as follows (U.S. EPA 1996q):
TABLE 2-6
ORAL SLOPE FACTORS FOR PCBs
Slope Factor (milligrams
per kilogram-day) J
2
0.4
(Not Typically Used)
0.07
Criteria for Use
Food chain exposure
Sediment or soil exposure
Early -life (infant and child) exposure by all routes to all PCB mixtures
Congeners with more than four chlorines comprise more than 0.5 percent of the total PCBs
Ingestion of water-soluble (less chlorinated) congeners
Inhalation of evaporated (less chlorinated) congeners
Congeners with more than four chlorines comprise less than 0.5 percent of the total PCBs
Source: U.S. EPA 1996q
An SF of 2 (milligrams per kilogram-day)"1 is typically used in most circumstances when conducting a
risk assessment. An SF of 0.07 (milligrams per kilogram-day)"1 is generally scientifically defensible for
adult exposures, when congener-specific analyses of emissions demonstrate that at least 99.5 percent of
the mass of the released PCB mixture has fewer than five chlorine atoms per molecule (U.S. EPA 1996q).
Acceptable congener-specific analyses include high-resolution gas chromatography/mass spectroscopy
(or similar means) for total PCB concentrations for each mono- through deca-isomer group. We don't
expect that the 0.4 SF will be widely used in combustion risk assessments, because the PCB mixture will
usually contain 0.5 percent or more PCB congeners with more than 4 chlorines.
2.3.9.2 Potential PCB Non-Cancer Effects
In addition to cancer risk associated with all PCBs, we generally recommend determining noncancer
hazard for those Aroclors having RfDs. IRIS specifies RfDs for Aroclor 1254 and Aroclor 1016 (U.S.
EPA 2005i). The RJD for Aroclor 1254 (2xlO~5 milligrams per kilogram-day) will typically be used in
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most circumstances when conducting a risk assessment. We consider this approach reasonable because
approximately 77 percent of Aroclor 1254 is composed of PCB congeners with more than 4 chlorines
(Hutzinger et al. 1974). The RfD for Aroclor 1016 (7x10~5 milligrams per kilogram-day) is scientifically
defensible for each homologue group demonstrating that at least 99.5 percent of the mass of the released
PCB mixture has more than four chlorine atoms per molecule (U.S. EPA 1996q). We generally consider
this approach reasonable because approximately 99 percent of Aroclor 1016 is comprised of PCB
congeners with 4 or fewer chlorines (Hutzinger et al. 1974).
We also recommend evaluating th risks to infants from exposure to coplanar PCBs in human breast milk.
Please see Section 2.3.10.2 for information on comparing estimated levels of coplanar PCBs (along with
dioxins and furans) to background. More information on the breast milk pathway is in Chapter 4, and
Tables C-3-1 and C-3-2.
2.3.9.3 Fate & Transport of PCBs
When evaluating coplanar PCB congeners, or PCB congener mixtures of which greater than 0.5 percent
contain more than 4 chlorines, we recommend using the fate and transport properties for Aroclor 1254.
When assessing risks and hazards from PCB congener mixtures of which less than 0.5 percent contain
more than 4 chlorines, we recommend using the fate and transport properties of Aroclor 1016.
2.3.10 Polychlorinated Dibenzo(p)dioxins and Dibenzofurans
As was the case with previous Agency guidance (U.S. EPA 1994i, 1994J, 1994n, 1994r, and 1998c), we
recommend including PCDDs and PCDFs in the risk assessment. Information in U.S. EPA (2000b)
suggests that there is adequate evidence that exposure to PCDDs and PCDFs results in a broad spectrum
of cancer and noncancer effects in animals, some of which may occur in humans.
PCDDs and PCDFs were first discovered as thermal decomposition products of poly chlorinated
compounds, including (1) the herbicide 2,4,5-T, (2) hexachlorophene, (3) PCBs, (4) pentachlorophenol,
and (5) intermediate chemicals used to manufacture these compounds. One mode in which PCDDs and
PCDFs form is in dry APCSs, where fly ash catalyzes reactions between halogens and undestroyed
organic material from the furnace. In recent years, as chemical analytical methods have become more
sensitive, additional sources of PCDDs and PCDFs have been identified, including (1) effluent from
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paper mills that use chlorine bleaches, and (2) combustion sources such as forest fires, municipal waste
and medical incinerators, and hazardous waste combustors. Duarte-Davidson et al. (1997) noted that
burning chlorine-containing materials in municipal solid waste is responsible for about two-thirds of the
total annual emissions of newly formed TCDDs and TCDFs in the United Kingdom. In the United States,
U.S. EPA (1998a) estimated that emissions of dioxin TEQs from municipal solid waste incinerators
accounted for 37 percent of all emissions of dioxins into the environment in 1995. The current Agency
draft dioxin sources inventory suggests that open barrel burning is the largest current single source of
release of these compounds (U.S. EPA 2000).
PCDDs and PCDFs are formed at these combustion sources from the reaction of chlorine-containing
chemicals and organic matter. Predicting the formation of PCDDs and PCDFs in a specific situation is
difficult because dechlorination, which produces PAHs from PCDDs and PCDFs, occurs under similar
conditions. Recent studies (Addink et al. 1996; Environment Canada 1987; Froese and Hutzinger 1996a,
1996b; Gullett et al.1994; Kilgroe et al. 1991; Luijk et al. 1994; and Shriver 1994) have explored some of
these complexities, including (1) the formation of PCDDs and PCDFs from simple organics (such as
ethane) and complex organics (such as dibenzofuran), and (2) the catalysis of these reactions by various
common metals, such as copper. Wikstrom et al. (1996) found that the form of chlorine—whether
organic, as with chlorinated solvents, or inorganic, as with bleach and salts—has little effect on the
quantity of PCDDs and PCDFs formed. However, their study found that the total concentration of
chlorine is important. In particular, if the waste being burned exceeds 1 percent chlorine, the PCDD and
PCDF formation rate increases significantly. In contrast, Rigo et al. (1995) analyzed over 1,700 test
results with varying chlorine feed concentrations and found no statistically significant relationship. The
formation rate of PCDDs and PCDFs may also depend on the physical characteristics of the waste feed
stream. Solid waste streams or high-ash-content liquid waste feed streams may increase particulate levels
in the combustion system between the combustion unit and the APCS. The increased particulate levels
provide additional surfaces for catalysis reactions to occur.
A review of currently available dioxin data for combustors reveals that total PCDD/PCDF emission rates
vary by more than 28-fold between different facilities, even though they use similar combustion units and
APCSs (U.S. EPA 1996b). Site-specific emission data are therefore needed to complete a more refined
risk assessment of each combustor.
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In evaluating fate-and-transport, it is important to consider the chemical and physical properties of
dioxins. In soil, sediment, and the water column, PCDDs and PCDFs are primarily associated with
particulate and organic matter because of their high lipophilicity and low water solubility. Ambient air
monitoring studies, in which researchers studied the partitioning of dioxin-like compounds between the
vapor and particle phases, suggest that the higher chlorinated congeners (the hexa through octa
congeners) principally sorb to airborne particulates. The tetra and penta congeners significantly, if not
predominantly, partition to the vapor phase (U.S. EPA 2000b). These findings are consistent with
vapor/particle partitioning as theoretically modeled in Bidleman (1988). Dioxin-like compounds exhibit
little potential for significant leaching or volatilization (U.S. EPA 2000b).
The following subsections clarify the procedures we recommend using (in conjunction with the
procedures described in Chapter 7) to estimate risks associated with PCDDs and PCDFs. Also, we're
aware of growing concern regarding the risks resulting from exposure to (1) fluorine- and
bromine-substituted dioxins and furans, and (2) sulfur analogs of PCDDs and PCDFs. Research
regarding these compounds is ongoing. Until such time as new information is released, though, you can
consider the following subsections as our guidance on how to evaluate fluorine, bromine, and sulfur
PCDD/PCDF Analogs as potential COPCs in hazardous waste combustor risk assessments.
2.3.10.1 PCDD/PCDF Cancer Risks
We recommend using the TEF method to assess carcinogenic risk on the basis of toxicity relative to
2,3,7,8-TCDD, which is the most toxic dioxin.
There are 210 individual compounds or "congeners" of PCDDs and PCDFs. Seventeen of these 210
congeners are considered to have "dioxin-like" toxicity. In the TEF method, each of these 17 congeners
is assigned a value, referred to as a toxicity equivalency factor (TEF), which compares its toxicity to that
of 2,3,7,8-TCDD. 2,3,7,8-TCDD then has a TEF of 1.0, and other dioxin-like congeners have TEFs
between 0.0 and 1.0. TEF values for these 17 congeners are listed in the Table 2-7.
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TABLE 2-7
PCDD/PCDF TOXICITY EQUIVALENCY FACTOR VALUES
Dioxin Congener
2,3,7,8-Tetrachlorodibenzo(p)dioxin
l,2,3,7,8-Pentachlorodibenzo(p)dioxin
l,2,3,4,7,8-Hexachlorodibenzo(p)dioxin
l,2,3,6,7,8-Hexachlorodibenzo(p)dioxin
l,2,3,7,8,9-Hexachlorodibenzo(p)dioxin
l,2,3,4,6,7,8-Heptachlorodibenzo(p)dioxin
l,2,3,4,6,7,8,9-Octachlorodibenzo(p)dioxin
TEF
(unitless)
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
•I
Furan Congener
2,3,7,8-Tetrachlorodibenzofuran
1 ,2,3,7,8-Pentachlorodibenzofuran
2,3,4,7,8-Pentachlorodibenzofuran
1 ,2,3,4,7,8-Hexachlorodibenzofuran
1 ,2,3,6,7,8-Hexachlorodibenzofuran
1 ,2,3,7,8,9-Hexachlorodibenzofuran
2,3,4,6,7,8-Hexachlorodibenzofuran
1,2,3,4,6,7,8-Heptachlorodibenzofuran
1,2,3,4,7,8,9-Heptachlorodibenzofuran
1 9 ^ 4 (S 7 & Q OrtflrhlnrnHihpnynfiirfin
TEF
(unitless)
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0 0001
Source: World Health Organization (1998)
Van den Berg etal (1998).
To estimate the exposure media concentrations for PCDDs and PCDFs, we recommend using the
congener-specific emission rates from the stack. Then, model the fate and transport of each of these 17
congeners to the exposure site to estimate congener-specific exposure media concentrations. The
HHRAP companion database includes congener-specific fate and transport parameter values, and the
media concentration equations are provided in Appendix B. After estimating congener-specific exposure
media concentrations, we recommend using the TEFs to estimate a "toxic equivalent" (TEQ) exposure
media concentration, and an overall TEQ exposure and cancer risk, as follows:
1. convert the exposure media concentrations of an individual congener to a TEQ
concentration for that congener by multiplying the congener's media concentrations by
the congener's TEF;
2. sum the TEQ concentrations of the individual congeners to get an overall exposure media
concentration;
3. estimate the lifetime average daily dose (LADD) for the TEQ concentration; and
4. assess the cancer risk on a TEQ basis using the cancer slope factor for 2,3,7,8-TCDD, in
combination with the TEQ-based LADD.
Please see Chapter 7 for a more complete discussion of the steps included in the TEF method.
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2.3.10.2 PCDD/PCDF Noncancer Hazards
We generally recommend comparing PCDD and PCDF oral exposure estimates to national average
background exposure levels, using 1 pg TEQ/Kg/day for adults and 60 pg TEQ/kg/day for nursing
infants. The pertinent exposure estimate would be the ADD, or Average Daily Dose, experienced over
the course of the exposure duration, rather than the LADD, which is this ADD averaged over a lifetime.
The Agency typically evaluates noncancer effects of chemicals by comparing exposure levels to health-
based reference doses or reference concentrations. However, for reasons discussed in the Agency's Draft
Dioxin Reassessment (U.S. EPA 2000b), the Agency has not developed these non-cancer benchmarks for
any of the PCDD or PCDF congeners, or for TEQ concentrations/doses.
One approach the Agency has taken to evaluate whether PCDDs and PCDFs emitted from hazardous
waste combustion facilities are likely to cause significant noncancer health effects is to compare estimated
TEQ exposures to national average background exposure levels (1 pg TEQ/kg/day for adults and 60 pg
TEQ/kg/day for nursing infants). The average background level of PCDD/PCDFs and co-planar, dioxin-
like PCBs in breast milk is 25 parts per trillion (ppt) of 2,3,7,8-TCDD TEQ (EPA 2000b). The 25- ppt
2,3,7,8-TCDD TEQ is the sum of the average breast milk concentration of 18-ppt TEQ from
PCDD/PCDFs and 7-ppt TEQ from co-planar, dioxin-like PCBs (EPA 200b). After normalization for
infant body weight, this breast milk concentration of 25 ppt TEQ results in an average, background intake
for the infant, ADIb-inf, of 93 picograms per kilogram per day (pg/kg-day) of 2,3,7,8-TCDD TEQ. If
exposures due to the facility's emissions during the exposure duration of concern are low compared to
background exposures, then the emissions aren't expected to cause an increase in noncancer effects.
In the future, the Agency may develop alternative approaches to evaluate noncancer effects from
exposures to PCDDs and PCDFs. In that case, those approaches may be included in future risk
assessments.
2.3.10.3 Fluorine, Bromine, and Sulfur PCDD/PCDF Analogs
We generally recommend deciding on a site-specific basis whether to evaluate these compounds, in
consultation with the permitting authority. Considering that neither the likelihood of the formation, nor
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the toxicity of these compounds is well understood, the permitting authority is not likely to request a
quantitative toxicity assessment of fluorine, bromine, and sulfur analogs.
The Agency is currently evaluating the potential for the formation of (1) fluorine- and bromine-
substituted dioxins and furans, and (2) sulfur analogs of PCDDs and PCDFs (U.S. EPA 1996h; 1996m).
Available information indicates that fluorinated dioxins and furans aren't likely to be formed as PICs;
although the presence of free fluorine in the combustion gases may increase the formation of chlorinated
dioxins (U.S. EPA 1996h). We aren't aware of any studies conducted to evaluate this relationship.
Available information indicates that there is potential for brominated or chlorobrominated dioxins to form
(U.S. EPA 1996i). The Agency has not assigned TEF values for brominated dioxins or furans (U.S. EPA
1994k). However, the toxicity of bromo- and chlorobromo-substituted dioxin analogs is comparable to
that of chlorinated dioxins in short-term toxicity assays (U.S. EPA 1996m).
Although chlorinated dibenzothiophenes (the sulfur analogs of dibenzofurans) have been reported to
form, no information is available indicating the formation of chlorinated dioxin thioethers (the sulfur
analogs of dibenzo[p]dioxins) (U.S. EPA 1996h). This may be because the carbon-oxygen bond is
stronger than the carbon-sulfur bond, and the compound furan (which is part of the dibenzofuran
structure) is more stable than thiophene (which is part of the dibenzothiophene structure) (U.S. EPA
1996c). Another possible reason that chlorinated dioxin thioethers have not been observed is the potential
instability of these compounds. Chlorinated dioxin thioethers contain two carbon-sulfur bonds in the
central ring of the structure (U.S. EPA 1996h).
Please Note. There is currently no U.S. EPA-approved method for the sampling or
analysis of these dioxin analogs.
We generally recommend using the TOE method (see Section 2.2.1.2) to account for the potential
presence of these compounds. The Uncertainty section of the risk assessment report (See Chapter 8)
could then discuss the potential for the formation of these analogs.
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RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
•• Description of any combustor-specific operating conditions that may contribute to the
formation of dioxins
•• Any facility-specific sampling information regarding PCDD and PCDF concentrations in air,
soil, water, or biota
•• Information regarding the concentrations of sulfur, fluorine, and bromine in the combustor
feed materials
2.3.11 Polynuclear Aromatic Hydrocarbons (PAHs)
As is the case in previous Agency guidance (U.S. EPA 1994i, 1994J, 1994r, 1998c), we recommend
evaluating PAHs as COPCs. The following are commonly detected PAHs:
benzo(a)pyrene (BaP);
• benzo(a)anthracene;
• benzo(b)fluoranthene;
benzo(k)fluoranthene;
• chrysene;
• dibenz(a,h)anthracene; and
• indeno(l,2,3-cd)pyrene.
The Agency considers all of these compounds to be carcinogenic. However, an IRIS oral cancer slope
factor is only available for benzo(a)pyrene.
PAHs are readily formed in combustors by either (1) dechlorination of other PAHs (such as dioxins)
present in the waste feed or emissions stream, or (2) the reaction of simple aromatic compounds (benzene
or toluene) present in the waste feed or emissions stream. PAHs are well-known as the principal organic
components of emissions from all combustion sources, including coal fires (soot), wood fires, tobacco
smoke ("tar"), diesel exhaust, and refuse burning (Sandmeyer 1981). They are generally the only
chemicals of concern in particulate matter (Manahan 1991), although the presence of metals and other
inorganics in the waste feed can add other contaminants of concern. Based on the toxicity and
combustion chemistry of PAHs, we generally recommend that stack gas testing confirm the absence of
these compounds from stack emissions.
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At present, BaP is the most studied PAH and the only one for which a 2-year feeding rodent bioassay has
been conducted (U.S. EPA 1991c). The studies available for the other carcinogenic PAHs were
conducted by injection, dermal or gavage. Multiple animal studies in rodent and nonrodent species
demonstrated BaP to be carcinogenic following administration by oral, intratracheal, inhalation, and
dermal routes. BaP also produced positive results in several in vitro bacterial and mammalian genetic
toxicity assays, in addition to numerous in vivo tests for deoxyribonucleic acid (DNA) damage. BaP
metabolizes to reactive electrophiles that are capable of binding to DNA (U.S. EPA 1990h). Therefore,
U.S. EPA (1993d) used various nonbioassay results to determine relative potency factors (RPFs) for the
class B2 carcinogen PAHs. RPFs for these seven PAHs are listed in Table 2-8.
TABLE 2-8
RELATIVE POTENCY FACTORS
FOR CLASS B2 CARCINOGEN PAHs
Compound
Benzo(a)pyrene
Benz(a)anthracene
Benzo(b)fluoranthene
Benzo(k)fluoranthene
Chrysene
Dibenz(a,h)anthracene
Indeno( 1 ,2,3-cd)pyrene
RPF
1.0
0.1
0.1
0.01
0.001
1.0
0.1
Source: U.S. EPA (1993d)
Obtaining test data for an individual chemical from a standard carcinogenesis bioassay that might be used
to develop a cancer slope factor requires:
1. at least 1 kilogram of relatively pure chemical (greater than 98 percent purity is the most
common specification),
2. $500,000 to $1,000,000, and
3. 5 to 6 years.
However, an alternative to the full carcinogenesis bioassay is to use in vitro studies to compare various
PAHs. In vitro studies, such as those conducted by Knebel et al. (1994) and many other groups, require a
few milligrams of each chemical, a few weeks, and about $1,000 per chemical. Because of these
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differences, we wouldn't anticipate that many full carcinogenesis bioassays of PAHs will be carried out in
the near future.
As with previous guidance (U.S. EPA 1994g), we generally recommend evaluating PAHs using an
approach similar to the BaP-RPF method. We generally recommend using the fate-and-transport
properties of specific PAHs (versus those of benzo[a]pyrene) to estimate exposure concentrations. Then
following the BaP-RPF method, you adjust the concentrations of the individual PAHs and sum them to
obtain an equivalent total concentration of BaP. Multiply this summed concentration by the BaP cancer
SFto estimate total risk from all carcinogenic PAHs.
We don't currently recommend a metabolism factor (MF) for PAHs. A published study (Hofelt et al.
2001), however, highlights the uncertainty in the HHRAP's approach and presents an alternative
metabolism factor for use. If this alternative metabolism factor is used, you may wish to consider the
following site-specific points:
1. If the PAHs under consideration are metabolized in the animal (beef, pork, etc) has it
been determined that the degradation products/metabolites aren't persistent in the
meat/and or milk? (This concern has been raised because it is the degradation products
of PAHs that cause the toxicity.);
2. Is the extrapolation from the rat to larger animal appropriate?; and
3. If the metabolism factor is appropriate, should it be used equally for all the PAHs being
evaluated?
Using an MF is discussed further in Section 5.4.4.7 and Appendix B, Table B-3-10.
In addition to carcinogenic effects, noncarcinogenic health effects are associated with exposure to PAHs.
However, RPFs for noncarcinogenic effects of PAHs (similar to those developed for carcinogenic effects)
have not been developed. The uncertainties associated with attempting to quantify the potential
noncarcinogenic effects of PAHs without RfDs or RfCs is typically considered greater than the
uncertainty associated with not evaluating these potential effects. However, if site-specific emissions data
indicate that significant amounts of noncarcinogenic PAHs are emitted, we generally recommend that the
potential to underestimate the noncarcinogenic health effects associated with exposure to PAHs be
discussed in the Uncertainty section of the risk assessment report.
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2.3.12 Radionuclides
Radionuclides exist in naturally occurring materials such as coal and other rocks, and as radioactive
by-products of industrial processes. The HHRAP doesn't consider the naturally occurring concentrations
of radioactive materials such as uranium and thorium (and their decay elements), based on Agency policy
and technical limitations for measuring such low levels. However, radioactive wastes and materials, as
defined by the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE),
are subject to evaluation through interagency agreements on this subject. The U.S. NRC considers
"radioactive waste" as waste that is, or contains, by-product material, source material, or special nuclear
material (as defined in 10 CFR Part 20.1003). The U.S. NRC considers "mixed waste" as waste that
contains both radioactive waste and hazardous waste (as defined by U.S. EPA). Both radioactive and
mixed wastes must be handled in accordance with all relevant regulations, including U.S. EPA and U.S.
NRC (10 CFR Part 20.2007) regulations. In particular, U.S. NRC licensees must comply with 10 CFR
Part 20.2004—"Treatment or Disposal by Incineration"—and applicable U.S. EPA regulations.
We generally recommend evaluating the burning of mixed waste and radioactive material if those
substances are components of the combustor feed. We also generally recommend including a
radionuclide as a COPC if it is in the combustor's feed, and has an available toxicity value (e.g., slope
factor). Slope factors for over 300 radionuclides are available in HEAST. The slope factor for a
particular radionuclide is multiplied by the intake (pCi) or soil concentration and years of exposure (pCi/g
times years of exposure) to estimate cancer risk.
Radionuclide exposure pathways typically evaluated in human health risk assessments include inhalation,
ingestion of food products (e.g., meat, milk, vegetables), incidental soil ingestion, external exposure from
ground surface deposits, and external exposure from air concentrations (air submersion). The submersion
exposure pathway may be of particular concern for radionuclides that emit high-energy beta particles.
Environmental transport and subsequent human exposure are commonly evaluated through the use of
radionuclide "dose" codes or computer code/spreadsheet combinations. A dose code combines air
dispersion/deposition modeling with terrestrial transport models, human exposure parameters, and
pre-calculated dose conversion factors [ e.g., from Federal Guidance Report No. 11 (U.S. EPA 1988c)
and Federal Guidance Report No. 12 (U.S. EPA 1993J)] to obtain dose and/or risk. The following are
several available dose codes for evaluating radionuclides from mixed waste combustion facilities:
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CAP-88 (Clean Air Act Assessment Package - 1988)
•• GENII (The Hanford Environmental Radiation Dosimetry Software System)
•• MEPAS (Multimedia Environmental Pollutant Assessment System)
ISCST3 (Industrial Source Complex Dispersion Model)
CALPUFF (California Puff Model)
To calculate air concentrations and ground deposition rates of radionuclides, we generally recommend the
ISCST3 air dispersion model using the exponential decay option. Intake can then be calculated with
appropriate exposure scenario equations and parameters. ISCST3 is a good choice for facilities with
multiple sources, complex terrain, building downwash, and wet/dry deposition requirements. A second
option is to use the air concentration and ground deposition rate output from another dose code (e.g.,
CAP-88 if the facility has completed its NESHAPs analysis).
We generally recommend that equations for fate and transport of radionuclides in soil and water be
consistent with those presented for non-radionuclides, while factoring in decay (and ingrowth if
applicable). The recommended food chain biotransfer parameters used to determine food concentrations
are available in the user's guides for most of the dose codes listed previously. However, a comprehensive
reference for obtaining these values is the Handbook of Parameter Values for the Prediction of
Radionuclide Transfer in Temperate Environments; IAEA Technical Report Series No. 364 (International
Atomic Energy Agency 1994).
Decay and ingrowth of radionuclides is a special consideration for integrating radioactive materials into
risk calculations. Most radioactive materials undergo radioactive decay through a series of
transformations rather than in a single step. Until the last step, these radionuclides emit energy or
particles with each transformation and become other radionuclides. As radioactive decay progresses, the
concentration of the original radionuclides decreases, while the concentration of their decay products
increases and then decreases as the decay products themselves transform. The increasing concentration of
decay products and activity is called ingrowth. We recommend that the assessment consider decay over
both the air transport time and the surface exposure duration. Ingrowth may be important, and we
generally recommend the assessment use radionuclide slope factors that include contributions from decay
elements ("+D" slope factors). Ingrowth that involves change of physical state is another situation
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needing special attention in the fate and transport modeling. For instance, solid radium-226 decays to
gaseous radon-222, which then decays through solid polonium-218 to further decay elements.
Options for estimating cancer risks from exposure to radionuclides include using the slope factor
methodology presented in the Agency's Estimating Radiogenic Cancer Risks (U.S. EPA 1994s), or using
estimates of the relationship between radiation dose and cancer risk to convert dose to risk. Federal
Guidance Report No. 13 (U.S. EPA 1999d) contains recent estimates of cancer risk for given radiation
dose, based on low-dose, low-Linear Energy Transfer uniform irradiation of the body. For these
conditions, radiation dose equivalent (rem) and absorbed dose (rad) are approximately equivalent. The
Report provides risk estimates in terms of mortality and morbidity. It is important to use the estimate
appropriate to the site and assessment in question.
The dose-conversion approach uses a single factor to convert dose to risk. It is limited, then, in that it
doesn't take into account variations among radionuclides in the relationship between dose and risk. In
general, though, this approach is protective. The slope factor approach generally provides a better
estimate of risk. Limitations of the slope factor conversion methodology, however, include:
•• It assumes a single chemical form, which is not necessarily site-specific or most
protective;
External radiation slope factors are only provided for soil contaminated to an infinite
thickness, which will over-estimate exposure from radionuclide concentrations near the
surface;
Slope factors aren't available for the submersion in water exposure pathway; and
Slope factors include decay chains for a limited number (18) of parent radionuclides
(although these are the most significant decay chains)
Some radioactive materials, such as uranium, also present a non-carcinogenic hazard that it is possible to
evaluate. We generally recommend also assessing these non-carcinogenic hazards. We also recommend
that the risk summary table in the risk assessment report present the cancer risk from radiological
contaminants alongside the risks from non-radiological contaminants.
To enhance transparency, we generally recommend that results include a discussion of additivity and the
uncertainties of additivity when combining risks from radiological and non-radiological contaminants.
There are fundamental differences between the slope factors for chemical and radionuclide carcinogens.
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Most notably, the slope factors for chemical carcinogens generally represent an upper bound or 95th
percent confidence limit value, while radionuclide slope factors are best estimate (50% confidence)
values.
Please Note. A prescriptive method for calculating risk from combustion facilities
burning mixed waste is beyond the scope of the HHRAP. The above information is
provided to outline the method we recommend.
2.3.13 Volatile Organic Compounds
U.S. EPA (1990e) reported that volatile organics listed as probable PICs (based on Freeman 1988 and
1989) produced by burning hazardous waste include:
• benzene;
• chloroform;
tetrachloroethylene;
1,1,1-trichloroethane;
• toluene; and
• methylene chloride.
However, the validity of evaluating volatile organic COPCs through the various indirect exposure
pathways (see Chapter 4) is subject to debate. One argument to exclude these COPCs from evaluation is
that there is no empirical evidence that VOC emissions pose a hazard via indirect pathways. We're
similarly not aware of any such evidence, but we're also unaware of any evidence to the contrary.
Another argument to exclude VOCs from evaluation is based on the conclusion that
1. volatile organic COPCs released into the air are expected to remain in the gas phase unless or
until they are transformed into low-volatility compounds, and
2. this transformation (or atmospheric chemical reaction), and the subsequent removal of the
reaction products, makes irrelevant the toxicity of the parent volatile organic COPC.
We disagree with both aspects of this argument. First, we're not aware of any information or research
documenting the fate-and-transport of volatile organic COPCs from hazardous waste combustors.
Second, although we agree that the toxicity of the parent COPC is irrelevant following transformation,
this argument ignores the potential toxicity of the reaction products. We're not aware of any available
quantitation methods that could be used to predict atmospheric chemical reactions of this nature. We
therefore generally believe that evaluating the fate-and-transport (and toxicity) of the parent COPC
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remains the best available method for protectively accounting for the potential reaction products to which
receptors are ultimately exposed.
Finally, another argument to exclude VOCs from evaluation is the assertion that there is no firm technical
basis for assessing the rate of deposition of VOCs to soils or uptake by plants (discussed in detail in
Appendix A-2). Although we agree with the basic premise of this issue, we're unaware of any other
method for evaluating the potential indirect exposure to volatile organic COPCs or their atmospheric
reaction products (empirical data aren't available).
To summarize, we agree in principle that the science regarding the fate-and-transport of volatile organic
COPCs in the environment is poorly understood. However, because the potential risks associated with
indirect exposure to these COPCs is also poorly understood, we believe that evaluating volatile organic
COPCs via the indirect exposure pathways—with the proper explanation of the uncertainties associated
with this process—generally provides the most reasonable (based on current science) and protective
estimate of these potential risks. We also believe that the risk equations generally address this issue
because a calculation cannot be completed unless there are sufficient fate and transport properties values
for each COPC.
Finally, current sampling and analytical methods aren't always able to positively identify all individual
organic compound in stack emissions. We recommend accounting for the mass of unidentified organic
compounds in stack emissions on the basis of TOE from the hazardous waste combustor. The
methodology for using TOE in a risk assessment is discussed further in Section 2.2.1.2.
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2.4 ESTIMATING COPC CONCENTRATIONS FOR NON-DETECTS
One particularly difficult issue in a risk assessment is how to treat data that are reported as below the
"detection limit" (i.e. "Non-detects"). The following subsections:
1. define commonly reported detection limits;
2. describe the use of non-detect data in the risk assessment;
3. describe statistical distribution techniques applied to address this issue;
4. summarize our recommendations on quantifying non-detects for use in risk assessments;
and
5. clarify use of data flagged as estimated maximum possible concentration (EMPC) in the
risk assessment.
2.4.1 Definitions of Commonly Reported Detection Limits
Generically, a "detection limit" is the lowest level of an analyte that can be detected using a particular
analytical method. The Agency's commonly-used definition for the detection limit for non-isotope
dilution methods is the method detection limit (MDL), as promulgated in 40 CFR Part 136, Appendix B
(U.S. EPA 1995i). A level above the MDL is the level at which reliable quantitative measurements can
be made; generically called the "quantitation limit" or "quantitation level." In practice, numerous terms
have been created to describe detection and quantitation levels. We have summarized below the
significance and applicability of the levels most widely reported by analytical laboratories. These
levels—listed generally from the lowest limit to the highest limit—include the following:
•• Instrument Detection Limit (IDL): The smallest signal above background that an instrument can
reliably detect, but not quantify. Also commonly described as a function of the signal-to-noise
(S/N) ratio.
•• Method Detection Limit (MDL): The minimum concentration of a substance that can be
measured (via non-isotope dilution methods) and reported with 99 percent confidence that the
analyte concentration is greater than zero. It is determined from analysis of a sample in a specific
matrix type containing the analyte. The MDL is considered the lowest level at which a
compound can be reliably detected. The MDL is based on statistical analyses of laboratory data.
In practice, MDLs are determined on analytical reagents (e.g., water) and not on the matrix of
concern. However, a laboratory may contract to do a matrix-specific "MDL Study" for a
particular project or a particular facility's waste matrix when needed. However, routine MDL
determinations (water reagent) are conducted on at least an annual basis or whenever equipment
changes occur. MDLs for a given method are laboratory- and compound-specific.
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To determine the MDL as specified in 40 CFR Part 136, Appendix A, for example, at least seven
replicate samples with a concentration of the compound of interest near the estimated MDL are
analyzed. The standard deviation among these analyses is calculated and multiplied by 3.14. The
result of the calculation becomes the MDL. The factor of 3.14 is based on a t-test with six
degrees of freedom and provides a 99 percent confidence that the analyte can be detected at this
concentration (U.S. EPA 1995i).
Please Note. 40 CFR Part 136 is specific to the Clean Water Act, and it identifies the use of
water as the matrix for the MDL determination. The MDL was promulgated in 1984, and is
incorporated in more than 130 Agency analytical methods for determining several hundred
analytes.
Reliable Detection Level (RDL): A detection level recommended by the National Environmental
Research Laboratory in Cincinnati. It is defined as 2.623 times the MDL (U.S. EPA 1995i).
Estimated Detection Limit (EDL): A quantitation level defined in SW-846 that has been applied
to isotope dilution test methods (e.g., SW-846 Method 8290). A variation of the SW-846 defined
EDL is also commonly reported by commercial laboratories, however, with the addition of a
multiplication factor that generally elevates the EDL value by 3.5 to 5 times that of the SW-846
definition. Commercial laboratories sometimes report EDLs for non-isotope dilution methods,
even though an EDL is not defined by the methods.
As defined in SW-846: The EDL is defined in SW-846 (presented in various methods,
e.g., Method 8280A) as the estimate made by the laboratory of the concentration of a given
analyte required to produce a signal with a peak height of at least 2.5 times the background signal
level. The estimate is specific to a particular analysis of the sample and will be affected by
sample size, dilution, etc. The presented equation defining EDL is as follows:
EDL =
Equation 2-3
where
EDL
2.5
Qis
Hn' andHn2
H1S' and HI?
D
Estimated detection limit (ng/L)
Peak height multiplier (unitless)
Nanograms of the appropriate internal standard added to
the sample prior to extraction (ng)
The peak heights of the noise for both of the quantitation
ions of the isomer of interest
The peak heights of both the quantitation ions of the
appropriate internal standards
Dilution factor - the total volume of the sample aliquot
in clean solvent divided by the volume of the sample
aliquot that was diluted (unitless)
Volume of sample extracted (L)
Calculated relative response factor from calibration
verification (unitless)
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Common commercial laboratory practice: The EDL generally reported by commercial
laboratories is defined as the detection limit reported for a target analyte that is not detected or
presents an analyte response that is less than 2.5 times the background level. The area of the
compound is evaluated against the noise level measured in a region of the chromatogram clear of
genuine GC signals times an empirically derived factor. This empirical factor approximates the
area to height ratio for a GC signal. This factor is variable between laboratories and analyses
performed, and commonly ranges from 3.5 to 5. The equation is as follows:
2.5-QR-(F-H)-D
EDL ° WAf-RRF, Equation 2-4
where
EDL = Estimated detection limit
2.5 = Minimum response required for a GC signal
Q.. = The amount of internal standard added to the sample before
extraction
F = An empirical factor that approximates the area to height ratio for
a GC signal
H = The height of the noise
D = Dilution factor
W = The sample weight or volume
RRF.. = The mean analyte relative response factor from the initial
calibration
A.. = The integrated current of the characteristic ions of the
corresponding internal standard
Practical Quantitation Limit (PQL): A quantitation level that is defined in 50 FR 46908 and
52 FR 25699 as the lowest level that can be reliably achieved with specified limits of precision
and accuracy during routine laboratory operating conditions (U.S. EPA 1992g; 1995i). The PQL
is constructed by multiplying the MDL, as derived above, by a factor (subjective and variable
between laboratories and analyses performed) usually in the range of 5 to 10. However, PQLs
with multipliers as high as 50 have been reported (U.S. EPA 1995i).
The PQL has been criticized because of the ambiguous nature of the multiplier and because the
resulting levels have been perceived as too high for regulatory compliance purposes (U.S.
EPA 1995i).
•• Target Detection Limit (TDL): A quantitation level constructed similar to the PQL.
•• Reporting Limit (RL): A quantitation level constructed similar to the PQL.
•• Estimated Quantitation Limit (EQL): A quantitation level constructed similar to the PQL.
•• Sample Quantitation Limit (SQL): A quantitation level that is sample-specific and highly
matrix-dependent because it accounts for sample volume or weight, aliquot size, moisture
content, and dilution. SQLs for the same compound generally vary between samples as moisture
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content, analyte concentration, and concentrations of interfering compounds vary. The SQL is
generally 5 to 10 times the MDL, however, it is often reported at much higher levels due to
matrix interferences.
Contract Required Quantitation Limit (CRQLVContract Required Detection Limit (CRDL): A
quantitation pre-set by contract, which may incorporate SW-846 methods, Office of Water
methods, or other methods deemed necessary to meet study objectives. These limits are typically
administrative limits and may actually be one or two orders of magnitude above the MDL.
2.4.2 Using Non-Detect Data In the Risk Assessment
In collecting waste feed or emissions data for use in risk assessments, or in setting regulatory compliance
levels, a permitting authority is often faced with data quality objectives that require analyses near or
below analytical detection or quantitation levels. In such situations, permittees often argue that the
detection levels should be set with a large factor of certainty in order to be confident that measurements
are reliable. Environmental groups frequently argue that a level of zero, or a level at which a single
researcher can demonstrate that the compound can be detected, should be used as the set level.
Measurements made below analytical detection and quantitation levels are associated with increased
measurement uncertainty, so it is important to understand the impact they may have when they are
applied.
Because of the quantitative differences between the various types of detection levels, "non-detected"
compounds pose two questions:
1. Is the compound really present?, and
2. If so, at what concentration?
The first question is generally hard to answer, and is dependent mainly on the analytical resources
available. The answer to the second question is "somewhere between true zero and the quantitation level
applied." In earlier guidance (U.S. EPA 1994i) we recommended applying emission rates of one-half the
"detection limit" for non-detects. However, which detection limit to use was not explicitly defined or
presented in quantitative terms.
For waste feed data (e.g., used as a surrogate when emissions data is not available), we generally
recommend using the SQL, since waste feeds are typically highly concentrated organic or inorganic
matrices that require special analytical clean-up procedures and dilutions of the sample. This approach is
consistent with that used in other program offices for highly contaminated media requiring quantification
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specifically for risk assessment purposes. However, in dealing with stack emissions data, concentrations
of constituents are typically found only at trace levels (assuming good combustion). Therefore, to
increase consistency and reproducibility in dealing with non-detects for emissions data, we generally
recommend using the MDL-derived RDL to quantify non-detects for COPCs analyzed with non-isotope
dilution methods, and using the method-defined EDL to quantify non-detects for COPCs analyzed with
isotope dilution methods. The procedures are as follows:
For COPCs Analyzed With Non-isotope Dilution Methods: Quantify non-detects by using an
MDL-derived RDL.
1. Require the laboratory to report the actual MDL as specified in the chosen SW-846
analytical method. The laboratory should report MDLs for every non-detect compound
analyzed, in addition to the commonly used reporting limit, such as an EDL, EQL, or
PQL.
Commonly used SW-846 non-isotope dilution methods such as Method 8260 (volatiles),
and Method 8270 (semivolatiles) don't themselves define the MDL. They reference 40
CFR Part 136 instead. Though specified in the Method, some laboratories do not always
report MDLs as defined in 40 CFR Part 136.
This would apply to the analysis of each individual component of multiple-component
samples (e.g., front half rinse, XAD resin, condensate, Tenax tube).
Note: Laboratories typically produce MDLs specific to each non-isotope dilution
method performed by the laboratory on an annual basis.
2. Calculate an MDL-derived RDL for each COPC non-detect by multiplying the MDL by
2.623 (interim factor) (U.S. EPA 19951).
Another option is to request that the laboratory derive the RDLs for you (per the
definition above), as part of the analysis. Good quality assurance/quality control
(QA/QC) suggests you check to make sure the RDLs have been generated properly.
3. Adjust the RDL, as appropriate, to account for sample-specific volumetric treatments
(e.g., splits and dilutions) that differ from those used in the Part 136 MDL
determinations.
Again, an option is to have the laboratory perform the adjustments for you. We
recommend you check to make sure the adjustments have been done properly.
For COPCs Analyzed With Isotope Dilution Methods: Quantify non-detects using the EDL as
defined by the analytical method, without the use of empirical factors or other mathematical
manipulations specific to the laboratory. Commonly used isotope dilution methods include
SW-846 Methods 8290, 1624, and 1625, as well as CARB 429.
Methods for Metals Analysis: Quantify non-detects for metal analysis in the risk assessment by
using the IDL as defined by the analytical method, without the use of empirical factors or other
mathematical manipulations specific to the laboratory.
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Please note this recommendation is an update to the information provided in U.S.
EPA [200 Ic] Risk Burn Guidance for Hazardous Waste Combustion Facilities.)
The MDL definition used in 40 CFR Part 136 addresses errors of the first type (i.e. false negatives). The
99 percent confidence limit states that the MDL has only a 1 percent chance the detects will be
misidentified as negative, when the compound of concern is actually present. Errors of the second type
(false positives) aren't addressed. By not addressing false positives, the statistically-defined default value
becomes 50 percent. In other words, where 40 CFR did not address false positives, the system required
that 50 percent of the detects at the MDL would be false positives. This is a very protective approach,
and biased toward not missing any compounds of potential concern that may be present. The
MDL-derived RDL, and to a lesser extent the EDL, somewhat indirectly addresses the false positive
issue. As described in defining the RDL (see Section 2.4.1), by the time the standard deviation has been
multiplied by 8, the possibility of false positives is usually less than 1 percent.
2.4.3 Statistical Distribution Techniques
Many statistical distribution techniques are available for calculating a range of standard deviations to
quantify non-detect concentrations of COPCs. These include random replacement scenarios, such as
(Cohen and Ryan 1989; Rao, Ku, and Rao 1991):
• the uniform fill-in (UFI) method, in which each LOD value is replaced with a randomly
generated data point by using a uniform distribution;
the log fill-in (LFI) method which is the same as UFI, except using a logarithmic
distribution;
• the normal fill-in (NFI) method which is the same as UFI, except using a log-normal
distribution; and
• the maximum likelihood estimation (MLE) technique.
Also, if the permitting authority determines it to be applicable, a Monte Carlo simulation might be used to
determine a "statistical" value for each non-detect concentration.
2.4.4 Our Recommendations on Quantifying Non-Detects
Using non-detects in a risk assessment depends on the analytical method(s) used to produce the data. In
most cases, the Agency estimates emission rates for undetected COPCs (see Section 2.3) by assuming
that COPCs are present at a concentration equivalent to the MDL-derived RDL for non-isotope dilution
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methods, or to the method-defined EDL for isotope dilution methods. We consider these methods
reasonable, and believe they represent a scientifically sound approach that supports maximum protection
of human health and the environment while recognizing the uncertainty associated with analytical
measurements at very low concentrations in a real world sample matrix. We also recognize that there are
subjective components and limitations to each of the non-detect methodologies presented in this and
previous guidance, including the recommended methods.
Some risk assessors have expressed the desire to obtain and use non-routine data (e.g., uncensored data)
of defensible quality in risk assessments, as a way to deal with non-detect issues. The HHRAP doesn't
address what forms or how such data might be used. The decision to use non-routine data in a risk
assessment is not precluded just because it is different. Neither does the HHRAP necessarily endorse
using non-routine data. We generally recommend consulting with the permitting authority on the
appropriateness of using non-routine data. If non-routine data is used, we generally recommend carefully
identifying and evaluating the limitations associated with the data, and clearly document this discussion in
the Uncertainty section of the risk assessment report.
As stated previously, a pretrial burn risk assessment can help to make sure that the trial burn test will
achieve the desired quantitation limit (and, therefore, DREs and COPC stack gas emission rates).
2.4.5 Estimated Maximum Possible Concentration (EMPC)
The EMPC as defined in SW-846 is in most cases only used with the isotope dilution methods. An
EMPC is calculated/or dioxin isomers that:
• have a response with a signal to noise ratio of at least 2.5 for both the quantitation ions;
and
meet all the relevant identification criteria specified in the analytical method, except the
ion abundance ratio.
Ion abundance ratios are affected by co-eluting interferences that contribute to the quantitative ion
signals. As a result, one or both of the quantitative ions signals may possess positive biases.
An EMPC is a worst-case estimate of the concentration. An EMPC is not a detection limit and should not
be treated as a detection limit in the risk assessment. We generally recommend using EMPC values as
detections without any further manipulation (e.g., dividing by 2). However, because EMPCs are worst-
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case estimates, you may wish to consult with the permitting authority on techniques to minimize EMPCs
when reporting trial and risk burn results. This is especially true when the EMPC values result in risk
estimates above regulatory levels of concern. Some techniques to minimize EMPCs include performing
additional cleanup procedures (as defined by the analytical method) on the sample or archived extract,
and/or reanalyzing the sample under different chromatographic conditions.
Please Note, using alternative quantitation ions might be acceptable, if the
signal-to-noise ratio of the ion signal is at least 2.5 and if the tune data indicate that the
mass spectrometer is operating within specifications.
Such actions to reduce the EMPC are expected to be more cost effective than the additional sample
cleanup and/or reanalysis.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
•• Actual MDLs for all non-detect stack emissions data, non-isotope dilution methods
•• EDLs for all non-detect stack emissions data, isotope dilution methods
•• SQLs for all non-detect waste feed or feedstream data used
•• Description of the method applied to quantify the concentration of non-detects
2.5 EVALUATING CONTAMINATION IN BLANKS
Blank samples are intended to provide a measure of any contamination that may have been introduced
into a sample:
1. in the field while the samples were being collected,
2. in transport to the laboratory, or
3. in the laboratory during sample preparation or analysis.
Blank samples are analyzed the same way as the site samples from the trail burn. To prevent including
non-site related compounds in the risk assessment, we generally recommend comparing the
concentrations of compounds detected in blanks to concentrations detected in site samples collected
during the trial burn. Four types of blanks are defined in the Risk Assessment Guidance for Superfimd
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(U.S. EPA 1989e): trip blanks, field blanks, laboratory calibration blanks, and laboratory reagent or
method blanks. Detailed definitions of each are provided below.
Trip Blank - A trip blank is used to indicate potential contamination due to migration of volatile
organic compounds from the air on the site or in sample shipping containers, through the septum
or around the lid of sampling vials, and into the sample. The blank accompanies the empty
sample bottles to the field as well as with the site samples returning to the laboratory for analysis.
The blank sample is not opened until it is analyzed in the lab with the site samples, thus making
the laboratory "blind" to the identity of the blanks.
Field Blank - A field blank is used to determine if field sampling or cleaning procedures
(e.g., insufficient cleaning of sample equipment) result in cross-contamination of site samples.
Like the trip blank, the field blank is transported to the field with empty sample bottles and is
analyzed in the laboratory along with the site samples. Unlike the trip blank, however, the field
blank sample is opened in the field and recovered in the same manner as the collected samples.
As with trip blanks, the field blanks' containers and labels may be the same as for site samples
and blind to the laboratory.
Instrument Blank - An instrument blank is distilled, deionized water injected directly into an
instrument without having been treated with reagents appropriate to the analytical method used to
analyze actual site samples. This type of blank is used to indicate contamination in the
instrument itself.
Laboratory Reagent or Method Blank - A laboratory reagent of method blank results from the
treatment of distilled, deionized water with all of the reagents and manipulations (e.g., digestions
or extractions) to which site samples will be subjected. Positive results in the reagent blank may
indicate either contamination of the chemical reagents or the glassware and implements used to
store or prepare the sample and resulting solutions. Although a laboratory following good
laboratory practices will have its analytical processed under control, in some instances method
blank contaminants cannot be entirely eliminated.
Water Used for Blanks - For all the blanks described above, results are reliable only if the water
comprising the blank was clean. For example, if the laboratory water comprising the trip blank
was contaminated with VOCs prior to being taken to the field, then the source of VOC
contamination in the trip blank cannot be isolated.
Blank data is generally compared to the results with which the blanks are associated. However, if the
association between blanks and data can't be made, blank data is compared to the results from the entire
sample data set.
U.S. EPA (1989e) makes a distinction between blanks containing common laboratory contaminants and
blanks containing contaminants not commonly used in laboratories. Compounds considered to be
common laboratory contaminants are
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acetone,
2-butanone (methyl ethyl ketone),
• methylene chloride,
• toluene, and
the phthalate esters.
If compounds considered to be common laboratory contaminants are detected in the blanks, then sample
results are usually not considered to be detected unless the concentrations in the sample are equal to or
exceed ten times the maximum amount detected in the applicable blanks. If the concentration of a
common laboratory contaminant in a sample is less than ten times the blank concentration, then the
compound is usually treated as a non-detect in that particular sample.
In some limited cases, it may be appropriate to consider blanks which contain compounds that aren't
considered by the Agency to be common laboratory contaminants. In these limited cases, sample results
aren't considered to be detected unless the concentrations in the sample exceed five times the maximum
amount detected in the applicable blanks. If the concentration in a sample is less than five times the
blank concentration, then the compound would be treated as a non-detect in that particular sample.
Carefully consider the evaluation of blank data in the overall context of the risk assessment and
permitting process. We generally expect issues related to non-laboratory contaminant blanks to be
minimal, because data collection and analysis efforts in support of trial/risk burns are expected to be of
high quality and in strict conformance to QA/QC plans and SOPs. Carefully evaluating the trial/risk burn
data will avoid the potential for contaminated blanks to compromise the integrity of the data. It will also
help prevent the need for retesting to properly address data quality issues.
We highly recommend practicing caution in applying blank results to correct or qualify sample results for
any purpose, as blanks are provided in minimal quantities (e.g., one per test condition or one per test) and
therefore are at best qualitative indicators of the validity of a data set. Blank correction can reduce
accuracy and often represent a non-conservative uncertainty. Consequently a permit authority might
dedice not to allow blank correction as a conservative assumption (consistent with a screening level risk
assessment).
When considering blank contamination in the COPC selection process, we recommend that permitting
authorities ensure that:
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(1) The facility or data gatherer has made every reasonable attempt to ensure good data
quality and has rigorously implemented the QA/QC Plan and good industry sampling and
testing practices.
(2) Trial/risk burn data has not been submitted to the permitting authority as "blank
corrected." Rather, the permitting authority has the full opportunity to review the data
absent additional manipulation by the data gatherer.
(3) The effect of the blank correction on the overall risk estimates, if such an effect is
considered, is clearly described in the uncertainty section of the risk assessment report.
(4) The risk assessment report lists emissions rates both as measured, and as blank corrected,
in situations where there is a significant difference between the two values.
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Chapter 3
Air Dispersion and Deposition Modeling
What's Covered in Chapter 3:
3.1 Background on Air Dispersion Models for Risk Assessment
3.2 Partitioning Emissions
3.3 Site-Specific Characteristics Required for Air Modeling
3.4 Meteorological Data Primer
3.5 Meteorological Preprocessor Data Needs
3.6 ISCST3 Model Input Files
3.7 ISCST3 Model Execution
3.8 Using Model Output
3.9 Modeling Fugitive Emissions
3.10 Mo deling Acute Risk
The burning of materials produces residual amounts of pollution that could be released to the
environment. Knowledge of atmospheric pollutant concentrations and deposition rates in the areas
around the combustion facility is an integral part of estimating potential human health risks associated
with these releases. Air concentrations and deposition rates are usually estimated using air dispersion
models. Air dispersion models are mathematical constructs that attempt to describe the effects of
physical processes that occur in the atmosphere on rates of dispersion of emissions from a source (such
as the stack of a combustor). These mathematical constructs are coded into computer programs to
facilitate the computational process.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The "you" to which we speak in this chapter is the air modeler: the person (or persons)
who will actually put the recommended air modeling methods into practice.
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This chapter provides guidance on criteria to consider when selecting an air model to use to support a
risk assessment. It also describes the development and use of the U.S. EPA air dispersion model—the
Industrial Source Complex Short-Term Model (ISCST3)—which best addresses the selection criteria for
most risk assessments. Although the air dispersion modeling methods presented in the HHRAP focus on
use of ISCST3, other available models may be more appropriate if they better meet the selection criteria
due to study-specific objectives or assessment area characteristics. Of particular interest is the American
Meteorological Society- Environmental Protection Agency Regulatory Model (AERMOD), which has
been proposed by EPA as a replacement for the ISCST3 model for Clean Air Act regulatory purposes.
More details about AERMOD and other alternative models are provided in Section 3.1.1.
The Guideline on Air Quality Models (GAQM) (U.S. EPA 1996k; 1999b; Federal Register 2000) is a
primary reference for all US EPA and state agencies on using air models for regulatory purposes. The
GAQM is Appendix W of 40 CFR Part 51. The Office of Air Quality Planning and Support (OAQPS)
provides the GAQM and extensive information on updates to air dispersion models, meteorological data,
data preprocessors, and user's guides on the Support Center for Regulatory Air Models (SCRAM) web
site at www.epa.gov/scramOO 1/index.htm. We suggest you review this web site periodically to check for
updates and changes to the recommended model. General questions regarding air modeling, or
information on the web site maybe addressed to atkinson.dennis@epamail.epa.gov. Specific questions
on the use of this guidance may be addressed to the appropriate permitting authority. Please refer to the
respective models User's Manual for:
*• a more in-depth discussion on the physical assumptions embodied in the ISCST3 or other
air dispersion model that may be considered; as well as
*• concepts of atmospheric processes that are embodied in dispersion models in general.
Discussions in Section 3.1 focus on a background of available models, and selection criteria to consider
when deciding which air model to use in a risk assessment. The remainder of the chapter provides
information on ISCST3 input data needs, and output file development. Chapter sections include:
• Partitioning emissions (Section 3.2)
• Site-specific characteristics needed for air modeling (Section 3.3)
Surrounding terrain (Section 3.3.1)
Surrounding land use (Section 3.3.2)
Facility building characteristics (Section 3.3.4)
• Meteorological data (Section 3.5)
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If you aren't familiar with the types of data used in air concentration and deposition models, Section 3.4
provides a short tutorial on the types of meteorological data needed, and potential data sources. Section
3.5 describes the data needs of the preprocessor computer program we recommend using to prepare,
organize, and format meteorological data for use in the ISCST3 model. Section 3.6 describes the
structure and format of ISCST3 input files. Section 3.7 describes limitations to consider in executing
ISCST3. Section 3.8 describes how model outputs are used in the risk assessment computations. Section
3.9 discusses air modeling of fugitive emissions. Section 3.10 discusses air modeling for acute exposure.
3.1 DESCRIPTION OF AIR MODELS
This section gives a brief background on air dispersion models for risk assessment. This section also
introduces some data preprocessing programs developed to aid in preparing air model input files (these
preprocessing programs are described in more detail in Sections 3.3.3 and 3.5).
3.1.1 Background on Air Dispersion Models for Risk Assessment
Before 1990, the Agency and the regulated community used several air dispersion models. These models
were of limited usefulness in risk assessments because they considered only air concentration, and not
the deposition of contaminants to land. The original Agency guidance on completing risk assessments
(U.S. EPA 1990e) identified two models that were explicitly formulated to account for the effects of
deposition:
COMPLEX I, from which a new model—COMPDEP—resulted
• Rough Terrain Diffusion Model (RTDM), from which a new
model—RTDMDEP—resulted
An updated version of the ISCST model in use at the time, COMPDEP included building wake effects.
Subsequent Agency guidance (U.S. EPA 1993f and 1994g) recommended using COMPDEP for air
deposition modeling. U.S. EPA (1993f) specified COMPDEP Version 93252, and U.S. EPA (1994g)
specified COMPDEP Version 93340. When these recommendations were made, a combined
ISC-COMPDEP model (a merger of the ISCST2 and COMPLEX I model) was still under development.
The merged model became known as ISCSTDFT. U.S. EPA guidance (1994r) recommended using the
ISCSTDFT model. After reviews and adjustments, this model was released as ISCST3. The ISCST3
model contains algorithms for dispersion in simple, intermediate, and complex terrain; dry deposition;
wet deposition; and plume depletion.
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Operating the COMPDEP, RTDMDEP, and ISCST models is described in more detail in the following
User's Manuals. Please note, though, that all models except the current version of ISCST3 are
considered obsolete:
• Environmental Research and Technology (ERT). 1987. User's Guide to the Rough
Terrain Diffusion Model Revision 3.20. ERT Document P-D535-585. Concord,
Massachusetts.
Turner, D.B. 1986. Fortran Computer Code/User's Guide for COMPLEX I Version
86064: An Air Quality Dispersion Model in Section 4. Additional Models for
Regulatory Use. Source File 31 Contained in UNAMAP (Version 6). National
Technical Information Service (NTIS) PB86-222361/AS.
• U.S. EPA. 1979. Industrial Source Complex Dispersion Model User's Guide, Volume I.
Prepared by the H.E. Cramer Company. Salt Lake City, Utah. Prepared for the Office of
Air Quality Planning and Standards. Research Triangle Park, North Carolina. EPA
450/4-79/030. NTIS PB80-133044.
U.S. EPA. 1980b. User's Guide for MPTER: A Multiple Point Gaussian Dispersion
Algorithm with Optional Terrain Adjustment. Environmental Sciences Research
Laboratory. Research Triangle Park, North Carolina. EPA 600/8-80/016. NTIS
PB80-197361.
U.S. EPA. 1982a. MPTER-DS: The MPTER Model Including Deposition and
Sedimentation. Prepared by the Atmospheric Turbulence and Diffusion Laboratory
(NOAA). Oak Ridge, Tennessee. Prepared for the Environmental Sciences Research
Laboratory. Research Triangle Park, North Carolina. EPA 600/8-82/024. NTIS
PB83-114207.
• U.S. EPA. 1987b. On-Site Meteorological Program Guidance for Regulatory Modeling
Applications. Office of Air Quality Planning and Standards. Research Triangle Park,
North Carolina.
• U.S. EPA. 1995f. User's Guide for the Industrial Source Complex (ISC3) Dispersion
Models, Volumes I and II. Office of Air Quality Planning and Standards. Emissions,
Monitoring, and Analysis Division. Research Triangle Park, North Carolina. EPA
454/B-95/003a. September.
Four air models in current regulatory use, or proposed by OAQPS, include ISCST3, AERMOD,
CALPUFF and ISC-PRIME. However, we generally recommend using the latest version of ISCST3 in
most situations to conduct air dispersion and deposition modeling for use in a risk assessment.
This recommendation is based on ISCST3's status as the:
• Model most used by regional, state and local agencies; and
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• Model satisfying the most of the following criteria:
*• Has broad regulatory acceptance and experience in evaluating the impacts of air
contaminants emitted from various industrial sources (e.g., short and tall stack
heights, fugitive emissions from process and storage areas);
*• Can evaluate impacts attributable to a single source, with capability to evaluate
multiple sources;
*• Can evaluate compounds emitted as vapor or particulate phase with
consideration of deposition and removal processes based on the physical
characteristics of the compound;
*• Accepts placement of grid nodes at any location within the applicable range of
the air model;
»• Can conduct stepwise evaluation of hourly meteorological conditions for
multiple years of data producing short-term (acute) and long-term (chronic)
averages as outputs; and
»• Can evaluate building downwash effects.
The other three air models are proposed by OAQPS as refined regulatory air models in the revisions to
the Guideline on Air Quality Models (U.S. EPA 1996k; 1999b). Of particular interest is AERMOD.
AERMOD is proposed to replace ISCST3 as the recommended air quality model for most regulatory
applications (Federal Register 2000). AERMOD has been evaluated extensively by the air modeling
community for improvements over the ISCST3 algorithms for vertical contaminant distribution from
sources with tall stacks, terrain effects in areas with terrain elevations above the top of emission source
stacks, and enhanced nighttime dispersion in urban areas. However, AERMOD currently has minimal
regulatory experience. AERMOD would be an important consideration for risk assessments if:
• it gains broad regulatory acceptance,
• the assessment area contains significant terrain features (i.e., terrain elevations above
emission source stacks)
OAQPS promulgated the California Puff Model (CALPUFF) model for use on a case-by-case basis, for
long-range transport (greater than 50 kilometers), as well as the special conditions of very light or calm
winds (less than 1 meter per second). CALPUFF may be an important consideration when modeling:
• chemical transformation of highly reactive contaminants;
• long residence times during extended periods of light winds or calm conditions;
• transport over long distances;
• recirculation of contaminants due to local wind effects; or
• hilly terrain and river valleys.
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Also, the proposed OAQPS revisions to the GAQM recommend using the version of ISCST with the new
downwash algorithm, ISC-PRIME, for applications where aerodynamic building downwash is critical
(Federal Register 2000). The necessary information and inputs to evaluate the effects of building
downwash are further discussed in Section 3.2.4. We generally recommend determining the need for the
improved building downwash calculation on a case-by-base basis.
Although air dispersion modeling methods presented in this guidance use ISCST3, you may want to
evaluate other models (such as ISC-PRIME, AERMOD, or CALPUFF, if the revised GAQM adopts
them), to see whether they more substantially meet the selection criteria. Specific to its ability to meet
the selection criteria listed above, the ISCST3 model is technically capable of evaluating:
• Gaussian dispersion rates in vertical and horizontal plume cross-section;
• Urban and rural dispersion coefficients;
• Terrain effects;
• Source characterization as a discrete point, two-dimensional area, or three-dimensional
volume;
• Short-term and long-term averages (1-hour and annual);
• Surface meteorology data includes hourly observations of wind speed (nearest
1/1 Oth mile per second), wind direction (nearest degree), stability class (6 categories),
and temperature (nearest degree);
• Mixing height data interpolated from twice daily upper air soundings corresponding to
each hour of surface data;
• Deposition processes for conservation of mass with particle wet and dry deposition and
removal, and vapor wet deposition and removal (dry vapor deposition implemented
within the air modeling; see Section 3.6.1);
• Hourly precipitation amount and type act on wet deposition and removal; and
• Single first order exponential decay rate.
In addition, emission sources can be defined in ISCST3 with either stack dimensions at a discrete point,
as a two-dimensional area source, or in three dimensions as a volume source. Therefore, ISCST3 offers
the flexibility to model sources based on source type (i.e., stack or fugitive area).
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3.1.2 Preprocessing Programs
ISCST3 needs the help of additional computer programs, referred to as "preprocessing" programs. These
programs format available information regarding surrounding buildings and meteorological data into a
format that ISCST3 can read. Currently, these programs include:
• Building Profile Input Program (BPIP) calculates the maximum crosswind widths of
buildings, which ISCST3 then uses to estimate the effects on air dispersion. These
effects by surrounding buildings are typically referred to as building downwash or wake
effects. The BPIP User's Guide contains detailed information for preparing the
necessary building dimensions (length, height, and width) and locations for the ISCST3
model (U.S. EPA 1995c).
• Meteorological Processor for Regulatory Models (MPRM) prepares meteorological data
for ISCST3. MPRM merges hourly measurements of surface parameters (e.g.,
precipitation, wind speed, and wind direction) into rows and columns of information that
ISCST3 can read. The MPRM User's Guide contains instructions for using on-site
meteorological data to prepare the necessary meteorological input file for ISCST3 (U.S.
EPA 1996J; 1999c). The Addendum to the MPRM User's Guide describes the additional
data needed in the meteorological input file for ISCST3 to model dry deposition of
vapors (U.S. EPA 1999c).
We generally recommend using MPRM to process the meteorological data, because MPRM provides all
the meteorological parameter values that ISCST3 needs to function. The peer review draft of the
HHRAP described the personal computer version of the meteorological preprocessor for the old RAM
program (PCRAMMET). Unfortunately, PCRAMMET doesn't provide the parameters needed for
evaluating dry vapor deposition in ISCST3. Therefore, we no longer recommend using PCRAMMET.
When sufficient site-specific data is available on the location, size and shape of structures in the vicinity
of the emissions sources, you can use the BPIP program to prepare the ISCST3 input file to consider the
effects of building downwash on pollutant transport. If sufficient site-specific data is not available to run
BPIP, then you will not be able to address building downwash in air modeling efforts. Prior to
performing the air dispersion modeling, we recommend that you consult with the appropriate parties (e.g.
regulatory authority and facility) on the decision to include or omit nearby structures.
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3.2 PARTITIONING EMISSIONS
COPC emissions to the environment occur in either vapor or particle phase. In general, you can assume:
• most metals and organic COPCs with very low volatility (fraction of COPC in vapor
phase [Fv] less than 0.05, see Appendix A-3) occur only in the particle phase;
• highly volatile organic COPCs occur only in the vapor phase (Fv of 1.0, see Appendix
A-3); and
• the remaining organic COPCs occur with a portion of the vapor condensed onto the
surface of particulates (i.e. particle-bound).
For modeling COPCs released only as particulates, the mass fractions allocated to each particle size are
different than the mass fractions used for modeling organics released in both the vapor and
particle-bound phases.
Due to model limitations, you need to run ISCST3 multiple times to generate estimates of vapor phase
COPCs, particle phase COPCs, and particle-bound COPCs. An example is the risk assessment for the
WTI incinerator located in East Liverpool, Ohio. The study used three runs; a vapor phase run for
organic COPCs, a particle run with mass weighting of the particle phase metals and organic COPCs with
very low volatility, and a particle run with surface area weighting of the particle-bound organic COPCs.
3.2.1 Vapor Phase Modeling
Vapor phase air modeling runs do not need a particle size distribution in the ISCST3 input file. ISCST3
output for vapor phase runs are vapor phase ambient air concentration, dry vapor deposition, and wet
vapor deposition at receptor grid nodes based on the unit emission rate.
3.2.2 Particle Phase Modeling (Mass Weighting)
ISCST3 uses algorithms to compute the rate at which dry and wet removal processes deposit particle
phase COPCs onto the Earth's surface. Particle size is the main determinant of the fate of particles in air
flow, whether dry or wet. The key to dry particle deposition rate is the terminal, or falling, velocity of a
particle. Particle terminal velocity is calculated mainly from the particle size and particle density. Large
particles fall more rapidly than small particles, and are deposited closer to the stack. Small particles have
low terminal velocities. Very small particles remain suspended in the air flow. Wet particle deposition
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also depends on particle size as larger particles are more easily removed, or scavenged, by falling liquid
(rain) or frozen (snow or sleet) precipitation. To estimate particle phase deposition rates, ISCST3 needs
an initial estimate of the particle size distribution, broken out by particle diameter.
The diameters of small particles contained in stack emissions are usually measured in micrometers. The
distribution of particle diameters will differ from one combustion process to another, and is greatly
dependent on the:
1. Furnace type;
2. Combustion chamber design;
3. Feed fuel composition;
4. Particulate removal efficiency;
5. APCS design;
6. Amount of air, in excess of stoichiometric amounts, that is used to sustain combustion;
and
7. Combustion temperature.
The particle size distribution cannot be calculated, but only directly measured or inferred from prior data.
Unfortunately, few studies have been performed to directly measure particle size distributions from a
variety of stationary combustion sources (U.S. EPA 1986a).
We recommend that existing facilities perform stack tests to identify particle size distribution. We
further recommend that these data represent actual operating conditions for the combustion unit and air
pollution control device (APCD) that remove particulate from the stack gas. A table of particle size
distribution data may be prepared using stack test data in the format similar to the example illustrated in
Table 3-1.
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TABLE 3-1
HYPOTHETICAL PARTICLE SIZE DISTRIBUTION DATA
TO SUPPORT EXAMPLE CALCULATIONS
1
Mean Particle
Diameter "
((im)
>15.0
12.5
8.1
5.5
3.6
2.0
1.1
0.7
<0.7
2
Particle
Radius
((im)
7.50
6.25
4.05
2.75
1.80
1.00
0.55
0.40
0.40
3
Surface
Area/
Volume
Oim1)
0.400
0.480
0.741
1.091
1.667
3.000
5.455
7.500
7.500
4
Fraction of
Total
Mass"
0.128
0.105
0.104
0.073
0.103
0.105
0.082
0.076
0.224
5
Proportion
Available
Surface
Area
0.0512
0.0504
0.0771
0.0796
0.1717
0.3150
0.4473
0.5700
1.6800
6
Fraction
of Total
Surface
Area
0.0149
0.0146
0.0224
0.0231
0.0499
0.0915
0.1290
0.1656
0.4880
a Geometric mean diameter in a distribution from U.S. EPA (1980a), as presented in U.S. EPA (1998c)
b The terms mass and weight are used interchangeably when using stack test data
We expect that actual stack test data will be different from the hypothetical values presented in Table 3-
1. This is because actual stack sampling will use particle "cut size" for the different cascade impactor
filters (or Coulter counter-based distributions). The test method will drive the range of particle sizes that
are presented in the results of the stack test. However, ISCST3 needs mean particle diameter for each
particle size distribution, and the stack test data identifies only the mass ("weight") of particles in a range
bounded by two specific diameters. To address this, we recommend that stack test data be converted into
a mean particle diameter which approximates the diameter of all the particles within a defined range. We
recommend using the following equation to calculate the mean particle diameter:
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Dmean = t°-25 ' (A' + D?D2 + D,Dl + £23)]1/3 Equation 3-1
where
Dmean = Mean particle diameter for the particle size category (um)
D, - Lower bound cut of the particle size category (um)
D2 = Upper bound cut of the particle size category (um)
For example, the mean particle diameter of 5.5 um in Table 3-1 is calculated from a lower bound cut size
(assuming a cascade impactor is used to collect the sample) of 5.0 urn to an upper bound cut size of
6.15 urn. In this example, the mean particle diameter is calculated as:
I0-25 C5-0' + (5-0)2(6.15) + (5.0)(6.15)2 + 6.153)]173 = 5.5
From Table 3-1, the mean particle diameter is 5.5 um. The mass of particulate from the 5.0 um stack test
data is then assigned to the 5.5 um mean particle diameter for the purpose of computing the "fraction of
total mass."
Typically, eight to ten mean particle diameters are available from stack test results. However, as
determined from a sensitivity analysis conducted by The Air Group-Dallas under contract to U.S. EPA
Region 6 (http://www.epa.gov/region06), a minimum of three particle size categories (> 10 microns, 2-10
microns, and < 2 microns) detected during stack testing are generally the most sensitive to air modeling
with ISCST3 (U.S. EPA 1997f). For facilities with stack test results which indicate mass amounts lower
than the detectable limit (or the filter weight is less after sampling than before), a single mean particle
size diameter of 1 .0 micron may be used to represent all mass (e.g., particle diameter of 1 .0 micron or a
particle mass fraction of 1 .0) in the particle and particle-bound model runs. Because rudimentary
methods for stack testing may not detect the very small size or amounts of COPCs in the particle phase,
the use of a 1.0 micron particle size will allow these small particles to be included properly as particles in
the risk assessment exposure pathways while dispersing and depositing in the air model similar in
behavior to a vapor. Consequently, we recommend that a minimum of three particle size categories be
used in the air modeling effort.
After calculating the mean particle diameter (Column 1), you can compute the fraction of total mass per
mean particle size diameter (Column 4) from the stack test results. For each mean particle diameter, the
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stack test data provides an associated particulate mass. Calculate the fraction of total mass for each mean
particle diameter by dividing the associated particulate mass for that diameter by the total particulate
mass in the sample. In many cases, the fractions of total mass will not sum to 1.0 because of rounding
errors. In these instances, we advocate forcing the total mass to 1.0 by adding the remaining mass
fraction into the largest mean particle diameter mass fraction.
Direct measurements of particle-size distributions may not be available at a proposed new facility. You
will need to supply ISCST3 with an assumed particle distributions. In such instances, you may use a
representative distribution. We recommend that the combustor on which the representative distribution
is based be as similar as practicable to the proposed combustor.
ISCST3 uses the mass-based particle size distribution to apportion the mass of particle phase COPCs
(metals and organics with Fv values less than 0.05) according to particle size. The ISCST3 input file uses
the data in Column 4 of Table 3-1 (as developed from actual stack test data) to perform a particulate run
with the particle phase COPCs apportioned per mass weighting.
3.2.3 Particle-Bound Modeling (Surface Area Weighting)
Use a surface area weighting of particles, instead of mass weighting, in separate particle runs of ISCST3.
Surface area weighting approximates the situation where a portion of a semivolatile organic contaminant,
volatilized in the high temperature environment of a combustion system, condenses to the surface of
particles entrained in the combustion gas after the gas cools in the stack. Apportioning emissions by
particle diameter becomes a function of the surface area of particles available for chemical adsorption
(U.S. EPA1998c).
The first step in apportioning COPC emissions by surface area is to calculate the proportion of available
surface area of the particles. If particle density is held constant (such as 1 g/cm3), the proportion of
available surface area of aerodynamically spherical particles is the ratio of surface area (S) to volume (V),
is estimated as follows:
• Assume aerodynamically spherical particles.
• Specific surface area of a spherical particle with a radius, r—S — 4 nr2
• Volume of a spherical particle with a radius, r—V = 4/3 nr3
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Ratio of S to V—S/V = 4^1 (4/3 nr3) = 3/r
The following example uses the hypothetical particle size distribution in Table 3-1 to apportion the
emission rate of the particle-bound portion of the COPC based on surface area. We generally
recommend following this procedure for apportioning actual emissions to the actual particle size
distribution measured at the stack.
In Table 3-1, a spherical particle having a diameter of 15 urn (Column 1) has a radius of 7.5 urn
(Column 2). The proportion of available surface area (assuming particle density is constant) is
S/V = 3/7.5 = 0.4, which is the value in Column 3. Column 4 shows that particles with a mean diameter
of 15 urn constitute 12.8 percent of the total mass. Multiplying Column 3 by Column 4 yields the value
in Column 5 of 0.0512. This value is an approximation of the relative proportion of total surface area,
based on the percent of particles that are 15 um in diameter. Totaling Column 5 yields the total surface
area of all particles in the particle size distribution. In this example, the sum is 3.4423. Column 6 is the
fraction of total surface area represented by the specific particle diameter in the distribution. Column 6 is
calculated by dividing the relative proportion of surface area (Column 5) for a specific diameter by the
total relative proportion of surface area (3.4423 square micrometers [um2]). In the example of the 15
um-diameter particle, the fraction of total surface area available for adsorption is 0.0149
(0.0512/3.4423). This procedure is then repeated for all particle sizes in the array.
ISCST3 uses the surface area-based particulate size distribution to apportion mass of particle-bound
COPCs (most organics) according to particle size. The ISCST3 input file uses Column 6 of Table 3-1 (as
developed from actual stack test data) to perform a particulate run for the particle-bound COPCs
apportioned according to surface area weighting.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Copies of all stack test data used to determine particle size distribution
Copies of all calculations made to determine particle size distribution, fraction of total mass,
and fraction of total surface area
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3.3 SITE-SPECIFIC INFORMATION NEEDED FOR AIR MODELING
We generally recommend that the site-specific information for the facility and surrounding area used in
air dispersion modeling include:
1. the elevation of the surrounding land surface or terrain;
2. surrounding land uses; and
3. characteristics of on-site buildings that may affect the dispersion of COPCs into the
surrounding environment.
You can often obtain some of the site-specific information needed for air dispersion modeling by
reviewing available maps and other graphical data on the area surrounding the facility. The first step in
the air modeling process is reviewing these resources. Using U.S. Geological Survey (USGS) 7.5-minute
topographic maps (1:24,000) extending to 10 kilometers from the facility, and USGS 1:250,000 maps
extending out to 50 kilometers, you can identify much of the following:
• site location,
• nearby terrain features,
• waterbodies and watersheds,
• ecosystems,
• nearby residences, and
• land use.
Aerial photographs are frequently available to supplement the depiction of the area. An accurate facility
plot plan—showing buildings, stacks, property and fence lines—is also needed. Facility information
including stack and fugitive source locations, building corners, plant property, and fence lines, in
Universal Transverse Mercator (UTM1) grid coordinates in meters east and north might be available in
both USGS reference systems. Therefore, knowledge of the horizontal datum of the geographic
coordinates from which the UTM coordinates were projected is required. We generally recommend
consulting with personnel experienced and knowledgeable in the intricacies of these types of coordinate
systems, and the various software programs used to conduct conversions.
UTM is a map coordinate projection of geographic coordinates.
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Most USGS paper 7.5-minute topographic maps are published in the North American Datum system
established in 1927 (NAD 27). However, most digital elevation data (e.g., USGS Digital Elevation
Mapping) is in the 1983 revised system (NAD 83). We recommend taking special care not to mix source
data based on NAD 27 with data based on NAD 83. Also, we recommend obtaining emission source
information in the original units from the facility data, and convert it to metric units for air modeling, if
necessary. You can get digital terrain data from USGS, or another source.
The following subsections describe the specific information we generally recommend collecting.
Entering this information into the ISCST3 input files is described in Section 3.6.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• All site-specific maps, photographs, or figures used in developing the air modeling approach
• Mapped identification of facility information including stack and fugitive source locations,
locations of facility buildings surrounding the emission sources, and property boundaries of
the facility
3.3.1 Surrounding Terrain Information
Terrain is important to air modeling because air concentrations and deposition rates are greatly
influenced by the height of the plume above local ground level. Terrain is characterized by elevation
relative to stack height. For air modeling purposes, terrain is referred to as "complex" if the elevation of
the surrounding land within the assessment area—typically defined as anywhere within 50 kilometers of
the stack—is above the top of the stack evaluated in the air modeling analysis. Terrain at or below stack
top is referred to as "simple." ISCST3 implements U.S. EPA guidance on the proper application of air
modeling methods in all terrain if you include terrain elevation for each receptor grid node and specify
the appropriate control parameters in the input file.
Even small terrain features may have a large impact on the air dispersion and deposition modeling results
and, ultimately, on the risk estimates. We generally recommend that air modeling include terrain
elevations for every receptor grid node. Some exceptions may be those sites characterized by very flat
terrain where the permitting authority has sufficient experience to comfortably defer the use of terrain
data because the terrain's historical effect on air modeling results have been minimal.
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We generally recommend including electronic copies of the digital terrain data used to extract receptor
grid node elevations in the risk assessment report. One way to obtain receptor grid node elevations is
using digital terrain data available from the USGS on the Internet at web site
http://edc.usgs.gov/guides/dem.html. For most locations, an acceptable degree of accuracy is provided
by the USGS "One Degree" (e.g., 90 meter data) data available as "DEM 250" 1:250,000 scale for the
entire United States free of charge. For areas requiring more accurate terrain, USGS 30-meter (1:24,000
scale) data might be considered, though it is not universally available. Either 90-meter or 30-meter data
is sufficient for most risk assessments which utilize 100 meter or greater grid spacing. Digital terrain
data is also available for purchase from a variety of commercial vendors, who may require
vendor-provided programs to extract the data. You can also manually extract the elevation of each
receptor grid node from USGS topographic maps.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Description of the terrain data used for air dispersion modeling
• Summary of any assumptions made regarding terrain data
• Description of the source of any terrain data used, including any procedures used to
manipulate terrain data for use in air dispersion modeling
3.3.2 Surrounding Land Use Information
Land use information is needed in the risk assessment for air dispersion modeling, as well as identifying
and selecting exposure scenario locations (see Chapter 4). Land use analysis for air dispersion modeling
usually occurs out to a radius of 3 kilometers from the centroid of the stacks from which emissions are
being modeled. Certain land uses, as defined by air modeling guidance, affect the selection of air
dispersion modeling variables. These variables are known as dispersion coefficients and surface
roughness. You typically use USGS 7.5-minute topographic maps, aerial photographs, or visual surveys
to define the air dispersion modeling land uses.
Land use information is also important for selecting exposure scenario locations, but at a radius further
(50 kilometers) from the emission source(s), to make sure all receptors that may be impacted are
identified. In most cases, though, air modeling performed out to a radius of 10 kilometers allows
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adequate characterization for evaluating exposure scenario locations. If you are evaluating a facility with
multiple stacks or emission sources, we generally recommend extending the radius from the centroid of a
polygon drawn connecting the various stack coordinates.
3.3.2.1 Land Use for Dispersion Coefficients
You need to specify the appropriate dispersion coefficients for ISCST3 to run properly. Land uses need
to be defined in order to specify dispersion coefficients. We generally recommend using the Auer
method specified in the Guideline on Air Quality Models (40 CFR Part 51, Appendix W) (U.S. EPA
1996k; 1999b) to define land use. Land use categories of "rural" or "urban" are taken from Auer (1978).
Areas typically defined as rural include residences with grass lawns and trees, large estates, metropolitan
parks and golf courses, agricultural areas, undeveloped land, and water surfaces. Auer typically defines
an area as "urban" if it has less than 35 percent vegetation coverage or the area falls into one of the
following use types:
TABLE 3-2
URBAN LAND USE TYPES
Urban Land Use
Type
11
12
Cl
R2
R3
Use and Structures
Heavy industrial
Light/moderate industrial
Commercial
Dense single/multi-family
Multi-family, two-story
Vegetation
Less than 5 percent
Less than 5 percent
Less than 15 percent
Less than 30 percent
Less than 35 percent
In general, the Auer method is described as follows:
Step 1 Draw a radius of 3 kilometers from the center of the stack(s) on the site map.
Step 2 Inspect the maps, and define in broad terms whether the area within the radius is rural or
urban, according to Auer's definitions.
Step 3 Classify smaller areas within the radius as either rural or urban, based on Auer's
definitions. (It may be prudent to overlay a grid [for example, 100 by 100 meters] and
identify each square as primarily rural or urban)
Step 4 Count the number of rural squares; if more than 50 percent of the total squares are rural,
the area is rural; otherwise, the area is urban.
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Alternatively, digital land use databases may be used in a computer-aided drafting system to perform this
analysis.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Description of the methods used to determine land use surrounding the facility
• Copies of any maps, photographs, or figures used to determine land use
• Description of the source of any computer-based maps used to determine land use
3.3.2.2 Land Use for Surface Roughness Height (Length)
Surface roughness height—also referred to as (aerodynamic) surface roughness length—is the height
above the ground at which the wind speed goes to zero. Surface roughness affects the height above local
ground level that a particle moves from the ambient air flow above the ground (for example in the plume)
into a "captured" deposition region near the ground. That is, ISCST3 assumes the particle is deposited to
the ground at some point above the actual land surface, based on surface roughness height. Surface
roughness height is defined by individual elements on the landscape, such as trees and buildings.
There are several methods for computing surface roughness. One uses measurements of hourly surface
roughness based on wind direction (fetch). Another uses the change in wind speed with height in the
surface layer, as measured on an instrumented tower operated at the site. We recommend deciding which
method to use to characterize surface roughness based on the variability of surface features and seasonal
values for the site. In lieu of any other method, the paragraphs below describe our recommendations for
computing surface roughness.
In order to be consistent with the recommended method for determining land use for dispersion
coefficients (Section 3.3.2.1), the land use within 3 kilometers generally is acceptable for determining
surface roughness. Surface roughness height values for various land use types are as follows:
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TABLE 3-3
SURFACE ROUGHNESS HEIGHTS
Surface Roughness Heights for Land Use Types and Seasons (meters)
Land Use Type
Water surface
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
Spring
0.0001
1.00
1.30
0.20
0.03
0.05
1.00
0.30
Summer
0.0001
1.30
1.30
0.20
0.20
0.10
1.00
0.30
Autumn
0.0001
0.80
1.30
0.20
0.05
0.01
1.00
0.30
Winter
0.0001
0.50
1.30
0.05
0.01
0.001
1.00
0.15
Source: Sheih, et al. (1979)
If a significant number of buildings are located in the area, higher surface roughness heights (such as
those for trees) maybe appropriate (U.S. EPA 1995g). Previous guidance documents do not recommend
a specific method for determining average surface roughness height. If you are using National Weather
Service surface meteorological data, we generally recommend setting the surface roughness height for
the measurement site at 0.10 meters (grassland, summer). If you intend to propose a different value for
the measurement site, we recommend using the following procedure to determine the value:
Step 1 Draw a radius of 3 kilometers from the center of the stack(s) on the site map.
Step 2 Inspect the maps, and use professional judgment to classify the area within the radius
according to land use type (for example water, grassland, cultivated land, and forest); a
site visit maybe necessary to verify some classifications.
Step 3 Divide the circular area into 12 angular sectors of 30 degrees.
Step 4 Identify a single representative surface roughness height for each sector, based on an
area-weighted average of the land use types within the sector using the categories
identified above.
Step 5 Input the area-weighted surface roughness height value representative of each of the 12
sectors into MPRM for preprocessing the meteorological data file.
Site-specific conditions might be such that you consider methods other than those described above more
appropriate for determining surface roughness height. In such instances, we recommend clearly
identifying and discussing the alternative methods with the appropriate parties (regulatory authority or
facility) prior to use.
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3.3.3 Information on Facility Building Characteristics
Building wake effects, also referred to as "building downwash," have a significant impact on the
concentration and deposition of COPCs near the stack. Building wake effects are flow lines that cause
plumes to be forced down to the ground much sooner than they would if the building was not there. The
ISCST3 model contains algorithms for evaluating this phenomenon. We recommend that the downwash
analysis consider all nearby structures with heights at least 40 percent of the height of the shortest stack
to be modeled. The 40 percent value is based on Good Engineering Practice (GEP) stack height of 2.5
times the height of nearby structures or buildings (stack height divided by 2.5 is equal to 0.40 multiplied
by the stack height [40 CFR Part 51 Appendix W]). BPIP uses Building dimensions and locations, and
stack heights and locations, to identify the potential for building downwash. BPIP and the BPIP User's
Guide are available for download from the SCRAM web site (http://www.epa.gov/scram001/) to address
specific questions. The BPIP output file is in a format that can be copied and pasted into the source (SO)
pathway of the ISCST3 input file. We generally recommend the following procedure to identify
buildings for input to BPIP:
Step 1 Lay out facility plot plan, with buildings and stack locations clearly identified (building
heights identified for each building); for buildings with more than one height or roof
line, identify each height (BPIP refers to each height as a tier).
Step 2 Identify the buildings to be included in the BPIP analysis by comparing building heights
to stack heights. The building height test specifies that only buildings at least 40 percent
of the height of a potentially affected stack be included in the BPIP input file. For
example, if a combustion unit stack is 50 feet high, only buildings at least 20 feet tall
(0.40 multiplied by 50 feet) will affect air flow at stack top. Any buildings shorter than
20 feet need not be included in the BPIP analysis. The building height test is performed
for each stack and each building.
Step 3 Use the building distance test to check each building to be included in BPIP from the
building height test. For the building distance test, only buildings "nearby" the stack will
affect air flow at stack top. "Nearby" is defined as "five times the lesser of building
height or crosswind width" (U.S. EPA 1995c). A simplified distance test may be used by
considering only the building height rather than the crosswind width. While some
buildings with more height than width will be included unnecessarily using this
simplification, BPIP will identify correctly only the building dimensions required for
ISCST3.
An example, hypothetical plot plan identifies a 25-foot tall building that is 115 feet from
the 50-foot tall combustion unit stack center to the closest building corner. The building
distance test, for this building only, is five times the building height, or 125 feet (five
multiplied by the building height, 25 feet). This building would be included in the BPIP
analysis, because it passes the building height test and building distance test.
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Step 4 Repeat steps 2 and 3 for each building and each stack, identifying all buildings to be
included in the BPIP. If the number of buildings exceeds the BPIP limit of eight
buildings, consider combining buildings, modifying BPIP code for more buildings, or
using third-party commercial software which implements BPIP. If two buildings are
closer than the height of the taller building, the two buildings may be combined. For
example, two buildings are 40 feet apart at their closest points. One building is 25 feet
high, and the other building is 50 feet high. The buildings could be combined into one
building for input to BPIP. For input to BPIP, the corners of the combined building are
the outer corners of the two buildings. For unusually shaped buildings with more than
the eight corners allowed by BPIP, approximate the building by using the eight corners
that best represent the extreme corners of the building. The BPIP User's Guide contains
additional description and illustrations on combining buildings, and BPIP model
limitations (U.S. EPA 1995c).
Step 5 Mark off the facility plot plan with UTM grid lines. Extract the UTM coordinates of
each building corner and each stack center to be included in BPIP input file. Although
BPIP allows the use of "plant coordinates," we recommend that all inputs to the air
model be prepared using UTM coordinates (meters) for consistency. UTM coordinates
are rectilinear, oriented to true north, and needed in metric units for ISCST3 modeling.
Almost all air modeling will need to use USGS topographic data (digital and maps) for
receptor elevations, terrain grid files, locating plant property, and identifying
surrounding site features. Therefore, using an absolute coordinate system will enable
you to check inputs at each step of the analysis. Also, the meteorological data are
oriented to true north. Significant errors will result from ISCST3 if incorrect stack or
building locations are used, plant north is incorrectly rotated to true north, or incorrect
base elevations are used. With computer run times requiring many hours (up to 40 hours
for one deposition run with depletion), verifying locations at each step while preparing
model inputs will prevent the need to remodel.
We recommend observing several precautions and guidelines while preparing input files for BPIP:
• Graphically confirming the correct locations before BPIP is run. One method is to plot
the buildings and stack locations using graphics software. Several commercial programs
incorporating BPIP provide graphic displays of BPIP inputs.
• In addition to using UTM coordinates for stack locations and building corners, also use
meters as the units for height.
• Carefully include the stack base elevation and building base elevations according to
BPIP User's Guide instructions.
• Note that the BPIP User's Guide (revised February 8, 1995) has an error on page 3-5,
Table 3-1, under the "TIER(ij)" description, which incorrectly identifies tier height as
base elevation.
• BPIP mixes the use of "real" and "integer" values in the input file. To prevent possible
errors in the input file, note that integers are used where a count is requested (for
example, the number of buildings, number of tiers, number of corners, or number of
stacks).
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• Make the stack identifiers (up to eight characters) in BPIP identical to those used in the
ISCST3 input file, or ISCST3 will report errors.
For most sites, BPIP executes in less than 1 minute. The array of 36 building heights and 36 building
widths (one for each of 36 10-degree direction sectors) are input into the ISCST3 input file by cutting
and pasting from the BPIP output file. We recommend deleting the five blank spaces preceding "SO" in
the BPIP output file, so that the "SO" begins in the first column of the ISCST3 input file.
One use of BPIP is to design stack heights for new facilities. Another is to determine what stack height
increases are necessary to avoid building influence on air flow, which may cause high concentrations and
deposition near the facility. The output for BPIP provides the GEP heights for stacks. Significant
decreases in concentrations and deposition rates will begin at stack heights at least 1.2 times the building
height, and further decreases occur at 1.5 times building height, with continual decreases of up to 2.5
times building height (GEP stack height) where the building no longer influences stack gas.
3.4 METEOROLOGICAL DATA PRIMER
Air dispersion and deposition modeling is extremely complicated, and uses data potentially unfamiliar if
your expertise lies outside meteorology and air modeling. Section 3.4 is an introduction to the types of
meteorological data used by air dispersion and deposition models, as well as information on potential
data sources. Section 3.5 continues this introduction into the specific data parameters required by
MPRM, the data preprocessor for the ISCST3 air model.
Generally speaking, air concentration and deposition models attempt to characterize the effects of
atmospheric forces on a mass of COPC found in the air. These forces can be divided between:
• those which transport the COPC (the dominant factor is wind);
• those which disturb the straight flow of the COPC away from the source (also known as
mixing, examples include building downwash effects, and atmospheric stability); and
• those actors which remove, or conditions which encourage the removal of, the COPC
from the atmosphere (An example of a removal actor would be rain, whereas an example
"condition" would be the point above the ground at which windspeed slows to zero).
Air concentration and deposition models need a variety of meteorological information, at different levels
of temporal definition. The ISCST3 model requires the following:
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• Hourly values (also know as "surface data, as they describe conditions closer to ground
level)
a. Wind direction (degrees from true north)
b. Wind speed (m/s)
c. Dry bulb (ambient air) temperature (K)
d. Opaque cloud cover (tenths)
e. Cloud ceiling height (m)
f. For dry particle deposition:
i. surface pressure (millibar)
ii. solar radiation (watts/m2)
g. For wet particle deposition:
i. Precipitation amount (inches)
ii. Precipitation type (liquid or frozen)
• Daily values (also called "upper air data" as they describe conditions higher in the
atmosphere)
a. Morning mixing height (m)
b. Afternoon mixing height (m)
The following subsections describe important characteristics of the needed data.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Identification of all sources of meteorological data
3.4.1 Wind Direction and Wind Speed
Wind direction and speed are two of the most critical parameters in air modeling. The wind direction
promotes higher concentration and deposition if it persists from one direction for long periods during a
year. For example, a predominantly south wind, such as on the Gulf Coast of Texas, will contribute to
high concentrations and depositions north of the facility. Wind speed is inversely proportional to
concentration predicted using air modeling: The higher the wind speed, the lower the concentration will
be. If wind speed doubles, the concentration and deposition will be reduced by one-half. Air models
need wind speed and wind direction at the stack top. Most air modeling is performed using government
sources of surface data. Wind data are typically measured by National Weather Service (NWS) stations
at a height of 10 meters. However, since some stations have wind speed recorded at a different height,
we recommend always verifying the anemometer height, and that the correct value is input into the
meteorological data preprocessing program. ISCST3 uses a wind speed profile to calculate wind speed at
stack top. This calculation exponentially increases the measured wind speed from the measured height to
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a calculated wind speed at stack height. ISCST3 assumes that wind direction at stack height is the same
as that measured at the NWS station height (U.S. EPA 1995c).
3.4.2 Dry Bulb Temperature
Dry bulb temperature, or ambient air temperature, is the same temperature reported on the television and
radio stations across the country each day. Dry bulb (ambient) temperature contributes to describing the
surface conditions that vary from hour to hour in the measured meteorological data needed for air
modeling. It therefore has a direct effect on the modeling results, as described below.
Dry bulb temperature is typically measured at 2 meters above ground level. ISCST3 uses air temperature
in the buoyant plume rise equations developed by Briggs (U.S. EPA 1995f). The model results are not
very sensitive to air temperature, except at extremes. However, buoyant plume rise is very sensitive to
the stack gas temperature. Buoyant plume rise is mainly a result of the difference between stack gas
temperature and ambient air temperature. Conceptually, it is similar to a hot air balloon: The higher the
stack gas temperature, and lower the ambient temperature, the higher will be the plume rise. High plume
heights result in low concentrations and depositions as the COPCs travel further and are diluted in a
larger volume of ambient air before reaching the surface. A large variation in ambient temperature will
affect buoyant plume rise, but not as much as variations in stack gas temperature. The fact that the stack
gas temperature is constant (for most modeling analyses) is the very point for noting that it is the changes
in the dry bulb temperature that creates the temperature differential affecting plume rise in the air model
(see also Section 3.6.2.2 regarding selection of stack gas exit temperature). The temperature is measured
in K, so a stack gas temperature of 450°F is equal to 505 K, an ambient temperature of 90°F = 305 K, and
32°F = 273 K.
When determining values for dry bulb temperature (or ambient air temperature), it is important not to
appear to be 'data shopping' by artificially selecting a period of meteorological data that has either
average or above-average ambient temperatures that will reduce plume rise. The skewing of results in
one direction or another may over-estimate concentrations near the source, but under-estimate
concentrations and depositions away from the source where more sensitive receptors may be located (see
Chapter 4 for more on Exposure Scenario location) . Attempts to increase protectiveness by applying
such approaches may not achieve the desired result.
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If the meteorological data sets you use, and your choice of dry bulb temperature are not consistent with
the HHRAP, we recommend clearly identifying and discussing them in the risk plan and/or cost estimate
(as appropriate) to ensure clarity and transparency of the final risk assessment results.
3.4.3 Opaque Cloud Cover
Observations of opaque cloud cover are used to calculate the stability of the atmosphere. Stability
determines the dispersion, or dilution, rate of COPCs in the atmosphere. Rapid dilution occurs in
unstable air conditions, while stable air results in very little mixing, or dilution, of the emitted COPCs.
With clear skies during the day, the sun heats the Earth's surface, which heats the air immediately above
it. The warm air rises and overturns with the cooler air above it. During this "unstable" condition, while
layers of air are moving through one another, the stack plume mixes as the air mixes.
A cool surface occurs at night because of radiative loss of heat on clear nights. With a cloud cover,
surface heating during the day and heat loss at night are reduced, resulting in moderate mixing rates, or
neutral stability.
Opaque cloud cover is a measure of the transparency of the clouds. For example, a completely overcast
sky with 10/10ths cloud cover may have only 1/lOth opaque cloud cover if the clouds are high,
translucent clouds that do not prevent sunlight from reaching the Earth's surface.
3.4.4 Cloud Ceiling Height
ISCST3 needs cloud ceiling height to calculate stability. Specifically, the height of the cloud cover
affects the heat balance at the Earth's surface.
3.4.5 Surface Pressure
The MPRM preprocessor for ISCST3 requires station (i.e. surface) pressure. MPRM uses station
pressure to compute Monin-Obukhov Length and Friction Velocity, two boundary layer parameters that
ISCST3 needs to perform dry particle deposition. ISCST3 is not very sensitive to surface pressure.
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3.4.6 Incoming Short-wave Radiation\Leaf Area Index
Solar radiation affects the respiratory activity of leaf surfaces, which affects the rate of dry vapor
deposition. We recommend using the method described in Section 3.6.1 to address the effects of dry
vapor deposition. Specify a single value in the ISCST3 input file for dry vapor deposition velocity for all
hours. Even though incoming solar radiation and leaf area index are not used when specifying the dry
vapor deposition velocity, the ISCST3 model will not run properly if values for these two data fields
aren't included in the meteorological input file.
Though we don't recommend it, ISCST3 is able to compute the hourly dry vapor deposition velocity by
combining hourly incoming short-wave (solar) radiation with the user-specified, site-specific leaf area
index. The default value specified in the Addendum to the MPRM User's Guide (U.S. EPA 1999c) may
be used for leaf area index when site-specific data is not available.
3.4.7 Precipitation Amount and Type
In order to calculate wet deposition of vapor and particles, ISCST3 requires that MPRM process
precipitation amount and type into the ISCST3 meteorological file. Precipitation is measured at 3 feet
(roughly 1 meter) above ground level. The amount of precipitation will directly influence the amount of
wet deposition at a specific location. Particles and vapor are both captured by falling precipitation,
known as precipitation scavenging.
Scavenging coefficients are needed as inputs to ISCST3 for vapors, with a rate specified each for liquid
and frozen precipitation. The precipitation type in a SAMSON weather report (see Section 3.4.9 for
more information on SAMSON) will identify to ISCST3 which event is occurring for appropriate use of
the scavenging coefficients entered (see Section 3.6.2.6). MPRM can also read supplemental
precipitation files from NCDC, and integrate the data with CD-144 data in the ISCST3 meteorological
file. We discuss the importance of precipitation to ISCST3 results further in Section 3.4.9 ( Potential
Data Sources).
3.4.8 Upper Air Data (Mixing Height)
Upper air data are needed to run the ISCST3 model. MPRM uses measurements of morning and
afternoon (twice daily) upper air data to calculate an hourly mixing height using interpolation methods
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(U.S. EPA 1996J). We recommend that only years with complete upper air data be used as input for air
modeling. It is important that the years you select for upper air data match the years you select for
surface data. If matching years of data are not available from a single upper air station, we recommend
another upper air station be used for completing the five years. We also recommend discussing your
choice of representative data with the appropriate authorities prior to performing air modeling.
3.4.9 Potential Data Sources
As shown in Figure 3-1, these data are available from several different sources. For most air modeling,
we recommend five years of data from a representative NWS station. However, in some instances where
the closest NWS data is clearly not representative of site-specific meteorological conditions, and there is
insufficient time to collect 5 years of onsite data, you might use 1 year of onsite meteorological data
(consistent with GAQM) to complete the risk assessment. We recommend clearly identifying and
discussing your choice of representative meteorological data with the appropriate parties (e.g. permitting
authority or facility) prior to air modeling.
FIGURE 3-1
SOURCES OF METEOROLOGICAL DATA
Meteorological Data Processing - Government Sources
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Meteorological Data Processing - Commercial Sources
Surface Data
NCDC
Precipitation
Data
Upper Air Data
Required if not
included incommercial
on-site source data
ISCST3
Meteorological File
Hourly data, also know as "surface data" because it tends to characterize meteorology nearer the surface,
can be obtained from the National Climatic Data Center (NCDC) web site at address
(http://lwf.ncdc.noaa.gov/oa/ncdc.html). We recommend data in the SAMSON format (available on
CD-ROM). SAMSON data are available for 239 airports across the U.S. for the period of 1961 through
1990. SAMSON data contain all of the needed input parameters used by ISCST3 to compute
concentration, dry and wet particle deposition, and dry and wet vapor deposition.
You could also get hourly surface data from NCDC in TD-3280 format, then reformat it to CD-144
format for input to MPRM. Precipitation data in TD-3240 format is also available from NCDC. TD-
3240 formatted data is processed by MPRM to supplement the hourly surface data.
National Climatic Data Center
Federal Building
37 Battery Park Avenue
Asheville, NC 28801-2733
Customer Service: (704) 271-4871
File type:
Hourly precipitation amounts
Hourly surface observations with precipitation type
Hourly surface observations with precipitation type
Twice daily mixing heights from nearest station
File name:
NCDC TC-3240
NCDC TD-3280
NCDC SAMSON CD-ROM (Vol. I , II, and/or III)
NCDC TD-9689
(also available on SCRAM web site for 1984 through 1991)
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Currently, MPRM is the most appropriate Agency meteorological preprocessor program for preparing the
surface and upper air data into an ISCST3 meteorological input file. Most air modeling analyses will use
MPRM to process the National Weather Service data.
We recommend using the most recent 5 years of complete meteorological data available on SAMSON (or
more recent sources) for the air modeling. It's desirable, but not mandatory, that the 5 years be
consecutive.
Each year of the 5 years of data should be complete before being processed by MPRM. If data gaps
exist, we recommend filling in all missing data. The procedures we recommend for filling missing
surface and upper air data are documented on the SCRAM web site under the meteorological data
section. If the missing data are not addressed by the Agency objective procedures, then with the approval
of the permitting authority, you can develop a subjective method for filling in missing data. If conditions
occur such that:
1. missing values are not able to be replaced; and
2. the permitting authority approves the use of the meteorological data in that condition
then specify the MSGPRO keyword in the COntrol pathway of the ISCST3 input file. Note that the
DEFAULT keyword can't be used with MSGPRO.
If you wish to use less than 5 years of meteorological data, we recommend clearly identifying and
discussing this with all appropriate parties (e.g. permitting authority, or facility).
To prevent the need to repeat air modeling activities, we recommend that your choices of representative
upper air and surface data be clearly identified and discussed with the appropriate parties (e.g. regulatory
authority, facility) before you begin preprocessing and air modeling. Also, we recommend completely
documenting all processing of meteorological data, including sources of data, selection criteria,
consideration for precipitation amounts, preprocessor options selected, and filled missing data. If your
choices of meteorological data (e.g. selection of upper air data, or determination of dry bulb temperature)
are inconsistent with this guidance, we further recommend clearly identifying and discussing these
choices in the risk plan and/or cost estimate (as appropriate) to ensure clarity and transparency of the
final risk assessment results.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Electronic copy of the ISCST3 input code used to enter meteorological information
Description of the selection criteria and process used to identify representative years used for
meteorological data
Identification of the 5 years of meteorological selected
Summary of the procedures used to compensate for any missing data
3.5 METEOROLOGICAL PREPROCESSOR DATA NEEDS
After selecting the appropriate surface and upper air data using the procedures outlined in Section 3.4,
you still need to put the data into a form that ISCST3 can use. As stated above, we recommend using the
meteorological preprocessor MPRM to do this. The following Section describes the data MPRM itself
requires in order to perform the preprocessing. Agency approval is recommended in the selection of
MPRM parameter values.
We recommend preparing an ISCST3 meteorological file that can be used to calculate either
concentration or deposition (what MPRM terms an "ISCGASW" file). All necessary parameters will
then be available to ISCST3 for any subsequent concentration or deposition modeling. For example, if
only the concentration option is selected in ISCST3 for a specific run, ISCST3 will ignore the
precipitation values in the meteorological file. For subsequent air deposition modeling, ISCST3 will
access the precipitation data from the same preprocessed meteorological file.
MPRM includes extensive QA/QC to check for values that are out of range. MPRM also checks for
missing data and summarizes values that require editing to fill missing data. After a complete surface file
passes the quality checks, it is processed with NCDC upper air data.
MPRM needs the following input parameters representative of the meteorological measurement site
(typically the nearest representative National Weather Station):
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• Minimum Monin-Obukhov length
• Anemometer height
• Surface roughness length (at measurement site)
• Noon-time albedo
• Bowen ratio
• Anthropogenic heat flux
• Fraction of net radiation absorbed at surface
MPRM also needs the following input parameter representative of the application site (e.g., source
location):
• Surface roughness length (at application site)
The MPRM User's Guide contains detailed information for preparing the ISCST3 meteorological input
file (U.S. EPA 1996J). The parameters listed are briefly described in the following subsections. These
data are not included in the surface or mixing height data files obtained from the U.S. EPA or NCDC.
We recommend taking special care, while using the tables in the MPRM User's Guide or reference
literature, to select values representative of the meteorological measurement site and the site to be
modeled. We recommend clearly identifying and discussing the selected values with the appropriate
parties (e.g. permitting authority, or facility) before processing the meteorological data.
3.5.1 Monin-Obukhov Length
The Monin-Obukhov length (L) is a measure of atmospheric stability. It is negative during the day, when
surface heating causes unstable air. It is positive at night, when the surface is cooled and the atmosphere
more stable. In urban areas during stable conditions, the estimated value of L may not adequately reflect
the less stable atmosphere associated with the mechanical mixing generated by buildings or structures.
However, MPRM needs an input for minimum urban Monin-Obukhov length, even if the area to be
analyzed by ISCST3 is rural. A nonzero value for L must be entered to run MPRM. We recommend
using a value of 2.0 meters forL when the land use surrounding the site is rural (see Section 3.3.2.1). For
urban areas, Hanna and Chang (1991) suggest that a minimum value ofL be set for stable hours to
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simulate building-induced instability. The following are general examples of L values for various land
use classifications:
TABLE 3-4
L VALUES FOR VARIOUS LAND USES
Land Use Classification
Agricultural (open)
Residential
Compact residential/industrial
Commercial (19 to 40-story buildings)
Commercial (>40-story buildings)
Minimum L
2 meters
25 meters
50 meters
100 meters
150 meters
MPRM will use the minimum L value in calculating urban stability parameters. These urban values will
be ignored by ISCST3 during the air modeling analyses for rural sites.
3.5.2 Anemometer Height
ISCST3 model results are very sensitive to small variations in wind speed. The height of the wind speed
measurements is needed by ISCST3 to calculate wind speed at stack top. The wind sensor (anemometer)
height for every National Weather Service station is identified in the station history section of the Local
Climatological Data Summary available fromNCDC. Since 1980, most National Weather Service
stations measure wind speed at the height of 10 meters. However, some stations operate at other heights
or have data at more than one height. We generally recommend verifying the correct measurement height
for each year of data prior to processing with MPRM and running the ISCST3 model.
3.5.3 Surface Roughness Length at Measurement Site
Surface roughness length (or height) is a measure of the height of obstacles to wind flow. It is important
in ISCST3 because it determines how close a particle must be to the ground before it is "captured" for
deposition on the ground. Slight variations in surface roughness can lead to dramatic differences in
ISCST3 results. For surface meteorological data from a National Weather Station, we typically
recommend using a value of 0.10 meters for the "measurement site." Surface roughness is proportional,
but not equal, to the physical height of the obstacles. Table 3-3 (in Section 3.3.2.2) lists available
roughness height values. These values are based on the general land use in the vicinity of the
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measurement site. We recommend considering these values in discussions with the appropriate parties
prior to air modeling.
3.5.4 Surface Roughness Length at Application Site
MPRM also needs the surface roughness length at the facility (application site) in order to prepare the
ISCST3 meteorological file. ISCST3 model results are very sensitive to the value used in MPRM for this
parameter. Table 3-3 in Section 3.3.2.2 is also applicable to the application site. Compute a single
surface roughness value representative of the site by using the method described in Section 3.3.2.2. We
recommend clearly identifying and discussing the computed surface roughness length for the application
site, along with maps or photographs illustrating land use, with the appropriate parties (e.g. permitting
authority or facility) prior to use.
3.5.5 Noon-Time Albedo
"Noon-time albedo" is the fraction of the incoming solar radiation that is reflected from the ground when
the sun is directly overhead. Albedo is used in calculating the hourly net heat balance at the surface. Net
heat balance at the surface is then used in calculating hourly values of Monin-Obukhov length. MPRM
automatically adjusts for the variation in albedo with solar elevation angle. Experience suggests that
ISCST3 modeling results are not sensitive to the value selected for this parameter. Table 3-5 presents
typical albedo values. Albedo values vary from 0.10 to 0.20 on water surfaces from summer to winter.
Cultivated farmland values vary the most, from 0.14 during spring when land is tilled to expose dark
earth, to 0.60 in winter when areas are snow-covered.
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TABLE 3-5
ALBEDO OF NATURAL GROUND COVERS FOR LAND USE TYPES AND SEASONS
Land Use Type
Water surface
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
Season"
Spring
0.12
0.12
0.12
0.12
0.14
0.18
0.14
0.30
Summer
0.10
0.12
0.12
0.14
0.20
0.18
0.16
0.28
Autumn
0.14
0.12
0.12
0.16
0.18
0.20
0.18
0.28
Winter
0.20
0.50
0.35
0.30
0.60
0.60
0.35
0.45
Source—Iqbal (1983)
a The various seasons are defined by Iqbal (1983) as follows:
Spring: Periods when vegetation is emerging or partially green; this is a transitional situation that applies for 1 to
2 months after the last killing frost in spring.
Summer: Periods when vegetation is lush and healthy; this is typical of mid-summer, but also of other seasons in
which frost is less common.
Autumn: Periods when freezing conditions are common, deciduous trees are leafless, crops are not yet planted or
are already harvested (bare soil exposed), grass surfaces are brown, and no snow is present.
Winter Periods when surfaces are covered by snow and temperatures are below freezing. Winter albedo depends
on whether a snow cover is present continuously, intermittently, or seldom. Albedo ranges from about
0.30 for bare snow cover to about 0.65 for continuous cover.
Based on the information in Table 3-5, rural area albedo estimates vary from 0.14 to 0.20 for cultivated
land, and from 0.18 to 0.20 for grassland. For urban areas without snow, values vary from 0.14 to 0.18.
For practical purposes, it is desirable to process a complete year of meteorological data with a single
value for noon-time albedo. For example, the single value of 0.18 may be appropriate to process all
meteorological data for an urban site. For rural sites, a single albedo value of 0.18 for grassland and
cultivated land may be appropriate for areas without significant snow cover during winter months. For
desert shrubland, a single value of 0.28 may be appropriate. A single value of 0.12 could be
representative of forested areas. We recommend discussing the proposed values with the permitting
authority prior to air modeling.
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3.5.6 Bowen Ratio
The Bowen ratio is the ratio of the sensible heat flux to the evaporative or latent heat flux at the ground
surface. The presence of moisture affects the heat balance through evaporative cooling, which, in turn,
affects the hourly Monin-Obukhov length calculated by MPRM. Surface moisture is highly variable.
Daytime Bowen ratios are presented in Table 3-6.
Bowen ratio values vary throughout the country. For example, in urban areas where annual rainfall is
less than 20 inches, a single Bowen ratio value of 4.0 may be representative. For rural areas, a Bowen
ratio value of 2.0 may be appropriate for grassland and cultivated land. For areas where annual rainfall is
greater than 20 inches, we recommend a single Bowen ratio value of 2.0 for urban areas; and 0.7 for rural
forests, grasslands, and cultivated lands. You can refine the Bowen ratio estimates provided in Table 3-6
by using site-specific precipitation and wind speed data. We recommend clearly identifying and
discussing the proposed values with the appropriate parties (e.g. permitting authority or facility) prior to
use.
TABLE 3-6
DAYTIME BOWEN RATIOS BY LAND USE, SEASON,
AND PRECIPITATION CONDITIONS
Land Use
Season"
Spring
Summer
Autumn
Winter
Dry Conditions
Water (fresh and salt)
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
0.1
1.5
1.5
0.2
1.0
1.0
2.0
5.0
0.1
0.6
0.6
0.2
1.5
2.0
4.0
6.0
0.1
2.0
1.5
0.2
2.0
2.0
4.0
10.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Average Conditions
Water (fresh and salt)
Deciduous forest
Coniferous forest
Swamp
Cultivated land
0.1
0.7
0.7
0.1
0.3
0.1
0.3
0.3
0.1
0.5
0.1
1.0
0.8
0.1
0.7
1.5
1.5
1.5
1.5
1.5
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TABLE 3-6
(contd.)
Land Use
Grassland
Urban
Desert shrubland
Season"
Spring
0.4
1.0
3.0
Summer
0.8
2.0
4.0
Autumn
1.0
2.0
6.0
Winter
1.5
1.5
6.0
Wet Conditions
Water (fresh and salt)
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
0.1
0.3
0.3
0.1
0.2
0.3
0.5
1.0
0.1
0.2
0.2
0.1
0.3
0.4
1.0
5.0
0.1
0.4
0.3
0.1
0.4
0.5
1.0
2.0
0.3
0.5
0.3
0.5
0.5
0.5
0.5
2.0
Source—Paine (1987)
a The various seasons are defined by Iqbal (1983) as follows:
Spring: Periods when vegetation is emerging or partially green; this is a transitional situation that applies for 1 to
2 months after the last killing frost in spring.
Summer: Periods when vegetation is lush and healthy, this is typical of mid-summer, but also of other seasons in
which frost is less common.
Autumn: Periods when freezing conditions are common, deciduous trees are leafless, crops are not yet planted or
are already harvested (bare soil exposed), grass surfaces are brown, and no snow is present.
Winter Periods when surfaces are covered by snow and temperatures are below freezing. Winter albedo depends
on whether a snow cover is present continuously, intermittently, or seldom. Albedo ranges from about
0.30 for bare snow cover to about 0.65 for continuous cover.
3.5.7 Anthropogenic Heat Flux
Anthropogenic heat is the surface heating caused by human activity, including automobiles and heating
systems. It is used to calculate hourly L values (Monin-Obukhov lengths). Table 3-7 presents
anthropogenic heat flux (Qj) values for several urban areas around the world (U.S. EPA 1995g). In rural
areas, we recommend using a value 0.0 Watts/m2 for the Qf. A value of 20.0 Watts/m2 is appropriate for
large urban areas, based on the annual value for Los Angeles.
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TABLE 3-7
ANTHROPOGENIC HEAT FLUX (Qf) AND NET RADIATION (g.)
FOR SEVERAL URBAN AREAS
Urban Area
(Latitude)
Manhattan
(40° North)
Montreal
(45° North)
Budapest
(47° North)
Sheffield
(53° North)
West Berlin
(52° North)
Vancouver
(49° North)
Hong Kong (22°
North)
Singapore
(1° North)
Los Angeles (34°
North)
Fairbanks
(64° North)
Population
(Millions)
1.7
1.1
1.3
0.5
2.3
0.6
3.9
2.1
7.0
0.03
Population
Density
(Persons/km2)
28,810
14,102
11,500
10,420
9,830
5,360
3,730
3,700
2,000
810
Per Capita
Energy Use
(MJ x 103/year)
128
221
118
58
67
112
34
25
331
740
(^(Watts/m2)
(Season)
117 (Annual)
40 (Summer)
198 (Winter)
99 (Annual)
57 (Summer)
153 (Winter)
43 (Annual)
32 (Summer)
51 (Winter)
19 (Annual)
21 (Annual)
19 (Annual)
15 (Summer)
23 (Winter)
4 (Annual)
3 (Annual)
21 (Annual)
19 (Annual)
fi.
(Watts/m2)
93 (Annual)
52 (Annual)
92 (Summer)
13 (Winter)
46 (Annual)
100 (Summer)
-8 (Winter)
56 (Annual)
57 (Annual)
57 (Annual)
107 (Summer)
6 (Winter)
110 (Annual)
110 (Annual)
108 (Annual)
18 (Annual)
Source—Oke (1978)
3.5.8 Fraction of Net Radiation Absorbed at the Ground
Also used for calculating hourly values of Monin-Obukhov length, fraction of net radiation absorbed at
the ground is the last component of radiative heat balance. Based on:
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• the net radiation (Q,) values presented in Table 3-7, and
• recommendations presented in the MPRM User's Manual, themselves based on Oke
(1982),
we recommend values of 0.15 for rural areas and 0.27 for urban areas (U.S. EPA 1995g).
3.6 ISCST3 MODEL INPUT FILES
The ISC3 User's Guide, Volume I (U.S. EPA 1995f) offers a thorough instruction on how to prepare the
ISCST3 input files. The User's Guide is available for downloading from the SCRAM at
http://www.epa.gov/scram001. We provide an example ISCST3 input file in Figure 3-2.
This example illustrates a single year run (1984), for particle phase COPC emissions from a single stack.
The run is used to compute acute (1-hour average) and chronic (annual average) values. It provides
single year results in a one hour and annual average plot file for post-processing. Specifying a terrain grid
file in the TG pathway is optional. You generally only consider it for modeling dry vapor deposition in
highly variable terrain. Each air modeling analysis has unique issues and concerns that we recommend
you address in the risk assessment report. We recommend using an air modeling methodology
consistently throughout, from data collection and model set-up, to model output. This will assist both
you and the permitting authority in interpreting and communicating model results. A transparent and
scientifically defensible risk assessment report will identify consistent methods while documenting each
section of the ISCST3 input file.
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FIGURE 3-2
EXAMPLE INPUT FILE FOR "PARTICLE PHASE"
CO STARTING
CO TITLEONE Example input file, particle phase run
CO TITLETWO 1984 met data, Baton Rouge Surface, Boothville Upper Air
CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT WETDPLT RURAL
CO AVERTIME 1 ANNUAL
CO POLLUTID UNITY
CO TERRHGTS ELEV
CO RUNORNOT RUN
CO SAVEFILE 84SAVE1 5 84SAVE2
** Restart incomplete runs with INITFILE, changing '**' to 'CO'
** INITFILE 84SAVE1
CO FINISHED
SO STARTING
SO LOCATION STACK1 POINT 637524. 567789. 347.
SO SRCPARAM STACK1 1.0 23.0 447.0 14.7 1.9
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29
SO BUILDWID STACK1 14.02 15.51 16.53 17.05 17.05 16.53 15.51 14.03
SO BUILDWID STACK1 12.10 14.02 15.51 16.53 17.05 17.05 16.53 15.51
SO BUILDWID STACK1 14.02 12.10 14.02 15.51 16.53 17.05 17.05 16.53
SO BUILDWID STACK1 15.51 14.02 12.10 14.02 15.51 16.53 17.05 17.05
SO BUILDWID STACK1 16.53 15.51 14.02 12.10
SO PARTDIAM STACK1 0.35 0.70 1.10 2.00 3.60 5.50 8.10 12.5 15.0
SO MASSFRAX STACK1 0.22 0.08 0.08 0.11 0.10 0.07 0.10 0.11 0.13
SO PARTDENS STACK1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
SO PARTSLIQ STACK1 7E-5 5E-5 6E-5 1.3E-4 2.6E-4 3.9E-4 5.2E-4 6.7E-4 6.7E-4
SO PARTSICE STACK1 2E-5 2E-5 2E-5 4E-5 9E-5 1.3E-4 1.7E-4 2.2E-4 2.2E-4
SO SRCGROUP ALL
SO FINISHED
RE STARTING
RE ELEVUNIT METERS
RE DISCCART 630000. 565000. 352.
RE DISCCART 630500. 565000. 365.
RE DISCCART 631000. 565000. 402.
(ARRAY OF DISCRETE RECEPTORS)
RE DISCCART 635000. 570000. 387.
RE FINISHED
ME STARTING
ME INPUTFIL 84BTR.WET
ME ANEMHGHT 10.0
ME SURFDATA 13970 1984 BATON_ROUGE
ME UAIRDATA 12884 1984 BOOTHVILLE
ME FINISHED
TG STARTING
TG INPUTFIL TERRAIN.TER
TG LOCATION 0.0 0.0
TG ELEVUNIT METERS
TG FINISHED
OU STARTING
OU RECTABLE ALLAVE FIRST
OU PLOTFILE 1 ALL FIRST BTR841.PLT
OU PLOTFILE ANNUAL ALL BTR84A.PLT
OU FINISHED
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As discussed in Section 3.2, ISCST3 requires separate runs to model vapor phase COPCs, particle phase
COPCs, and particle-bound phase COPCs, for a total of three runs per COPC source (stack or fugitive).
The ISCST3 "Control Secondary Keywords" used for these three runs are:
Vapor Phase:
Particle Phase:
Particle-Bound Phase:
CONG DDEP WDEP DEPOS
CONG DDEP WDEP DEPOS
CONG DDEP WDEP DEPOS
ISCST3 needs site-specific inputs for source parameters, receptor locations, meteorological data, and
terrain features. Prepare the model for execution by creating an input file. The input file is
structured in five (or six, if a terrain grid file is used) sections, or pathways, designated by two-letter
abbreviations:
TABLE 3-8
ISCST3 INPUT FILE SECTIONS
Section
Control
Source
Receptor
Meteorology
Terrain Grid (Optional)
Output
Abbreviation
CO
so
RE
ME
TG
OU
The following subsections describe how to specify the parameters for each pathway in the ISCST3 input
file.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Electronic and hard copies of ISCST3 input file for all air modeling runs
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3.6.1 COntrol Pathway
Your choice of model options (MODELOPT) in the COntrol pathway will direct ISCST3 in the types of
computations to perform. We generally recommend specifying the DFAULT parameter for particle and
particle-bound phase runs, so that ISCST3 will implement the following regulatory default options:
• Use stack-tip downwash (except for Schulman-Scire downwash).
• Use buoyancy-induced dispersion (except for Schulman-Scire downwash).
• Do not use final plume rise (except for building downwash).
• Use the calms processing routines.
• Use upper-bound concentration estimates for sources influenced by building downwash
from super-squat buildings.
• Use default wind speed profile exponents.
• Use default vertical potential temperature gradients.
The CONG parameter specifies calculation of air concentrations. The DDEP and WDEP parameters
specify dry and wet deposition. The DEPOS specifies computation of total (wet and dry) deposition flux.
DRYDPLT and WETDPLT are used for plume depletion resulting from dry and wet removal. We
recommend the following command lines for the vapor and two particle runs (these are for rural areas;
substitute URBAN for urban areas):
Vapor: CO MODELOPT TOXICS CONG DDEP WDEP DEPOS DRYDPLT
WETDPLT RURAL
CO GASDEPVD 0.01
Particle Phase: CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT
WETDPLT RURAL
Particle-Bound: CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT
WETDPLT RURAL
Note that for vapor phase model runs, the DFAULT option is replaced by the TOXICS option. This
directs ISCST3 to execute the dry vapor deposition algorithm in addition to the regulatory default
parameters. You need to add an additional COntrol command line for the vapor phase run, to provide the
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single value of the dry vapor deposition velocity. In order to set a single dry vapor deposition velocity in
ISCST3, use the following options:
Remove the DEFAULT keyword from the CO MODELOPT card. (This option is
incompatible with the TOXICS option.)
Add the TOXICS keyword on the CO MODELOPT card. (This option is needed when
using dry vapor deposition.)
• Add the keyword CO GASDEPVD, where Uservd in the dry vapor deposition
velocity (meters/second).
We recommend a dry vapor deposition velocity of 0.5 centimeter per second (cm/s) for organic
contaminants, chlorine, and HC1. We recommend a dry vapor deposition velocity of 2.9 cm/s for divalent
mercury. The recommended dry vapor deposition velocity value of 0.5 cm/s for organic contaminants is
consistent with the range specified for pesticides (0.01 -1.1 cm/s) and dioxins and furans (0.27 - 0.78
cm/s) (U.S. EPA 2000b). A recent review of dry deposition (Wesely and Hicks 2000) demonstrates
considerable uncertainty about dry deposition even for well-measured species such as ozone and sulfur
dioxide. Uncertainty is greater for organic compounds, with very few measurements available to support
defensible values (see following table).
TABLE 3-9
DRY DEPOSITION VELOCITY ESTIMATES AVAILABLE IN LITERATURE
Chemical
Acetic acid
Formic acid
Chlordane
Arochlor 1242
Arochlor 1254
p,p'-DDT
TCDD
PCDD/Fs
Dry Deposition Velocity (cm/s)
0.64-1.0
0.7
1.1
0.01 - 0.04
0.02-0.1
0.08-0.2
0.1 -0.7
0.5
0.5
0.27 - 0.78
0.19 (0.06-0.60)
Reference
Hartmann et al. (199 1)1
Sanheuza et al. (1992)1
Sanheuza et al. (1992)1
Bidleman(1988)
Trapp and Matthies (1995)
McLachlan et al. (1995)
Smith and Hemhold (1995)
Koester and Kites (1992)
' As cited in Wesely and Hicks (2000)
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The modeling reported in the Mercury Study Report to Congress (U.S. EPA 1997c) used a value of
0.3 cm/s for nighttime dry deposition of divalent mercury, but used daytime values ranging from 0.20 to
4.83 cm/s depending upon atmospheric stability and land-use category. The daytime values were based
on data developed from nitric acid data, not from measurements of divalent mercury. U.S. EPA (1997c)
used an average ISC model-calculated dry deposition velocity of 2.9 cm/s for divalent mercury vapor and
0.06 cm/s for elemental mercury. Higher values were expected for chemicals with greater reactivity than
acetic acid or formic acid, but no measured values were identified for any organic compounds higher
than 1.1 cm/s. As a result, we recommend the default of 2.9 for divalent mercury.
U.S. EPA (1997c) also calculates site- or contaminant-specific dry vapor deposition velocities based on
various parameters including molecular diffusivity, a solubility enhancement factor, pollutant reactivity,
mesophyll resistance, and Henry's law constant. If you are assessing a facility surrounded by land uses
other than pine forest (for instance urban, agricultural lands, or wetlands), you may wish to consider how
a site-specific value for this parameter could be calculated. Two important parameters are the stability
class (from the air modeling) and the land use surrounding the facility. The following information can be
found in Section 5.1.2.3 (Dry Deposition of Vapors), page 5-12, in the technical background document of
the MACT rule: "Human Health and Ecological Risk Assessment Support to the Development of
Technical Standards for Emissions from Combustion Units Burning Hazardous Wastes: Background
Document, Final Report (F-1999-RC2F-S0014)", which explains that:
To calculate the weighted dry deposition velocity, it is recommended that land use be obtained
from 1:250,000 scale quadrangles of land use and GIRAS spatial data obtained from EPA
website and placed in an ARC-INFO format (U.S. EPA, 1994b). Table B-6 of Appendix B of the
MACT rule background document shows the land use data for the sites that were detailed in the
MACT rule. In the MACT rule, the fraction of time in each stability class was based on 5-year
hourly meteorological files used in the ISCST3 modeling. Table B-8 of Appendix B in the
MACT rule background document shows the weighted dry deposition velocity for divalent
mercury vapor at the modeled facilities. Dry deposition of elemental mercury was not included
in the MACT analysis, which is consistent with the 1997 Mercury Report to Congress.
Site-specific dry deposition velocity for divalent mercury vapor can be calculated by weighting
land use and stability class. Values for dry deposition velocity for each land use category and
stability class can be found in the "Mercury Study Report to Congress Volume III: Fate and
Transport of Mercury in the Environment, December 1997, EPA-452/R-97-003" in Table 4-3 and
Table 4-4, respectively. It is recommended that these values be averaged to annualized values.
Generally, night time dry deposition velocities can be treated as constant across all stability.
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As more research to measure deposition velocities of more organic compounds under a variety of
conditions becomes available, it may be appropriate to develop site- and chemical- specific default dry
deposition of vapor velocities. It is important to note that although we recommend inputting the dry
vapor deposition velocity directly into ISCST3, you might instead elect to execute the dry gas deposition
algorithms within ISCST3 to calculate a deposition velocity. However, we caution you to read
Section 3.1.2 (Preprocessing Programs) and Section 3.4.6 (Solar Radiation), which note additional data
needs and potential limitations to ISCST3 calculating deposition velocities. Having ISCST3 calculate a
deposition velocity may also require that you conduct compound-specific air modeling runs, deviating
from the unit emission rate approach as outlined in this guidance. This may significantly increase the
number of air modeling runs needed.
Note that for each of the three runs for each emission source, 5 complete years of off-site (e.g., National
Weather Service from SAMSON) meteorological data are used. For sites with meteorological data
collected on-site, we recommend that the permitting authority be notified of the data period needed for a
risk assessment. Specify 'ANNUAL' for the averaging times (AVERTIME) to compute chronic (annual
average) health risk, and/or ' 1' to compute acute health risks based on the maximum 1 -hour average
concentrations over the 5-year period (see Section 3.10). We generally recommend repeating each phase
run five times (one for each year, or a total of 15 ISCST3 runs) to complete a set of 15 runs for the full
five years of meteorological data.
Alternatively, you can combine the 5 years of meteorological data into a single meteorological data file
and complete only 3 runs for each emission source (one run for each phase). Section 3.5.1.1 of the ISC3
User's Guide (U.S. EPA 1995f), includes a complete discussion of combining multiple years of
meteorological data into a single file prior to running ISCST3. You may select the 'ANNUAL' averaging
time for all risk assessment runs, regardless of the number of years in the meteorological data file. The
incorrect selection of 'PERIOD' will not compute the correct deposition rates needed by the risk
assessment equations (refer to Section 3.2.3 of the ISC3 User Guide, Volume I). No additional ISCST3
model execution time is needed to obtain 1-year or 5-year air modeling values.
ISCST3 also allows you to specify COPC half-life and decay coefficients. Unless clearly identified and
discussed with the appropriate parties (regulatory authority or facility), we don't recommend using these
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keywords when conducting air modeling for risk assessments. You typically use the TERRHGTS
keyword with the ELEV parameter to model terrain elevations at receptor grid nodes. The FLAGPOLE
keyword specifies receptor grid nodes above local ground level and is not typically used for most air
modeling to perform impacts at ground level.
We also recommend using SAVEFIL to restart ISCST3 in the event of a computer or power failure
during long runs. SAVEFIL is best used by specifying two save files, each with a different name. We
recommend that the save interval not be longer than 5 days for large runs. If two save files are used, and
a failure occurs during writing to the save file, no more than 10 days will be lost. Use the INITFILE
command to restart the runs after the failure, as shown in the following example:
CO SAVEFILE SAVE1 5 SAVE2
** INITFILE SAVE1
ISCST3 will save the results alternately to SAVE1 and SAVE 2 every 5 days. If the run fails after
successfully writing to SAVE 1, you can restart the ISCST3 run by replacing the two asterisks (*) in the
INITFILE line with CO and running ISCST3 again. The run will begin after the last day in SAVE1.
Note that you don't use the MULTYEAR keyword for computing long-term averages, and it should not be
specified.
The following is an example of the COntrol pathway computer code for a single-year ISCST3 particle
run:
CO STARTING
CO TITLEONE Example input file, particle phase run, 1 year
CO TITLETWO 1984 met data, Baton Rouge Surface, Boothville Upper Air
CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT WETDPLT RURAL
CO AVERTIME 1 ANNUAL
CO POLLUTID UNITY
CO TERRHGTS ELEV
CO RUNORRUN RUN
CO SAVEFILE 84SAVE1 5 84SAVE2
** Restart incomplete runs with INITFILE, changing * * *' to 'CO'
** INITFILE SAVE1
CO FINISHED
The corresponding COntrol pathway computer code for a single-year ISCST3 vapor run is:
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CO STARTING
CO TITLEONE Example input file, vapor phase run, 1 year
CO TITLETWO 1984 met data, Baton Rouge Surface, Boothville Upper Air
CO MODELOPT TOXICS CONG DDEP WDEP DEPOS DRYDPLT WETDPLT RURAL
CO GASDEPVD 0.01
CO AVERTIME 1 ANNUAL
CO POLLUTID UNITY
CO TERRHGTS ELEV
CO RUNORRUN RUN
CO SAVEFILE 84SAVE1 5 84SAVE2
** Restart incomplete runs with INITFILE, changing * * *' to 'CO'
** INITFILE SAVE1
CO FINISHED
Additional runs for the other 4 years are set up with the same COntrol pathway, except for the title
description and SAVEFILE filenames.
3.6.2 SOurce Pathway
As discussed in Section 3.8, ISCST3 normally uses a unit emission rate of 1.0 g/s. Additional source
characteristics the model needs (typically found in the Part B permit application and trial burn report)
include the following:
• Source type (point source for stack emissions; area or volume for fugitive emissions)
• Source location (UTM coordinates, m)
• Source base elevation
• Emission rate (1.0 g/s)
• Stack height (m)
• Stack gas temperature (K)
• Stack gas exit velocity (m/s)
• Stack inside diameter (m)
• Building heights and widths (m)
• Particle size distribution (percent)
• Particle density (g/cm3)
• Particle and gas scavenging coefficients (unitless)
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Input values with supporting documentation for each parameter identified in Section 3.7.2
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3.6.2.1 Source Location
The location keyword of the SOurce path way (so LOCATION) identifies the:
• source type,
• location, and
• base elevation.
The source type for any stack is referred to as a "point" source inlSCSTS. Fugitive source emissions are
discussed in section 3.9. The source location must be entered into ISCST3. We recommend entering
source locations in UTM coordinates. The easterly and northerly coordinates are entered to the nearest
meter; for example, 637524 meters UTM-E, or 4567789 meters UTM-N (no commas are used). Enter the
base elevation of each stack in meters. Sources for base elevations include USGS topographic maps,
facility plot plans or USGS digital data bases.
An example input for the location keyword on the SOurce pathway includes source type, location, and
base elevation in the following format:
SO LOCATION STACK1 POINT 637524. 4567789. 347.
3.6.2.2 Source Parameters
The source parameters keyword of the SOurce pathway (so SRCPARAM) identifies the:
• emission rate,
• stack height,
• stack temperature,
• stack velocity, and
• stack diameter.
Enter the unit emission rate as 1.0 g/s. Stack height is the height above plant base elevation on the SO
LOCAT ION keyword. Stack gas exit temperature is the most critical stack parameter for influencing
concentration and deposition. High stack gas temperatures result in high buoyant plume rise, which, in
turn, lowers concentration and deposition rates. We recommend basing stack gas temperatures on stack
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sampling tests for existing stacks. For new or undefined stacks, you might use manufacturer's data for
similar equipment. Calculate stack gas exit velocity from actual stack gas flow rates and stack diameter.
Actual stack gas flow rates can be measured during existing stacks during stack sampling. You can get
representative values for new or undefined sources from manufacturer's data on similar equipment.
Stack diameter is the inside diameter of the stack at exit.
We highly recommend using a site- or unit-specific stack temperature (low, average, high). Similar to the
Chapter 2 discussion on emissions testing, we recommend choosing the stack gas temperature based on
the objectives of the risk assessment. For example, if permitting is the objective, 'typical' operating
temperatures with all control devices operating might be a possible testing condition to select. On the
other hand, you might select low or high operating temperatures because of the potential for emitting
certain COPCs, or the production of specific PICs, regardless of plume rise considerations. We
recommend avoiding the practice of always choosing the lowest stack temperature, in order to decrease
buoyancy and thereby increase concentration and deposition near the source. This is a gross over-
simplification of conditions. Increases in concentration and deposition near the source may
underestimate concentrations and deposition rates away from the source where more critical receptors
may be located (see Chapter 4).
Following is an example of the source parameter input in the SOurce pathway for emission rate (grams
per second), stack height (meters), stack temperature (K), stack velocity (meters per second), and stack
diameter (meters):
SO SRCPARAM STACK1 1.0 23.0 447.0 14.7 1.9
3.6.2.3 Building Parameters
The building height and width keywords of the SOurce pathway (so BUILDHGT; so BUILDWID)
identify the building dimensions that most influence the air flow for each of the 36 10-degree directions
surrounding a stack. Calculate the dimensions using BPIP software, as described in Section 3.2.4.
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The BPIP output file is transferred into the ISCST3 SOurce pathway as follows:
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29
SO BUILDWID STACK1 14.02 15.51 16.53 17.05 17.05 16.53 15.51 14.03
SO BUILDWID STACK1 12.10 14.02 15.51 16.53 17.05 17.05 16.53 15.51
SO BUILDWID STACK1 14.02 12.10 14.02 15.51 16.53 17.05 17.05 16.53
SO BUILDWID STACK1 15.51 14.02 12.10 14.02 15.51 16.53 17.05 17.05
SO BUILDWID STACK1 16.53 15.51 14.02 12.10
3.6.2.4 Particle Size Distribution
ISCST3 needs a particle size distribution for determining deposition velocities. We recommend using
site-specific stack test data for existing sources.
The following example is an ISCST3 input for a particle phase run. From Table 3-1, the distribution for
9 mean diameter sizes includes the data needed for the keywords of the SOurce pathway
(so PARTDIAM; so MASSFRAX). The PARTDiAM is taken from Column 1 (Mean Particle Diameter).
The MASSFRAX is taken from Column 4 (Fraction of Total Mass).
SO PARTDIAM STACK1 0.35 0.70 1.10 2.00 3.60 5.50 8.10 12.5 15.0
SO MASSFRAX STACK1 0.22 0.08 0.08 0.11 0.10 0.07 0.10 0.11 0.13
The example for the ISCST3 input for the particle-bound run is described below. From Table 3-1, the
PARTDIAM is the same. The MASSFRAX is taken from Column 6 (Fraction of Total Surface Area).
SO PARTDIAM STACK1 0.35 0.70 1.10 2.00 3.60 5.50 8.10 12.5 15.0
SO MASSFRAX STACK1 0.49 0.17 0.13 0.09 0.05 0.02 0.02 0.01 0.02
3.6.2.5 Particle Density
ISCST3 also needs particle density in order to model the air concentration and deposition rates of
particles. We recommend determining site-specific measured data on particle density for all modeled
sources when possible. For new or undefined sources requiring air modeling, we recommend using a
default particle density of 1.0 g/cm3. Particles from combustion sources, however, may have densities
that are less than 1.0 g/cm3 (U.S. EPA 1994d), which would reduce the modeled deposition flux.
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Following is an example of the particle density portion of the SOurce pathway (so PARTDENS) for the
9 mean particle size diameters of the previous example:
SO PARTDENS STACK1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
3.6.2.6 Scavenging Coefficients
ISCST3 calculates wet deposition flux by multiplying a scavenging ratio times the vertically integrated
concentration. The scavenging ratio is the product of a scavenging coefficient and a precipitation rate.
Studies show that best fit values for the scavenging coefficients vary with particle size. For vapors, wet
scavenging depends on the properties of the COPCs involved. However, not enough data are now
available to adequately develop COPC-specific scavenging coefficients. Therefore, assume that vapors
are scavenged at the rate of the smallest particles, with behavior in the atmosphere that is influenced
more by the molecular processes that affect vapors than by the physical processes that may dominate the
behavior of larger particles (U.S. EPA 1995f).
Wet deposition only occurs during precipitation. To use the wet deposition option in ISCST3, input
scavenging coefficients for each particle size and a file that has hourly precipitation data. For wet
deposition of vapors, we suggest using a scavenging coefficient for a 0.1-fun particle, to simulate wet
scavenging of very small (molecular) particles. ISCST3 will also accept site-specific measured washout
data, or a value calculated based on Henry's Law constant. If you choose an option other than the
coefficient for a 0.1 -fim particle, we recommend clearly identifying and discussing it with the appropriate
parties (e.g. permitting authority, or facility) prior to analysis. You can establish scavenging coefficients
for each particle size from the best fit of the curves presented in the ISC3 User's Guide (U.S. EPA
1995f). The curves are based on the work of Jindal and Heinhold (1991). The curves are limited to a
maximum particle size of 10-um. Assume that all scavenging coefficients for particle sizes greater than
or equal to 10-(un are equal. This assumption follows research on wet scavenging of particles (Jindal
and Heinhold 1991).
The ISCST3 model differentiates between frozen and liquid scavenging coefficients. Research on sulfate
and nitrate data shows that frozen precipitation scavenging coefficients are about one-third of the values
of liquid precipitation (Scire et al. 1990; Witby 1978). It is protective to assume that the frozen
scavenging coefficients are equal to the liquid scavenging coefficients (Pei and Cramer 1986). If desired,
you may input separate scavenging coefficients for frozen precipitation.
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September 2005
The following is an example of the particle liquid (rain) and frozen (sleet or snow) scavenging
coefficients input in the SOurce pathway for 9 mean particle size diameters, assuming particles are
scavenged by frozen precipitation at 1/3 the rate of liquid precipitation:
SO PARTSLIQ STACK1 7E-5 5E-5 6E-5 1.3E-4 2.6E-4 3.9E-4 5.2E-4 6.7E-4 6.7E-4
SO PARTSICE STACK1 2E-5 2E-5 2E-5 4E-5 9E-5 1.3E-4 1.7E-4 2.2E-4 2.2E-4
The complete SOurce pathway for the example particle phase input file is as follows:
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
STARTING
LOCATION
SRCPARAM
BUILDHGT
BUILDHGT
BUILDHGT
BUILDHGT
BUILDHGT
BUILDWID
BUILDWID
BUILDWID
BUILDWID
BUILDWID
PARTDIAM
MASSFRAX
PARTDENS
PARTSLIQ
PARTSICE
SRCGROUP
FINISHED
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
ALL
POINT
637524
1.0 23.0
18.29
18.29
18.29
18.29
18.29
14.02
12.10
14.02
15.51
16.53
0 . 35
0 . 22
1 . 0
7E-5
2E-5
18 .
18 .
18 .
18 .
18 .
15 .
14 .
12 .
14 .
15 .
0.70
0.08
1 . 0
5E-5
2E-5
447
29
29
29
29
29
51
02
10
02
51
1 .
0 .
1 .
6E
2E
. 456778
. 0
18 .
18 .
18 .
18 .
18 .
16 .
15 .
14 .
12 .
14 .
10
08
0
-5
-5
14.7
.29 18
.29 18
.29 18
.29 18
.29 18
.53 17
.51 16
.02 15
.10 14
. 02 12
2.00
0.11
1 . 0
1 . 3E-
4E-
9 . 347 .
1 . 9
. 29
. 29
. 29
. 29
. 29
. 05
. 53
. 51
. 02
. 10
3 . 60
0.10
1 . 0
4 2 .
5
18.29
18.29
18.29
18.29
17.05
17.05
16.53
15.51
5 . 50
0.07
1 . 0
6E-4
9E-5
18.29
18.29
18.29
18.29
16.53
17.05
17.05
16.53
8.10
0.10
1 . 0
3 . 9E-4
1 . 3E-4
18.29
18.29
18.29
18.29
15.51
16.53
17.05
17.05
12.5 1
0.11 0
1.0 1
5 . 2E-
1 . 7E-
18 .
18 .
18 .
18 .
14 .
15 .
16 .
17 .
5 . 0
. 13
. 0
4 6 .
4 2 .
. 29
. 29
. 29
. 29
. 03
. 51
. 53
. 05
.7E-4 6.7E-4
.2E-4 2.2E-4
When modeling air vapors using ISCST3, the following is an example of the SOurce pathway input for
wet vapor scavenging coefficients that replaces the PARTDIAM, MASSFRAX, PARTDENS,
PARTSLIQ and PARTSICE lines in the above example:
SO GAS-SCAV STACK1 LIQ 1.7E-4
SO GAS-SCAV STACK1 ICE 0.6E-4
3.6.3 REceptor Pathway
The REceptor pathway identifies sets or arrays of receptor grid nodes (identified by UTM coordinates)
for which ISCST3 generates estimates of air parameters including:
• air concentration,
• dry and wet deposition, and
• total deposition.
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Previous U.S. EPA guidance (19941) recommended using a polar receptor grid to identify maximum
values. Polar grids provide coverage over large areas with fewer receptor grid nodes than other grid
types, thereby reducing computer run times. However, U.S. EPA Region 6 experience indicates that,
although polar grids may reduce computer run times, air modelers typically choose a different option.
The benefit of reduced run time is offset by difficulties in identifying polar grid locations in absolute
UTM coordinates for (1) extracting terrain values from digital terrain files, and (2) selecting exposure
scenario locations (see Chapter 4). Using a Cartesian grid is an alternative to a polar grid.
One method of obtaining a Cartesian grid with terrain elevations is to open both the grid array and the
USGS DEM file in a graphics program (e.g., SURFER®). The software samples the DEM file at the
user-specified locations (i.e. the grid nodes), each defined as the intersection of east (x) and north (y)
values. The software extracts terrain elevation (z) from the DEM file associated with the desired
location. These x, y, and z values are saved as a text file with one receptor grid node per line. Use a text
editor to prefix each line with "RE DISCCART," to specify a discrete receptor grid node in ISCST3
format. Commercial software is available that generates the recommended receptor grid node array and
extracts terrain elevations from the USGS DEM downloaded files, or any terrain file in x-y-z format.
The following is an example of the REceptor pathway for discrete receptor grid nodes at 500-meter
spacing that includes terrain elevations (in meters):
RE STARTING
RE ELEVUNIT METERS
RE DISCCART 630000. 3565000. 352.
RE DISCCART 630500. 3565000. 365.
RE DISCCART 631000. 3565000. 402.
i
RE DISCCART 635000. 3570000. 387.
RE FINISHED
We recommend that air modeling for each risk assessment include, at a minimum, an array of receptor
grid nodes covering the area within 10 kilometers of the facility. Locate the origin of the grid at the
centroid of a polygon formed by the locations of the stack emission sources. This receptor grid node
array may consist of a Cartesian grid with grid nodes spaced 100 meters apart extending from the
centroid out to 3 kilometers. For the distances from 3 kilometers out to 10 kilometers, the receptor grid
node spacing can be increased to 500 meters. The single grid node array contains both grid node
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spacings. Include the same receptor grid node array in the REceptor pathway for all ISCST3 runs for all
years of meteorological data and for all emission sources.
We recommend specifying individual terrain elevations for all receptor grid nodes. Several methods are
available for assigning terrain elevations to grid nodes using digital terrain data. The 1:250,000 scale
DEM digital data are available for download at the USGS Internet site
(http://edc.usgs.gov/guides/dem.html). This data has horizontal spacing between digital terrain values of
approximately 90 meters, which provides sufficient accuracy for air modeling.
During the risk assessment, select air parameter (concentration and deposition) values for a single
receptor grid node to evaluate a specific exposure scenario location. You can also compute an area
average of air parameter values across multiple receptor grid nodes, to represent the average
concentration or deposition over a watershed or water body (see Chapter 4). However, depending on
site-specific considerations, a different receptor grid node array may be more appropriate.
In addition to the receptor grid node array evaluated out to 10 kilometers, you might consider additional
grid node arrays for evaluating water bodies and their watersheds located beyond 10 kilometers. We
recommend a grid node spacing of 500 meters between nodes for arrays that are more than 10 kilometers
from the emission source. An equally spaced grid node array facilitates subsequent computation of area
averages for deposition rates onto waterbodies and their associated watersheds.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Summary of all information regarding the coordinates and placement of the receptor grid node
array used in air modeling
Copies of any maps, figures, or aerial photographs used to develop the receptor grid node
array
Map presenting UTM locations of receptor grid nodes, along with other facility information.
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3.6.4 MEteorological Pathway
The file containing meteorological data is specified in the MEteorological pathway. MPRM creates
individual files for each of the 5 years as ASCII files. ISCST3 uses them to compute hourly
concentrations and deposition rates. You can either specify a single year of meteorological data in each
ISCST3 run, or combine the total period of meteorological data into a single meteorological file, which
ISCST3 will process in a single 5-year run. When combining meteorological files, we caution you to
consider the following:
• Preprocess each year separately using MPRM into an ASCII format
• Combine the years into a single file (using a text editor or DOS COPY command)
• ISCST3 compares the first line (header) of the combined file to the Surface and Upper
Air Station ID numbers specified in the input file ME pathway
• ISCST3 reads the headers of subsequent years unless they're deleted in the combined
file. If subsequent year headers are included in the combined file, ISCST3 will compare
the station IDs to the input file station ID.
Your analysis might use meteorological data from more than one surface station or upper
air station (e.g., the upper air station is moved after the third year of the period and
assigned a new station ID by the National Weather Service). If this is the case, delete the
headers for subsequent years in the combined file.
• For sites where the anemometer height is changed during the 5 year period (e.g., during
the period 1984-1988, the anemometer was relocated on December 15, 1985, changing
the height from 20 feet to 10 meters), we recommend running each year separately. Use
the anemometer height in the ISCST3 input file ME pathway which corresponds to that
year's meteorological data.
We recommend completing each year in the file with a full year of data (365 days, or 366 days for leap
years). Verify the anemometer height for the surface station from Local Climate Data Summary records,
or other sources, such as the state climatologist office. We recommend identifying the correct
anemometer height ANEMHGHT for the wind speed measurements at the surface station before air
modeling. Details of specifying the meteorological data file are in the ISC3 User's Guide, Section
3.5.1.1.
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The following is an example input section for the MEteorological pathway, using the 1984 Baton Rouge
file, with an anemometer height of 10 meters and station identification numbers:
ME STARTING
ME INPUTFIL 84BR.WET
ME ANEMHGHT 10.0
ME SURFDATA 13970 1984 BATON_ROUGE
ME UAIRDATA 12884 1984 BOOTHVILLE
ME FINISHED
3.6.5 Terrain Grid (TG) Pathway
Computing dry plume depletion is sensitive to terrain elevation. In the absence of a terrain grid file,
ISCST3 automatically assumes that the terrain slope between the stack base and the receptor grid node
elevation is linear. In concept, this assumption may underestimate plume deposition. However, based on
experience, the magnitude of the differences in computed concentrations and deposition rates is nominal.
Since including a terrain grid file in the TG pathway significantly increases model execution time, we
suggest that a terrain grid file may not be necessary for all sites. If a terrain grid file is desired for a
specific site based on highly variable terrain over short distances, the format of the TG file is available in
the ISC3 User's Guide.
The location keyword of the TG pathway (TG LOCAT i ON) identifies the x and y values to be added to the
source and receptor grid to align with the terrain file coordinates. If the source and receptor grid nodes
are in relative units such that the source is at location 0,0, the location keywords in the TG pathway
would be the UTM coordinates of the source. We recommend specifying all emission sources and
receptor grid nodes in UTM coordinates (note NAD27 or NAD83 format), and that the TG file, if used,
be in UTM coordinates. Therefore, the location of the origin of the TG file relative to the source location
will be 0,0. Also, we recommend presenting the terrain elevations in the TG file in meters. Following is
an example of the TG pathway:
TG STARTING
TG INPUTFIL TERRAIN.TER
TG LOCATION 0.0 0.0
TG ELEVUNIT METERS
TG FINISHED
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3.6.6 OUtput Pathway
ISCST3 output comes in two basic file types: the mandatory "output summary file" type (mandatory
because ISCST3 automatically creates the file), and numerous optional file formats. As part of its input
file structure, ISCST3 requires the name of the output summary file (See Section 3.8.2). Using text and
tables, this file summarizes the ISCST3 run results, including repeating back all input data (e.g., sources
with parameters, control pathways, grid node list, model options selected), and results summaries for
each annual and 1-hour averaging period. It also states if the run finished successfully. The summary
file also alerts you to special meteorological conditions that were found during runtime (e.g. number of
calm hours, and relationships between source base elevation and grid node elevations). It provides
impacts for the highest grid node values, not the results at every grid node required to perform the
methods found in the remainder of the HHRAP. We recommend using the output summary file for
quality checking, to make sure the run executed successfully and correctly.
ISCST3 provides numerous optional output file formats in addition to the results in the output summary
file, as specified in receptor tables (RECTABLE). The "plot files" format is the most useful option for
facilitating post-processing of the air parameter values in the model output. For this reason, though "plot
files" are optional in executing ISCST3, they are necessary for HHRAP methods. There are two plot
files for each ISCST3 run on a single source - the 'annual' and ' 1-hour' plot files. The 'annual' plot file
contains all air modeling results at each grid node, for the annual average of five years of met data. The
' 1 -hour' plot file contains the air modeling results at each grid node for the liighest 1 -hour' impact for all
five years. The file presents data in tabular form, with impacts at every grid node.
The plot file lists the x and y coordinates, and the concentration or deposition rate values for each
averaging period. Data are listed in a format that can easily be pulled into a post-processing program
(e.g. spreadsheet). Note that the ISCST3-generated/>/of file is not the same format as the ISCST3-
generatedpost file. We recommend using the plot file, not the post file.
Following is an example OUtput file specification for single-year run of 1-hour and annual average plot
files:
OU STARTING
OU RECTABLE ALLAVE FIRST
OU PLOTFILE 1 ALL FIRST BTR841.PLT
OU PLOTFILE ANNUAL ALL BTR84A.PLT
OU FINISHED
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The second line directs ISCST3 to create a table of values for each receptor grid node for all averaging
periods in the model run (1-hour and annual). The third line directs ISCST3 to create a separate plot file
(named BTTR841.PLT) of the 1-hour average results for all emission sources in the run using the first
highest (e.g., maximum) value for all hours of results during the year for each receptor grid node. The
fourth line directs ISCST3 to create another separate plot file (named BTR84A.PLT) of the annual
average results for all sources in the run for each receptor grid node.
3.7 ISCST3 MODEL EXECUTION
Consider model execution time needs for each analysis. A complete air modeling run—including air
concentration, wet and dry deposition, and plume depletion—may require 10 times the run time for the
same source and receptor grid nodes for air concentration only. You can avoid wasted modeling effort
and analysis time by verifying input parameters and conducting test runs prior to executing ISCST3.
Long run times result mainly from two algorithms—plume depletion and terrain grid file. ISCST3 run
times increase as much as tenfold for runs applying plume depletion. We believe that constituent mass is
conserved between suspended concentration and deposition rate by allowing for depletion of deposited
mass from the plume concentration in ISCST3. The overestimate of plume concentration, and the
subsequent overestimate of deposition which results when plume depletion is not allowed, is too
protective. At the same time, we don't believe the nominal benefits of including a terrain grid file justify
the added run times. Therefore, we recommend that plume depletion always be included. We don't
recommend terrain grid files.
3.8 USING MODEL OUTPUT
ISCST3 output (air concentrations and deposition rates) are usually provided on a unit emission rate
(1.0 g/s) basis from the combustor or emission source, and aren't COPC-specific. This is to preclude
having to run the model for each individual COPC. Use the COPC-specific emission rates from the trial
burn (see Chapter 2) to adjust the unitized concentration and deposition output from ISCST3. The
resulting COPC-specific air concentrations and deposition rates will be used in the estimating media
concentration equations (see Chapter 5). Concentration and deposition are directly proportional to the
unit emission rate used in the ISCST3 modeling. However, there might be some instances where the risk
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assessor will need to convert modeled output to COPC-specific output for the risk assessment. For
example, the risk assessor may want to compare modeled COPC concentrations in ambient media to
concentrations actually measured in the field.
For facilities with multiple stacks or other emission sources, we recommend that each source be modeled
separately. This is because modeling multiple sources at one time results in an inability to estimate
source-specific risks. This limits the ability of a permitting agency to evaluate which source is
responsible for the resulting risks. Such ambiguity makes it impossible for the agency to specify
protective, combustion unit-specific permit limits. If a facility has two or more sources with identical
characteristics (emissions, stack parameters, and nearby locations), it maybe appropriate to model them
with a single set of runs. We recommend getting the approval of the permitting authority prior to such
multi-source modeling.
TABLE 3-10
ISCST3 AIR PARAMETER OUTPUT
Air Parameter
Description
Units
(Used for most soil-based exposure pathways)
Cyv
Cyp
Dydv
Dywv
Dydp
Dywp
Unitized yearly average air concentration from vapor phase
Unitized yearly average air concentration from particle phase
Unitized yearly average dry deposition from vapor phase
Unitized yearly average wet deposition from vapor phase
Unitized yearly average dry deposition from particle phase
Unitized yearly average wet deposition from particle phase
ug-s/g-m3
ug-s/g-m3
s/m2-yr
s/m2-yr
s/m2-yr
s/m2-yr
(Used for fish and drinking water ingestion exposure pathways)
Cywv
Dytwv
Dytwp
Unitized yearly (water body or watershed) average air concentration
from vapor phase
Unitized yearly (water body or watershed) average total (wet and dry)
deposition from vapor phase
Unitized yearly (water body or watershed) average total (wet and dry)
deposition from particle phase
ug-s/g-m3
s/m2-yr
s/m2-yr
(Used for evaluation of acute risk via direct inhalation exposure pathway)
Chv
Chp
Chpb
Unitized hourly air concentration from vapor phase
Unitized hourly air concentration from particle phase
Unitized hourly air concentration from particle-bound phase
ug-s/g-m3
Hg-s/g-m3
Ug-s/g-m3
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3.8.1 Unit Rate Output vs. COPC-Specific Output
The relationship between the unit emission rate and the unit air parameter values (air concentrations and
deposition rates) is linear. Similarly, the relationship between the COPC-specific emission rate (Q) and
the COPC-specific air parameter values (air concentrations and deposition rates) is also linear if the
COPC-specific emission rate is used in the air model.
In this Section we discuss using the unit emission rate. We also advance the rationale that you should
use a unit emission rate instead of the COPC-specific emission rate. Using a unit emission rate
precludes having to run ISCST3 separately for each individual COPC. We advocate using a unit
emission rate in the air modeling because you can develop a common ratio relationship between the unit
emission rate and the COPC-specific emission rate. The ratio is based on the fact that both individual
relationships are linear in the air model. This ratio relationship is expressed by the following equation:
COPC- Specific Air Concentration _ Modeled Output Air Concentration
COPC- Specific Emission Rate Unit Emission Rate "
To use this equation, you must know three of the variables. ISCST3 provides the modeled output air
concentration (or deposition rate). The unit emission is 1.0 g/s. You can get the COPC-specific emission
rate directly from stack or source test data.
3.8.1.1 Determining the COPC-Specific Emission Rate (0
The COPC-specific emission rate from the stack, prior to any applicable adjustments (e.g., upset
emissions scaling, revisions based on hours of operation, feed rate adjustments, etc.) is a function of the
stack gas flow rate and the stack gas concentration of each COPC. It can be calculated from the
following equation:
SGC-CFO2
Q - SGF • Equation 3-3
IxlO6
where
Q = COPC-specific emission rate (g/s)
SGF = Stack gas flow rate at dry standard conditions (dscm/s)
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SGC = COPC stack gas concentration at 7 percent O2 as measured in the trial burn
(ng/dscm)
CFO2 = Correction factor for conversion to actual stack gas concentration O2 (unitless)
1 x 106 = Unit conversion factor (fJ-g/g)
Guidance for adjusting COPC-specific emission rates, as well as determining emission rates for fugitive
emission sources, is available in Chapter 2. Also, it is sometimes necessary to derive the COPC-specific
emission rate from surrogate data, such as for a new facility that has not yet been constructed and trial
burned (see Chapter 2).
3.8.1.2 Converting Unit Output to COPC-Specific Output
Once three of the four variables in Equation 3-1 are known, you can derive the COPC-specific air
concentrations and deposition rates by multiplying as follows:
COPC-Specific _ Modeled Output Air Concentration-COPC-Specific Emission Rate
Air Concentration = Unit Emission Rate Equation 3-4
For example, if COPC A is emitted at a rate of 0.25 g/s, and the ISCST3 modeled concentration at a
specific receptor grid node is 0.2 ng/m3 per the 1.0 g/s unit emission rate, the concentration of COPC A
at that receptor grid node is 0.05 (ig/m3 (0.25 multiplied by 0.2). Calculating deposition is similarly
proportional to the emission rate of each COPC. You are reminded once again that the process of
converting modeled unitized output into COPC-specific output is taken directly into account in the
estimating media concentration equations in Chapter 5 and Appendix E.
3.8.2 ISCST3 Model Output
It is important that the risk assessor understand how to read the ISCST3 output structure, in order to
ensure accurate use of modeled output in the risk assessment. The output from each ISCST3 model run
is written to two separate file formats. The 'output summary file' is specified by name at run time in the
execution command. Typical command line nomenclature is:
ISCST3 inputfile.INP outputfile.OUT
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where
i s c s T 3: specifies executing the ISCST3 model
inputfiie.iNp: is the input file name you (the modeler) select
outputfiie.ouT: is the output summary file name you select, typically the same as the
input file name (but with a different suffix)
For example, the following ISCST3 input line would run the input file (PART84R.INP) created by the
modeler for particulate emissions using 1984 meteorological data on the receptor grid. ISCST3 will
automatically write the output summary file (PART84R.OUT) during execution.
ISCST3 PART84R.INP PART84R.OUT
You specify the second file format, the output 'plot file,' in the ISCST3 input file OUtput pathway.
ISCST3 creates it during the run (see Section 3.6.6). The "plot file" is typically then imported into a
post-processing program (e.g. a spreadsheet) before entry into the risk assessment computations.
Vapor phase and Particle-bound phase runs produce similar plot files. The plot files for the vapor phase
runs will include average concentrations and wet deposition rates. The plot files for the particle and
particle-bound phase runs will include average concentrations, dry depositions, wet depositions and total
depositions. You can average the 1-year values at each receptor grid node for a 5-year value at each
node, unless a single five-year ISCST3 run using a combined meteorological file is used. If you use the
5-year combined file, the risk assessor can use the results from the ISCST3 plot file directly in the risk
assessment without averaging over the five years. All values are used in the estimating media
concentration equations (see Chapter 5).
3.8.3 Using Model Output to Estimate Media Concentrations
Section 3.2 discussed how partitioning of the COPCs affects the development of ISCST3 modeling runs.
The choices of which modeled air parameter values (air concentrations and deposition rates) to use in the
estimating media concentration equations is based on this same partitioning theory.
3.8.3.1 Vapor Phase COPCs
ISCST3 generates the following output for vapor phase air modeling runs:
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• vapor phase air concentrations (unitized Cyv and unitized Cywv),
• dry vapor deposition (unitized Dydv), and
• wet vapor depositions (unitized Dywv and unitized Dywwv)
for organic COPCs at receptor grid nodes based on the unit emission rate. These values are used in the
estimating media concentration equations for all COPC organics except the polycyclic aromatic
hydrocarbons (PAHs) dibenzo(a,h)anthracene and indeno(l,2,3-cd)pyrene2. The air concentration
(unitized Cyv), dry vapor deposition (unitized Dydv), and wet vapor deposition (unitized Dywv) from the
vapor phase run is also used in the estimating media concentration equations for mercury. You select
values for these COPCs from the vapor phase run because the mass of the COPC emitted by the
combustor is assumed to have either all or a portion of its mass in the vapor phase (see Appendix A-3).
3.8.3.2 Particle Phase COPCs
ISCST3 generates the following output for particle phase air modeling runs:
• air concentration (unitized Cyp),
• dry deposition (unitized Dydp),
• wet deposition (unitized Dywp), and
• combined deposition (unitized Dytwp)
for inorganics and relatively non-volatile organic COPCs at receptor grid nodes based on the unit
emission rate. These values are used in the estimating media concentration equations for all COPC
inorganics (except mercury, see Chapter 2 and Appendix A-3) and polycyclic aromatic hydrocarbons
with fraction of vapor phase, Fv , less than 0.05 (e.g., dibenzo(a,h)anthracene and
indeno(l,2,3-cd)pyrene). You choose values for inorganic and relatively non-volatile COPCs from the
particle phase run because the all the mass of the COPC is assumed to be in the particulate phase (see
Appendix A-3). The mass is apportioned across the particle size distribution based on mass weighting.
These two PAHs have vapor phase fractions, Fv, less than five percent.
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3.8.3.3 Particle-Bound COPCs
ISCST3 particle-bound runs generate the following output:
• air concentration (unitized Cyp),
• dry deposition (unitized Dydp),
• wet deposition (unitized Dywp), and
• combined deposition (unitized Dytwp)
for organic COPCs and mercury (see Chapter 2 and Appendix A-3) at each receptor grid node. Use these
values in the "estimating media concentration" equations to account for the portion of the vapor
condensed onto particulate surfaces. Select values for these COPCs from the particle-bound run because
the mass of the COPC emitted by the combustor is assumed to have a portion of its mass condensed on
particulates (see Appendix A-3). ISCST3 uses surface area weighting to apportion the particle-bound
mass across the particle size distribution.
3.9 MODELING FUGITIVE EMISSIONS
The procedures presented in this chapter for modeling stack source emissions are also effective for
modeling fugitive source emissions, as defined in Chapter 2. However, you can represent fugitive
emissions in the ISCST3 input file SOurce pathway as either "area" or "volume" source types. Model
fugitive emissions of volatile organics only in the vapor phase. Model fugitive emissions of ash only in
the particle and particle-bound phases, not vapor phase.
As discussed in Chapter 2, fugitive emissions of volatile organic vapors are associated with combustors
that include storage vessels, pipes, valves, seals and flanges. Enter the horizontal area of the fugitive
source into the ISCST3 input file according to the instructions found in the ISC3 User's Guide, Volume I
(U.S. EPA 1995f). You can get the horizontal area from the facility plot plan. The height of the fugitive
source is defined as the top of the vertical extent of the equipment. If the vertical extent isn't known, you
can use a default height of ground level (release height of zero). This provides a protective estimate of
potential impacts. The ISCST3 model run time is faster for volume source types than for area source
types. We generally recommend considering the volume source type for most applications. We
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recommend following the methods in the ISCST3 User's Guide to define the input parameters
representing the fugitive source.
The following example is for organic fugitive emissions only (modeling only vapor phase emissions)
modeled as a volume source type. The example includes a facility with two stack emission sources (B1,
B2) and two fugitive emission sources (areas Fl, F2). A total of four runs for each year (or 5-year
combined file) of meteorological data is needed.
Plot Plan
R9
F1
ISC3 Volume
F1A
F1B
F1C
F1D
Perform one run for each of the two stacks as point sources. Perform one run for each of the two fugitive
areas as volume sources (Note: or you could model as area sources). Since the emissions are fugitive
volatile organics, model only the vapor phase. The vertical extent of the pipes, valves, tanks and flanges
associated with each fugitive emission area is 15 feet (about 5 meters) above plant elevation. To define
the sources for input to ISCST3, specify the release height as 2.5 meters (!/2 of vertical extent of fugitive
emissions). Specify the initial vertical dimension as 1.16 meters (vertical extent of 5 meters divided by
4.3, as described in the ISC3 User's Guide).
The initial horizontal dimension is the side length of the square fugitive area (footprint) divided by 4.3. If
fugitive area F2 has a measured side of 30 meters, the initial horizontal dimension is 6.98 (30 meters
divided by 4.3). For fugitive area Fl, subdivide the area on the plot plan (ISC3 Volume) to create square
areas for input to ISCST3. The four areas depicted represent subdivision into square areas. Fugitive
source Fl is input into a single ISCST3 run as four separate volume sources (F1A, FIB, F1C, FID). The
initial horizontal dimension for each volume source is the side of the square divided by 4.3.
It is very important to allocate the unit emission rate (1.0 gram per second) proportionately among the
subdivided areas. For example, if the areas of the subdivided squares in the ISC3 Volume figure results
in F1A equal to FIB each with l/8th the total area, the proportion of the unit emissions allocated to each
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of these volume sources is 0.125 grams per second. The remaining two areas are each 3/8ths of the total
area of fugitive Fl, so that 0.375 grams per second is specified for the emission rate from each source.
The total emissions for the four volume sources sum to the unit emission rate for the Fl fugitive source
(0.125 + 0.125 + 0.375 + 0.375 = 1.0 g/s).
If you tell ISCST3 to include all sources in the model results (SO SRCGROUP ALL), the model will
appropriately combine all four volume source subdivisions into combined impact results for fugitive
source Fl. You can use the resulting air parameter values in the plot files directly in the risk assessment
equations, the same as if the emissions were modeled as a single point source. The initial vertical
dimension is defined the same as F2, using the vertical extent of 5 meters divided by 4.3 and a release
height of 2.5 meters (% vertical extent). For volume sources, the location is specified by the x andy
coordinates of the center of each square area.
The COntrol parameters can follow the recommendations for setting up a vapor phase computation.
CO CONG WDEP
Fugitive emissions of ash particles are from the storage piles associated with combustion units. Enter the
horizontal area of the storage pile into the ISCST3 input file according to the ISCST3 User's Guide,
Volume I (U.S. EPA 1995 f). The height of emissions is the top of the pile. If you don't know the
vertical extent, you could use ground level (or zero height). You'll typically model fugitive ash as area
source type. However, the permitting authority might consider volume source type prior to air modeling.
We generally recommend following the methods in the ISCST3 User's Guide when defining the input
parameters to represent the ash release as an area source.
We recommend the COntrol parameters follow the recommendations for setting up a particulate phase
computation.
CO CONG DDEP WDEP DEPOS
We recommend documenting the emissions characterization and source type.
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3.10 MODELING ACUTE RISK
It might be important for you to evaluate the acute, or short-term, effects due to the direct inhalation of
vapor phase, particle phase and particle-bound phase COPCs in the risk assessment. We recommend
considering site- or unit-specific factors such as unit operating conditions during emissions testing,
emission rates to be used in the acute screen, exposure scenario locations, etc. (see Sections 2.2.1, 4.2,
4.3, and 7.5) prior to conducting air modeling for an acute evaluation.
Since only ambient air concentrations are included in the direct inhalation pathway, You can compute the
air parameters needed for a screening acute assessment in the same ISCST3 runs (i.e., same input values
for meteorological data, stack gas exit temperature and velocity, etc.) that compute the air parameters for
the long-term chronic effects. However, as with chronic modeling, a non-steady state dispersion model
(e.g., CALPUFF) might provide a more representative estimate of concentration and deposition. This is
because of differences in methodology for modeling time-varying emissions and time- and space-varying
meteorological fields.
Methods outlined in this section focus on supporting a screening type acute assessment using the existing
air modeling runs executed for the chronic risk assessment. However, it is important to note that while
this approach provides some obvious efficiencies with regard to air modeling, you might need to execute
separate air modeling runs. For example, you might consider site- or unit-specific characteristics
(i.e., conditions of most concern for a short term or acute release scenario) in order to provide the most
protective or appropriate acute assessment. As discussed in Section 3.1, site-specific conditions and
assessment objectives might result in your choosing a different air model for the acute portion than for
the chronic assessment.
In air modeling for an acute type assessment, the goal is typically to compute the highest 1-hour average
air concentration for each phase (particle-bound, etc) for each source, for the entire period of analysis. In
most cases, this period is the five years of meteorological data. For ISCST3 to identify the highest
one-hour average concentration at each grid node, we recommend making two specifications in the
ISCST3 input files.
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First, you must specify the 1 -hour average as one of the averaging times in the COntrol pathway. The
example of this specification is included in Section 3.6.1. The ISCST3 input file includes:
CO AVERTIME 1 ANNUAL
where "1" specifies the 1-hour averaging time ISCST3 computes for each hour of meteorological data.
Remember that the 'ANNUAL' is specified for the chronic effects.
Second, we recommend that the OUtput pathway include the specification of a 1-hour average plot file
which contains the highest 1 -hour concentration at each receptor grid node for all the hours evaluated in
the ISCST3 run. For a single year run, the 1-hour concentration reported in the plot file is the highest
value computed at each x, y grid node location for the total hours in the year (8760, or 8784 for leap
years). The acute risk assessment needs the highest 1-hour concentration for the 5-year period. We
recommend reviewing each of the five single year values to identify the highest for all five years at each
receptor grid node. However, if you run a combined 5-year meteorological input file, the resulting plot
file will already identify the highest value for the 5-year period at each grid node with no additional
processing needed. The OUtput pathway instructions to create the plot file for one-hour average
concentrations are:
OU PLOTFILE 1 ALL FIRST BTR841.PLT
where
1: specifies the 1 -hour averaging period,
ALL: instructs ISCST3 to include all sources in the run,
FIRST: instructs the model to include only the first highest value at each
receptor grid node, and
BTR841 .PLT: is the name for the plot file.
The plot file name is unique for the run and the one-hour averaging period results.
The highest air concentration for the 1 -hour averaging period is input as the air parameters, Chv, Chp,
and Chpb in the acute risk assessment equations (see Section 7.5 and Appendix B, Table B-6-1).
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Chapter 4
Exposure Scenario Identification
What's Covered in Chapter 4:
4.1 Characterizing the Exposure Setting
4.2 Recommended Exposure Scenarios
Farmer
Farmer Child
Resident
Resident Child
Fisher
Fisher Child
Acute Receptor
4.3 Selecting Exposure Scenario Locations
The purpose of this chapter is to provide guidance on identifying "exposure scenarios" to evaluate in the
risk assessment. Evaluating exposure scenarios will estimate the type and magnitude of human exposure
to COPC emissions from hazardous waste combustors (including fugitive emissions). In this document,
identifying exposure scenarios consists of:
• characterizing the exposure setting,
• identifying recommended exposure scenarios, and
• selecting exposure scenario locations.
Characterizing the exposure setting includes defining the dimensions of the assessment area (or "study
area"). It also includes identifying the current and potential human activities and land uses within those
boundaries. Within the context of the exposure setting, an exposure scenario is a combination of
"exposure pathways" to which a "receptor" may be subjected.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak in this chapter is the performer of a risk assessment: the person (or
persons) who will actually put the recommended methods into practice.
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For this guidance, we define a receptor as a human being potentially exposed to COPCs emitted to the
atmosphere from a hazardous waste combustion facility. An exposure "route" is the particular means of
entry into the body. For the purposes of the HHRAP, receptors come into contact with COPCs via two
primary exposure routes: either directly—via inhalation; or indirectly—via COPC deposition and
subsequent ingestion of water, soil, vegetation, and animals that have been contaminated by COPCs
through the food chain.
An exposure "pathway" is the course a chemical takes from its source to the person being exposed. An
exposure pathway consists of four fundamental components:
1. a source and mechanism of COPC release (see Chapter 2);
2. a retention medium, or a transport mechanism and subsequent retention medium in cases
involving media transfer of COPCs (see Chapter 3 for air transport of COPCs, and
Chapter 5 for bioaccumulation of COPCs in the food chain);
3. a point of potential human contact with the contaminated medium; and
4. an exposure route.
Exposure to COPCs can occur via numerous exposure pathways, such as ingestion of diary products and
home grown produce (see Section 4.2).
The HHRAP identifies a number of generic exposure scenarios (Farmer, Farmer Child; Fisher, Fisher
Child; Resident, and Resident Child). Used as presented, these standardized scenarios should be
reproducible across most sites and land use areas. We intend these scenarios to be appropriate for a
broad range of situations, rather than to represent actual scenarios. We believe that the recommended
exposure scenarios and associated assumptions are reasonable. They represent a scientifically sound
approach that is protective of human health and the environment, while recognizing the uncertainties
associated with evaluating real world exposures. For example, the scenarios are designed with a level of
protectiveness intended to address potential receptors not directly evaluated, such as populations with
somewhat higher exposures than the general public. At the same time, you can easily alter these
scenarios to more closely reflect site-specific conditions. To be transparent, we recommend well-
documenting, supporting and discussing any changes (i.e. deletions, additions, or modifications) to a
recommended exposure scenario or scenario location with the appropriate parties (regulatory agency,
facility, interested community members).
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Selecting exposure scenario locations involves identifying the physical positions of the exposure
scenarios within the study area. For example, you can position scenarios based on current or future
human activities or land use. Alternatively, you can position scenarios artificially, as part of a screening
assessment. You could, for example, locate all selected receptors at the area of greatest contaminant
deposition, to maximize potential exposure. The HHRAP focuses on placement based on actual or
potential activities and land use.
The following sections describe how we recommend
1. characterizing the exposure setting,
2. identifying which of the recommended exposure scenarios are appropriate for the risk
assessment, and
3. selecting the exposure scenario locations.
4.1 CHARACTERIZING THE EXPOSURE SETTING
The purpose of characterizing the exposure setting is to identify the human receptors, their land uses and
activities, which might be impacted by exposure to emissions from the facility being assessed. The
exposure setting might include multiple sources (e.g., multiple stacks, fugitive emissions), as well as
terrain both inside and outside the facility boundary (or "fenceline"). We believe both current and
reasonable potential human activities or land uses are relevant, when determining which recommended
exposure scenarios are appropriate for the risk assessment (see Section 4.2).
Experience has shown us that most significant deposition occurs within a 10 km radius, as measured
from the centroid of a polygon centered on the stacks of the facility being assessed. Consequently,
resources for characterizing the exposure setting might initially be focused within this area. Also, most
recommended exposure scenarios appropriate for the assessment will likely be located within this area.
It may be prudent, however, to also characterize the exposure setting beyond the 10 km radius, to
determine if conditions exist which warrant additional exposure scenarios. Such conditions might
include (but are not limited to) recommended exposure scenarios or special populations (see Section
4.1.3) not found within the 10 km area, or topographic features - such as hills - that tend to increase
potential deposition. A 50 km radius is the recognized limit of the ISCST3 air dispersion model, and can
be used as the outer boundary for characterizing additional exposure settings (See Chapter 3 for
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information on air modeling beyond a 50 km radius). All affected parties (i.e. regulators, facilities,
interested community members) can then discuss if additional scenarios need to be assessed, and if so,
their locations.
The study area might include land use and water bodies both inside and outside the facility fenceline.
It's important to understand that some of the recommended scenarios might most appropriately be placed
within facility boundaries. For example, some facilities located on substantial property rent portions of
the property to the public for farming, ranching, or recreational purposes (e.g., fishing).
When characterizing the exposure setting, we highly recommend considering
• current and reasonable potential future land use,
• waterbodies and their associated watersheds, and
• special populations.
The following subsections provide information on these aspects.
4.1.1 Current and Reasonable Potential Future Land Use
Land use is an important factor in characterizing the exposure setting. When land use is overlaid with
the air dispersion modeling results, the combination will demonstrate which recommended exposure
scenarios (and their locations) are most relevant for the risk assessment. We recommend considering
both current and reasonable potential future land use (i.e. "future land use"), because risk assessments
typically evaluate the potential risks from facilities over long periods of time (greater than 30 years).
One can typically identify current land use, and indications of future land use, by reviewing hard copy
and/or electronic versions of Land Use/Land Cover (LULC) maps, topographic maps, and aerial
photographs. We list some sources below, and general information associated with several potential data
and map resources. Also, as noted in Chapter 3, we recommend verifying that all mapping information
you use is in the same Universal Transverse Mercator (UTM) coordinate system format (NAD27 or
NAD83), to ensure consistency and prevent erroneous geo-referencing of locations and areas.
Land Use/Land Cover (LULC) Maps - you can download LULC maps directly from the United
States Geological Survey (USGS) web site (http://mapping.usgs.gov/index.html), at a scale of
1:250,000, in the GIRAS file format. LULC maps are also available from the EPA web site
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(ftp://ftp.epa.gov/pub), at a scale of 1:250,000, in an Arc/Info export format. Within your study
area, we recommend verifying the exact boundaries of polygons defining land use areas using
available topographic maps and aerial photographs.
Topographic Maps - Topographic maps are readily available in both hard copy and electronic
format directly from the USGS or numerous other vendors. These maps are commonly at a scale
of 1:24,000, and in TIFF file format with a TIFF World File included for georeferencing.
Aerial Photographs - You can purchase hard copy aerial photographs directly from the USGS in
a variety of scales and coverages. Electronic format aerial photographs or Digital Ortho Quarter
Quads (DOQQs) are also available for purchase directly from the USGS, or from an increasing
number of commercial sources.
Properly georeferenced DOQQs covering a 3-km or more radius of the assessment area, combined with
overlays of the LULC map coverage and the ISCST3 modeled receptor grid node array, provide an
excellent reference for identifying land use areas and justifying your choices of exposure scenario
locations. The information above does not represent the universe of data available on human activities
or land use. They are, however, readily available for little or no cost from a number of government
sources, often via the Internet.
If feasible, we recommend verifying the accuracy of land use information with a site visit. Also,
organizations exist that routinely collect and evaluate land use data (agricultural extension agencies,
U.S. Department of Agriculture, natural resource and park agencies, and local governments). You may
find discussions with these organizations helpful in updating current land use information or providing
information regarding future land use. Local planning and zoning authorities are also potential sources
of information on reasonable potential future land use. These authorities have information on the level of
development allowed under current regulations, and what development may be expected in the future.
The general public is another excellent source of information about land use in the area. Conducting a
public workshop early in the data gathering process for the risk assessment can provide valuable
information on land use, crops, special populations, etc. as well as starting a positive dialogue with the
community. For example, by communicating with local tribes you might find that certain locations hold
special significance for cultural or religious activities.
You can also use site-specific data on physiographic features (e.g., plant types, soil characteristics, land
use, etc.) to verify the land uses identified using the resources listed above. You can readily determine
the presence, type, and extent of physiographic features from the following sources:
• USGS topographic maps,
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• Soil Conservation Service reports,
• county and local land use maps, and
• information from state departments of natural resources or similar agencies.
A study area might include multiple land uses, with differing current or potential human activity/land use
characteristics. Your activity/land use analysis could identify multiple population centers (e.g.,
communities, residential developments, or rural residences), farms and ranches, or other land use types in
the study area that would support recommended exposure scenarios. For example, if a study area
includes a farm and a small residential community, you could consider both areas as possible exposure
scenario locations (see Sections 4.2 and 4.3).
Once you've identified current land uses, we generally recommend also identifying areas with different
reasonable potential future land use characteristics. For example, if a study area includes undeveloped
property which could be converted to a residential community in the future, you might consider both of
these land use types (i.e. undeveloped property, and residential community) in the risk assessment (see
Sections 4.2 and 4.3). We recommend considering only potential future land uses which might
reasonably be expected to occur. For example:
1. A rural area currently characterized as undeveloped open fields, could reasonably be
expected to become farmland if it is able to support agricultural activities;
2. A rural area currently characterized by open fields and intermittent housing, could
reasonably be expected to become a residential subdivision; and
3. An area currently characterized as a tidal swamp would not reasonably be expected to
become farm land.
For transparency and clarity, we recommend describing any current or reasonably expected future land
use in the risk assessment report. Of all the land use areas you identify, we generally recommend
focusing on those areas that could be impacted by the COPC emissions you're evaluating in the risk
assessment.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Identification and/or mapping of current land uses in the area, a description of the use, the
area of the land described by the use, and the source of the information. You might choose to
focus initially on those land use areas impacted by emissions of COPCs.
Identification and/or mapping of the reasonable potential future land use areas, a description
of the use, the source or rationale on which the description is based. You might choose to
focus initially on those land use areas impacted by emissions of COPCs.
4.1.2 Water Bodies and Their Associated Watersheds
Surface water bodies and their associated watersheds are important factors in evaluating some of the
recommended exposure scenarios. Specifically, water bodies can be important sources offish for the
fish ingestion pathway, or sources of water for the drinking water pathway (see Section 4.2). Your
careful consideration is warranted when identifying which water bodies in the study area to assess. For
the Fisher scenario, an appropriate water body (and/or its associated watershed) would receive deposition
from the emission source, and be able to sustain a fish population harvested by humans. For the drinking
water ingestion pathway, an appropriate waterbody (and/or its associated watershed) would receive
deposition from the emission source, and be used as a direct drinking water source (i.e. not processed by
a drinking water treatment facility). We recommend considering both current and potential human uses
of water bodies found within the study area. In addition to identifying the human uses of water bodies,
we recommend defining the surface areas and exact locations of the water bodies, and their associated
watersheds. See Section 4.3 for a further discussion of selecting exposure scenario locations and their
associated water bodies.
You can typically identify the use, area, and location of water bodies and their associated watersheds by
reviewing the same hard copy and /or electronic versions of LULC maps, topographic maps, and aerial
photographs used to identify land uses. We present sources and general information associated with each
of these data types or maps in Section 4.1.1.
You might also get information on water body use from local authorities (e.g., state environmental
agencies, fish and wildlife agencies, or local water control districts). This might include information
about viability to support fish populations and drinking water sources. Surface water bodies that are used
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As drinking water sources in the assessment area are generally evaluated in the risk assessment. While
water bodies closest to the facility will generally have higher deposition rates, risk estimates are also
affected by other physical parameters (e.g. the size of the water body and the associated watershed) and
by the properties of the COPCs being emitted.
Once you've selected a water body, we recommend identifying the area extent (defined by UTM
coordinates) of its watershed. Watershed runoff can be a significant contributor to overall water body
COPC loadings. Media concentration equations use the extent of pervious and impervious areas in the
watershed, as well as COPC concentrations in watershed soil, to calculate the water body COPC
concentrations (see Chapter 5 and Appendix B). We therefore recommend clearly identifying and
discussing the area extent of the watershed with the interested parties (both permitting authority and
facility).
You generally define the area extent of a watershed by identifying topographic highs that result in
downs lope drainage into the water body. We recommend ensuring that the watershed and it's
contribution to the water body are defined relative to the exposure scenario location associated with the
water body (e.g. location on the water body of the drinking water intake, fishing pier, etc.), and
subsequent risk estimates. Please keep in mind that the total watershed area can be very extensive
relative to the area that is impacted from facility emissions.
For example, if facility emissions principally impact an area of land which drains into a specific tributary
of a large river system and immediately upstream of a private drinking water intake point, you may wish
to consider evaluating an "effective" watershed area rather than the entire watershed area of the large
river system. For such a large river system, the watershed area can be on the order of thousands of
square kilometers and can include numerous tributaries draining into the river at points that would have
no net impact on the drinking water intake or on the water body COPC concentration at the exposure
point of interest.
To use the HHRAP as recommended, you will need the following water body and watershed parameters
(on an average annual basis):
• Water body surface area
• Watershed surface area
• Impervious watershed area
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• Average water body volumetric flow rate
• Water body current velocity
• Depth of water column
• Total suspended solids (TSS)
• Universal Soil Loss Equation (USLE) rainfall/erosivity factor
The impervious watershed area is generally a function of urbanization within the watershed, and is
typically presented as a percentage of the total watershed area. Volumetric flow rate and water body
current velocity are typically annual average values. State or local geologic surveys often keep records
on flow rate and current velocity of larger water bodies. You can calculate the volumetric flow rates for
smaller streams or lakes by multiplying the watershed area by one-half of the local average annual
surface runoff. Lacking site-specific data, you can calculate current velocities by dividing the volumetric
flow rate by the cross-sectional area (NOTE: current velocities are not used in the equations for lakes).
State or local sources sometimes have information on the depths of water bodies available. Discussions
on determining the USLE rainfall/erosivity factor are included in Chapter 5 and Appendix B.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Identification and/or mapping of water bodies and associated watersheds potentially impacted
by facility emissions of COPCs, including surface area of the water body and area extent of
the contributing watershed, defined by UTM coordinates
Rationale for selecting or excluding water bodies within the assessment area from evaluation
Information on water body use that may justify including or excluding the water body from
evaluation
Documentation of water body area, watershed area, impervious area, volumetric flow rate,
current velocity, depth of water column, total suspended solids (TSS), and the USLE
rainfall/erosivity factor
Description of assumptions made to limit the watershed area to an "effective" area
Copies of all maps, photographs, or figures used to define water body and watershed
characteristics
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4.1.3 Special Population Characteristics
Special populations are human receptors or segments of the population that may be at higher risk due to
increased sensitivity and/or increased exposure to COPCs. Fetuses, infants and children, and the elderly
are examples of human life stages (i.e. populations) which might be more sensitive to COPC exposure.
You might consider some tribal groups a special population because their ingestion of fish at rates higher
than the general public increases their exposure to chemicals thatbioaccumulate. Subsistence residents
are also likely to have higher exposures from ingestion of meat (locally harvested game), produce (wild
berries and onions, for example), and soil. There may be special locations where cultural activities are
conducted, or that are sacred to the tribes, and we encourage evaluating exposures at these locations.
We've developed the assumptions specified in this guidance - such as the protective nature of the
recommended exposure scenarios (see Section 4.2), and the use of RfDs which have been developed to
account for toxicity to sensitive receptors - to also protect the health of special populations. However,
you may also need to specifically address populations that are located in impacted areas because of
unique characteristics of the exposure setting or to address particular community concerns. For example,
a day care center or hospital may be located in an area that is directly impacted by the facility stack
emissions. Receptors at these locations may be especially sensitive to the adverse effects and/or the
exposure setting is particularly conducive to exposure. Consequently, due to site-specific exposure
characteristics, exposure to children at the day care center, or to the sick in the hospital, might need to be
specifically evaluated. Section 4.2 provides additional discussion on evaluating potential exposure of
special populations, as part of evaluating recommended exposure scenarios. Additionally, the Agency
has a stated policy focused on consistently and explicitly evaluating environmental health risks to infants
and children in all risk assessments (U.S. EPA 1995J).
Concerns about special populations can arise at anytime in the permitting process. We therefore
recommend identifying special populations as part of characterizing the exposure setting. You can
identify special populations in the assessment area based on the location of schools, hospitals, nursing
homes, day care centers, parks, community activity centers, etc. If available information indicates that
there are children exhibiting pica behavior (defined for risk assessment purposes as "an abnormally high
soil ingestion rate") in the assessment area, these children could also represent a special population (see
Section 6.2.3.1).
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RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Identification and/or mapping of the locations of special populations at potentially higher risk
from exposure to facility sources (anticipated to be located in areas impacted by facility
emissions); focusing on the characteristics of the exposure setting to ensure that selected
exposure scenario locations are protective of the special populations.
4.2 RECOMMENDED EXPOSURE SCENARIOS
We recommend evaluating the following exposure scenarios when they are consistent with site-specific
exposure settings (also see Table 4-1):
• Farmer
• Farmer Child
• Resident
Resident Child
• Fisher
Fisher Child
• Acute Receptor
• Nursing Infant (Covered as exposure pathway under adult exposure scenarios)
These are the same exposure scenarios recommended by earlier OSW guidance, with the exception of the
Farmer Child, Fisher Child, and acute receptor. The Farmer Child scenario was introduced into the
indirect screening process in the risk assessment completed to support the proposed Hazardous Waste
Combustion Rule and by NC DEEINR (1997). We include the Fisher Child scenario in order to be
consistent with the adult/child pairings we recommend for the Resident and Farmer scenarios. We
include the acute receptor scenario to ensure that the assessment evaluates all receptors that may be
significantly exposed to emissions from facility sources.
In addition to the recommended exposure scenarios listed above, we recommend evaluating, where
appropriate, special populations (as defined in Section 4.1.3) and communities of concern. Do this by
identifying their locations, and determining whether they are located in areas with exposure setting
characteristics that are particularly conducive to COPC impacts from facility emissions. Examples of
additional exposure scenarios include hunters, trespassers, workers (see below), recreational fishers, etc.
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You could evaluate some populations using a combination of a recommended exposure scenario expected
to overestimate exposure compared to the populations, and maximum modeled air parameter values
specific to the location (see Section 4.3). If this initial evaluation suggests that the receptors are
protected, then no additional assessment is necessary. If, on the other hand, this evaluation estimates
levels of risk which are of concern, a refined evaluation may be needed. The refined exposure scenario
would evaluate the specific exposure pathways appropriate to the special population.
Take, for example, a children's school or day care center located in an area receiving deposition of
facility emissions. You could evaluate potential exposure of children at this location using the Resident
Child scenario at the location of the school or day care center. In most cases, evaluating this scenario at
the school location will over-estimate exposure. This is because the Resident Child scenario includes an
exposure pathway (ingestion of homegrown produce) which is most likely not occurring at that location.
Also, the residential scenario assumes that a child breaths the air 24 hours/day, ingests 100 mg of
soil/day; and is exposed for 6 years - when the child is probably only at day care 5 days/week and up to
10 hours/day. If this generates risk estimates of concern, you could conduct a more refined evaluation
that adjusts the exposure assumptions to be more representative of the site.
We don't routinely recommend assessing workers at a facility that burns hazardous waste in the risk
assessment, because we assume that those workers are protected by regulation and guidance of the U.S.
Occupational Safety and Health Administration (OSHA). There are, however, some instances where
workers impacted by exposure to facility emissions are not covered by the appropriate OSHA
regulations. For example, workers located at a nearby but separate facility or commercial area, whose
duties are independent of combustor operations, are not necessarily covered by the appropriate OSHA
regulations. Also, on a site with multiple on-site activities (e.g., manufacturing, hazardous waste
combustion, and military operations) the OSHA regulations would address the worker at the
manufacturing operations with respect to those operations and not the emissions from the separate
hazardous waste combustion operations. Considering such instances in the risk assessment may be
appropriate.
We no longer refer to our recommended farmer and fisher exposure scenarios as "subsistence" scenarios.
The associated daily consumption amounts (see Table 4-2, as well as Appendix C) are more comparable
to reasonable (versus subsistence) amounts.
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May 2005
As mentioned above, an exposure scenario is defined as a combination of exposure pathways to which a
receptor is subjected at a particular location. Table 4-1 presents the exposure pathways we recommend
evaluating for each of the exposure scenarios. Food-related ingestion pathways could represent
significant potential exposure to COPCs released from combustion sources (U.S. EPA 19941; 1994g;
1998c; NC DEHNR 1997), due primarily to the potential for COPCs to bioaccumulate up the food chain.
TABLE 4-1
RECOMMENDED EXPOSURE SCENARIOS FOR A
HUMAN HEALTH RISK ASSESSMENT
Exposure Pathways
Inhalation of Vapors and Particulates
Incidental Ingestion of Soil
Ingestion of Drinking Water from Surface Water Sources
Ingestion of Homegrown Produce
Ingestion of Homegrown Beef
Ingestion of Milk from Homegrown Cows
Ingestion of Homegrown Chicken
Ingestion of Eggs from Homegrown Chickens
Ingestion of Homegrown Pork
Ingestion of Fish
Ingestion of Breast Milk
Recommended Exposure Scenarios"
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Notes:
Pathway is included in exposure scenario.
Pathway is not included in exposure scenario.
Exposure scenarios are defined as a combination of exposure pathways evaluated for a receptor at a specific location.
The acute receptor scenario evaluates short-term 1-hour maximum COPC air concentrations (see Chapter 3) at any
land use area that would support the other recommended exposure scenarios, as well as commercial and industrial
land use areas (excluding workers at the facility being directly evaluated in the risk assessment).
Infant exposure to PCDDs, PCDFs, and dioxin-like PCBs via the ingestion of their mother's breast milk is evaluated
as an additional exposure pathway, separately from the recommended exposure scenarios identified in this table (see
Chapter 2).
Site-specific exposure setting characteristics (e.g., presence of ponds on farms, or presence of ponds or small
livestock within semi-rural residential areas) may warrant the permitting authority consider adding this exposure
pathway to the scenario (see Section 4.2).
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As indicated in Table 4-1, some exposure setting characteristics may warrant you consider including
additional exposure pathways when evaluating a particular exposure scenario. For example, the
recommended Farmer exposure scenario doesn't typically include the fish ingestion exposure pathway.
However, in some areas of the country it's common for farms to have stock ponds that are fished on a
regular basis for the farm family's consumption. Since the ingestion rates we recommend for those food
pathways already considered in the evaluation are not significant enough to preclude the Farmer also
ingesting the fish caught from the local pond, the fish ingestion exposure pathway may also be relevant in
such locations. You could use the same rationale for residential scenarios where residents are located in
semi-rural areas which allow small livestock (e.g., free range poultry for eggs), and/or residents located
by small ponds suitable for fishing, or wetlands that support crawfish harvest.
We also recommend evaluating infant exposure to PCDDs and PCDFs via the ingestion of their mother's
breast milk as an additional exposure pathway at all recommended adult exposure scenario locations.
Chapter 2 and Appendix C further describe the ingestion of breast milk exposure pathway.
In addition, although some risk assessments conducted by U.S. EPA (1996b) have discounted the direct
inhalation risks to all receptors except the adult Resident (nonfarmer) and Resident Child (nonfarmer),
we generally recommend evaluating the direct inhalation exposure pathway for all receptors.
We don't typically recommend evaluating the following exposure pathways as part of an exposure
scenario:
Ingestion of Ground Water - U.S. EPA (1998c) found that ground water is an insignificant
exposure pathway for combustion emissions; in addition, U.S. EPA (1994k) noted that uptake
from ground water into food crops and livestock is minimal because of the hydrophobic nature of
dioxin-like compounds. We anticipate potential exposure to COPCs through ingestion of
drinking water from surface water bodies to be much more significant. Ingestion of ground water
is further discussed in Section 6.2.4.2.
Inhalation of Resuspended Dust - U.S. EPA (1990e) found that risk estimates from inhalation of
resuspended dust was insignificant. We anticipate exposure through direct inhalation of vapor
and particle phase COPCs and incidental ingestion of soil to be much more significant.
Inhalation of resuspended dust is further discussed in Section 6.2.3.3.
Dermal Exposure to Surface Water, Soil, or Air - Available data indicate that the contribution
of dermal exposure to soils to overall risk is typically small (U.S. EPA 1996g; 1995h). For
example, the risk assessment conducted for the Waste Technologies Industries, Inc., hazardous
waste incinerator in East Liverpool, Ohio, indicated that—for an adult farmer in a subarea with
high exposures—the risk resulting from soil ingestion and dermal contact was 50-fold less than
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the risk from any other exposure pathway and 300-fold less than the total estimated risk (U.S.
EPA 1996g; 1995h). Also, there are significant uncertainties associated with estimating potential
COPC exposure via the dermal exposure pathway. The most significant of these uncertainties
are associated with determining the impact of soil characteristics and the extent of exposure (e.g.,
the amount of soil on the skin and the length of exposure) on estimating compound-specific
absorption fractions (ABS).
We don't generally recommend evaluating the dermal exposure to soil pathway as part of the
recommended exposure scenarios. However, if either a facility or a permitting authority feel
that site-specific conditions indicate dermal exposure to soil may contribute significantly to total
soil-related exposures, we recommend following the relevant methods described in U.S. EPA
NCEA document, Methodology for Assessing Health Risks Associated with Multiple Pathways of
Exposure to Combustor Emissions (U.S. EPA 1998c). Dermal exposure is further discussed in
Section 6.2.3.2 of this guidance.
Inhalation ofCOPCs and Ingestion of Water by Animals - We don't recommend these animal
exposure pathways in calculating animal tissue concentration because we expect their
contributions to total risk to be negligible compared to the contributions of the recommended
animal exposure pathways. However, you might need to evaluate these exposure pathways on a
case-by-case basis considering site-specific exposure setting characteristics.
Our recommended exposure scenarios are further discussed in the following subsections.
4.2.1 Farmer
The Farmer exposure scenario is made up of the exposure pathways through which an adult member of a
farming or ranching family could be exposed. We recommend including this scenario when farming or
ranching takes place, or may reasonably take place some time in the future, in the study area. As
indicated in Table 4-1, we recommend assuming the Farmer is exposed to COPCs emitted from the
facility through the following exposure pathways:
• Direct inhalation of vapors and particles
• Incidental ingestion of soil
• Ingestion of drinking water from surface water sources
• Ingestion of homegrown produce (i.e. fruits and vegetables)
• Ingestion of homegrown beef
• Ingestion of milk from homegrown cows
• Ingestion of homegrown chicken
• Ingestion of eggs from homegrown chickens
• Ingestion of homegrown pork
• Ingestion of breast milk (evaluated separately, for an infant of the Farmer; see Chapter 2)
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While on the farm property, the Farmer inhales air containing COPC-impacted vapors and suspended
particles. Through daily activities, the Farmer ingests incidental amounts of soil. If site characterization
suggests that impacted surface waterbodies are used as direct drinking water sources (see Section 4.1.2),
the farm family receives its water from a surface waterbody. The farm family raises and consumes beef
and milk cattle, pigs, and free-range chickens (including eggs). Cattle ingest soil while foraging on a
grazing field, as well as being fed silage and grain grown on the farm. Pigs are contained within a yard
or small field, where they are assumed not to forage, but ingest soil while being fed a combination of
silage and grain grown on the farm. Free-range chickens are contained within a yard or field, where they
ingest soil while being fed grain grown on the farm. The Farmer grows enough fruits and vegetables to
supply the family with produce.
The scenario assumes that a portion of the Farmer's diet comes from each homegrown food type listed
above (see Table 6-1 and Appendix C for consumption rates). All of these portions are impacted by
emissions from the facility being assessed. The recommended consumption rates don't represent the
Farmer's entire intake of each food type, but rather only the homegrown portion of the Farmer's diet. It
is therefore reasonable to assume that 100% of this subset of each food type (i.e. the homegrown portion)
is contaminated. Also, because the portions represent only the homegrown portion of the Farmer diet,
assuming ingestion of all meat groups by the Farmer does not grossly overestimate the total amount of
meat a farmer or rancher could reasonably consume. Breaking out consumption by food type is an
important step in estimating the relative contributions to COPC-specific risk from ingestion of each food
type.
Previous Agency guidance (for example, U.S. EPA 1993f and U.S. EPA 1994f) didn't include the
ingestion of chicken and eggs exposure pathways. NC DEEINR (1997) considers chicken and egg
ingestion pathways only for exposure to dioxins and furans, because biotransfer factors were only
available for dioxins and furans when that guidance was published. U.S. EPA (1998c) includes ingestion
of both poultry and eggs. Currently, biotransfer factors can be derived from literature data for other
organic compounds and metals. Therefore, we generally recommend including the chicken and egg
ingestion exposure pathways for all COPCs with available biotransfer factors. Further discussion of
these exposure pathways, including numeric equations, parameters values, and COPC-specific inputs, can
be found in Chapter 5 and Appendices A, B, and C.
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When evaluating the ingestion of drinking water from surface water for the Farmer scenario, we
generally recommend also considering the potential for ingestion of cistern water at farm or ranch
locations, in addition to surface water sources. If site-specific information (e.g. interviews with local
health departments) suggests that cistern water is likely used, or could be used for a drinking water
source, you could evaluate ingestion of cistern water in a manner similar to that used to evaluate
ingestion of water from a surface water body (see Chapter 5 and Appendix B). Site-specific information
(e.g. do cisterns in the study area tend to be covered or uncovered?) can educate decision makers as to
appropriate equations and parameter values to use in assessing the ingestion of drinking water from
cisterns.
We don't usually recommend the ingestion offish exposure pathway for the Farmer exposure scenario.
However, as indicated in the notes to Table 4-1, we do recommend that you consider evaluating the fish
ingestion pathway if regional or site-specific exposure setting characteristics (e.g., presence of ponds on
farms or ranches that support fish for human consumption) are identified that warrant consideration. You
can use the applicable estimating media concentration equations for ingestion of fish as presented in
Chapter 5 and Appendix B. Also, evaluating the Fisher and Fisher Child exposure scenarios (see
Sections 4.2.5 and 4.2.6) at farm or ranch locations may be appropriate where on-site ponds are used as
sources of fish for human consumption.
We recommend evaluating the exposure of an infant to PCDDs, PCDFs, and dioxin-like PCBs via the
ingestion of breast milk as an additional exposure pathway, separately from this exposure scenario (see
Chapter 2).
If site-specific information is available indicating that farmers in the study area don't raise a type of
livestock, nor is raising that type of livestock likely to occur in the future, then you could reasonably
consider eliminating the related exposure pathway (or pathways, in the case of chicken and egg
ingestion). However, if one meat source is not used, its place in the diet is often taken by one or more of
the remaining exposure pathways. Take care, therefore, to consider the intake rates of the remaining
exposure pathways, to ensure that the total amount consumed (summed fraction from each food group) is
representative. See Chapter 6 (Quantifying Exposure) for further discussion of the implications of
modifying our recommended exposure pathways.
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4.2.2 Farmer Child
The Farmer Child exposure scenario is made up of the exposure pathways through which a child member
of a farming or ranching family may reasonably be expected to be exposed. Agency policy recommends
consistently and explicitly evaluating environmental health risks to infants and children in all risk
assessments (U.S. EPA 1995J). As indicated in Table 4-1, the scenario assumes the Farmer Child is
exposed to COPCs emitted from the facility through the same exposure pathways as the Farmer. The
primary differences between the Farmer and Farmer Child are in exposure duration (6 years for the child
vs . 40 years for the adult), and consumption rates (e.g. 1.4 homegrown produce servings per week for
child vs. 2.8 homegrown produce servings per week for adult, see Table 6-1).
4.2.3 Resident
The Resident exposure scenario is made up of the exposure pathways through which an adult receptor
may be exposed in an urban or nonfarm rural setting. We recommend including the adult Resident
scenario, because potential exposure to COPCs through ingesting homegrown produce has been shown to
be potentially significant. This exposure scenario equates with the "Home Gardener" scenario
recommended by U.S. EPA (1994g) and NC DEHNR (1997). As indicated in Table 4-1, the scenario
assumes the adult Resident is exposed to COPCs from the emission source through the following
exposure pathways:
• Direct inhalation of vapors and particles
• Incidental ingestion of soil
• Ingestion of drinking water from surface water sources
• Ingestion of homegrown produce
• Ingestion of breast milk (evaluated separately, for an infant of the Resident; see Chapter
2)
While on their property, the Resident inhales air containing COPC-impacted vapors and suspended
particles. Through daily activities, the Resident ingests incidental amounts of soil. If site
characterization suggests that impacted surface waterbodies are used as direct drinking water sources
(see Section 4.1.2), the resident family receives its water from a surface waterbody. The Resident grows
fruits and vegetables for home consumption (NC DEHNR 1997). Breaking out consumption by exposure
pathway is an important step in estimating the relative contributions to COPC-specific risk from
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ingestion of each food type. Further discussion of these exposure pathways, including equations,
parameter values, and COPC-specific inputs, can be found in Chapter 5 and Appendices A, B, and C.
We don't usually recommend evaluating the ingestion offish exposure pathway for the Resident
exposure scenario. However, as indicated in the notes to Table 4-1, we do recommend that you consider
evaluating the fish ingestion pathway if exposure setting characteristics (e.g., presence of ponds within
semi-rural residential areas that support fish for human consumption) are identified that warrant
consideration. It may be appropriate to evaluate the Fisher and Fisher Child exposure scenarios (see
Sections 4.2.5 and 4.2.6) at residential locations where ponds or surface water bodies are used as a
potential source of fish for human consumption.
We recommend evaluating exposure of an infant to PCDDs, PCDFs, and dioxin-like PCBs via the
ingestion of breast milk as an additional exposure pathway, separately from this exposure scenario (see
Chapter 2).
4.2.4 Resident Child
The Resident Child exposure scenario is made up of the exposure pathways through which a child
receptor may be exposed in an urban or nonfarm rural setting. This exposure scenario equates with the
"Child of the Home Gardener" scenario recommended by U.S. EPA (1994g) andNC DEHNR (1997).
Agency policy recommends consistently and explicitly evaluating environmental health risks to infants
and children in all risk assessments (U.S. EPA 1995J). As indicated in Table 4-1, the scenario assumes
the Resident Child is exposed to COPCs emitted from the facility through the same exposure pathways as
the Resident adult. The primary differences between the Resident and Resident Child are in exposure
duration (6 years for the child vs . 30 years for the adult), and consumption rates (e.g. 1.2 homegrown
produce servings per week for the child vs. 2.3 homegrown produce servings per week for the adult, see
Table 6-1).
4.2.5 Fisher
The Fisher exposure scenario is made up of the exposure pathways through which an adult receptor may
be exposed in an urban or nonfarm rural setting where fish is the main source of protein in the receptor
diet. We recommend including the Fisher scenario, because food-related ingestion routes may represent
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significant potential exposure to COPCs released from combustion sources (U.S. EPA 19941; 1994g;
1998c; NC DEHNR 1997). The potential exposure is due primarily to the potential for COPCs to
bioaccumulate up the food chain. Breaking out consumption by exposure pathway is an important step in
estimating the relative contributions to COPC-specific risk from ingestion of each food type. As
indicated in Table 4-1, the scenario assumes the Fisher is exposed to COPCs emitted from the facility
through the following exposure pathways:
• Direct inhalation of vapors and particles
• Incidental ingestion of soil
• Ingestion of drinking water from surface water sources
• Ingestion of homegrown produce
• Ingestion of fish
• Ingestion of breast milk (evaluated separately, for an infant of the Fisher; see Chapter 2)
While on their property (i.e. where they reside), the Fisher inhales air containing COPC-impacted vapors
and suspended particles. Through daily activities, the Fisher ingests incidental amounts of soil. If site
characterization suggests that impacted surface waterbodies are used as direct drinking water sources
(see Section 4.1.2), the fisher family receives its water from a surface waterbody. The Fisher grows
fruits and vegetables for home consumption (NC DEEINR 1997). The Fisher harvests enough fish from
waterbodies in the study area impacted by facility emissions to supply the family with a significant
portion of their protein. Further discussion of these exposure pathways, including numeric equations,
parameters values, and COPC specific inputs, can be found in Chapter 5 and Appendices A, B, and C.
We recommend evaluating the exposure of an infant to PCDDs, PCDFs, and dioxin-like PCBs via the
ingestion of breast milk as an additional exposure pathway, separately from this exposure scenario (see
Chapter 2).
4.2.6 Fisher Child
The Fisher Child exposure scenario is made up of the exposure pathways through which a child receptor
may be exposed in an urban or nonfarm rural setting where fish is the main source of protein in the
receptor diet. Evaluating this exposure scenario is the same as the adult/child pairings recommended for
the Farmer and Resident scenarios. In addition, Agency policy recommends consistently and explicitly
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evaluating environmental health risks to infants and children in all risk assessments (U.S. EPA 1995J).
As indicated in Table 4-1, the scenario assumes the Fisher Child is exposed to COPCs emitted from the
facility through the same exposure pathways as the Fisher. The primary differences between the Fisher
and Fisher Child are in exposure duration (6 years for child vs. 30 years for the adult), and consumption
rates (e.g. 1.2 homegrown produce servings per week for the child vs. 2.3 homegrown produce servings
per week for the adult, see Table 6-1).
4.2.7 Acute Receptor Scenario
In addition to long-term chronic effects evaluated in the other recommended exposure scenarios, we
generally recommend evaluating the acute exposure scenario. The acute receptor scenario accounts for
short-term effects of exposure to maximum 1-hour concentrations of COPCs in emissions from the
facility (see Chapter 3) through direct inhalation of vapors and particles (see Table 4-1 and Chapter 7).
A receptor could be exposed in an urban or rural setting where human activity or land use supports any of
the recommended exposure scenarios. The receptor could also be exposed in commercial and industrial
land use areas (excluding workers from the facility) not typically covered by the other recommended
exposure scenarios. As mentioned in Section 4.2 above, we assume that workers from the facility being
assessed in the risk assessment are protected by OSHA programs, and therefore aren't generally included
in hazardous waste combustion risk assessments.
We discuss further this recommended exposure scenario and associated exposure pathway, including
numeric equations, parameters values, and COPC-specific inputs, in Chapter 7 and Appendices A, B, and
C.
4.3 SELECTING EXPOSURE SCENARIO LOCATIONS
Exposure scenario locations are the physical places within the study area selected for evaluating one or
more of the recommended exposure scenarios. We generally recommend choosing exposure scenario
locations based on COPC air concentrations and deposition rates from ISCST3 (see Chapter 3) specific
to land use areas defined during exposure setting characterization (see Section 4.1). Location-specific air
concentrations and deposition rates are then used as inputs to the equations which estimate media
concentrations.
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We would like to emphasize that the method and resulting selection of exposure scenario locations is one
of the most critical steps of the risk assessment process, with huge impacts on standardization across all
facilities evaluated, and reproducibility of results. This is, at least partly, because ISCST3-modeled air
parameter values (and the resulting media concentration estimates) can vary significantly, even within
individual land use areas.
To ensure consistent and reproducible risk assessments, we recommend using the following procedures
to select your exposure scenario locations. These procedures also reduce the chances that the location(s)
you select to evaluate a land use area overlook locations within that same land use area that would result
in higher risk estimates. This can be important given the complexity of multiple modeled air parameters
and phases per location, possibly multiple facility emission sources, each with multiple source-specific
COPCs. This approach also provides a more complete risk evaluation of areas surrounding the facility.
This information often becomes relevant later in the permitting process and in risk communication to the
surrounding public.
As detailed in Chapter 3, ISCST3 estimates COPC concentrations in the air above, and deposition rates
onto, specific locations (i.e. receptor grid nodes) around a central point (e.g. combustor facility stack). If
all the locations modeled by ISCST3 are viewed as a group, they form a grid of horizontal and vertical
lines on a map, with each location a node, or intersection between vertical and horizontal lines; hence the
name "grid nodes" for modeled locations. Also, Section 4.1 of this chapter explained the steps and issues
involved in characterizing the various uses of the land in the study area. Figure 4-1 is a graphic
representation of these two sets of information, and demonstrates some of the relationship between them.
For example, a single land use area can have multiple grid nodes associated with it, each node with its
own air concentration and deposition levels. Choosing exposure scenario location(s) for a land use area
is a matter of choosing which grid node(s) will provide the data used to generate media concentrations
used in the exposure scenario. We recommend the following steps:
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FIGURE 4-1
ISCST3 GRID NODES AND LAND USE DESIGNATIONS
\
7: Define Land Use Areas To Evaluate - To avoid confusion and misidentification, land
use areas, water bodies, and watersheds identified during the exposure setting characterization
step, are best defined and mapped using UTM coordinates in a format consistent with that used
to define locations of facility emission sources and the ISCST3 receptor grid nodes. Formats
include NAD27 orNAD83 UTM.
Step 2: Identify Receptor Grid Node(s) Within Each Defined Land Use Area - For each defined
land use area, identify the receptor grid nodes within or on the boundary of that area (defined in
Step 1) that represent the location of highest yearly average concentration for each ISCST3 air
parameter output (i.e., air concentration, dry deposition, wet deposition) for each phase
(i.e., vapor, particle, particle-bound). We recommend choosing concentrations specific to each
facility emission source (e.g., stacks, fugitives), as well as all emission sources at the facility
combined. This results in selecting one or more receptor grid nodes (and therefore the exposure
scenario locations for that land use area), with the following attributes:
• Highest vapor phase air concentration
• Highest vapor phase dry deposition rate
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• Highest vapor phase wet deposition rate
• Highest particle phase air concentration
• Highest particle phase wet deposition rate
• Highest particle phase dry deposition rate
• Highest particle-bound phase air concentration
• Highest particle-bound phase wet deposition rate
• Highest particle-bound phase dry deposition rate
With the exception of water bodies and watersheds (discussed in Step 4 below), we recommend
using only air parameters for a single receptor grid node as inputs into the media equations for
each exposure scenario location. We also recommend using actual parameter values, without
averaging or other statistical manipulation. However, based generally on the number and
location of facility emission sources, you might select multiple exposure scenario locations for a
specific land use area.
U.S. EPA Region 6 applied these criteria to actual sites, using actual modeled air parameters, and
found that only 1 to 3 receptor grid nodes were typically selected per land use area. This was
because, in most cases, the highest air concentration and deposition rate occurred at the same
receptor grid node.
Please note: while these criteria tend to minimize the chances of overlooking maximum
risk within a land use area, they do not preclude you from selecting additional exposure
scenario locations within that same land use area based on site-specific risk
considerations (see Step 3 below).
Step 3: Identify Receptor Grid Nodes For Acute Risk and Site-Specific Risk Considerations -
In addition to the receptor grid nodes selected in Steps 1 and 2 above, you might consider
additional receptor grid nodes to evaluate acute risk or site-specific risk considerations (e.g.,
special populations).
To evaluate a land use area (including commercial and industrial land use areas) for acute risk,
choose location(s) from receptor grid nodes with the highest modeled hourly vapor phase air
concentration and highest hourly particle phase air concentration (see Chapter 3) specific to each
emission source, as well as all emission sources combined. For site-specific risk considerations,
we recommend considering the receptor grid node closest to the exposure point being evaluated
(e.g. school, hospital). However, in some cases, a more protective approach might select the
closest receptor grid node or nodes with the highest modeled air parameter values.
Step 4: Identify Receptor Grid Nodes For Water Bodies and Watersheds - For recommended
exposure scenarios that include evaluating water bodies and their associated watersheds, we
recommend considering the receptor grid nodes within their area extent or "effective" areas
(defined and mapped in Step 1). For water bodies, you could select the receptor grid node with
the highest modeled air parameter values. You could also average the air parameter values for all
receptor grid nodes within the area of the water body. For watersheds, you could average the
modeled air parameter values of all receptor grid nodes within the drainage basin (excluding the
area of the water body). Media concentration equations for water bodies and watersheds need
the same air parameter values as found in Step 2 above; yearly averages for each ISCST3
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modeled air parameter (e.g., air concentration, dry deposition, wet deposition) for each phase
(e.g., vapor, particle, particle-bound); specific to each emission source (e.g., stacks, fugitives) as
well as all emission sources at the facility combined.
For evaluating potential exposure routes other than ingestion of fish, we consider it reasonable to assume
that the Fisher and Fisher Child reside at the same exposure scenario locations as the Resident scenario.
You can similarly assume that the Fisher and Fisher Child exposure scenarios are exposed through
ingestion of fish from the water body with the highest modeled combined deposition, that can or does
support fish populations. As a result of some site specific conditions, it may be appropriate to evaluate
the Fisher and Fisher Child assuming exposure through ingestion of fish calculated using COPC water
concentrations from one water body, and exposure from ingestion of drinking water calculated using
COPC water concentrations from a different water body.
To reiterate, we recommend initially evaluating current and reasonable potential future land use areas
defined during the exposure setting characterization, using the most representative recommended
exposure scenario(s), at actual receptor grid nodes selected using the four-step process explained above.
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Chapter 5
Estimating Media Concentrations
What's Covered in Chapter 5:
5.1 Calculating COPC Concentrations in Air for Direct Inhalation
5.2 Calculating COPC Concentrations in Soil
5.3 Calculating COPC Concentrations in Produce
5.4 Calculating COPC Concentrations in Beef and Dairy Products
5.5 Calculating COPC Concentrations in Pork
5.6 Calculating COPC Concentrations in Chicken and Eggs
5.7 Calculating COPC Concentrations in Drinking Water and Fish
5.8 Using Site-Specific vs. Default Parameter Values
The purpose of this chapter is to describe the equations (and associated parameters) for estimating media
concentration that we recommend using to evaluate the exposure scenarios presented in Chapter 4. In
most cases, we include the origin and development of each of these equations, and describe the associated
parameters. We also present the equations in Appendix B in a more condensed form (i.e. without
derivation), and organize them according to exposure pathway. Discussions of ISCSTS-modeled unitized
air parameters are presented in Chapter 3. Appendix B also includes equations for modeling phase
allocation and speciation of mercury concentrations. Appendix A-2 lists compound-specific parameters
the equations need to estimate media concentrations, as well as our recommended hierarchies of sources.
The HHRAP companion database provides recommended values for compound-specific parameters.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak in this chapter is the performer of a risk assessment: the person (or
persons) who will actually put the recommended methods into practice.
Section 5.1 describes the equations that estimate air concentrations for evaluating direct inhalation of
COPCs. Section 5.2 describes equations for estimating COPC concentrations in soils. Section 5.3
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describes equations for estimating COPC concentrations in produce. Sections 5.4 through 5.6 describe
equations for estimating COPC concentrations in animal products (such as milk, beef, pork, poultry, and
eggs) resulting from animals ingesting contaminated feed and soil. Section 5.7 describes equations for
estimating COPC concentrations in fish through bioaccumulation (or, for some compounds,
bioconcentration) from the water column, dissolved water concentration, or bed sediment—depending on
the COPC.
Please Note, references made throughout Chapter 5 to particle phase are generic and made
without distinction between particle and particle-bound.
5.1
CALCULATING COPC CONCENTRATIONS IN AIR FOR DIRECT INHALATION
recommend calculating COPC concentrations in air by summing the vapor phase and particle
phase air concentrations of COPCs. To evaluate long-term or chronic exposure via direct
inhalation, we generally recommend using unitized yearly air parameter values to calculate air
concentrations, as specified in Appendix B, Table B-5-1. To evaluate short-term or acute exposure via
direct inhalation, we recommend using unitized hourly air parameter values to calculate air
concentrations, as specified in Appendix B, Table B-6-1.
Figure 5-1 - COPC Concentration in Air for Direct
Inhalation
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5.2 CALCULATING COPC CONCENTRATIONS IN SOIL
recommend estimating COPC concentrations in soil by summing the vapor phase and
•'. •;:' particle phase deposition of COPCs to the soil. We generally recommend considering wet and
dry deposition of particles and vapors. Calculate dry deposition of vapors from the vapor air
concentration and the dry deposition velocity. We consider it appropriate for soil concentration
calculations to account for loss of COPCs by several mechanisms, including leaching, erosion, runoff,
degradation (biotic and abiotic), and volatilization. These loss mechanisms all lower the soil
concentration associated with the deposition rate. We present our recommended equations for calculating
soil concentration and soil losses of COPCs in Appendix B, Tables B-l for land use areas, and Tables B-4
for watersheds (see Section 5.7).
FIGURE 5-2 -
COPC CONCENTRATION IN SOIL
Leaching,
Erosion,
and Runoff
COPC
Soil Losses
^
COPC Concentration
in Soil
Degradation
(Biotic + Abiotic)
and Volatilization
Soil concentrations might require many years to reach steady state. As a result, the equations we suggest
to calculate the average soil concentration over the period of deposition were derived by integrating the
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instantaneous soil concentration equation over the period of deposition. For carcinogenic COPCs, we
recommend using two variations of the equation (average soil concentration over exposure duration):
1. one variation if the exposure duration (T2) is greater than or equal to the operating
lifetime of the emission source or time period of combustion, and
2. the other form if the exposure duration is less than the operating lifetime of the emission
source or time period of combustion.
For noncarcinogenic COPCs, we recommend using the second form of the carcinogenic equation. This
equation calculates the highest annual average COPC soil concentration occurring during the exposure
duration. We describe these equations in more detail in Section 5.2.1.
Soil conditions—such as pH, structure, organic matter content, and moisture content—affect the
distribution and mobility of COPCs. Modeling the loss of COPCs from soil uses rates specific to the
physical and chemical characteristics of the soil. We describe these variables and their use in the
following subsections, along with the recommended equations.
5.2.1 Calculating Cumulative Soil Concentration (Cs)
U.S. EPA (1990e) recommended using Equation 5-1—adapted from Travis, et al. (1983)—to calculate
cumulative soil concentration:
r = 100 • (Dydp + Dywv) • [1.0 - exp (-ks-tD )]
Z-BD-ks Equation 5-1
S
where
Cs = Average soil concentration over exposure duration (mg COPC/kg soil)
100 = Units conversion factor (mg-m2/kg-cm2)
Dydp — Unitized yearly dry deposition from particle phase (s/m2-yr)
Dywv = Unitized yearly wet deposition from vapor phase (s/m2-yr)
ks = COPC soil loss constant due to all processes (yr ')
tD — Time period over which deposition occurs (time period of combustion)
(yr)
Zs = Soil mixing zone depth (cm)
BD — Soil bulk density (g soil/cm3 soil)
U.S. EPA (1993 f) stated that Equation 5-1 evaluated deposition of particle phase COPCs, but failed to
consider vapor phase deposition or diffusion. To account for vapor phase diffusion, U.S. EPA (1998c)
recommended using the following equation:
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100 • (Dydp + Dywv + Ldif) • [1.0 - exp (- ks • tD)]
~—TTT—; Equation 5-1A
Zs-BD-ks 4
where
Cs — Average soil concentration over exposure duration (mg COPC/kg soil)
100 = Units conversion factor (mg-m2/kg-cm2)
Dydp — Unitized yearly dry deposition from particle phase (s/m2-yr)
Dywv = Unitized yearly wet deposition from vapor phase (s/m2-yr)
Ldy = Dry vapor phase diffusion load to soil (g/m2-yr)
ks — COPC soil loss constant due to all processes (yr ')
tD = Time period over which deposition occurs (time period of combustion)
(yr)
Zs — Soil mixing zone depth (cm)
BD = Soil bulk density (g soil/cm3 soil)
Other guidance (U.S. EPA 1994g) recommended the original Equation 5-1, but only for calculating Cs for
2,3,7,8-TCDD. U.S. EPA (1994g) also recommended setting the COPC soil loss constant (ks) equal to 0
for all other COPCs. For COPCs other than 2,3,7,8-TCDD, U.S. EPA (1994g) recommended Equation 5-
1B—which eliminates the COPC soil loss constant:
r> - mn Dyd + Dyw n
C|S ~ 1UU'—„ __—'tL} Equation 5-1B
S
where
Cs = Average soil concentration over exposure duration (mg COPC/kg soil)
100 = Units conversion factor (mg-m2/kg-cm2)
Dyd = Yearly dry deposition rate of pollutant (g/m2-yr)
Dyw = Yearly wet deposition rate of pollutant (g/m2-yr)
tD — Time period over which deposition occurs (time period of combustion)
(yr)
Zs = Soil mixing zone depth (cm)
BD — Soil bulk density (g soil/cm3 soil)
More recent guidance documents—U.S. EPA (1994r) and NC DEHNR(1997)—recommended using two
different equations (Equations 5-1C and 5-1D) with carcinogenic COPCs. Equation 5-1C was
recommended for T2 < tD and Equation 5-1D was recommended for T,< tD < T2. For noncarcinogenic
COPCs, Equation 5-1E was recommended.
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We recommend using Equations 5-1C, 5-1D, and 5-1E to calculate Cs. Appendix B, Table B-l-1
discusses further how to use these equations.
Recommended Equations for Calculating:
Cumulative Soil Concentration (Cs)
Carcinogens:
For T2 < tD
Cs =
Ds
ks-(tD- Tj
+ exp
-\T
'
Equation 5-1C
For T, < tD < T2
Ds • tD - Cs
(
Cs =
tn
Cs
' (1 - exp [- fe • (T2 - tD)])
Equation 5-ID
Noncarcinogens:
= Ds-[l - exp(-ks-tD)]
ks
Equation 5-IE
where
Cs
Ds
ks
tD
T-,
Average soil concentration over exposure duration (mg COPC/kg soil)
Deposition term (mg COPC/kg soil/yr)
COPC soil loss constant due to all processes (yr ')
Time period over which deposition occurs (time period of combustion) (yr)
Time period at the beginning of combustion (yr)
Soil concentration at time tD (mg/kg)
Length of exposure duration (yr)
We discuss the deposition term further in this Section, as well as Section 5.2.3. Section 5.2.2 discusses
the COPC-specific soil loss constant (ks). Chapter 2 discusses how the period of time at the beginning of
combustion (Tt) relates to characterizing site conditions immediately preceding the study period. Chapter
2 also addresses the time period during which burning - and therefore deposition - occurs (tD), as it
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relates to setting emission rates. Chapter 3 addresses air dispersion modeling aspects of tD. Chapter 6
further discusses how the duration of exposure (T2) relates to characterizing exposure.
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-1C when you model
an exposure duration that is less than or equal to the operating lifetime of the emission source or
hazardous waste combustor (T2 < tD). We recommend using Equation 5-ID when you model an
exposure duration greater than the operating lifetime of the hazardous waste combustor (T, < tD < T2).
For noncarcinogenic COPCs, we recommend Equation 5-IE.
We generally recommend using the COPC soil concentration averaged over the exposure duration
(represented by Cs) for carcinogenic compounds. Carcinogenic risk is averaged over the lifetime of an
individual. Because the hazard quotient associated with noncarcinogenic COPCs is based on a threshold
dose rather than a lifetime exposure, we recommend using the highest annual average COPC soil
concentration (Cs^) occurring during the exposure duration period for noncarcinogenic COPCs. CstD
typically occurs at the end of the operating life of the emission source or the time period of combustion.
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using the highest 1-year annual average
soil concentration, determined using Equation 5-1E, to evaluate risk from noncarcinogenic COPCs (see
Chapter 7).
5.2.2 Calculating the COPC Soil Loss Constant (ks)
Organic and inorganic COPCs can be lost from the soil by several processes that may or may not occur
simultaneously. The rate at which a COPC is lost from the soil is known as the soil loss constant (ks).
We recommend determining ks by using the soil's physical, chemical, and biological characteristics, to
estimate the COPC-specific loss resulting from:
(1) leaching,
(2) runoff,
(3) erosion,
(4) biotic and abiotic degradation, and
(5) volatilization.
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U.S. EPA (1990e) recommended Equation 5-2 to calculate ks:
ks = ksl + ksg + ksv Equation 5-2
where
ks = COPC soil loss constant due to all processes (yr ')
ksl — COPC loss constant due to leaching (yr ')
ksg = COPC loss constant due to biotic and abiotic degradation (yr')
ksv — COPC loss constant due to volatilization (yr')
We recommend using Equation 5-2A to calculate ks. We describe this equation further in Appendix B,
Table B-l-2. Using Equation 5-2A is consistent with U.S. EPA (1994g), U.S. EPA (1994r), U.S. EPA
(1998c) and NC DEHNR (1997).
Recommended Equation for Calculating:
COPC Soil Loss Constant (ks)
ks = ksg + kse + ksr + ksl + ksv Equation 5-2A
where
ks — COPC soil loss constant due to all processes (yr')
ksg = COPC loss constant due to biotic and abiotic degradation (yr ')
kse — COPC loss constant due to soil erosion (yr ')
ksr = COPC loss constant due to surface runoff (yr ')
ksl — COPC loss constant due to leaching (yr ')
ksv = COPC loss constant due to volatilization (yr')
Section 5.2.2.1 discusses loss due to biotic and abiotic degradation (ksg). Section 5.2.2.2 discusses loss
due to erosion (kse). Section 5.2.2.3 discusses loss due to surface runoff (ksr). Section 5.2.2.4 discusses
Loss due to leaching (ksl). Section 5.2.2.5 discusses loss due to volatilization (ksv).
As highlighted in Section 5.2.1, using Equation 5-2A in Equations 5-1C and 5-1D assumes that you can
define COPC loss using first-order reaction kinetics. First-order reaction rates depend on the
concentration of one reactant (Bohn et al. 1985). The loss of a COPC by a first-order process depends
only on the concentration of the COPC in the soil, and a constant fraction of the COPC is removed from
the soil over time. Those processes that apparently exhibit first-order reaction kinetics without implying a
mechanistic dependence on a first-order loss rate are termed "apparent first-order" loss rates (Sparks
1989). The assumption that COPC loss follows first-order reaction kinetics may be an oversimplification
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because—at various concentrations or under various environmental conditions—the loss rates from soil
systems will resemble different kinetic expressions. However, at low concentrations, a first-order loss
constant may be adequate to describe the loss of the CO PC from soil (U.S. EPA 1998c).
CO PC loss in soil can also follow zero or second-order reaction kinetics. Zero-order reaction kinetics are
independent of reactant concentrations (Bohn et al. 1985). Zero-order loss rates describe processes in
which the reactants are present at very high concentrations. Under zero-order kinetics, a constant amount
of a COPC is lost from the soil over time, independent of its concentration. Processes that follow
second-order reaction kinetics depend on the concentrations of two reactants or the concentration of one
reactant squared (Bohn et al. 1985). The loss constant of a COPC following a second-order process can
be contingent on its own concentration, or on both its concentration and the concentration of another
reactant, such as an enzyme or catalyst.
Because COPC loss from soil depends on many complex factors, it may be difficult to model the overall
rate of loss. In addition, because the physical phenomena that cause COPC loss can occur
simultaneously, using Equation 5-2A might also overestimate loss rates for each process (Valentine
1986). We recommend, when possible, taking into account the common occurrence of all loss processes.
It's possible to derive combined rates of soil loss by these processes experimentally. U.S. EPA (1986c)
presents values for some COPCs.
5.2.2.1 COPC Loss Constant Due to Biotic and Abiotic Degradation (ksg)
Soil losses resulting from biotic and abiotic degradation (ksg) are determined empirically from field
studies and should be available in the literature (U.S. EPA 1998c). According to Lyman et al. (1982), it's
reasonable to assume that degradation rates follow first order kinetics in a homogenous media. You're
therefore able to relate the half-life of a compound to the degradation rate constant. Ideally, ksg is the
sum of all biotic and abiotic rate constants in the soil media. Therefore, if the half-life of a compound (for
all of the mechanisms of transformation) is known, you can calculate the degradation rate. However,
literature sources don't provide sufficient data for all such mechanisms, especially for soil. Earlier
Agency guidance (U.S. EPA 1994g) recommended setting ksg for all COPCs other than 2,3,7,8-TCDD
equal to zero. The HHRAP companion database presents our recommended values for this COPC-
specific variable.
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Recommended Values for:
COPC Loss Constant Due to Biotic and Abiotic Degradation (ksg)
CO PC-Specific
(See the HHRAP companion database)
The rate of biological degradation in soils depends on the concentration and activity levels of the
microbial populations in the soil, the soil conditions, and the COPC concentration (Jury and Valentine
1986). First-order loss rates often fail to account for the high variability of these features in a single soil
system. However, using simple rate expressions may be appropriate at low chemical concentrations (e.g.,
nanogram per kilogram soil). A first-order dependence on chemical concentration may be reasonable at
low chemical concentrations. The rate of biological degradation is COPC-specific, and depends on the
complexity of the COPC and the usefulness of the COPC to the microorganisms. Some substances, rather
than being used by the organisms as a nutrient or energy source, are simply degraded with other similar
COPCs, which can be further utilized. Environmental- and COPC-specific factors that can limit the
biodegradation of COPCs in the soil environment (Valentine and Schnoor 1986) include:
availability of the COPC;
• nutrient limitations;
toxicity of the COPC; and
• inactivation or nonexistence of enzymes capable of degrading the COPC.
Chemical degradation of organic compounds can be a significant mechanism for removing COPCs from
soil (U.S. EPA 1998c). Hydrolysis and oxidation-reduction reactions are the primary chemical
transformation processes occurring in the upper layers of soils (Valentine 1986). General rate expressions
describing the transformation of some COPCs by all non-biological processes are available. These
expressions are helpful when division into component reactions isn't possible.
Hydrolysis in aqueous systems is characterized by three processes: acid-catalyzed, base-catalyzed, and
neutral reactions. The overall rate of hydrolysis is the sum of the first-order rates of these processes
(Valentine 1986). In soil systems, sorption of the COPC can increase, decrease, or not affect the rate of
hydrolysis, as numerous studies cited in Valentine (1986) have shown. We recommend predicting the
overall (i.e. total) rate of hydrolysis in soil by adding the rates in the soil and water phases. We
recommend assuming that these rates are first-order reactions at a fixed pH (Valentine 1986). Lyman et
al. (1982) describes methods for estimating these hydrolysis constants.
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Organic and inorganic compounds also undergo oxidation-reduction (redox) reactions in the soil
(Valentine 1986). Organic redox reactions involve the reacting molecules exchanging oxygen and
hydrogen atoms. Inorganic redox reactions may involve the reactants exchanging atoms or electrons. In
soil systems where the identities of oxidant and reductant species aren't known, you can acquire a
first-order rate constant for describing loss by redox reactions (Valentine 1986). Redox reactions
involving metals may promote losses from surface soils by making metals more mobile (e.g., leaching to
subsurface soils).
5.2.2.2 COPC Loss Constant Due to Soil Erosion (kse)
U.S. EPA (1998c) recommended using Equation 5-3 to calculate the constant for soil loss resulting from
erosion (kse).
O.l-X -SD-ER Kd -BD
' Equatlon "
where
kse = COPC soil loss constant due to soil erosion
0.1 = Units conversion factor (1,000 g-kg/10, 000 cm2-m2)
Xe = Unit soil loss (kg/m2-yr)
SD = Sediment delivery ratio (unitless)
ER — Soil enrichment ratio (unitless)
BD = Soil bulk density (g soil/cm3 soil)
Zs — Soil mixing zone depth (cm)
Kds — Soil/water partition coefficient (ml water/g soil)
Qsw = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
We recommend using the Universal Soil Loss Equation (USLE) to calculate unit soil loss (JQ(See
Section 5.7.2). We describe soil bulk density (BD) in Section 5.2.4.2. We describe Soil mixing depth
(Zs) in Section 5.2.4.1. We describe soil volumetric water content (6SW) in Section 5.2.4.4. We discuss
site-specific variables associated with Equation 5-3 further in Appendix B.
U.S. EPA(1994gand 1994r) recommended setting all kse values equal to zero. U.S. EPA (1994r)
recommended setting kse equal to zero because contaminated soil erodes both onto and off of the site.
As in U.S. EPA (1994g and 1994r), we recommend setting kse equal to zero.
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Recommended Value for:
COPC Loss Constant Due to Erosion (kse)
0
For additional information on addressing kse, we recommend consulting U.S. EPA (1998c). We also
further describe using kse values in Appendix B, Table B-l-3.
5.2.2.3 COPC Loss Constant Due to Runoff (ksr)
Earlier U.S. EPA guidance (1994g) recommended setting all ksr values equal to zero.
As in U.S. EPA (1994r; 1998c) and NC DEHNR (1997), we recommend using Equation 5-4 to calculate
ksr. We further discuss using Equation 5-4 in Appendix B, Table B-l-4.
Recommended Equation for Calculating:
COPC Loss Constant Due to Runoff (far)
ksr =
RO
1 +
Equation 5-4
where
ksr — COPC loss constant due to runoff (yr ')
RO = Average annual surface runoff from pervious areas (cm/yr)
6SW = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
Zs = Soil mixing zone depth (cm)
Kds — Soil/water partition coefficient (ml water/g soil)
BD = Soil bulk density (g soil/cm3 soil) =1.5 g/cm3
The average annual surface runoff from pervious surfaces (RO) is a site-specific water loss term discussed
in Section 5.2.4.3. Section 5.2.4.4 describes soil volumetric water content (6SW). Section 5.2.4.1 discusses
the depth of soil mixing (Zs). Appendix A-2 explains how we recommend calculating the COPC-specific
soil/water partition coefficient (Kds). Section 5.2.4.2 describes soil bulk density (BD).
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5.2.2.4 COPC Loss Constant Due to Leaching (ksl)
Losses of soil COPCs due to leaching (ksl) depend on the amount of water available to generate leachate
and soil properties such as bulk density, soil moisture, soil porosity, and soil sorption properties.
U.S. EPA (1990e) recommended using Equation 5-5 to calculate ksl.
E<"""'0"5-5
where
ksl = COPC loss constant due to leaching (yr ')
P — Average annual precipitation (cm/yr)
/ = Average annual irrigation (cm/yr)
Ev — Average annual evapotranspiration (cm/yr)
6SW = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
Zs = Soil mixing zone depth (cm)
Kds — Soil/water partition coefficient (ml water/g soil)
BD = Soil bulk density (g soil/cm3 soil)
U.S. EPA (1993f) determined that Equation 5-5 does not properly account for surface runoff. U.S. EPA
(1994g) recommended setting all ksl values to zero.
More recent guidance (U.S. EPA 1994r; 1998c; NC DEHNR 1997) have recommended using Equation 5-
5Ato calculate ksl. As with U.S. EPA (1994r),U.S. EPA (1998c), and NC DEHNR (1997), we
recommend using Equation 5-5A to account for runoff while calculating ksl. We further discuss the use
of this equation in Appendix B, Table B-l-5.
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Recommended Equation for Calculating:
COPC Loss Constant Due to Leaching (ksl)
P+ I- RO- Ev
where
ksl = COPC loss constant due to leaching (yr ')
P — Average annual precipitation (cm/yr)
/ = Average annual irrigation (cm/yr)
RO — Average annual surface runoff from pervious areas (cm/yr)
Ev = Average annual evapotranspiration (cm/yr)
Qsw = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
Zs = Soil mixing zone depth (cm)
BD — Soil bulk density (g soil/cm3 soil) = 1.5 g/cm3
Kds = Soil/water partition coefficient (cm3 water/g soil)
Appendix B describes how we suggest acquiring site-specific variables associated with Equation 5-5 A.
The average annual volume of water available to generate leachate is the mass balance of all water inputs
and outputs from the area under consideration (P + I - RO - Ev). These variables are described in
Section 5.2.4.3. Section 5.2.4.4 describes soil volumetric water content (6SW). Section 5.2.4.1 describes
the soil mixing depth (Zs). Section 5.2.4.2 soil bulk density (BD). Appendix A-2 describes how we
recommend calculating the COPC-specific soil/water partition coefficient (Kds).
5.2.2.5 COPC Loss Constant Due to Volatilization (ksv)
Semi-volatile and volatile COPCs emitted in high concentrations may become adsorbed to soil particles
and exhibit volatilization losses from soil. The loss of a COPC from the soil by volatilization depends on
the rate of movement of the COPC to the soil surface, the chemical vapor concentration at the soil
surface, and the rate at which vapor is carried away by the atmosphere (Jury 1986).
U.S. EPA (1990e; 1993f; 1998c) recommended using Equation 5-6 to calculate ksv.
ksv = Ke-Kt Equation 5-6
where
ksv = COPC loss constant due to volatilization (yr')
Ke — Equilibrium coefficient (s/cm-yr)
Kt = Gas phase mass transfer coefficient (cm/s)
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U.S. EPA (1990e; 1993f; 1998c) don't identify a reference for Equation 5-6. U.S. EPA (1993f) stated
that Equation 5-6 had been independently verified as accurately representing volatilization loss, but that
the equation for Kt (Equation 5-8) appeared to fitto data empirically. U.S. EPA (1993f) also stated that
ksv is modeled as a means of limiting soil concentration. Because this mass flux never experiences rain
out, or washout and subsequent re-deposit, soil COPC concentrations are underestimated for soluble
volatile CO PCs. U.S. EPA (1993f) further recommended that additional research be conducted to
determine the magnitude of the uncertainty introduced for volatile COPCs. U.S. EPA (1998c)
recommended not considering the volatilized residues of semi-volatile COPCs (such as dioxin). U.S.
EPA (1994g) recommended setting all ksv values to zero.
U.S. EPA guidance (1994r) and NC DEHNR (1997) recommended using Equation 5-6A to calculate ksv.
Equation 5-6A appears to incorporate equations that U.S. EPA (1990e) recommended for calculating Ke
(equilibrium coefficient) andKt (gas phase mass transfer coefficient).
ksv =
3.1536xlQ7-#
Zs'Kds-R-Ta-BD
0.482
Pa
P«'D«.
-0.67
*
>
-
4-A
it
-0.11^
/
Equation 5-6A
where
ksv
3.1536 x 107
H
Zs
Kds
R
Ta
BD
0.482
W
0.78
V-a
Pa
Da
-0.67
A
-0.11
COPC loss constant due to volatilization (yr ')
Units conversion factor (s/yr)
Henry's Law constant (atm-mVmol)
Soil mixing zone depth (cm)
Soil/water partition coefficient (cm3 water/g soil)
Universal gas constant (atm-m3/mol-K)
Ambient air temperature (K) = 298.1 K
Soil bulk density (g soil/cm3 soil)
Empirical constant (unitless) Units conversion factor
[(3600 s/hr)078(100 cm/m)/(3600 s/hr)] • (empirical constant
0.0292)
Average annual wind speed (m/s)
Empirical constant (unitless)
Viscosity of air (g/cm-s)
Density of air (g/cm3)
Diffusivity of COPC in air (cm2/s)
Empirical constant (unitless)
Surface area of contaminated area (m2)
Empirical constant (unitless)
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U.S. EPA (1990e) recommended using Equation 5-7 to calculate Ke and Equation 5-8 to calculate Kt.
Ke =
3.1536 x !Q7-(# x 1Q3)
Zs-Kds-R-Ta-BD
Equation 5-7
Kt = 0.482 •W0-n-Sca0-61'de-°-n
Equation 5-8
where
Ke = Equilibrium coefficient (s/cm-yr)
3.1536 x 107 = Units conversion factor (s/yr)
H = Henry's Law constant (atm-L/mol)
103 = Units conversion factor (L/m3)
Zs — Soil mixing zone depth (cm)
Kds = Soil/water partition coefficient (cm3 water/g soil)
R — Universal gas constant (atm-m3/mol-K)
Ta = Ambient air temperature (K) = 298.1 K
BD — Soil bulk density (g soil/cm3 soil)
Kt = Gas phase mass transfer coefficient (cm/s)
0.482 = Units conversion factor [(3600 s/hr)078(100 cm/m)/(3600 s/hr)] •
(empirical constant 0.0292)
W = Average annual wind speed (m/s)
Sca — Schmidt number for gas phase (unitless)
de — Effective diameter of contaminated media (m)
U.S. EPA (1990e) also recommended using Equation 5-9 to calculate the Schmidt number for gas phase
(Sca), and Equation 5-10 to calculate the effective diameter of contaminated media (d ).
Sca =
Equation 5-9
4-A
71
Equation 5-10
where
Pa
Schmidt number for gas phase (unitless)
Viscosity of air (g/cm-s)
Density of air (g/cm3)
Diffusivity of COPC in air (cmVs)
Effective diameter of contaminated media (m)
Surface area of contaminated area (m2)
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As in U.S. EPA (1998c), we recommend using Equation 5-7A to calculate ksv, in cases where high
concentrations of volatile organic compounds are expected to be present in the soil.
ksv =
Recommended Equation for Calculating:
COPC Loss Constant Due to Volatilization (ksv)
3.1536
Zs-Kds-R-Ta-BD
D
1 -
BD
soil I
Equation 5-7A
where
ksv
3.1536 x 107
H
Zs
Kds
R
Ta
BD
Da
Psoil
COPC loss constant due to volatilization (yr ')
Units conversion factor (s/yr)
Henry's Law constant (atm-m3/mol)
Soil mixing zone depth (cm)
Soil/water partition coefficient (ml/g)
Universal gas constant (atm-m3/mol-K)
Ambient air temperature (K) = 298.1 K
Soil bulk density (g soil/cm3 soil) =1.5 g/cm3
Diffusivity of COPC in air (cmVs)
Solids particle density (g/cm3) = 2.1 g/cm3
Soil volumetric water content (ml/cm3 soil) = 0.2 ml/cm3
Henry's Law constants are compound-specific, and we supply recommended values in the HHRAP
companion database. We describe the soil mixing depth (Zs) in. Appendix A-2 describes how we
recommend calculating the COPC-specific soil/water partition coefficient (Kds). The Universal gas
constant (R) and ambient air temperature (Ta) are discussed further in Appendix B, Table B-l-6. Soil
bulk density (BD) is described below, as well as in Section 5.2.4.2. Appendix A-2 discusses the
diffusivity of a COPC in air (Da). Solids particle density (psoil) is discussed in this Section, below. Soil
volumetric water content (6SW) is further described below, as well as in Section 5.2.4.4.
Equation 5-7A is based on gas equilibrium coefficients and gas phase mass transfer, and combines
Equations 5-7, 5-7B, and 5-7C. You can derive ksv by adapting the Hwang and Falco (1986) equation for
soil vapor phase diffusion, to obtain Equation 5-6, as previously reported by U.S. EPA (1990e). Based on
general soil properties, you can also write the gas-phase mass transfer coefficient, Kt, as follows (Hillel
1980; Miller and Gardiner 1998):
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V — a v
A-t ~ —~— Equation 5-7B
where
Kt = Gas phase mass transfer coefficient (cm/s)
Zs — Soil mixing zone depth (cm)
Da = Diffusivity of COPC in air (cm2/s)
6V = Soil void fraction (cmVcm3)
We describe Soil mixing depth (Zs) in Section 5.2.4.1. The soil void fraction (6V) is the volumetric
fraction of a soil that does not contain solids or water, and can be expressed as:
6V = 1 ( ) ~ ®SW Equation 5-7C
PS
where
6V = Soil void fraction (cm3/cm3)
BD = Soil bulk density (g/cm3) = 1.5 g/cm3
psoil = Solids particle density (g/cm3) = 2.7 g/cm3
Qsw = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
The expression containing bulk density (BD) divided by solids particle density (psoil) gives the volume of
soil occupied by pore space or voids (Miller and Gardiner 1998). Soil bulk density is affected by the soil
structure, such as looseness or compactness of the soil, and depends on the water and clay content of the
soil (Hillel 1980). A range for bulk density of 0.83 to 1.84 was originally cited in Hoffman and Baes
(1979). Blake and Hartge (1996) and Hillel (1980) both suggest that the mean density of solid particles is
about 2.7 g/cm3. We recommend a default soil bulk density of 1.5 g/cm3, based on a mean value for loam
soil from Carsel et al. (1988).
The soil water content (6SW) depends on both the available water and the soil structure of a particular soil.
Values for 6SW range from 0.03 to 0.40 ml/cm3 depending on soil type (Hoffman and Baes 1979). The
lower values are typical of sandy soils, which can't retain much water; the higher values are typical of
soils such as clay or loam soils which can retain water. If site-specific information isn't available, we
recommend a mid-point default value of 0.2 ml water/cm3 soil. However, since 6SW is unique for each soil
type, we highly recommend using site-specific information.
We discuss ksv further in Appendix B, Table B-l-6.
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5.2.3 Calculating the Deposition Term (Ds)
We recommend using Equation 5-11 to calculate the deposition term (Ds). This equation is further
described in Appendix B, Table B-l-1. Using Equation 5-11 to calculate Ds is consistent with U.S. EPA
(1994r) andNC DEHNR (1997), which both incorporate Ds into Equation 5-1C.
Recommended Equation for Calculating:
Deposition Term (Ds)
Ds
100 -Q
Zs-BD
• [F-(Dydv + Dywv) + (Dydp + Dywp) • (1 - FJ]
Equation 5-11
where
Ds
100
Q
Zs
BD
Fv
Dydv
Dywv
Dydp
Dywp
Deposition term (mg COPC/kg soil/yr)
Units conversion factor (mg-m2/kg-cm2)
COPC emission rate (g/s)
Soil mixing zone depth (cm)
Soil bulk density (g soil/cm3 soil) =1.5 g/cm3
Fraction of COPC air concentration in vapor phase (unitless)
Unitized yearly average dry deposition from vapor phase (s/m2-yr)
Unitized yearly average wet deposition from vapor phase (s/m2-yr)
Unitized yearly average dry deposition from particle phase (s/m2-yr)
Unitized yearly average wet deposition from particle phase (s/m2-yr)
Chapters 2 and 3 explain how we suggest quantifying the COPC emission rate (Q). Chapter 3 describes
generating modeled air parameters Cyv, Dydv, Dywv, Dydp, and Dywp. We describe the soil mixing
depth (Zs) in Section 5.2.4.1. Soil bulk density (BD) is described in Sections 5.2.2.5 and 5.2.4.2, as well
as Appendix B. Appendix A-2 describes how we suggest determining the COPC-specific parameter Fv.
5.2.4 Site-Specific Parameters for Calculating Cumulative Soil Concentration
Calculating Cs requires the following site-specific parameters:
• Soil mixing zone depth (Zs)
Soil bulk density (BD)
Available water (P + I - RO - Ev)
• Soil volumetric water content (6SW)
We discuss these parameters further in the following subsections, and in Appendix B.
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5.2.4.1 Soil Mixing Zone Depth (Zs)
When modeling exposures to COPCs in soils, the depth of contaminated soils is important in calculating
the appropriate soil concentration. Tilling might mix deposited COPCs deeper into the soil, whether
manually in a garden or mechanically in a large field. Increasing the volume of soil through which
COPCs are mixed will tend to decrease (i.e. dilute) concentrations. The value of Zs you choose may
affect the outcome of the risk assessment, because soil concentrations that are based on soil depth are
used to calculate exposure via several pathways:
• ingestion of plants contaminated by root uptake;
• direct ingestion of soil by humans, cattle, swine, or chickens; and
• surface runoff into water bodies.
For example, in calculations of exposures resulting from uptake through plant roots, the average
concentration of COPCs over the depth of the plant root determines plant uptake.
In general, U.S. EPA (1992d, 1998c) estimated that if the area under consideration is likely to be tilled,
soil depth is about 10 to 20 centimeters, depending on local conditions and the equipment used. If soil is
not moved, COPCs were assumed to be retained in the shallower, upper soil layer. In this case, earlier
Agency guidance (U.S. EPA 1990e; U.S. EPA 1998c) typically recommended a value of 1 centimeter.
U.S. EPA (1998c) recommended selecting Zs as follows:
Soil Depth (Zs)
1 cm
1 cm
20cm
20cm
Exposure
Direct ingestion of soil
Surface water runoff in
nonagricultural areas
Plant uptake for agricultural
soils
Surface water runoff in
agricultural areas
Description
Human exposure: in gardens, lawns, landscaped areas, parks, and
recreational areas.
Animal exposure: in pastures, lawns, and parks (untilled soils).
These areas are typically assumed to be untilled.
The root depth is assumed to equal the tilling depth of 20 centimeters.
In untilled soils, the root zone does not directly reflect tilling depth,
although it is assumed that tilling depth is an adequate substitute for
root zone depth.
These areas are typically assumed to be tilled.
We recommend the following values for Zs:
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Recommended Values for:
Soil Mixing Zone Depth (Zs)
2 cm - untilled
20 cm - tilled
We recommend a default Zs of 2 cm for estimating surface soil concentrations in untilled soils, based on a
study that profiled dioxin measurements within soil (Brzuzy et al. 1995). We recommend a default Zs of
20 cm for estimating surface soil concentrations in tilled soils, as in U.S. EPA (1998c).
5.2.4.2 Soil Dry Bulk Density (BD)
BD is the ratio of the mass of soil to its total volume. This variable is affected by the soil structure, type,
and moisture content (Hillel 1980).
U.S. EPA (1994r) recommended deriving wet soil bulk density by weighing a thin-walled, tube soil
sample (e.g., a Shelby tube) of known volume and subtracting the tube weight (ASTM Method D2937).
Moisture content can then be calculated (ASTM Method 2216) to convert wet soil bulk density to dry soil
bulk density.
As in U.S. EPA (1994g; 1998c) and presented in Hoffman and Baes (1979), we recommend the following
value for BD:
Recommended Value for:
Soil Dry Bulk Density (BD)
1.50g/cm3
5.2.4.3 Available Water (P + I - RO - Ev)
The average annual volume of water available (P + I - RO - Ev) for generating leachate is the mass
balance of all water inputs and outputs from the area under consideration. A wide range of values for
these site-specific parameters could apply in the various Agency regions.
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The average annual precipitation (/*), irrigation (7), runoff (RO), and evapotranspiration (Ev) rates and
other climatological data are available from either data recorded on site or from the Station Climatic
Summary for a nearby airport.
Meteorological variables—such as the evapotranspiration rate (Ev) and the runoff rate (RO)—might also
be found in resources such as Geraghty et al. (1973). You could also estimate surface runoff by using the
Curve Number Equation developed by the U.S. Soil Conservation Service (NC DEHNR 1997). U.S.
EPA (1985b) cited isopleths of mean annual cropland runoff corresponding to various curve numbers
developed by Stewart et al. (1975). Curve numbers were assigned to an area on the basis of soil type,
land use or cover, and the hydrologic conditions of the soil (NC DEHNR 1997).
The wide range of available values, however, demonstrates the uncertainties and limitations in our ability
to estimate these parameters. For example, Geraghty et al. (1973) presented isopleths for annual surface
water contributions that include interflow and ground water recharge. U.S. EPA (1994g) recommended
reducing these values by 50 percent, to represent surface runoff only.
5.2.4.4 Soil Volumetric Water Content (•£,)
The soil volumetric water content (• ^,) depends on the available water and the soil structure. A wide
range of values for these variables may apply in the various Agency regions. As in earlier guidance
documents, (U.S. EPA 1993i; U.S. EPA 1994g; NC DEHNR 1997), we recommend using a default value
of 0.2 ml/cm3 for • •.
Recommended Value for:
Soil Volumetric Water Content (••,,)
0.2 ml/cm3
5.3 CALCULATING COPC CONCENTRATIONS IN PRODUCE
Indirect exposure resulting from ingestion of produce depends on the total concentration of
COPCs in the leafy, fruit, and tuber portions of the plant. Because of general differences in
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contamination mechanisms, we recommend separating produce into two broad categories—aboveground
produce and belowground produce. In addition, aboveground produce can be further subdivided into
exposed and protected aboveground produce.
Aboveground Produce
Aboveground exposed produce is typically assumed to be contaminated by three possible mechanisms:
Direct deposition of particles—wet and dry deposition of particle phase COPCs on the
leaves and fruits of plants (Section 5.3.1).
Vapor transfer—uptake of vapor phase COPCs by plants through their foliage
(Section 5.3.2).
Root uptake—root uptake of COPCs available from the soil and their transfer to the
aboveground portions of the plant (Section 5.3.3).
FIGURE 5-3
COPC CONCENTRATION IN PRODUCE
Deposition
of Particles
(Section 5.3.1)
Vapor
Transfer
(Section 5.3.2)
Root Uptake
from Soil
(Section 5.3.3)
COPC Concentration in
Aboveground Produce
As in U.S.EPA (1998c), we recommend calculating the total COPC concentration in aboveground
exposed produce as a sum of contamination occurring through all three of these mechanisms. However,
edible portions of aboveground protected produce, such as peas, corn, and melons, are covered by a
protective covering. They are therefore protected from contamination from deposition and vapor transfer.
Root uptake of COPCs is the primary mechanism through which aboveground protected produce becomes
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contaminated (Section 5.3.3). Appendix B further describes the equations and parameters we recommend
to calculate COPC concentrations in exposed and protected aboveground produce.
Belowground Produce
For belowground produce, we recommend assuming contamination occurs only through one
mechanism—root uptake of COPCs available from soil (Section 5.3.3). The HHRAP doesn't address
contamination of belowground produce via direct deposition of particles and vapor transfer because we
assume that the root or tuber is protected from contact with contaminants in the vapor phase. Appendix B
further describes the equations and parameters we recommend to calculate COPC concentrations in
belowground produce.
Generally, we don't consider risks associated with exposure to VOCs via food-chain pathways
significant. This is primarily because VOCs are typically low-molecular-weight COPCs that do not
persist in the environment and do not bioaccumulate (U.S. EPA 1994r; U.S. EPA 1996g). However, as
discussed in Chapter 2, we recommend evaluating all COPCs, including VOCs, for each exposure
pathway.
5.3.1 Aboveground Produce Concentration Due to Direct Deposition (Pd)
9-
A
Some earlier guidance documents (U.S. EPA 1990e; 1998c) recommended using Equation 5-
13 to calculate COPC concentrations in aboveground vegetation resulting from wet and dry
deposition onto plant surfaces of leafy plants and exposed produce (Pd):
Equation 5-13
where
Pdt — Concentration of pollutant due to direct deposition in the rth plant group
(Hg COPC/g plant tissue DW))
1,000 = Units conversion factor (kg/103 g and 106 |o,g/g pollutant)
Dyd = Yearly dry deposition from particle phase (g/m2-yr)
Fw — Fraction of COPC wet deposition that adheres to plant surfaces (unitless)
Dywv = Yearly wet deposition from vapor phase (g/m2-yr)
Rpi — Interception fraction of the edible portion of plant tissue for the z'th plant
group (unitless)
kp — Plant surface loss coefficient (yr ')
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Tpi = Length of plant's exposure to deposition per harvest of the edible portion
of the rth plant group (yr)
Ypt = Yield or standing crop biomass of edible portion of the rth plant group
(kg DW/m2)
U.S. EPA (1994r) modified Equation 5-13 to include stack emissions adjusted to remove the fraction of
air concentration in vapor phase [Q (1 - Fv)] (Equation 5-14).
We recommend using Equation 5-14 to calculate Pd. We further discuss the use of this equation in
Appendix B, Table B-2-7.
Recommended Equation for Calculating:
Aboveground Produce Concentration Due to Direct Deposition (Pd)
1,000 • Q • (1 - Fv) • [Dydp + (Fw • Dywp)] - Rp • [1.0- exp(- kp • Tp)}
Pd= Equation 5-14
Yp-kp
where
Pd — Plant (aboveground produce) concentration due to direct (wet and dry)
deposition (mg COPC/kg DW)
1,000 = Units conversion factor (mg/g)
Q — COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Dydp — Unitized yearly average dry deposition from particle phase (s/m2-yr)
Fw = 0.2 for anions, 0.6 for cations & most organics (unitless)
Dywp — Unitized yearly wet deposition from particle phase (s/m2-yr)
Rp = Interception fraction of the edible portion of plant (unitless)
kp — Plant surface loss coefficient (yr ')
Tp — Length of plant exposure to deposition per harvest of the edible portion of the
rth plant group (yr)
Yp — Yield or standing crop biomass of the edible portion of the plant (productivity)
(kg DW/m2)
Chapters 2 and 3 explain how we recommend quantifying the COPC emission rate (Q). Appendix A-2
describes how we recommend determining the COPC-specific parameter Fv. Chapter 3 describes how the
modeled air parameters Dydp and Dywp are generated. Appendix B explains our recommendations for
Fw. Rp, kp, Tp, and Yp are neither site- nor COPC-specific, and are described in Sections 5.3.1.1 through
5.3.1.4.
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5.3.1.1 Interception Fraction of the Edible Portion of Plant (Rp)
U.S. EPA (1998c) stated thatNRC models assumed a constant of 0.2 for Rp for dry and wet deposition of
particles (Boone et al. 1981). However, Shor et al. (1982) suggested that diversity of plant growth
necessitated vegetation-specific Rp values.
As summarized in Baes et al. (1984), experimental studies of pasture grasses identified a correlation
between initial Rp values and productivity (standing crop biomass [Yp]) (Chamberlain 1970):
Rp = 1- ejYp Equation 5-14A
where
Rp = Interception fraction of the edible portion of plant (unitless)
y — Empirical constant (Chamberlain [1970] gives the range as 2.3 to 3.3 for
pasture grasses; Baes et al. [1984] used the midpoint, 2.88, for pasture
grasses.)
Yp = Standing crop biomass (productivity) (kg DW/m2 for silage; kg WW/m2
for exposed produce)
Baes et al. (1984) also developed methods for estimating Rp values for leafy vegetables, silage, and
exposed produce. However, these vegetation class-specific calculations produced Rp values that were
independent of productivity measurements. This independence led to potentially unreasonable estimates
of surface plant concentrations. Therefore, Baes et al. (1984) proposed using the same empirical
relationship developed by Chamberlain (1970) for other vegetation classes. Baes et al. (1984) developed
class-specific estimates of the empirical constant (y) by forcing an exponential regression equation
through several points. Points included average and theoretical maximum estimates of Rp and Yp. The
following class-specific empirical constants (y) were developed:
• Exposed produce = 0.0324
Leafy vegetables = 0.0846
Silage = 0.769
U.S. EPA (1994r) and U.S. EPA (1995e) proposed a default aboveground produce Rp value of 0.05,
based on a weighted average of class-specific Rp values. Specifically, class-specific Rp values were
calculated using the equation developed by Chamberlain (1970) and the following empirical constants:
• Leafy vegetables were assigned the same empirical constant (0.0846) developed by Baes
etal. (1984).
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• Fruits, fruiting vegetables, and legumes were assigned the empirical constant (0.0324)
originally developed by Baes et al. (1984) for "exposed produce."
Vegetables and fruits included in each class are as follows:
• Fruits—apple, apricot, berry, cherry, cranberry, grape, peach, pear, plum/prune, and
strawb erry
• Fruiting Vegetables—asparagus, cucumber, eggplant, sweet pepper, and tomato
• Legumes—snap beans
• Leafy Vegetables—broccoli, brussel sprouts, cauliflower, celery, lettuce, and spinach
The class-specific Rp values were then weighted by relative ingestion (by humans) of each class, to
determine a weighted average Rp value of 0.05. However, the produce classes and relative ingestion
values used by U.S. EPA (1994r) and U.S. EPA (1995e) to calculate and weightthe Rp values are not
current with the U.S. EPA 1997 Exposure Factors Handbook (U.S. EPA 1997b). In addition, the overall
Rp value presented in U.S. EPA (1994r; 1995e) was based on limited information; subsequent revision to
U.S. EPA(1994r; 1995e) resulted in an overall Rp value of 0.2 (RTI 1997).
For purposes of consistency, we combined the produce classes into two groups—exposed fruit and
exposed vegetables. We used the exposed produce empirical constant (y) to calculate Rp. Since the
exposed vegetable category includes leafy and fruiting vegetables, we calculated Rp for leafy and fruiting
vegetables. We then calculated the exposed vegetable Rp by a weighted average based on productivity
(Yp) of leafy and fruiting vegetables, respectively. The relative ingestion rates used to determine an
average weighted Rp value we derived from the intake of homegrown produce discussion presented in the
1997 Exposure Factors Handbook (U.S. EPA 1997b). We recommend using the weighted average Rp
value of 0.39 as a default Rp value, because it represents the most current parameters, including standing
crop biomass and relative ingestion rates.
Recommended Value for:
Interception Fraction of the Edible Portion of Plant (Rp)
0.39
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Unweighted Rp and ingestion rates used for the weighting are as follows:
Aboveground Produce Class
Exposed fruits
Exposed vegetables
Rp
0.053
0.982
Ingestion Rate (g DW/kg-day)
0.19
0.11
One of the primary uncertainties associated with this variable is whether the algorithm developed by
Chamberlain (1970) and the empirical constants developed by Baes et al. (1984) for use in this algorithm
accurately represent aboveground produce. Specifically, Chamberlain (1970) based his algorithm on
studies of pasture grass rather than aboveground produce. Baes et al. (1984) noted that their approach to
developing class-specific Rp values is "at best ad hoc, " but stated that this approach was justified,
because the consequences of using Rp estimates that are independent of productivity are "serious."
5.3.1.2 Plant Surface Loss Coefficient (kp)
U.S. EPA (1 998c) identified several processes — including wind removal, water removal, and growth
dilution — that reduce the amount of contaminant that has deposited on plant surfaces. The term kp is a
measure of the amount of contaminant that is lost to these physical processes over time. U.S. EPA
(1998c) cited Miller and Hoffman (1983) for the following equation:
_.,,
'365 Equation 5-15
where
kp — Plant surface loss coefficient (yr ')
tia = Half-life (days)
365 = Units conversion factor (days/yr)
Miller and Hoffman (1983) reported half-life values ranging from 2.8 to 34 days for a variety of
contaminants on herbaceous vegetation. These half-life values converted to kp values of 7.44 to
90.36 (yr"1). U.S. EPA (1994r; 1998c) recommended a kp value of 18, based on a generic 14-day half-life
corresponding to physical processes only. The 14-day half- life is approximately the midpoint of the
range (2.8 to 34 days) estimated by Miller and Hoffman (1983).
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Lacking experimental data supporting chemical- and/or site-specific values, we recommend using a
default kp value of 18. This kp value is the midpoint of Miller and Hoffman's (1983) range of values.
Based on this range (7.44 to 90.36), plant concentrations could range from about 1.8 times higher to about
48 times lower than the plant concentrations, based on a kp value of 1 8. If chemical- or site-specific data
is available, you could also calculate site- and chemical-specific kp values using the equation in Miller
and Hoffman (1983).
Recommended Value for:
Plant Surface Loss Coefficient (kp)
ISyr1
The primary uncertainty associated with kp relates to its position as the sole surface loss term in Equation
5-14. As defined by Miller and Hoffman (1983) and U.S. EPA (1998c), kp only represents potential
losses from the physical processes listed above, not all potential losses (e.g. chemical degradation).
However, information regarding chemical degradation of contaminants on plant surfaces is limited.
Including chemical degradation processes would decrease half-life values and thereby increase kp values.
Note that effective plant concentration decreases as kp increases. Therefore, using a kp value that does
not consider chemical degradation processes is protective.
In addition, there are uncertainties associated with the half-life values reported by Miller and Hoffman
(1983) with regard to how accurately these values represent the behavior of risk assessment COPCs on
aboveground produce. However, the relative impact of this second uncertainty is less than the omission
of chemical degradation processes.
5.3.1.3 Length of Plant Exposure to Deposition per Harvest of Edible Portion of Plant (Tp)
U.S. EPA (1990e; 1993f; 1994r; 1998c), and NC DEHNR (1997) recommended treating Tp as a constant,
based on the average period between successive hay harvests. Belcher and Travis (1989) estimated this
period at 60 days (0.164 years), which represents the length of time that aboveground vegetation (in this
case, hay) is exposed to contaminant deposition before being harvested. Calculate Tp as follows:
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„, 60 days ~ 1/:,
TP = -.^ 7 / = °-164J"" Equation 5-16
365 dayslyr
where
Tp = Length of plant exposure to deposition per harvest of the edible portion
of plant (yr)
60 = Average period between successive hay harvests (days)
365 = Units conversion factor (days/yr)
As in previous guidance, we recommend using a Tp value of 0.164 year.
Recommended Value for:
Length of Plant Exposure to Deposition per Harvest of Edible Portion of Plant (Tp)
0.164 years
The primary uncertainty associated with using this value is that it is based on the growing season for hay
rather than aboveground produce. The average period between successive hay harvests (60 days) may not
reflect the length of the growing season or the period between successive harvests for aboveground
produce at specific sites. To the extent that information documenting the growing season or period
between successive harvests for aboveground produce is available, this information could be appropriate
to estimate a site-specific Tp value. The greater the difference between site-specific Tp and our
recommended value, the greater the effect on plant concentration estimates.
5.3.1.4 Standing Crop Biomass (Productivity) (Yp)
U.S. EPA (1998c) recommended that the best estimate of Yp is productivity, which Baes et al. (1984) and
Shor et al. (1982) define as follows:
Yh
Equation 5-17
Aht
where
Yht — Harvest yield of the rth crop (kg DW)
Ah t = area planted to the rth crop (m2)
U.S. EPA (1994r) and NC DEHNR (1997) recommended using this equation and calculated a Yp value of
1.6 for aboveground produce, based on weighted average Yh and Ah values for four aboveground produce
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classes (fruits, fruiting vegetables, legumes, and leafy vegetables). Vegetables and fruits included in each
class were as follows:
• Fruits—apple, apricot, berry, cherry, cranberry, grape, peach, pear, plum/prune, and
strawb erry
• Fruiting Vegetables—asparagus, cucumber, eggplant, sweet pepper, and tomato
• Legumes—snap beans
• Leafy Vegetables—broccoli, brussel sprouts, cauliflower, celery, lettuce, and spinach.
Class-specific Yp values were estimated using U.S. average Yh and Ah values for a variety of fruits and
vegetables for 1993 (USDA 1994a; USD A 1994b). Yh values were converted to dry weight using
average class-specific conversion factors (Baes etal. 1984). U.S. EPA (1994r and 1995e) calculated
class-specific Yp values and then used relative ingestion rates of each group to calculate the weighted
average Yp value of 1.6. However, the produce classes and relative ingestion values used by U.S. EPA
(1994r and!995e) to calculate and weight the Yp values are not current with the U.S. EPA 1997 Exposure
Factors Handbook. In addition, overall Yp value presented in U.S. EPA (1994r and U.S. EPA 1995e)
was based on limited information; subsequent revision to U.S. EPA (1994r and 1995e) has resulted in an
overall Yp value of 1.7 (RTI 1997).
For consistency, we combined the produce classes into two groups—exposed fruit and exposed
vegetables. We derived the exposed vegetable Yp summing Yh values for leafy and fruiting vegetables
and dividing by the sum of Ah values for leafy and fruiting vegetables. We derived the relative ingestion
rates used to calculate an overall average weighted Yp value from the homegrown produce discussions
presented in the 1997 Exposure Factors Handbook (U.S. EPA 1997b). We recommend using the
weighted average Yp value of 2.24 as a default Yp value, because this value represents the most complete
and thorough information available.
Recommended Value for:
Standing Crop Biomass (Productivity) (Yp)
2.24
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Unweighted Yp and ingestion rates used for the weighting are as follows:
Aboveground Produce Class
Exposed fruits
Exposed vegetables
YP
0.25
5.66
Ingestion Rate (g DW/kg-day)
0.19
0.11
The primary uncertainty associated with this variable is that the harvest yield (Yh) and area planted (Ah)
may not reflect site-specific conditions. To the extent to which site-specific information is available, you
can estimate the magnitude of the uncertainty introduced by the default Yp value.
5.3.2 Aboveground Produce Concentration Due to Air-to-Plant Transfer (Pv)
The method we recommend for estimating COPC concentrations in exposed and aboveground
produce due to air-to-plant transfer (Pv) was developed with consideration of items which
might limit the transfer of COPC concentrations from plant surfaces to the inner portions of the
plant. These limitations result from mechanisms responsible for
• inhibiting the transfer of lipophilic COPCs (e.g., the shape of the produce); and
• removing COPCs from the edible portion of the produce (e.g., washing, peeling, and
cooking).
We recommend using Equation 5-18 to calculate Pv. We further discuss the use of this equation in
Appendix B, Table B-2-8.
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Recommended Equation for Calculating:
Aboveground Produce Concentration Due to Air-to-Plant Transfer (Pv)
Cyv -Bv -VG
Pv = Q-Fv--?- 5* 5£ EquationS-18
where
Pv = Concentration of CO PC in the plant resulting from air-to-plant transfer
(|ig COPC/g DW)
Q — COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Cyv — Unitized yearly average air concentration from vapor phase (|ig-s/g-m3)
fivag = COPC air-to-plant biotransfer factor ([mg COPC/g DW plant]/[mg COPC/g
air]) (unitless)
VGag = Empirical correction factor for aboveground produce (unitless)
pa = Density of air (g/m3)
Chapters 2 and 3 explain how we recommend quantifying the COPC emission rate (Q). Appendix A-2
describes how we recommend determining the COPC-specific parameters Fv and Bvag. Chapter 3
describes generating the modeled air parameter Cyv. As discussed below in Section 5.3.2.1, the
parameter VGag depends on the lipophilicity of the COPC. Appendix B further describes how we
recommend using Equation 5-18, including calculating pa.
5.3.2.1 Empirical Correction Factor for Aboveground Produce (VGag)
The parameter VGag was incorporated into Equation 5-18 to address the potential to overestimate the
transfer of lipophilic COPCs to the inner portions of bulky produce, such as apples. Because of the
protective outer skin, size, and shape of bulky produce, transfer of lipophilic COPCs (log Kow greater than
4) to the center of the produce is not as likely as for non-lipophilic COPCs. As a result, the inner portions
will be less affected.
To address this issue, U.S. EPA (1994m) recommended an empirical correction factor (VGag) of 0.01 for
lipophilic COPCs to reduce estimated vegetable concentrations. The factor of 0.01 is based on a similar
correction factor (VGrootveg) for below ground produce. VGrootveg was estimated for unspecified vegetables
as follows:
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'rootveg ~ ~Tr Equation 5-19
vegetable
where
VGrootveg = Correction factor for belowground produce (g/g)
MMn — Mass of a thin (skin) layer of belowground vegetable (g)
^-vegaaue = Mass of the entire vegetable (g)
Assuming that the density of the skin and the whole vegetable are the same, this equation becomes a ratio
of the volume of the skin to that of the whole vegetable. U.S. EPA (1994m) assumed that the vegetable
skin is 0.03 centimeters, which is the leaf thickness of a broad-leaf tree, as was used in experiments
conducted by Riederer (1990). Using this assumption, U.S. EPA (1994m) calculated VGrootveg values of
0.09 and 0.03 for carrots and potatoes, respectively.
Based on the work by Wipf et al. (1982), U.S. EPA (1994m) identified other processes—such as peeling,
cooking, and cleaning—that further reduce the vegetable concentration. U.S. EPA (1994m)
recommended a VGmotveg value of 0.01 for lipophilic COPCs. These are less than the estimates of 0.09 and
0.03 for the carrots and potatoes mentioned earlier, but greater than the estimate would be if the
correction factor was adjusted for cleaning, washing, and peeling, as described by Wipf et al. (1982).
Following this line of reasoning, U.S. EPA (1994m) recommended a lipophilic COPC VGag value of 0.01
for all aboveground produce except leafy vegetables. As with VGrootveg, U.S. EPA (1994m) noted that
assignment of this value is based on the consideration that it "should be less than estimated just based on
surface volume to whole fruit volume ratios."
U.S. EPA (1994m) recommended a lipophilic COPC VG of 1.0 for pasture grass because of a direct
analogy to exposed azalea and grass leaves (for which data were available). Pasture grass is described as
"leafy vegetation." However, the leafy vegetable group, as defined in Section 5.3.1.1, is composed of
bulkier produce such as broccoli, brussel sprouts, cauliflower, celery, lettuce, and spinach. In addition,
the outer leaves of most of the produce in this category are removed during preparation. Therefore, the
VGag value of 1.0 for leafy vegetables is inappropriate and may overestimate COPC concentrations. A
default lipophilic COPC VGag value of 0.01 for leafy vegetables is more appropriate for leafy vegetables,
because the leafy vegetable category represents bulkier, more protected plants as compared to single
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leaves of grass blades. U.S. EPA (1994r) and NC DEHNR (1997) recommended a lipophilic COPC VGag
value of 0.01, for all classes of aboveground produce.
For COPCs with a log Kow greater than 4, we recommend using a lipophilic VGag value of 0.01 for all
aboveground exposed produce. For COPCs with a log Kow less than 4, we recommend using a VGag value
of 1.0, because we assume these COPCs pass more easily through the skin of produce.
Recommended Values for:
Empirical Correction Factor for Aboveground Produce (VGag)
0.01 for COPCs with a log Kow greater than 4
1.0 for COPCs with a log Kow less than 4
Uncertainty may be introduced by assuming VGag values for leafy vegetables (such as lettuce) and for
legumes (such as snap beans). Assuming a VGag value of 0.01 for legumes and leafy vegetables may
underestimate concentrations because these species often have a higher ratio of surface area to mass than
other bulkier fruits and fruiting vegetables, such as tomatoes.
5.3.3 Produce Concentration Due to Root Uptake (Pr)
Root uptake of contaminants from soil may contribute to COPC concentrations in
aboveground exposed produce, aboveground protected produce, and belowground produce.
As in previous guidance (U.S. EPA 1994m; U.S. EPA 1994r; and U.S. EPA 1995e), we
recommend using Equations 5-20A and 5-20B to calculate Pr. We discuss the use of these
equations further in Appendix B.
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Recommended Equation for Calculating:
Produce Concentration Due to Root Uptake (Pr)
Exposed and protected aboveground produce:
Pr = Cs • Br Equation 5-20A
Belowground produce:
^ = S Kds • 1 kg/I"**8 Equation 5-20B
where
Pr — Concentration of COPC in produce due to root uptake (mg/kg)
Cs = Average soil concentration over exposure duration (mg COPC/kg soil)
Br — Plant-soil bioconcentration factor for produce (unitless)
RCF = Root concentration factor (unitless)
VGrootveg = Empirical correction factor for belowground produce (unitless)
Kds — Soil/water partition coefficient (L/kg)
Appendix B and Section 5.2 explain how we recommend calculating Cs. Appendix A-2 describes how
we recommend calculating the COPC-specific parameters Br, RCF, and Kds. Similar to VGag and as
discussed in Section 5.3.2.1, VGrootveg is based on the lipophilicity of the COPC.
Equation 5-20A is based on the soil-to-aboveground plant transfer approach developed by Travis and
Arms (1988). This approach is appropriate for evaluating exposed and protected aboveground produce;
however, it might not be appropriate for soil-to-belowground plant transfers. For belowground produce,
U.S. EPA (1994m) and U.S. EPA (1995e) recommended Equation 5-20B, which includes a root
concentration factor (RCF) developed by Briggs et al. (1982). RCF is the ratio of COPC concentration in
the edible root to the COPC concentration in the soil water. Since Briggs et al. (1982) conducted their
experiments in a growth solution, in order to use this equation you must divide the COPC soil
concentration (Cs) by the COPC-specific soil/water partition coefficient (Kds) (U.S. EPA 1994m).
As in U.S. EPA (1994m), we recommend using a VGrootveg value of 0.01 for lipophilic COPCs (log Kow
greater than 4) based on root vegetables like carrots and potatoes. A value of 0.01 appears to represent
the most complete and thorough information available. For COPCs with a log Kow less than 4, we
recommend a VGroot value of 1.0.
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Recommended Values for:
Empirical Correction Factor for Belowground Produce (VGrootve^
0.01 for COPCs with a log Kow greater than 4
1.0 for COPCs with a log Kow less than 4
5.4 CALCULATING COPC CONCENTRATIONS IN BEEF AND DAIRY PRODUCTS
We generally recommend that you estimate COPC concentrations in beef tissue and milk
products on the basis of the amount of COPCs that cattle are assumed to consume through
their diet. The HHRAP assumes the cattle's diet consists of:
• forage (primarily pasture grass and hay),
• silage (forage that has been stored and fermented), and
• grain.
Additional contamination may occur through the cattle ingesting soil. The HHRAP calculates the total
COPC concentration in the feed items (e.g., forage, silage, and grain) as a sum of contamination occurring
through the following mechanisms:
• Direct deposition of particles—wet and dry deposition of particle phase COPCs onto
forage and silage (Section 5.4.1).
• Vapor transfer—uptake of vapor phase COPCs by forage and silage through foliage
(Section 5.4.2).
• Root uptake—root uptake of COPCs available from the soil and their transfer to the
aboveground portions of forage, silage, and grain (Section 5.4.3).
Feed items consumed by animals can be classified as exposed or protected, depending on whether they
have a protective outer covering. Because the outer covering on protected feed acts as a barrier, we
assume that there is negligible contamination of protected feed through deposition of particles and vapor
transfer. In the HHRAP, grain is classified as protected feed. As a result, we recommend that you
assume grain contamination occurs only through root uptake. We also recommend assuming that
contamination of exposed feed items, including forage and silage, occurs through all three mechanisms.
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COPC Concentration
in Beef & Dairy Products
FIGURE 5-4
COPC CONCENTRATION IN BEEF AND DAIRY PRODUCTS The HHRAP assumes that
the amount of grain,
silage, forage, and soil
consumed varies between
dairy and beef cattle.
Sections 5.4.4 (beef) and
5.4.5 (dairy) describe the
methods we recommend
to estimate consumption
rates and subsequent
COPC concentrations in
cattle. As in previous
guidance (U.S. EPA
1990eandl994a;NC
DEHNR 1997), we
recommend assuming that 100 percent of the plant materials eaten by cattle were grown on soil
contaminated by emission sources. Therefore, we recommend assuming that 100 percent of the feed
items are contaminated.
Appendix B, Tables B-3-1 through B-3-11, describe how we recommend calculating (1) the COPC
concentrations in soil and feed items (forage, silage, and grain) consumed by beef and dairy cattle, and
(2) the resulting COPC concentrations in beef and milk.
5.4.1 Forage and Silage Concentrations Due to Direct Deposition (Pd)
..'.'•;'. COPC concentrations in forage and silage result from wet and dry deposition onto exposed
/ plant surfaces; similar to aboveground produce (Section 5.3.1). Therefore, we recommend
/' also using Equation 5-14 to calculate Pd for forage and silage. We discuss calculating Pd
for Forage and silage further in Appendix B. Appendix A-2 explains how we recommend
calculating COPC-specific Fv values for forage and silage (i.e. exactly as they are calculated for
aboveground produce). Sections 5.4.1.1 through 5.4.1.4 describe how we recommend calculating Rp, kp,
Tp, and Yp for use in calculating forage and silage concentrations.
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5.4.1.1 Interception Fraction of the Edible Portion of Plant (Rp)
As discussed in Section 5.3.1.1, Chamberlain (1970) found a correlation between Rp and productivity, Yp
(standing crop biomass). This correlation is expressed in Equation 5-14A.
Based on U.S. EPA (1994r and 1995b) and NC DEHNR (1997), we recommend using Equation 5-14 to
calculate Rp values for forage and silage.
Substituting the Baes et al. (1984) empirical constant (•) value of 2.88 for pasture grass, and the standing
crop biomass value of 0.24 kg DW/m2 (these variables are discussed in Section 5.3.1.1) into Equation 5-
14, the forage-specific Rp is 0.5. Substituting the Baes et al. (1984) empirical constant (•) value of 0.769
for silage, and the standing crop biomass value of 0.8 kg DW/m2 into Equation 5-14, the silage-specific
Rp value is 0.46.
Recommended Value for:
Interception Fraction of the Edible Portion of Plant (Rp)
Forage = 0.5
Silage = 0.46
Several uncertainties are associated with the Rp variable:
• The empirical relationship developed by Chamberlain (1970) is based on a study of
pasture grass, and therefore may not accurately represent site-specific silage types.
The empirical constant for silage developed by Baes et al. (1984) used in Chamberlain's
empirical relationship may also fail to accurately represent site-specific silage types.
• The range of empirical constants recommended by Baes et al. (1984) for pasture grass
does not result in a significant range of estimated Rp values for forage (the calculated Rp
range is 0.42 to 0.54). Therefore, using the empirical constant midpoint (2.88 for pasture
grass) does not significantly affect the Rp value and the resulting estimate of plant COPC
concentration.
5.4.1.2 Plant Surface Loss Coefficient (kp)
We recommend using Equation 5-15 (Section 5.3.1.2) to calculate the plant surface loss coefficient &p for
aboveground produce. The kp factor is derived in the same manner for cattle forage and silage. The
uncertainties of kp for cattle forage and silage are similar to the uncertainties for aboveground produce.
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5.4.1.3 Length of Plant Exposure to Deposition per Harvest of the Edible Portion of Plant (Tp)
As discussed in Section 5.3.1.3, the HHRAP treats I)? as a constant, based on the average period between
successive hay harvests. This period, which Belcher and Travis (1989) estimated at 60 days, represents
the length of time that aboveground vegetation (in this case, hay) would be exposed to particle deposition
before being harvested. We used Equation 5-16 (Section 5.3.1.3), to calculate a Tp of 0.16 year for cattle
silage.
For cattle forage, we modified Equation 5-16 to consider the average of :
1. the average period between successive hay harvests, and
2. the average period between successive grazing.
Based on Belcher and Travis (1989), the we assumed the average period between hay harvests is 60 days,
and the average period between successive grazing is 30 days. We therefore calculated Tp as follows:
„ 0.5 • (60 days + 30 days) n ,-
Tp = ^77^ ; = V-V-y Equation 5-21
365 dayslyr
Recommended Value for:
Plant Exposure Length to Deposition per Harvest of the Edible Portion of Plant (Tp)
Forage = 0.12 yr
Silage = 0.16 yr
The primary uncertainties associated with Tp are similar to those for aboveground produce, as discussed
in Section 5.3.1.3.
5.4.1.4 Standing Crop Biomass (Productivity) (Yp)
As discussed in Section 5.3.1.4, U.S. EPA (1998c) stated that the best estimate of Yp is productivity, as
defined in Equation 5-17. Consequently, under this approach, you would consider dry harvest yield (Yh)
and area harvested (Ah).
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We calculated forage Yp as a weighted average of the calculated pasture grass and hay Yp values. We
assumed weightings of 0.75 for forage and 0.25 for hay. The weightings are based on the fraction of a
year that cattle are assumed to be pastured and eating grass (9 months per year) or not pastured and fed
hay (3 months per year). We assumed an unweighted pasture grass Yp of 0.15 kg DW/m2 (U.S. EPA
1994r; U.S. EPA 1994m). We then calculated an unweighted hay Yp of 0.5 kg DW/m2 using Equation
5-17 and the following Yh and Ah values:
Yh = 1.22x 1011 kg DW, calculated from the 1993 U.S. average wet weight Yh of
1.35x 1011 kg (USDA 1994b) and a conversion factor of 0.9 (Fries 1994).
Ah = 2.45 x 1011 m2, the 1993 U.S. average for hay (USDA 1994b).
The unweighted pasture grass and hay Yp values were multiplied by their weighting factors (0.75 and
0.25, respectively), and summed to calculate the recommended weighted forage Yp of 0.24 kg DW/m2.
We recommend assuming a production-weighted U.S. average Yp of 0.8 kg DW/m2 for silage (Shor, et al.
1982).
Recommended Values for:
Standing Crop Biomass (Productivity) (Yp)
Forage = 0.24 kg DW/m2
Silage = 0.8 kg DW/m2
The primary uncertainty associated with this variable is that the harvest yield (Yh) and area planted (Ah)
may not reflect site-specific conditions. To the extent that site-specific information is available, it's
feasible to estimate the magnitude of the uncertainty introduced by the default Yp value. In addition, the
weightings assumed in this discussion for the amount of time that cattle are pastured (and foraging) or
stabled (and being fed silage) could be adjusted to reflect site-specific conditions, as appropriate.
5.4.2 Forage and Silage Concentrations Due to Air-to-Plant Transfer (Pv)
We recommend using Equation 5-18 (Section 5.3.2) to calculate the COPC concentration in aboveground
produce resulting from air-to-plant transfer (Pv). Pv is calculated for cattle forage and silage similarly to
the way that it's calculated for aboveground produce. We provide a detailed discussion of Pv in Section
5.3.2. We present differences in VGag values for forage and silage, as compared to the values for
aboveground produce described in Section 5.3.2.1, in Section 5.4.2.1. We discuss calculating Pv further
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in Appendix B. Appendix A-2 explains how we recommend calculating COPC-specific Bv values for
forage and silage (i.e. the same as they are calculated for aboveground produce).
5.4.2.1 Empirical Correction Factor for Forage and Silage (VGa^
Please Section 5.3.2.1 for a detailed, general introduction to VGag. Using such a factor while estimating
COPC concentrations specifically for forage and silage assumes that there is insignificant translocation of
COPCs deposited on the surface of bulky silage to the inner parts of the vegetation. Applying a silage
VGag would be relevant if the silage can't be characterized as leafy (e.g., if grain is used as silage). As a
point of clarification, forage and silage are considered vegetative plant parts, and grains are considered
reproductive plant parts.
U.S. EPA (1994m) didn't recommend a VGag value for silage. NC DEHNR (1997) recommended a VGag
factor of 0.5 for bulky silage but didn't present a specific rationale for this value. U.S. EPA (1995b)
noted that a volume ratio of outer whole surface area to volume of vegetation could be used to assign a
silage VGag value, if one knew the proportions of each type of vegetation of which silage consisted. In the
absence of specific data concerning the quantities of different silage material (e.g., hay and grain), U.S.
EPA (1995b) recommended assuming a VGag of 0.5 for silage without rigorous justification.
We recommend using VGag values of 1.0 for forage and 0.5 for silage. As discussed, the primary
uncertainty associated with this variable is the lack of specific information on the proportions of each
vegetation type of which silage may consist, leading to the default assumption of 0.5.
Recommended Values for:
Empirical Correction Factor for Forage and Silage (VGag)
Forage = 1
Silage = 0.5
5.4.3 Forage, Silage, and Grain Concentrations Due to Root Uptake (Pr)
We recommend using Equations 5-20A and 5-20B (Section 5.3.3) to calculate the COPC
£ concentration in aboveground and belowground produce resulting from root uptake. Pr is
calculated for cattle forage, silage, and grain in the same way that it is calculated for
aboveground produce, except that we recommend using forage/silage- and grain-specific
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bioconcentration factors (Brfarage and Brgrain respectively). Appendix A-2 explains how we recommend
calculating COPC-specific Br values for forage and silage (i.e. exactly as it's calculated for aboveground
produce). We provide a detailed discussion on how we recommend calculating Pr in Section 5.3.3. We
further discuss the calculation of Pr in Appendix B.
5.4.4 BMBKoncentration Resulting from Plant and Soil Ingestion (Abeef)
As in U.S. EPA (1995h), we recommend using Equation 5-22 to calculate COPC concentration
in beef tissue (Abee^. The equation was modified from an equation presented in U.S. EPA
(1990c), U.S. EPA (1994r), U.S. EPA (1995b), andNC DEHNR (1996) by introducing a metabolism
factor (MF). Equation 5-22 calculates the daily amount of a COPC that is consumed by cattle through the
ingestion of contaminated feed items (plant) and soil. The equation includes biotransfer and metabolism
factors to transform the daily animal intake of a COPC (mg/day) into an animal COPC tissue
concentration (mg CO PC/kg tissue). We further discuss using this equation in Appendix B, Table 3-10.
Recommended Equation for Calculating:
Concentration of COPC in Beef (Abeef)
Equation 5-22
where
Abeef = Concentration of COPC in beef (mg COPC/kg FW tissue)
Ft — Fraction of plant type r grown on contaminated soil and ingested by the animal
(cattle) (unitless)
Qpt — Quantity of plant type r eaten by the animal (cattle) per day (kg DW plant/day)
P i = Concentration of COPC in each plant type r eaten by the animal (cattle)
(mg/kgDW)
Qs = Quantity of soil eaten by the animal (cattle) each day (kg/day)
Cs — Average soil concentration over exposure duration (mg COPC/kg soil)
Bs — Soil bioavailability factor (unitless)
Babeef = COPC biotransfer factor for beef (day/kg FW tissue)
MF — Metabolism factor (unitless)
Sections 5.4.4.1 through 5.4.4.7 describe the parameters Ft, Qpt, Pt, Qs, Cs, Bs, and MF, respectively.
Appendix A-2 explains how we recommend calculating the COPC-specific parameter Babeef.
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5.4.4.1 Fraction of Plant Type i Grown on Contaminated Soil and Eaten by the Animal (Cattle)^.)
As in U.S. EPA(1990e and 1994r), and NC DEHNR (1997), we recommend assuming that 100 percent
of the plant materials eaten by cattle were grown on soil contaminated by the emission sources being
evaluated. This assumption translates to a default value of 1.0 for Ft.
Recommended Value for:
Fraction of Plant Type i Grown on Contaminated Soil and Eaten by the Animal (Cattle)
I
5.4.4.2 Quantity of Plant Type i Eaten by the Animal (Cattle) Each Day (Qp,)
The daily quantity of plants eaten by cattle can be estimated (kg DW/day) for each category of plant feed.
U.S. EPA (1994rand 1998c) andNC DEHNR (1997) recommended including forage, silage, and grain
feeds in this estimate.
NC DEHNR (1997) recommended plant ingestion rates forthe cattle of either subsistence beef farmers or
typical beef farmers. Subsistence beef farmers rely on a higher percentage of forage and silage to feed
cattle, whereas typical beef farmers rely on greater amounts of grain to feed cattle. U.S. EPA (1990e) and
U.S . EPA (1 994r) identified plant ingestion rates only for subsistence farmers. The following daily
quantities of forage, grain, and silage eaten by cattle were recommended by NC DEHNR (1997), U.S.
EPA (1994r and 1990e), and Boone et al. (1981):
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Source
NCDEHNR(1997)
Subsistence Farmer Beef
Cattle
NCDEHNR(1997)
Typical Farmer Beef
Cattle
U.S. EPA(1994r)
Subsistence Farmer Beef
Cattle
U.S. EPA(1990e)
Subsistence Farmer Beef
Cattle
Booneetal. (1981)
Forage
(kg DW/day)
8.8
3.8
8.8
8.8
8.87
Grain
(kg DW/day)
0.47
3.8
Not reported
0.47
1.9
Silage
(kg DW/day)
2.5
1.0
Not reported
2.5
2.5
References
Booneetal. (1981)
NAS (1987)
Rice (1994)
Booneetal. (1981)
NAS (1987)
Booneetal. (1981)
McKone and Ryan
(1989)
Booneetal. (1981)
With the exception of a higher grain ingestion rate, Boone et al. (1981) rates are consistent with those
recommended by U.S. EPA (1990e and 1994r), and NC DEHNR (1997). For typical farmer beef cattle,
NC DEHNR (1997) cites Rice (1994) as a reference for the Qpt variables and notes that the values include
grain supplemented during the growing phase for beef cattle.
U.S. EPA (1990e) noted that McKone and Ryan (1989) reported an average total ingestion rate of
12 kg DW/day for the three plant feeds, which is consistent with the total recommended by U.S. EPA
(1990e) and NC DEHNR (1997) (forage, grain, and silage total of 11.8 kg DW/day). U.S. EPA (1994r)
and NC DEHNR (1997) also noted that NAS (1987) reported a daily dry matter intake that is 2 percent of
an average beef cattle body weight of 590 kilograms. This results in a daily total intake rate of
11.8 kg DW/day. NAS (1987) reported that a nonlactating cow eats dry matter equivalent to 2 percent of
its body weight.
We recommend using the following beef cattle ingestion rates of forage, silage, and grain. These values
are based on the total daily intake rate of about 12 kg DW/day.
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Recommended Values for:
Quantity of Plant Type i Eaten by the Animal (Cattle) Each Day (Qp,)
Forage = 8.8 kg DW/day
Silage = 2.5 kg DW/day
Grain = 0.47 kg DW/day
The principal uncertainty associated with Qpi is the variability between forage, silage, and grain ingestion
rates for cattle.
5.4.4.3 Concentration of COPC in Plant Type i Eaten by the Animal (Cattle) (/»,.)
We generally recommend using Equation 5-23 to calculate the total COPC concentration in forage, silage,
and grain. We recommend deriving values for Pd, Pv, and Pr for each type of feed by using Equations
5-14, 5-18, and 5-20, respectively.
Recommended Equation for Calculating:
Concentration of COPC in Plant Type i Eaten by the Animal (Cattle) (/»,)
P. = ^(Pd + Pv+ Pr) Equation 5-23
where
Pt — Concentration of COPC in each plant type i eaten by the animal (mg
COPC/kgDW)
Pd — Plant concentration due to direct deposition (mg COPC/kg DW)
Pv = Plant concentration due to air-to-plant transfer (mg COPC/kg DW)
Pr — Plant concentration due to root uptake (mg COPC/kg DW)
This equation is further described in Appendix B.
5.4.4.4 Quantity of Soil Eaten by the Animal (Cattle) Per Day (Qs)
Additional cattle contamination occurs through ingestion of soil.
NC DEHNR (1997) and U.S. EPA (1994r) recommended a soil ingestion rate for subsistence beef cattle
of 0.5 kg/day. This rate is based on Fries (1994). U.S. EPA (1994r) and NC DEHNR (1997) noted that
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Fries (1994) reported soil ingestion to be 4 percent of the total dry matter intake. NAS (1987) was also
referenced. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate
(nonlactating cows) of 2 percent of body weight. This results in a daily dry matter intake rate of 11.8 kg
DW/day and a daily soil ingestion rate of about 0.5 kg/day. U.S. EPA (1990e) reported a soil ingestion
rate that is 3 percent of the forage intake rate of 8.8 kg DW/day, resulting in a daily soil ingestion rate of
approximately 0.3 kg/day. Simmonds and Linsley (1981) and Thornton and Abrams (1983) were cited as
the references for this assumption.
We recommend using 0.5 kg/day for the quantity of soil ingested by the animal (cattle).
Recommended Value for:
Quantity of Soil Ingested by the Animal (Cattle) Per Day (Qs)
0.5 kg/day
5.4.4.5 Average Soil Concentration Over Exposure Duration (Cs)
We recommend using Equations 5-1C, 5-ID, and 5-IE to calculate the COPC concentration in soil as
discussed in Section 5.2.1. Also, Appendix B further describes how we recommend calculating the soil
concentration.
Please Note: You might need to generate soil concentration estimates for grain separate
from those for forage and silage. Currently, the HHRAP assumes that forage and silage
are grown on untilled land, and grain is grown on tilled land. We highly recommend that
your Cs calculations include the appropriate Zs (1 for untilled land, 20 for tilled land).
5.4.4.6 Soil Bioavailability Factor (Bs)
The efficiency of transfer from soil may differ from the efficiency of transfer from plant material for some
COPCs. If the transfer efficiency is lower for soils, then the ratio would be less than 1.0. If it is equal to
or greater than that of vegetation, the Bs value would be equal to or greater than 1.0.
Until more COPC-specific data becomes available for this parameter, we recommend a default value of 1
forBs.
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Recommended Values for:
Soil Bioavailability Factor (Bs)
1.0
5.4.4.7 Metabolism Factor (MF)
The metabolism factor (MF) estimates the amount of COPC that remains in fat and muscle. Based on a
study by Ikeda et al. (1980), U.S. EPA (1995h) used a COPC-specific MF to account for metabolism in
animals and humans. Evidence indicates BEHP is more readily metabolized and excreted by mammalian
species than other contaminants (ATSDR 1987). As in U.S. EPA (1995h), we recommend aMF of 0.01
for bis(2-ethylhexyl)phthalate (BEHP). Lacking data to support derivation of other chemical-specific
MFs, we recommend using aMF of 1.0 for all chemicals other than BEHP. Using the recommended
values for this variable, MF has no quantitative effect on Aheef (with the exception of BEHP).
Recommended Values for:
Metabolism Factor (MF)
bis(2-ethylhexyl)phthalate (BEHP) = 0.01
All other COPCs = 1.0
The MF presented above for BEHP applies only to mammalian species, including beef cattle, dairy cattle,
and pigs. It does not relate to metabolism in produce, chicken, or fish. In addition, since exposures
evaluated in this guidance are intake driven, using an MF applies only to estimating COPC concentrations
in food sources used in evaluating indirect human exposure, including ingestion of beef, milk, and pork.
In summary, anMF is not applicable for direct exposures to air, soil, or water, or to ingestion of produce,
chicken, or fish.
5.4.5 COPC Concentration In Milk Due to Plant and Soil Ingestion (Amilk)
We recommend modifying Equation 5-22 (Section 5.4.4) to calculate COPC milk concentrations
(Amiik), as follows:
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Recommended Equation for Calculating:
Concentration of COPC in Milk (Amilk)
Amilk = (E (Fi' QPi' Pl> + Qs'Cs-Bs}' Bamm • MF Equation 5-24
where
Amilk = Concentration of COPC in milk (mg COPC/kg milk)
Ft = Fraction of plant type r grown on contaminated soil and ingested by the animal
(dairy cattle) (unitless)
Qpt = Quantity of plant type i eaten by the animal (dairy cattle) each day (kg D W
plant/day)
Pt = Concentration of COPC in plant type r eaten by the animal (dairy cattle)
(mg/kg DW)
Qs = Quantity of soil eaten by the animal (dairy cattle) each day (kg soil/day)
Cs = Average soil concentration over exposure duration (mg COPC/kg soil)
Bs — Soil bioavailability factor (unitless)
Bamilk = COPC biotransfer factor for milk (day/kg WW tissue)
MF — Metabolism factor (unitless)
Appendix A-2 explains how we recommend calculating the COPC-specific parameter 5amilk. The
discussion in Section 5.4.4 of the variables Ft, Qpt, Pt, Qs, Cs, and MF for beef cattle generally applies to
the corresponding variables for dairy cattle. However, there are some differences in assumptions made
for dairy cattle; these differences are summarized in the following subsections.
We recommend using Equation 5-24 to estimate Amilk. Using Equation 5-24 is described further in
Appendix B, Table B-3-11.
5.4.5.1 Fraction of Plant Type i Grown on Contaminated Soil and Eaten by the Animal (Dairy
Cattle) (F,.)
The calculation ofFt for dairy cattle is identical to that for beef cattle (Section 5.4.4.1).
5.4.5.2 Quantity of Plant Type i Eaten by the Animal (Dairy Cattle) Per Day (gp.)
As discussed in Section 5.4.4.2, the daily quantity of forage, silage, and grain feed consumed by cattle is
estimated for each category of feed material. However, daily ingestion rates for dairy cattle are estimated
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differently than for beef cattle. We generally recommend estimating the daily quantity of feed consumed
by cattle on a dry weight basis for each category of plant feed.
NC DEHNR (1997) recommended using plant ingestion rates for either subsistence dairy farmer or
typical dairy farmer cattle. In addition, subsistence dairy farmers rely on a higher percentage of forage
and silage to feed cattle, whereas typical dairy farmers rely on greater amounts of grain to feed cattle.
U.S. EPA (1990e and 1994r) identified plant ingestion rates only for subsistence farmers.
The following daily quantities of forage, grain, and silage eaten by dairy cattle were recommended by NC
DEHNR (1997), U.S. EPA (1994r), U.S. EPA (1990e), and Boone et al. (1981):
Source
NC DEHNR (1997)
Subsistence Dairy
Farmer Cattle
NC DEHNR (1997)
Typical Dairy Farmer
Cattle
U.S. EPA (1994r)
Subsistence Dairy
Farmer Cattle
U.S. EPA (1990e)
Subsistence Dairy
Farmer Cattle
Boone etal. (1981)
Forage
(kg/day DW)
13.2
6.2
13.2
11.0
11.0
Grain
(kg/day DW)
3.0
12.2
Not reported
2.6
2.6
Silage
(kg/day DW)
4.1
1.9
Not reported
3.3
3.3
References
Boone etal. (1981)
NAS(1987)
Rice (1994)
Boone etal. (1981)
NAS(1987)
Boone etal. (1981)
McKone and Ryan
(1989)
Boone etal. (1981)
U.S. EPA (1990e) noted that McKone and Ryan (1989) reports an average total ingestion rate of
17 kg/day DW for the three plant feeds, which is consistent with the total ingestion rate recommended by
U.S. EPA (1990e). U.S. EPA (1994r) and NC DEHNR (1997) noted that NAS (1987) reports a daily dry
matter intake that is 3.2 percent of an average dairy cattle body weight of 630 kilograms. This results in a
daily total intake rate of approximately 20 kg/day DW, which is consistent with the average total
ingestion rates for the three plant feeds recommended by U.S. EPA (1994r) and NC DEHNR (1997) .
NAS (1987) reported that dairy cows eat dry matter equivalent to 3.2 percent of their body weight; the
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630-kilogram average dairy cow body weight was not confirmed. U.S. EPA (1995b) also cited a feed
ingestion rate of 20 kg/day DW, citing U.S. EPA (1993c).
Based on more recent references (NAS 1987; U.S. EPA 1993c) which recommend a feed ingestion rate of
20 kg/day DW, we recommend a default total ingestion rate of 20 kg DW/day for dairy cattle.
Recommended Values for:
Quantity of Plant Type i Eaten by the Animal (Dairy Cattle) Per Day (Qp,)
Forage = 13.2 kg DW/day
Silage = 4. 1kg DW/day
_ Grain = 3. Okg DW/day _
Uncertainties associated with estimating Qpi include estimating forage, grain, and silage ingestion rates,
which will vary from site to site. Assuming uniform contamination of plant materials consumed by cattle
also introduces uncertainty.
5.4.5.3 Concentration of COPC in Plant Type i Eaten by the Animal (Dairy Cattle) (P;)
The estimation of Pi for dairy cattle is identical to that for beef cattle (Section 5.4.4.3).
5.4.5.4 Quantity of Soil Eaten by the Animal (Dairy Cattle) Per Day (Qs)
As discussed in Section 5.4.4.4, contamination of dairy cattle also results from the ingestion of soil. We
generally recommend the following soil ingestion rate for dairy cattle:
Recommended Values for:
Quantity of Soil Eaten by the Animal (Dairy Cattle) Per Day (Qs)
_ 0.4 kg/day _
U.S. EPA (1994r) and NC DEHNR (1997) recommended a soil ingestion rate of 0.4 kg/day for dairy
cattle, based on Fries (1994). U.S. EPA (1994r) and NC DEHNR (1997) noted that Fries (1994) reported
soil ingestion rates as 2 percent of the total dry matter intake. NAS (1987) was also referenced, which
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reported an average dairy cattle weight of 630 kilograms and a daily dry matter intake rate (nonlactating
cows) of 3.2 percent of body weight. This resulted in a daily dry matter intake rate of 20 kg/day DW, and
a daily soil ingestion rate of approximately 0.4 kg/day. NC DEHNR (1997) recommended a soil
ingestion rate of 0.2 kg/day for the cattle of typical dairy farmers, citing Rice (1994). U.S. EPA (1990e)
reported soil ingestion rates as 3 percent of the forage intake rate. U.S. EPA (1990e) assumed that the
more protective forage intake rate of 13.2 kg/day DW results in a daily soil ingestion rate of about
0.4 kg/day. Simmonds and Linsley (1981) and Thornton and Abrams (1983) were cited as the references
for this assumption.
Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion rates
for dairy cattle. Assuming uniform contamination of soil ingested by cattle also adds uncertainty.
5.4.5.5 Average Soil Concentration Over Exposure Duration (Cs)
The calculation of Cs for dairy cattle is the same as for beef cattle (Section 5.4.4.5).
Please Note: You might need to generate soil concentration estimates for grain separate
from those for forage and silage. Currently, the HHRAP assumes that forage and silage
are grown on untilled land, and grain is grown on tilled land. We highly recommend
making sure that your Cs calculations include the appropriate Zs (2 for untilled land, 20
for tilled land).
5.4.5.6 Soil Bioavailability Factor (Bs)
The calculation of & for dairy cattle is the same as for beef cattle (Section 5.4.4.6).
5.4.5.7 Metabolism Factor (MF)
The recommended values for MF are identical to those we recommend for beef cattle (Section 5.4.4.7).
5.5 CALCULATING COPC CONCENTRATIONS IN PORK
Under the approach recommended in this guidance, COPC concentrations in pork tissue are
^^^^^
wBVlK estimated on the basis of the amount of COPCs that swine consume through a diet consisting
of silage and grain. Additional COPC contamination of pork tissue may occur through their
ingestion of soil.
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Equation 5-22 (Section 5.4.4) describes how
we recommend calculating COPC
concentration in beef cattle (Abeef). We suggest
modifying Equation 5-22 to calculate COPC
concentrations in swine (A k), as follows:
September 2005
FIGURE 5-5
COPC CONCENTRATION IN PORK
Recommended Equation for Calculating:
Concentration of COPC in Pork (Apork)
Equation 5-25
where
P
Cs
Bs
Bapo
MF
Concentration of COPC in pork (mg COPC/kg FW tissue)
Fraction of plant type z grown on contaminated soil and ingested by the animal
(swine)(unitless)
Quantity of plant type z eaten by the animal (swine) each day (kg DW
plant/day)
Concentration of COPC in plant type z eaten by the animal (swine)
(mg/kg DW)
Quantity of soil eaten by the animal (swine) (kg/day)
Average soil concentration over exposure duration (mg COPC/kg soil)
Soil bioavailability factor (unitless)
COPC biotransfer factor for pork (day/kg FW tissue)
Metabolism factor (unitless)
Appendix A-2 explains how we recommend calculating the COPC-specific parameter BapoA. The
discussions in Section 5.4.5 of the variables Ft, Qpt, Pt, Qs, Cs and MF for beef cattle generally apply to
the corresponding variables for pork. However, some different assumptions are made for pork. These
differences are summarized in the following subsections.
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We generally recommend using Equation 5-25 to calculate COPC pork concentrations (Apork). This
equation is further described in Appendix B, Table B-3-12.
5.5.1 Fraction of Plant Type i Grown on Contaminated Soil and Eaten by the Animal (Swine) (F/)
The calculation ofFt for pork is identical to that for beef cattle (Section 5.4.4.1).
5.5.2 Quantity of Plant Type i Eaten by the Animal (Swine) Each Day (Qp,)
Section 5.4.4.2 discusses estimating the daily quantity of forage, silage, and grain feed consumed by beef
cattle for each feed category. However, daily ingestion rates for pork are estimated differently than for
beef cattle. U.S. EPA (1994r and 1998c), and NC DEHNR (1997) recommended only including silage
and grain feeds to estimate daily plant quantity eaten by swine. Because swine are not grazing animals,
they are assumed not to eat forage (U.S. EPA 1998c). We therefore generally recommend estimating the
daily quantity of plant feeds (kilograms of DW) consumed by swine for each category of plant feed.
U.S. EPA (1990e) and NC DEHNR (1997) recommended grain and silage ingestion rates for swine of 3.0
and 1.3 kg DW/day, respectively. NC DEHNR (1997) references U.S. EPA (1990e) as the source of
these ingestion rates. U.S. EPA (1990e) reported total dry matter ingestion rates for hogs and lactating
sows as 3.4 and 5.2 kg DW/day, respectively. U.S. EPA (1990e) cites Boone et al. (1981) as the source
of the ingestion rate for hogs, and NAS (1987) as the source of the ingestion rate for a lactating sow.
Boone et al. (1981) reported a grain ingestion rate of 3.4 kg DW/day for a hog. NAS (1987) reported an
average ingestion rate of 5.2 kg DW/day for a lactating sow. U.S. EPA (1990e) recommended using the
average of these two rates (4.3 kg DW/day).
U.S. EPA (1990e) assumed that 70 percent of the swine diet is grain and 30 percent silage to obtain the
grain ingestion rate of 3.0 kg DW/day and the silage ingestion rate of 1.3 kg DW/day. U.S. EPA (1990e)
cited U.S. EPA (1982b) as the source of the grain and silage dietary fractions. U.S. EPA (1995b)
recommended an ingestion rate of 4.7 kg DW/day for a swine, referencing NAS (1987). NAS (1987)
reported an average daily intake of 4.36 kg DW/day for a gilt (young sow) and a average daily intake of
5.17 kg DW/day for a sow, which averages out to 4.7 kg/DW/day. Assuming the 70 percent grain to
30 percent silage diet noted above, estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4
kg DW/day (silage) are derived.
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Recommended Values for:
Quantity of Plant Type i Eaten by the Animal (Swine) Each Day (Qpt)
Grain = 3.3kgDW/day
_ Silage = 1.4kgDW/day _
Uncertainties associated with this variable include the variability of actual grain and silage ingestion rates
from site to site. You could use site-specific data to mitigate this uncertainty. In addition, assuming
uniform contamination of the plant materials consumed by swine produces some uncertainty.
5.5.3 Concentration of COPC in Plant Type i Eaten by the Animal (Swine) (P;)
The suggested calculation ofPj for pork is identical to that for beef cattle (Section 5.4.4.3).
5.5.4 Quantity of Soil Eaten by the Animal (Swine) Each Day (Qs)
As discussed in Section 5.4.4.4, additional contamination of swine results from ingestion of soil. The
following Qs values were recommended by earlier guidance:
Guidance
U.S. EPA (1990e)
NCDEHNR(1997)
U.S. EPA(1998c)
Quantity of Soil Eaten by Swine Each Day (gs)
Stated that sufficient data are not available to estimate swine soil
ingestion rates.
0.37 kg/day
Estimated by assuming a soil intake that is 8% of the plant
ingestion rate of 4.3 kg DW/day). U.S. EPA (1993f) was cited as
the reference for the soil ingestion rate of 8 percent of dry matter
intake.
Cites a companion "Parameters Guidance Document" for detailed
recommendations on Q. The "Parameters" document has not been
published.
As in NC DEHNR (1997), we recommend the following soil ingestion rate for swine:
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Recommended Value for:
Quantity of Soil Eaten by the Animal (Swine) Each Day (gs)
0.37 kg DW/day
Uncertainties associated with this variable include the lack of current empirical data to support soil
ingestion rates for swine, and assuming uniform contamination of the soil ingested by swine.
5.5.5 Average Soil Concentration Over Exposure Duration (Cs)
Our suggested calculation of Cs for pork is the same as for beef cattle (Section 5.4.4.5).
Please Note: You might need to generate soil concentration estimates for grain separate
from those for silage. We recommend assuming that silage is grown on untilled land, and
grain is grown on tilled land. We highly recommend that you make sure that your Cs
calculations include the appropriate Zs (2 for untilled land, 20 for tilled land).
5.5.6 Soil Unavailability Factor (Bs)
Our suggested calculation ofBs for pork is the same as for beef cattle (Section 5.4.4.6)
5.5.7 Metabolism Factor (MF)
Our recommended values for MF are identical to those we recommended for beef cattle (Section 5.4.4.7).
5.6 CALCULATING COPC CONCENTRATIONS IN CHICKEN AND EGGS
Under the approach outlined in this guidance document, estimates of the COPC
concentrations in chicken and eggs are based on the amount of COPCs that chickens
_ 'AA
consume through ingestion of grain and soil. We recommend assuming that the uptake of
COPCs via inhalation and via ingestion of water are insignificant relative to other pathways. The
HHRAP assumes that chickens are housed in a typical manner that allows contact with soil. Because of
this, chickens are assumed to consume 10 percent of their diet as soil. Assuming 10 percent is consistent
with the study from which the biotransfer factors were obtained (Stephens et al. 1995). We recommend
assuming that the remainder of the diet (90 percent) consists of grain grown at the exposure scenario
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location. Therefore, it's appropriate to assume 100 percent of the grain consumed is contaminated. The
equations don't account for the uptake of COPCs via ingestion of contaminated insects and other
organisms (e.g., worms, etc.), which may also contribute to the ingestion of COPCs. This may be a
limitation, depending on the site-specific conditions under which the chickens are raised.
FIGURE 5-6
COPC CONCENTRATION IN CHICKEN & EGGS
We generally recommend using the
algorithm for aboveground produce
described in Section 5.3 to estimate the
COPC concentration in grain. Grain is
considered to be protected from direct
deposition of particles, and vapor transfer.
This approach considers only contamination
due to root uptake of COPCs in calculating
COPC concentrations in grain. Our
recommended equations for calculating
concentrations in chicken and eggs are
presented in Appendix B. The method we
used to derive biotransfer factors, and the
COPC-specific values for chicken and eggs are presented in Appendix A-2.
As in NC DEHNR (1997), we recommend using Equation 5-26 to calculate COPC concentrations in
chicken and eggs (Stephens et al. 1995). We generally recommend calculating COPC concentrations in
chicken and eggs separately. Parameters and variables in Equation 5-26 are further described in
Appendix B, Tables B-3-13 and B-3-14.
COPC
Concentration
in Chicken and Egg
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Recommended Equation for Calculating:
Concentration of COPC in Chicken and Eggs (Achickm or Aegg)
Achicken °r Aegg= &\-Fi ' QPi ' Pi~\ + Qs ' Cs ' Bs) ' (Baegg °r BachickJ Equation 5-26
where
Achicken = Concentration of COPC in chicken (mg COPC/kg FW tissue)
Aegg = Concentration of COPC in eggs (mg COPC/kg FW tissue)
Fj = Fraction of plant type /' (grain) grown on contaminated soil and ingested by the
animal (chicken)(unitless)
Qpi = Quantity of plant type /' (grain) eaten by the animal (chicken) each day (kg
DW plant/day)
Pj = Concentration of COPC in plant type / (grain) eaten by the animal (chicken)
(mg/kg DW)
Qs = Quantity of soil eaten by the animal (chicken) (kg/day)
Cs = Average soil concentration over exposure duration (mg COPC/kg soil)
Bs = Soil bioavailability factor (unitless)
Bcichicken = COPC biotransfer factor for chicken (day/kg FW tissue)
Baegg = COPC biotransfer factor for eggs (day/kg FW tissue)
Appendix A-2 explains how we recommend determining the COPC-specific parameters Bachicken and Baegg.
The remaining parameters are discussed in Appendix B and in the following subsections.
5.6.1 Fraction of Plant Type i Grown on Contaminated Soil and Eaten by the Animal
(Chicken)^)
The calculation ofFt for chicken is identical to that for beef cattle (Section 5.4.4.1).
5.6.2 Quantity of Plant Type i Eaten by the Animal (Chicken) Each Day (Qpt)
Section 5.4.4.2 discusses estimating the daily quantity of forage, silage, and grain feed consumed by beef
cattle for each feed category. However, daily ingestion rates for chicken are estimated differently than for
beef cattle. NC DEHNR (1997) recommended that only grain feeds be included in this estimate. Because
chickens are not grazing animals, they are assumed not to eat forage (U.S. EPA 1998c). Chickens are
similarly assumed not to consume any silage. We recommend only estimating the daily quantity of plant
feeds (kilograms of DW) consumed by chicken (Qp) for grain feed.
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As in Ensminger (1980), Fries (1982), and NAS (1987), we recommend using the following ingestion
rate:
Recommended Value for:
Quantity of Plant Type i Eaten by the Animal (Chicken) Each Day (Qp,)
Grain = 0.2 kg DW/day
Uncertainties associated with this variable include the variability of actual grain ingestion rates from site
to site. In addition, assuming uniform contamination of plant materials consumed by chicken produces
some uncertainty.
5.6.3 Concentration of COPC in Plant Type i Eaten by the Animal (Chicken) (/».)
The total COPC concentration is the COPC concentration in grain. We recommend using Equation 5-27
to calculate Pt. This equation is further described in Appendix B.
Recommended Equation for Calculating:
Concentration of COPC in Plant Type i Eaten by the Animal (Chicken) (IV)
Equation 5-27
where
PI = Concentration of COPC in each plant type i eaten by the animal (mg
COPC/kgDW)
Pr — Plant concentration due to root uptake (mg COPC/kg DW)
We generally recommend calculating plant concentration due to root uptake (Pr) using Equation 5-20, as
discussed in Section 5.3.3.
5.6.4 Quantity of Soil Eaten by the Animal (Chicken) Each Day (Qs)
COPC concentration in chickens also results from intake of soil. As discussed earlier, The HHRAP
assumes that chickens consume 10 percent of their total diet as soil, a percentage that is consistent with
the study from Stephens et al. (1995). We recommend the following soil ingestion rate for chicken:
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Recommended Value for:
Quantity of Soil Eaten by the Animal (Chicken) Each Day (Qs)
0.022 kg DW/day
Uncertainties associated with this variable include the lack of current empirical data to support soil
ingestion rates for chicken, and assuming uniform contamination of soil ingested by chicken.
5.6.5 Average Soil Concentration Over Exposure Duration (Cs)
The calculation of Cs for chicken is the same as for beef cattle (Section 5.4.4.5).
Please Note: We recommend assuming that forage and silage are grown on untilled land,
and grain is grown on tilled land. We highly recommend making sure that your Cs
calculations include the appropriate Zs (20 for tilled land).
5.6.6 Soil Unavailability Factor (Bs)
The calculation of & for chicken is the same as for beef cattle (Section 5.4.4.6)
5.7 CALCULATING COPC CONCENTRATIONS IN DRINKING WATER AND FISH
We generally recommend calculating COPC concentrations in surface water for all
water bodies you selected to evaluate in the risk assessment. Specifically, those
waterbodies selected as potential sources for the drinking water and/or fish ingestion exposure pathways.
Mechanisms we suggest considering in determining COPC loading of the water column include:
• Direct deposition,
• Runoff from impervious surfaces within the watershed,
Runoff from pervious surfaces within the watershed,
• Soil erosion over the total watershed,
• Direct diffusion of vapor phase COPCs into the surface water, and
Internal transformation of compounds chemically or biologically.
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Considering other potential mechanisms may be appropriate, due to site-specific conditions (e.g., tidal
influences). Typically, though, we assume that contributions from other potential mechanisms are
negligible compared to those evaluated in the HHRAP.
FIGURE 5-7
COPC LOADING TO THE WATER BODY
Runoff to
Water Body
Soil Erosion
(Sediments)
f f f
^ ///
Particle
Deposition
i m
'" "'- '•'
Volatilization
///>
J J ) )
/
/ \fepor
/ Transfer
Bed
Direct
Deposition
(Section 5.7.1.1)
(
Benthic
Burial
Section 5.7.4."
Benthic {
Burial •
Vapor
Transfer
(Section 5.7.1.2
ol
' V/
^J
\ \ Sediment
Runoff from Runoff from
Impervious Pervious
, Surfaces Surfaces
; (Section 5.7.1.3) (Section 5.7.1 .4)
'
Soil
Erosion
(Section 5.7.1.5)
r
Toal Water Body ^
Concentration
Volatilization
(Section 5.7.4.3)
The total concentration of each COPC partitions between the sediment and the water column.
Partitioning between water and sediment varies with the COPC. The HHRAP uses the Universal Soil
Loss Equation (USLE) and a sediment delivery ratio to estimate the rate of soil erosion from the
watershed. The equations we recommend for estimating surface water concentrations include a sediment
mass balance, in which the amount of sediment assumed to be buried and lost from the water body is
equal to the difference between the amount of soil introduced to the water body by erosion and the
amount of suspended solids lost in downstream flow. As a result, we typically assume that sediments do
not accumulate in the water body over time, and an equilibrium is maintained between the surficial layer
of sediments and the water column. The total water column COPC concentration is the sum of the COPC
concentration dissolved in water and the COPC concentration associated with suspended solids.
Appendix B-4 presents the equations we recommend using to estimate surface water concentrations.
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To evaluate the COPC loading to a water body from its associated watershed, we generally recommend
calculating watershed soil-specific COPC concentrations. The equation in Section 5.2 for estimating
COPC concentration in soil includes a loss term that considers the loss of contaminants from the soil after
deposition. These loss mechanisms all lower the soil concentration associated with a specific deposition
rate. Appendix B (Tables B-4-1 through B-4-28) provides the equations we recommend for calculating
COPC concentrations in watershed soils and in the water body.
The equations presented in Appendix B for modeling COPC loading to a water body represent a simple
steady-state model to solve for a water column in equilibrium with the upper sediment layer. These
equations (Appendix B) predict the steady-state mass of contaminants in the water column and underlying
sediments, and don't address the dynamic exchange of contaminants between the water body and the
sediments following changes in external loadings. While appropriate for calculating risk under long-term
average conditions, evaluating complex water bodies or shorter term loading scenarios might be improved
by using a dynamic modeling framework [e.g., Exposure Analysis Modeling System (EXAMS), or Water
Quality Analysis Simulation Program (WASP), both of which can be downloaded from the EPA Center
for Exposure Assessment Modeling]. Although typically more resource intensive, such analysis may be
able to refine modeling of contaminant loading to a water body. Also, the computations may better
represent the exposure scenario you are evaluating.
For example, EXAMS allows performing computations for each defined segment or compartment of a
water body or stream. These compartments are considered physically homogeneous and are connected
via advective and dispersive fluxes. Compartments can be defined as littoral, epilimnion, hypolimnion, or
benthic. Such resolution also makes it possible to assign receptor locations specific to certain portions of
a water body where evaluating exposure is of greatest interest.
The following are some considerations regarding the selection and use of a dynamic modeling framework
or simulation model to evaluate water bodies:
• Will a complex surface water modeling effort provide enhanced results over the use of
the more simplistic steady-state equations presented in Appendix B?
Are the resources needed to conduct, as well as review, a more complex modeling effort
justified compared to the more refined results?
• Has the model been used previously for regulatory purposes, and therefore, already has
available documentation to support such uses?
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• Can the model conduct steady-state and dynamic analysis? and
• Does the model require calibration with field data, and if so, are there sufficient quantity
and quality of site-specific data available to support calibration?
As mentioned previously in Chapter 2 (Section 2.3.5.3 - "Mercury"), the SERAFM model offers a
dynamic modeling framework for mercury that enables the user to model specific water body mercury
transformation processes in lieu of applying default speciation assumptions.
5.7.1 Total COPC Load to the Water Body (LT)
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-28 to calculate the
total COPC load to a water body (LT). This equation is described in detail in Appendix B, Table B-4-7.
Recommended Equation for Calculating:
Total COPC Load to the Water Body (LT)
LT = LDEp + Ldy + Lm + LR+ LE+ Lj Equation 5-28
where
LT — Total COPC load to the water body (including deposition, runoff, and erosion)
(g/y)
LDEP = Total (wet and dry) particle phase and vapor phase COPC direct deposition
load to water body (g/yr)
/, = Vapor phase COPC diffusion load to water body (g/yr)
LRI — Runoff load from impervious surfaces (g/yr)
LR = Runoff load from pervious surfaces (g/yr)
LE = Soil erosion load (g/yr)
Lj — Internal transfer (g/yr)
Due to the limited data and uncertainty associated with the chemical or biological internal transfer, Lj, of
compounds into degradation products, we generally recommend a default value for this variable of zero.
However, if a permitting authority determines that site-specific conditions indicate calculating internal
transfer may need to be considered, we recommend following the methods described in U.S. EPA
(1998c). The remaining variables (LDEP, Ldif, LRI, LR, and LE) are discussed in the following subsections.
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5.7.1.1 Total (Wet and Dry) Particle Phase and Vapor Phase COPC Direct Deposition Load to
Water Body (LDEP)
As in U.S. EPA (1994r) and NC DEHNR (1997), with the inclusion of the direct deposition of total
vapor, we recommend using Equation 5-29 to calculate the load to the water body from the direct
deposition of wet and dry particles and vapors onto the surface of the water body (LDEP). The equation is
described in detail in Appendix B, Table B-4-8.
Recommended Equation for Calculating:
Total Particle Phase and Vapor Phase Direct Deposition Load to Water Body (LDEP)
LDEP = Q ' [ fv-DytWV + (1 - Fv )-DytWp ] • Aw Equation 5-29
where
LDEP = Total (wet and dry) particle phase and vapor phase COPC direct deposition
load to water body (g/yr)
Q — COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Dytwv — Unitized yearly (water body or watershed) average total (wet and dry)
deposition from vapor phase (s/m2-yr)
Dytwp = Unitized yearly (water body or watershed) average total (wet and dry)
deposition from particle phase (s/m2-yr)
Aw = Water body surface area (m2)
Appendix A-2 describes how we recommend determining the COPC-specific parameter Fv. Chapter 3
describes generating the modeled air parameters, Dytwv and Dytwp. Methods for determining the water
body surface area, Aw, are described in Chapter 4 and Appendix B.
5.7.1.2 Vapor Phase COPC Diffusion Load to Water Body (Ldif)
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-30 to calculate Ldif.
The equation is described in detail in Appendix B, Table B-4-12.
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Recommended Equation for Calculating:
Vapor Phase COPC Diffusion Load to Water Body (LDif)
1-6
H
Equation 5-30
R-T
where
L — Vapor phase COPC diffusion load to water body (g/yr)
Kv = Overall COPC transfer rate coefficient (m/yr)
Q — COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Cywv — Unitized yearly (water body or watershed) average air concentration from
vapor phase (|o,g-s/g-m3)
Aw — Water body surface area (m2)
10"6 — Units conversion factor (g/(J.g)
H = Henry's Law constant (atm-m3/mol)
R — Universal gas constant (atm-m3/mol-K)
Twk — Water body temperature (K)
Calculating the overall COPC transfer rate coefficient (Kv) is described in Section 5.7.4.4, as well as in
Appendix B, Table B-4-19. Chapters 2 and 3 explain how we recommend quantifying the COPC
emission rate (Q). Appendix A-2 describes how we recommend determining the CO PC-specific
parameters Fv, H, and R. Chapter 3 describes generating the modeled air parameter, Cywv. Methods for
determining the water body surface area, Aw, are described in Chapter 4 and Appendix B. Consistent with
U.S. EPA (1994r) and U.S. EPA (1998c), we recommend a default water body temperature (Twk) of 298 K
(or25°C).
5.7.1.3 Runoff Load from Impervious Surfaces (LRI)
In some watershed soils, a portion of the total (wet and dry) deposition in the watershed will be to
impervious surfaces. This deposition may accumulate and be washed off during rain events. As in U.S.
EPA (1994r) and NC DEHNR (1997), with the inclusion of total (wet and dry) vapor phase deposition,
we recommend using Equation 5-31 to calculate impervious runoff load to a water body (Lsl). The
equation is also presented in Appendix B, Table B-4-9.
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Recommended Equation for Calculating:
Runoff Load from Impervious Surfaces (LRI)
LRI = Q'[ Fv-DytWV + (1.0 - Fv ) • Dytwp ] • Aj Equation 5-31
where
Lju = Runoff load from impervious surfaces (g/yr)
Q — CO PC emission rate (g/s)
Fv = Fraction of CO PC air concentration in vapor phase (unitless)
Dytwv — Unitized yearly (water body or watershed) average total (wet and dry)
deposition from vapor phase (s/m2-yr)
Dytwp — Unitized yearly (water body or watershed) average total (wet and dry)
deposition from particle phase (s/m2-yr)
Aj = Impervious watershed area receiving COPC deposition (m2)
Chapters 2 and 3 explain how we recommend quantifying the COPC emission rate (Q). Appendix A-2
describes how we recommend determining the COPC-specific parameter Fv. Chapter 3 describes a
method for generating the modeled air parameters, Dytwv and Dytwp. Impervious watershed area
receiving COPC deposition (Aj) is the portion of the total effective watershed area that is impervious to
rainfall (such as roofs, driveways, streets, and parking lots) and drains to the water body. Our
recommended method for determining AI is described in Chapter 4 and Appendix B.
5.7.1.4 Runoff Load from Pervious Surfaces
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-32 to calculate the
runoff dissolved COPC load to the water body from pervious soil surfaces in the watershed (Ls). The
equation is also presented in Appendix B, Table B-4-10.
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Recommended Equation for Calculating:
Runoff Load from Pervious Surfaces (L)
J
LR
RO
•Vi-
^)"
Cs-BD
Q™ + to*.'*
• 0 01
D
Equation
5-32
where
LR = Runoff load from pervious surfaces (g/yr)
RO — Average annual surface runoff from pervious areas (cm/yr)
AL = Total watershed area receiving COPC deposition (m2)
Aj = Impervious watershed area receiving COPC deposition (m2)
Cs — Average soil concentration over exposure duration (in watershed soils)
CO PC/kg soil)
BD — Soil bulk density (g soil/cm3 soil) =1.5 g/cm3
Qsw = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
Kds — Soil-water partition coefficient (cm3 water/g soil)
0.01 = Units conversion factor (kg-cm2/mg-m2)
Appendix B describes how we recommend determining the site-specific parameters RO, AL, A}, BD, and
Qsw. We also address soil bulk density (BD) in Section 5.2.4.2. We also address soil water content (6SW) in
Section 5.2.4.4. Our recommended method for calculating the COPC concentration in watershed soils
(Cs) is discussed in Section 5.2.1 and Appendix B, Table B-4-1. Appendix A-2 describes how we
recommend calculating the COPC-specific soil/water partition coefficient (Kds).
5.7.1.5 Soil Erosion Load (LE)
As in U.S. EPA (1994r) andNCDEHNR (1997), we recommend using Equation 5-33 to calculate soil
erosion load (LE). The equation is also presented in Appendix B, Table B-4-11.
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Recommended Equation for Calculating:
Soil Erosion Load (LE)
Cs-Kd • BD
LE = Xe • (AL - Aj) -SD-ER- ' • 0.001 Equation 5-33
0™+ Kds-BD
where
LE = Soil erosion load (g/yr)
Xe = Unit soil loss (kg/m2-yr)
AL = Total watershed area (evaluated) receiving COPC deposition (m2)
Aj = Impervious watershed area receiving COPC deposition (m2)
SD = Sediment delivery ratio (watershed) (unitless)
ER = Soil enrichment ratio (unitless)
Cs = Average soil concentration over exposure duration (in watershed soils) (mg
COPC/kg soil)
BD = Soil bulk density (g soil/cm3 soil) = 1.5 g/cm3
•^ = Soil volumetric water content (ml water/cm3 soil) = 0.2 ml/cm3
Kds = Soil-water partition coefficient (ml water/g soil)
0.001 = Units conversion factor (k-cm2/mg-m2)
Section 5.7.2 describes unit soil loss (Xe). Chapter 4 and Appendix B describe how we recommend
determining the site-specific parameters AL and^47. We generally recommend calculating the watershed
sediment delivery ratio (SD) as described in Section 5.7.3 and in Appendix B, Table B-4-14. COPC
concentration in soils (Cs) is described in Section 5.2.1, and Appendix B, Table B-4-1. Soil bulk density
(BD) is described in Section 5.2.4.2. Soil water content (•£,) is described in Section 5.2.4.4. Appendix B,
Table B-4-11 describes how we recommend determining the COPC-specific soil enrichment ration (ER).
5.7.2 Universal Soil Loss Equation - USLE
As in U.S. EPA (1994g and 1994r), we generally recommend using the universal soil loss equation
(USLE), Equation 5-33A, to calculate the unit soil loss (XJ specific to each watershed. This equation is
further described in Appendix B, Table B-4-13. Appendix B also describes how we suggest determining
the site- and watershed-specific values for each of the variables associated with Equation 5-33A.
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Recommended Equation for Calculating:
Unit Soil Loss (Xe)
007 i o
Xe = RF'K'LS'C'PF'- Equation 5-33A
where
Xe = Unit soil loss (kg/m2-yr)
RF — USLE rainfall (or erosivity) factor (yr ')
K — USLE erodibility factor (ton/acre)
LS = USLE length-slope factor (unitless)
C — USLE cover management factor (unitless)
PF = USLE supporting practice factor (unitless)
907.18 = Units conversion factor (kg/ton)
4047 = Units conversion factor (mVacre)
The USLE RF variable, which represents the influence of precipitation on erosion, is derived from data
on the frequency and intensity of storms. This value is typically derived on a storm-by-storm basis, but
average annual values have been compiled (U.S. Department of Agriculture 1982). Information on
determining site-specific values for variables used in calculating^ is provided in U.S. Department of
Agriculture (U.S. Department of Agriculture 1997) and U.S. EPA guidance (U.S. EPA 1985b).
Refer to Appendix B, TableB-4-13 for additional discussion of the USLE.
5.7.3 Sediment Delivery Ratio (SD)
We recommend using Equation 5-34 to calculate the sediment delivery ratio (SD). The use of this
equation is further described in Appendix B, Table B-4-14.
Recommended Equation for Calculating:
Sediment Delivery Ratio (SD)
SD = a-(AL)~b Equation 5-34
where
SD = Sediment delivery ratio (watershed) (unitless)
a — Empirical intercept coefficient (unitless)
b = Empirical slope coefficient (unitless)
AL — Total watershed area (evaluated) receiving COPC deposition (m2)
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The sediment delivery ratio (SD) for a large land area (i.e. a watershed or part of a watershed) can be
calculated, on the basis of the area of the watershed, by using an approach proposed by Vanoni (1975).
Accordingly, U.S. EPA (1998c) recommended using Equation 5-34 to calculate the SD.
According to Vanoni (1975), sediment delivery ratios vary approximately with the -0.125 power of the
drainage area. Therefore, the empirical slope coefficient is assumed to be equal to 0.125. An inspection
of the data presented by Vanoni (1975) indicates that the empirical intercept coefficient varies with the
size of the watershed, as illustrated in Appendix B, Table B-4-14.
AL is the total watershed surface area evaluated that is affected by deposition and drains to the body of
water (see Chapter 4). In assigning values to the watershed surface area affected by deposition, we
generally consider the following relevant:
• the distance from the emission source,
• the location of the area affected by deposition fallout with respect to the point at which
drinking water is extracted or fishing occurs
• the watershed hydrology.
5.7.4 Total Water Body COPC Concentration (CMot)
We recommend using Equation 5-35 to calculate the total water body COPC concentration (Cw(0(). Cwtot
includes both the water column and the bed sediment. The equation is also presented in Appendix B,
Table B-4-15.
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Recommended Equation for Calculating:
Total Water Body COPC Concentration (CMJ
~
where
Cvtot = Total water body COPC concentration (including water column and bed
sediment) (g COPC/m3 water body)
LT — Total COPC load to the water body (including deposition, runoff, and erosion)
(g/yr)
Vfx — Average volumetric flow rate through water body (mVyr)
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
kvt — Overall total water body COPC dissipation rate constant (yr ')
Aw = Water body surface area (m2)
dvc — Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
The total COPC load to the water body (LT)—including deposition, runoff, and erosion—is described in
Section 5.7.1 and Appendix B, Table B-4-7. Average volumetric flow rate through the water body (FjQ
and water body surface area (Aw) are discussed in Appendix B. Section 5.7.4.1 discusses the fraction of
total COPC concentration in the water column (/^c). Section 5.7.4.2 discusses the COPC dissipation rate
constant (kwt). Chapter 4 discusses the water body-specific dwc. We discuss the depth of the upper benthic
sediment layer (dbs) below.
The depth of the upper benthic layer (dbs), which represents the portion of the bed that is in equilibrium
with the water column, cannot be precisely specified; however, U.S. EPA (1998c) recommended values
ranging from 0.01 to 0.05. As in U.S. EPA (1994r), we recommend a default value of 0.03, which
represents the midpoint of the specified range. Issues related to the remaining parameters are summarized
in the following subsections.
Recommended Default Value for:
Depth of Upper Benthic Sediment Layer (dbs)
0.03 m
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5.7.4.1 Fraction of Total Water Body COPC Concentration in the Water Column (fwc) and Benthic
Sediment (fts)
We generally recommend using Equation 5-36A to calculate the fraction of total water body COPC
concentration in the water column (fwc), and Equation 5-36B to calculate the total water body contaminant
concentration in benthic sediment (fbs). The equations are also presented in Appendix B, Table B-4-16.
Recommended Equation for Calculating:
Fraction of Total Water Body COPC Concentration in
the Water Column (fwc) and Benthic Sediment (fbs)
, _ swwcz _
Jwc ~ - ; - Equation 5-36A
ixio-6)-^/^^* Kdbs-cBS)-djdz
Jbs ~ 1 Jwc Equation 5-36B
where
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
fbs = Fraction of total water body COPC concentration in benthic sediment
(unitless)
Kdsv — Suspended sediments/surface water partition coefficient (L water/kg
suspended sediment)
TSS — Total suspended solids concentration (mg/L)
1 x 10"6 = Units conversion factor (kg/mg)
dz — Total water body depth (m)
Qbs = Bed sediment porosity (Lwater/Lsediment)
Kdbs — Bed sediment/sediment pore water partition coefficient (L water/kg bottom
sediment)
CBS = Bed sediment concentration (g/cm3 [equivalent to kg/L])
dvc — Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
The CO PC-specific partition coefficient (Kdsv) describes the partitioning of a contaminant between
sorbing material, such as soil, surface water, suspended solids, and bed sediments (see Appendix A-2).
Total suspended solids (TSS), total water body depth (dz), bed sediment porosity (Qbs) and bed sediment
concentration (CBS) are addressed below. Bed sediment and sediment pore water partition coefficient
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(Kdbs) is discussed in Appendix A-2. Depth of water column (dwc) and depth of upper benthic layer (dbs)
are discussed in Section 5.7.4.
U.S. EPA(1998c) andNCDEHNR (1997) recommended using Equations 5-36 A and 5-36B to calculate
fwc and/fcs. NC DEHNR (1997) also recommended adding the depth of the water column to the depth of
the upper benthic layer (dwc + dbs) to calculate the total water body depth (dz).
NC DEHNR (1997) recommended a default total suspended solids (TSS) concentration of 10 mg/L, which
was adapted from U.S . EPA (1 993 e). However, due to variability in water body specific values for this
variable, we recommend using water body-specific measured TSS values representative of long-term
average annual values. Average annual values for TSS are generally expected to be in the range of 2 to
300 mg/L. Additional information on anticipated TSS values is available in U.S. EPA (1998c).
If measured data are not available, or of unacceptable quality, it's possible to calculate a TSS value for
non-flowing water bodies using Equation 5-36C.
where
X'(Ar-AT)'SD-
TSS = — - - - - - Equation 5-36C
Vfx+ Dss • Aw
TSS = Total suspended solids concentration (mg/L)
Xe — Unit soil loss (kg/m2-yr)
AL = Total watershed area (evaluated) receiving COPC deposition (m2)
Aj — Impervious watershed area receiving COPC deposition (m2)
SD = Sediment delivery ratio (watershed) (unitless)
Vfx — Average volumetric flow rate through water body (value should be 0 for
quiescent lakes or ponds) (mVyr)
Dss — Suspended solids deposition rate (a default value of 1,825 for quiescent
lakes or ponds) (m/yr)
Aw = Water body surface area (m2)
The default value of 1,825 m/yr provided for Dss is characteristic of Stoke's settling velocity for an
intermediate (fine to medium) silt.
Also, it's possible to evaluate the appropriateness of watershed-specific values used in calculating the unit
soil loss (JQ, as described in Section 5.7.2 and Appendix B, by comparing the water-body specific
measured TSS value to the estimated TSS value obtained using Equation 5-36C. If the measured and
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calculated TSS values differ significantly, we recommend re-evaluating the parameter values used to
calculate Xe. You might also re-evaluate TSS andXe if the calculated TSS value is outside of the normal
range expected for average annual measured values, as discussed above.
One approach to calculating bed sediment porosity (6fts) from the bed sediment concentration is by using
the following equation (U.S. EPA 1998c):
Equation 5-37
where
8fcs = Bed sediment porosity (Lwater/Lsediment)
ps = Bed sediment density (kg/L)
CBS — Bed sediment concentration (kg/L)
We recommend the following default value for bed sediment porosity (6fcs), adapted from NC DEHNR
(1997):
Recommended Value for:
Bed Sediment Porosity (6As)
6fcS = 0.6 Lwater/Lsediment
assuming
ps = 2.65 kg/L [bed sediment density]
and
CBS= 1.0 kg/L [bed sediment concentration])
U.S. EPA (1994r) and NC DEHNR (1997) recommended a benthic solids concentration (CBS) ranging
from 0.5 to 1.5 kg/L, which was adapted from U.S. EPA (1993e). We recommend the following default
value for bed sediment concentration (CBS):
Recommended Default Value for:
Bed Sediment Concentration (CBS)
1.0 kg/L
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5.7.4.2 Overall Total Water Body COPC Dissipation Rate Constant (kwt)
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-38 to calculate the
overall dissipation rate of COPCs in surface water, resulting from volatilization and benthic burial. The
equation is also presented in Appendix B, Table B-4-17.
Recommended Equation for Calculating:
Overall Total Water Body COPC Dissipation Rate Constant (£„,,)
^wt = f\vc ' kv + fbs ' kb Equation 5-3 8
where
£ = Overall total water body dissipation rate constant (yr ')
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
kv — Water column volatilization rate constant (yr ')
fbs = Fraction of total water body COPC concentration in benthic sediment
(unitless)
kb = Benthic burial rate constant (yr ')
The variable s/j,,,, andfbs are discussed in Section 5.7.4.1. The water column volatilization rate constant
(&„) is discussed in Section 5.7.4.3. The benthic burial rate constant (kb)is discussed in Section 5.7.4.7.
5.7.4.3 Water Column Volatilization Rate Constant (&„)
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-39 to calculate kv.
The equation is also presented in Appendix B, Table B-4-18.
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Recommended Equation for Calculating:
Water Column Volatilization Rate Constant (kv)
v , ,, r^, „,„„ , ^_fi. Equation 5-39
where
kv — Water column volatilization rate constant (yr ')
Kv — Overall CO PC transfer rate coefficient (m/yr)
dz = Total water body depth (m)
Kdsv — Suspended sediments/surface water partition coefficient (L water/kg
suspended sediments)
TSS — Total suspended solids concentration (mg/L)
1 x 10"6 = Units conversion factor (kg/mg)
The overall transfer rate coefficient (Kv) is discussed in Section 5.7.4.4. Total water body depth (dz),
suspended sediment and surface water partition coefficient (Kdsw), and total suspended solids
concentration (TSS), are described in Section 5.7.4.1. Kdsw is also discussed in Appendix A-2.
5.7.4.4 Overall COPC Transfer Rate Coefficient (#„)
Volatile organic chemicals can move between the water column and the overlying air. The overall
transfer rate Kv, or conductivity, is determined by a two-layer resistance model that assumes that two
"stagnant films" are bounded on either side by well-mixed compartments. Concentration differences
serve as the driving force for the water layer diffusion. Pressure differences drive the diffusion for the air
layer. From balance considerations, the same mass must pass through both films; the two resistances
thereby combine in series, so that the conductivity is the reciprocal of the total resistance.
As in U.S. EPA (1993f; 1993e; 1998c), andNCDEHNR (1997), we recommend using Equation 5-40 to
calculate Kv. The equation is also presented in Appendix B, Table B-4-19.
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Recommended Equation for Calculating:
Overall COPC Transfer Rate Coefficient (#„)
where
Kv
KL
KG
H
R
Twk
6
K,
'1
Kr
H
- 1
- 293
Overall COPC transfer rate coefficient (m/yr)
Liquid phase transfer coefficient (m/yr)
Gas phase transfer coefficient (m/yr)
Henry's Law constant (atm-m3/mol)
Universal gas constant (atm-m3/mol-K)
Water body temperature (K)
Temperature correction factor (unitless)
Equation 5-40
The liquid and gas phase transfer coefficients, KL and KG, respectively, vary with the type of water body.
We discuss the liquid phase transfer coefficient (KL) in Section 5.7.4.5, and the gas phase transfer
coefficient (KG) in Section 5.7.4.6.
Henry's Law constants generally increase with increasing vapor pressure of a COPC and generally
decrease with increasing solubility of a COPC. Henry's Law constants are COPC-specific and we offer
recommended default values in the HHRAP companion database. The universal ideal gas constant, R, is
8.205 x 10"5 atm-mVmol-K, at 20°C. The temperature correction factor (6), which is equal to 1.026,
adjusts for the actual water temperature. Equation 5-40 assumes that volatilization occurs much less
readily in lakes and reservoirs than in moving water bodies.
The value of the conductivity Kv depends on the intensity of turbulence in the water body and the
overlying atmosphere. As Henry's Law constant increases, the conductivity tends to be increasingly
influenced by the intensity of turbulence in water. Conversely, as Henry's Law constant decreases, the
value of the conductivity tends to be increasingly influenced by the intensity of atmospheric turbulence.
5.7.4.5 Liquid Phase Transfer Coefficient (KL)
We generally recommend using Equations 5-41A and 5-41B to calculate liquid phase transfer coefficient.
(KL). The use of these equations is further described in Appendix B, Table B-4-20.
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Recommended Equation for Calculating:
Liquid Phase Transfer Coefficient (KL)
September 2005
For flowing streams or rivers:
KL
\
V J ~w
dz
'3.1536xl07
For quiescent lakes or ponds:
n* P Jr°'33 U
^ = (Cj • W) - (—)°-5 • -—•( w )"°-67-3.1536xlQ7
Equation 5-41A
Equation 5-41B
where
KL
Dw
u
1 x 10"4
dz
Cd
W
pa
pw
k
Xz
|iw
3.1536 x 107
Liquid phase transfer coefficient (m/yr)
Diffusivity of COPC in water (cm2/s)
Current velocity (m/s)
Units conversion factor (mVcm2)
Total water body depth (m)
Drag coefficient (unitless)
Average annual wind speed (m/s)
Density of air (g/cm3)
Density of water (g/cm3)
von Karman's constant (unitless)
Dimensionless viscous sublayer thickness (unitless)
Viscosity of water corresponding to water temperature (g/cm-s)
Units conversion factor (s/yr)
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using the following default values:
• a diffusivity of chemical in water ranging (Dw) from 1.0 x 10 5 to 8.5 x 10"2 cm2/s,
a drag coefficient (Cd) of 0.0011 which was adapted from U.S. EPA (1998c),
• a density of air (pa) of 0.0012 g/cm3 at standard conditions (temperature = 20°C or 293 K,
pressure = 1 atm or 760 millimeters of mercury) (Weast 1986),
• a density of water (pw) of 1 g/cm3 (Weast 1986),
• a von Karman's constant (k) of 0.4,
• a dimensionless viscous sublayer thickness (Xz) of 4,
• a viscosity of water (|iw) of a 0.0169 g/cm-s corresponding to water temperature
(Weast 1986).
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The values above are further discussed in Appendix A-2. Chapter 4 discusses the current velocity (u).
Chapter 3 describes methods for determining the average annual wind speed (W). Section 5.7.4.1
discusses the total water body depth (dz) for liquid phase transfer coefficients.
For a flowing stream or river, the transfer coefficients are controlled by flow-induced turbulence. For
these systems, we recommend calculating KL using Equation 5-41 A, which is the O'Connor and Dobbins
(1958) formula, as presented in U.S. EPA (1998c).
For a stagnant system (quiescent lake or pond), the transfer coefficient is controlled by wind-induced
turbulence. For quiescent lakes or ponds, we recommend calculating KL using Equation 5-41B
(O'Connor 1983; U.S. EPA 1998c).
5.7.4.6 Gas Phase Transfer Coefficient (KG)
We generally recommend using Equations 5-42A and 5-42B to calculate gas phase transfer coefficient
(KG). The equation is also discussed in Appendix B, Table B-4-21.
Recommended Equation for Calculating:
Gas Phase Transfer Coefficient (KG)
For flowing streams or rivers:
KG = 36500 m/yr Equation 5 -42 A
For quiescent lakes or ponds:
L0.33 M,
KG = (Cf -W)-±—' (-^-)-°'67 ' 3.1536X 107 Equation 5-42B
\ f>a'Da
where
KG — Gas phase transfer coefficient (m/yr)
Cd = Drag coefficient (unitless)
W — Average annual wind speed (m/s)
k = von Karman's constant (unitless)
Az = Dimensionless viscous sublayer thickness (unitless)
[ia = Viscosity of air corresponding to air temperature (g/cm-s)
pa = Density of air corresponding to water temperature (g/cm3)
Da = Diffusivity of COPC in air (cmVs)
3.1536\IQ7 — Units conversion factor (s/yr)
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The following parameters, including default values, are discussed in Section 5.7.4.5, and in Appendix A-
2: Cd, k, Az, and pa. Chapter 3 describes methods for determining the average annual wind speed (W).
AsinU.S.EPA(1994r)andNC DEHNR (1997), we recommend using a value of 1.81 x 10'4 g/cm-s for
the viscosity of air corresponding to air temperature (|ia). Appendix A-2 discusses the CO PC-specific
parameter Da.
U.S. EPA (1998c) indicated that the rate of transfer of a COPC from the gas phase for a flowing stream or
river is assumed to be constant, in accordance with O'Connor and Dobbins (1958) (Equation 5-42A).
For a stagnant system (quiescent lake or pond), the transfer coefficients are controlled by wind-induced
turbulence. For quiescent lakes or ponds, we recommend calculating the gas phase transfer coefficient
using the equation presented in O'Connor (1983) (Equation 5-42B).
5.7.4.7 Benthic Burial Rate Constant (kb)
We generally recommend using Equation 5-43 to calculate benthic burial rate (kb). The equation is also
discussed in Appendix B, Table B-4-22.
Recommended Equation for Calculating:
Benthic Burial Rate Constant (kt)
where
** =
Xe • AL • SD • 1 x 103 - Vfx- TSS
SD
TSS
1 x 10~6 =
1 x 103 =
Aw • TSS
10
-6
CBS ' dbs
Benthic burial rate constant (yr ')
Unit soil loss (kg/m2-yr)
Total watershed area (evaluated) receiving deposition (m2)
Sediment delivery ratio (watershed) (unitless)
Average volumetric flow rate through water body (m3/yr)
Total suspended solids concentration (mg/L)
Water body surface area (m2)
Bed sediment concentration (g/cm3)
Depth of upper benthic sediment layer (m)
Units conversion factor (kg/mg)
Units conversion factor (g/kg)
Equation 5-43
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Section 5.7.2 discusses the unit soil loss (Xe). Section 5.7.3 discusses watershed area evaluated receiving
COPC deposition (AL) and sediment delivery ratio (SD). Average volumetric flow rate through the water
body (Vfx) and water body surface area (Aw) are discussed in Appendix B. Aw is also discussed in
Appendix A-2. Section 5.7.4.1 discusses total suspended solids concentration (TSS) and bed sediment
concentration (CBS). Section 5.7.4 discusses the depth of the upper benthic sediment layer (dbs).
It's possible to express the benthic burial rate constant (kb), which is calculated in Equation 5-43, in terms
of the rate of burial (Wb):
Wb = kb • dbs Equation 5-44
where
Wb = Rate of burial (m/yr)
kb = Benthic burial rate constant (yr ')
dbs = Depth of upper benthic sediment layer (m)
According to U.S. EPA (1994r) and NC DEHNR (1997), COPC loss from the water column resulting
from burial in benthic sediment can be calculated using Equation 5-43.
We expect kb values to range from 0 to 1.0: Low kb values for water bodies with limited or no
sedimentation (rivers and fast flowing streams), and kb values closer to 1.0 for water bodies characteristic
of higher sedimentation (lakes). This range of values is based on the relation between the benthic burial
rate and rate of burial expressed in Equation 5-44; with the depth of upper benthic sediment layer held
constant. If you calculate a negative kb value (water bodies with high average annual volumetric flow
rates in comparison to watershed area evaluated), we recommend using a kb value of 0 in calculating the
total water body COPC concentration (Cwtot) in Equation 5-35. If the calculated kb value exceeds 1.0, we
recommend re-evaluating the parameter values used in calculating Xe. Our experience has shown that the
value calculated for Xe is the most likely reason for estimating a large and potentially unrealistic benthic
burial rate. Information about determining site-specific values and variables for calculating Xe are in the
references cited in Section 5.7.2.
5.7.4.8 Total COPC Concentration in Water Column (Cwctot)
As in U.S. EPA (1994r) andNC DEHNR (1997), we generally recommend using Equation 5-45 to
calculate total COPC concentration in water column (Cwctot). The equation is also discussed in Appendix
B, Table B-4-23.
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Recommended Equation for Calculating:
Total COPC Concentration in Water Column (
(
c - f • c
wctot Jwc wtot
C + dhs
j Equalioii 5-45
we
where
Cvctot = Total COPC concentration in water column (mg COPC/L water column)
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
Cwtot = Total water body COPC concentration, including water column and bed
sediment (mg COPC/L water body)
dwc = Depth of water column (m)
dbs — Depth of upper benthic sediment layer (m)
We discussed the fraction of total water body COPC concentration in the water column (fwc) in Section
5.7.4.1. We discussed the total COPC Concentration in the water column (Cwaot), as well as depth of the
water column (dwc) and benthic sediment layer (dbs) in Section 5.7.4.
5.7.4.9 Dissolved Phase Water Concentration (CdK)
We recommend using Equation 5-46 to calculate the concentration of COPC dissolved in the water
column (Cdw). The equation is discussed in detail in Appendix B, Table B-4-24.
Recommended Equation for Calculating:
Dissolved Phase Water Concentration (Cdw)
wctot
1 + Kd • TSS- lxl(T6
Equation 5-46
where
Cdw = Dissolved phase water concentration (mg COPC/L water)
Cwctot = Total COPC concentration in water column (mg COPC/L water column)
Kdsv — Suspended sediments/surface water partition coefficient (L water/kg
suspended sediment)
TSS — Total suspended solids concentration (mg/L)
1 x 10"6 = Units conversion factor (kg/mg)
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We discussed Cwctot in Section 5.7.4.8. We discussed Kd^ and TSS in Section 5.7.4.1.
Using Equation 5-46 to calculate the concentration of COPC dissolved in the water column is consistent
with recommendations in U.S. EPA (1994r) and NC DEHNR (1997).
5.7.4.10 COPC Concentration Sorbed to Bed Sediment (CJ
We recommend using Equation 5-47 to calculate COPC concentration sorbed to bed sediment (Csb). The
equation is also presented in Appendix B, Table B-4-25.
Recommended Equation for Calculating:
COPC Concentration Sorbed to Bed Sediment (Csb)
_ f ft I os I I we bs I
~ Jbs ' ^wtot' ^——^ 7T~ ' ~, Equation 5-47
where
Csb = COPC concentration sorbed to bed sediment (mg COPC/kg sediment)
fbs = Fraction of total water body COPC concentration in benthic sediment
(unitless)
Cwtot = Total water body COPC concentration, including water column and
bed sediment (mg COPC/L water body)
Kdbs = Bed sediment/sediment pore water partition coefficient (L COPC/kg
water body)
' fc = Bed sediment porosity (Lpore water/Lsediment)
CBS = Bed sediment concentration (g/cm3)
dwc = Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
We discussed^, 'fs, and CBS in Section 5.7.4.1. We discussed Cwtot, dvc, and dbs in Section 5.7.4. We
discuss Kdbs in Appendix A-2.
As in U.S. EPA (1994r) and NC DEHNR (1997), we continue to recommend using Equation 5-47 to
calculate the COPC concentration sorbed to bed sediment.
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5.7.5 Concentration of COPC in Fish (Cfish)
We generally recommend calculating the COPC concentration in fish using either a
COPC-specific bioconcentration factor (BCF), a COPC-specific bioaccumulation factor
(BAF), or a COPC-specific biota-sediment accumulation factor (BSAF). Under this approach you would
use BCFs for COPCs with a log Km less than 4.0. We assume that COPCs with a log Kow greater than
4.0 (except for extremely hydrophobic compounds such as dioxins, furans, and PCBs), have a high
tendency to bioaccumulate. As a result, BAFs are used. While we assume that extremely hydrophobic
COPCs like dioxins, furans, and PCBs also have a high tendency to bioaccumulate, they are expected to
be sorbed to the bed sediments more than associated with the water phase. Therefore, we recommend
using BSAFs to calculate concentrations of dioxins, furans, and PCBs in fish. Appendix A-2 provides a
detailed discussion on the sources of the COPC-specific BCF, BAF, and BSAF values, and the method we
used to derive them.
Soil
Concentration
Volatilization
Dissolved-Phase
Water
Concentration
FIGURE 5-8
COPC CONCENTRATION IN FISH
COPC Concentration
in Fish
Direct
Deposition
Total Water Body
Concentration
Total Water
Column
Concentration
COPC Concentration
in Fish
Vapor
Transfer
Benthic
Burial
COPC Concentration
in Fish
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BCF and BAF values are generally based on dissolved water concentrations. Therefore, when you use
BCF or BAF values, it's appropriate to calculate the COPC concentration in fish using dissolved water
concentrations. BSAF values are based on benthic sediment concentrations. Therefore, when using BSAF
values, we recommend calculating COPC concentrations in fish using benthic sediment concentrations.
We describe our recommended equations for calculating fish concentrations in the subsequent
subsections.
5.7.5.1 Fish Concentration (Cfish) from Bioconcentration Factors Using Dissolved Phase Water
Concentration
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-48 to calculate fish
concentration from BCFs using dissolved phase water concentration. Using this equation is further
described in Appendix B, Table B-4-26.
Recommended Equation for Calculating:
Fish Concentration (Cflsh) from Bioconcentration Factors (BCFfish)
Using Dissolved Phase Water Concentration
Cf,sh = Cdw'BCFflsh Equation 5-48
where
Cfish = Concentration of COPC in fish (mg COPC/kg FW tissue)
Cdw — Dissolved phase water concentration (mg COPC/L)
BCFfish = Bioconcentration factor for COPC in fish (L/kg)
We discussed Cdw in Section 5.7.4.9. CO PC-specific BCFflsh values are presented in the HHRAP
companion database.
5.7.5.2 Fish Concentration (Cflsh) from Bioaccumulation Factors Using Dissolved Phase Water
Concentration
We recommend using Equation 5-49 to calculate fish concentration from BAF?, using dissolved phase
water concentration. The equation is also presented in Appendix B, Table B-4-27.
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Recommended Equation for Calculating:
Fish Concentration (Cfish) from Bioaccumulation Factors (BAFfish)
Using Dissolved Phase Water Concentration
Cfish = Cdw ' BAFfish Equation 5-49
where
Cfish = Concentration of COPC in fish (mg COPC/kg FW tissue)
Cdw = Dissolved phase water concentration (mg COPC/L)
BAFflsh — Bioaccumulation factor for COPC in fish (L/kg FW tissue)
We discussed Cdvl in Section 5.7.4.9. COPC-specific bioaccumulation factor (BAFfisl) values are presented
in the HHRAP companion database.
5.7.5.3 Fish Concentration (Cflsh) from Biota-To-Sediment Accumulation Factors Using COPC
Sorbed to Bed Sediment
As in U.S. EPA (1994r) and NC DEHNR (1997), we recommend using Equation 5-50 to calculate fish
concentration from BSAFs using COPC concentrations sorbed to bed sediment. We recommend using
BSAFs for very hydrophobic compounds such as dioxins, furans, and PCBs. The equation is also
presented in Appendix B, Table B-4-28.
Recommended Equation for Calculating:
Fish Concentration (Cfish) from Biota-To-Sediment Accumulation Factors (BSAF)
Using COPC Sorbed to Bed Sediment
BSAF
Equation 5-50
where
Cflsh = Concentration of COPC in fish (mg COPC/kg FW tissue)
Csb = Concentration of COPC sorbed to bed sediment (mg COPC/kg bed sediment)
flipid — Fish lipid content (unitless)
BSAF = Biota-to-sediment accumulation factor (unitless)
OC-sed = Fraction of organic carbon in bottom sediment (unitless)
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We discussed Csb in Section 5.7.4.10. We discuss flipid and OCsedbelow. Our recommended default values
fwfupid and OCsed are given in Appendix B, Table B-4-28. We offer biota-to-sediment accumulation
factors (BSAF), which are applied only to dioxins, furans, and PCBs, in the HHRAP companion database.
Values recommended by U.S. EPA (1998c) range from 0.03 to 0.05 for OCSED. These values are based on
an assumption of a surface soil organic carbon (OC) content of 0.01. U.S. EPA (1998c) states that the
organic carbon content in bottom sediments is higher than the organic carbon content in soils because (1)
erosion favors lighter-textured soils with higher organic carbon contents, and (2) bottom sediments are
partially comprised of detritus materials.
The fish lipid content (fiipid) value is site-specific and dependent on the type offish consumed. As stated
in Appendix B, Table B-4-28, we recommend a default range of 0.03 to 0.07 specific to warm or cold
water fish species. U.S. EPA (2000c) provides information supporting a value of 0.03 (3 percent lipid
content of the edible portion). U.S. EPA (1993e) recommended a default value of 0.04 for OCSED, which is
the midpoint of the specified range. U.S. EPA (1993f; 1993e) recommended using 0.07, which was
originally cited in Cook et al. (1991).
5.8 USING SITE-SPECIFIC vs. DEFAULT PARAMETER VALUES
As initially discussed in Chapter 1, many of the parameter values we recommend in the HHRAP are not
site-specific. After completing a risk assessment using HHRAP default values, you might choose to
investigate using site-specific parameter values. More site-specific values might provide a more
representative estimate of site-specific risk. If you use parameter values other than those specified in the
HHRAP, we recommend that you clearly described them in the risk assessment report. We also
recommend that you discuss them with the permitting authority prior to using them. We recommend that
requests to change default parameter values include the following information, as appropriate:
1. An explanation of why using a more site-specific parameter value is warranted (e.g., the
default parameter is based on data or studies at sites in the northwestern U.S., but the
facility is located in the southeast);
2. The technical basis of the site-specific parameter value, including readable copies of any
relevant technical literature or studies;
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3. A comparison of the weight-of-evidence between the competing studies (e.g., the
site-specific parameter value is based on a study that is more representative of site
conditions, a specific exposure setting being evaluated, or a more scientifically valid
study than the default parameter, the site-specific parameter is based on the analysis of 15
samples as opposed to 5 for the default parameter, or the site-specific study used more
stringent quality control/quality assurance procedures than the study upon which the
default parameter is based);
4. A description of other risk assessments or projects that used the site-specific parameter
value, and how such risk assessments or projects are similar to the current risk
assessment.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Identification of site-specific or alternate default media equations and/or inputs; including
justification and full referencing
Media concentration calculations
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Chapter 6
Quantifying Exposure
What's Covered in Chapter 6:
6.1 Inhalation Exposure Pathways
6.2 Ingestion Exposure Pathways
6.3 Dermal Exposure Pathways
6.4 Exposure Frequency
6.5 Exposure Duration
6.6 Averaging Time
This chapter describes the factors to evaluate in quantifying the exposure received under each of the
recommended exposure scenarios described in Chapter 4. Calculating COPC-specific exposure rates for
each exposure pathway involves some or all of the following, depending upon the medium being
assessed:
the estimated COPC media concentrations calculated in Chapter 5,
• consumption rates of the medium,
receptor body weight, and
• the frequency and duration of exposure.
We recommend repeating the appropriate calculation for each COPC and for each exposure pathway
included in an exposure scenario, to generate multiple exposure concentration estimates, as recommended
in the EPA information quality guidelines (see Chapter 1, page 1-11). We present recommended
exposure pathway-specific equations in Appendix C. The following sections describe a general exposure
rate calculation and the exposure pathway-specific variables that may affect this calculation. Acute
exposure resulting from direct inhalation is discussed as a separate issue in Chapter 7, Section 7.4.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak is the performer of a risk assessment: the person (or persons) who will
actually put the recommended methods into practice.
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6.1 INHALATION EXPOSURE PATHWAYS
We recommend using COPC air concentrations calculated using the equation in Table B-5-1 to represent
air concentrations for estimating exposure via inhalation by all exposure scenarios in the risk assessment
(see Table 4-1).
Direct inhalation of vapors and particulate emissions from combustion sources is a potential pathway of
exposure. Chapter 2 presented various variables and conditions that affect the rate, type, and quantity of
combustor emissions. Chapter 3 presented the air dispersion and deposition modeling techniques we
recommend using to estimate airborne concentrations of vapors and particulates in the assessment area.
As a result of normal respiration, receptors in the assessment area could be exposed to COPCs in vapor,
particle, and particle-bound phases. Examples of factors that affect exposure from vapor and particulate
inhalation include vapor and particulate COPC concentrations, particle size, and length of exposure.
Exposure can occur over a period of time. To calculate an average exposure per unit of time (Exposure
Concentration, or EC), we recommend dividing the total exposure by the time period. We generally
recommend using total COPC air concentrations (Ca, estimated using the equation in Table B-5-1) when
estimating EC values. Estimating ECs doesn't involve or require adjustment for respiration rates, as
those are inherent to inhalation toxicity factors. Sections 6.4 through 6.6 discuss exposure time-related
parameters, and Appendix C Tables C-2-1 and C-2-2 further discuss estimating EC.
We consider it appropriate to estimate noncarcinogenic hazards and carcinogenic risks associated with
direct inhalation exposure by combining ECs with inhalation toxicity factors (reference concentrations
[RfCs] or unit risk factors [URFs]). These toxicity factors are developed for all human populations,
including sensitive subpopulations (including children) who might be exposed to continuous
concentrations over a lifetime. Inhalation risk parameters and inhalation pharmacokinetics are largely
chemical-specific and a "one size fits all" approach to convert the standard RfC or URF parameters into
scenario-specific toxicity values may not be appropriate. We therefore generally recommend that a
single (lifetime) inhalation risk be predicted for each receptor identified in Chapter 4, Table 4-1.
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Inhalation exposure concentrations for vapors and particles (arising from outdoor sources) can be
influenced by the relative amount of time that a receptor spends indoors. Although vapors entering
buildings and residences as a result of air exchange are likely to remain airborne and, therefore may be
inhaled, particulates entering these same buildings are more likely to settle out and not be inhaled.
However, for the purpose of the risk assessment, we recommend assuming that both vapor and
particulates are inhaled throughout the day, both indoors and outdoors.
6.1.1 Soil Inhalation Resulting from Dust Resuspension
We don't typically recommend evaluating the soil inhalation of resuspended dust exposure pathway.
However, site-specific exposure setting characteristics might support evaluating it (e.g. arid, windy
climates). This section therefore discusses exposure to soil resulting from dust resuspension.
Inhalation of soil resulting from dust resuspension could be an issue for site-specific exposure scenario
locations at which there is little vegetative cover. Wind erosion could resuspend pollutants in
contaminated soil as particles in the air. As dust is resuspended, receptors could inhale the pollutant
particles (direct inhalation of particulate matter is addressed in Section 6.1). The amount resuspended
depends on the:
• moisture content of the soil,
• fraction of vegetation cover,
• wind velocity,
• soil particle size,
• pollutant concentration in the soil, and
• size of the contaminated area.
Study of estimated exposures to deposited combustor emissions via dust resuspension indicates that dust
resuspension by wind erosion is usually not a significant pathway (U.S. EPA 1998c). Methods have also
been developed to assess the exposure to pollutants resuspended by wind erosion for landfills and
Superfund sites (U.S. EPA 1985a; 1988b; 1994q). We recommend consulting these reference documents
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if you'll be evaluating this exposure pathway. Also, it may be useful to review the methods described in
U.S. EPA(1998c).
6.2
INGESTION EXPOSURE PATHWAYS
Exposure can occur over a period of time. To calculate an average exposure per unit of time, we
recommend dividing the total exposure by the time period. Express an average exposure in terms of body
weight. Ingestion exposures quantified per the HHRAP are
• unitized for time and body weight,
• presented in units of milligrams per kilogram of body weight per day, and
• termed "intakes."
Equation 6-1 is a generic equation for calculating ingestion chemical intake (U.S. EPA 1989e):
C -CR-EF-ED
I =
gen
BW-AT
Equation 6-1
where
gen
CR
EF
ED
BW
AT
Intake—the amount of COPC at the exchange boundary (mg/kg/day); for
evaluating exposure to noncarcinogenic COPCs, the intake is referred to
as average daily dose (ADD); for evaluating exposure to carcinogenic
compounds, the intake is referred to as lifetime average daily dose
(LADD)
Generic COPC concentration in medium of concern (e.g., mg/kg for soil
or mg/L for surface water; see Chapter 5)
Consumption rate—the amount of contaminated medium consumed per
unit of time or event (e.g., kg/day for soil or L/day for water)
Exposure frequency (days/year)
Exposure duration (years)
Average body weight of the receptor over the exposure period (kg)
Averaging time—the period over which exposure is averaged (days); for
carcinogens, the averaging time is 25,550 days, based on a lifetime
exposure of 70 years; for noncarcinogens, averaging time equals ED
(years) multiplied by 365 days per year.
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We recommend variations of Equation 6-1 to calculate pathway- and receptor-specific exposures to
COPCs. We present the equations recommended for each exposure pathway in Appendix C. The
variation of input variables is also described in Appendix C.
The exposures calculated using the HHRAP are intended to represent reasonable maximum exposure
(RME) conditions, as further described in U.S. EPA (1989e). Studies of the compounding of
conservatism in probabilistic risk assessments show that setting as few as two factors at RME levels or
high end (e.g., near the 90th percentile), while setting the remaining variables at less protective typical, or
"central tendency" values (e.g., near the 50th percentile) results in output insignificantly different from
output generated using all input variables at an RME level (e.g., 99th percentile value) (Cullen 1994).
As described in Chapter 2 (Section 2.2.1), the estimated air concentrations and deposition rates are based
on RME emissions from trial or risk burns. We recommend setting the following variables set at RME
levels:
• the highest ISCST3 modeled air concentrations and deposition rates at chosen exposure
scenario locations,
• the exposure frequency, and
• the exposure duration.
We generally recommend setting other exposure parameters (e.g. body weight) at average levels.
6.2.1 Body Weight
The choice of body weight to use in the risk characterization equations presented in Appendix C depends
on the definition of the receptor at risk—which, in turn, depends on exposure and susceptibility to
adverse effects. U.S. EPA (1990e) defines the body weight of the receptor as either adult weight
(70 kilograms) or child weight (1 to 7 years; 17 kilograms) on the basis of data presented in Nelson et al.
(1969). However, as in other Agency guidance (U.S. EPA 1991b; 1994r; 1994g), we recommend using a
weight of 15 kilograms for the child (exposure duration of 6 years) in the risk assessment.
The daily intake for an exposure pathway is expressed as the dose per body weight. Because children
have lower body weights than adults. Typical ingestion exposures per body weight, such as for soil,
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milk, and fruits & vegetables, can be substantially higher for children. This is the primary reason to
evaluate the child resident scenario (U.S. EPA 1996g). However, using these two body weights may not
account for significant differences between weights of infants and toddlers or weights of teenagers and
adults. Please remember that, for the purposes of the risk assessment, the child scenario is defined by the
average body weight, rather than the chronological age. Obviously, the weight of a child changes
significantly over the first several years. We assume 15 kilograms is a realistic average estimate for an
exposure duration of 6 years. 15 kilograms overestimates the weight of the child for the early years, and
then underestimates it for the later years (U.S. EPA 1996g).
6.2.2 Food (Ingestion) Exposure Pathways
Plants and animals impacted by emission sources may take up emitted COPCs in the air or deposited
COPCs in soil. Humans could then be exposed to COPCs via the food chain when they consume these
plants and animals as food. We generally recommend determining human intake of COPCs based on the:
• types of foods consumed,
• amount of food consumed per day,
• concentration of COPCs in the food, and
• percentage of the diet contaminated by COPCs.
Chapter 6 describes procedures for determining the concentration of COPCs in food. It also considers
the variations in exposure resulting from food preparation methods and type of food item (e.g.,
aboveground versus belowground - i.e. root - vegetables). Other variables, described below, may also
significantly affect exposure estimates.
6.2.2.1 Types of Foods Consumed
The types of foods consumed will affect exposure, because different plant and animal tissues take up
different COPCs, and take them up at different rates. The COPC concentrations a receptor is exposed to
will then vary with the types of food in the diet. Therefore, it is important to determine COPC
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concentrations in food according to the type of food. The types of food consumed might also vary with
the age of the receptor, geographical region, and socio-cultural factors.
6.2.2.2 Food Consumption Rate
Consumption rate is the amount of contaminated medium (soil, food) consumed per unit of time or event.
The soil at an exposure location are inherent to the location. Food consumed at an exposure location,
however, may or may not have originated there. The HHRAP assumes that only food produced at the
exposure location is contaminated by emissions from the facility being assessed. Food not produced at
the point of exposure is not assumed to be contaminated, and is irrelevant to the assessment. Therefore,
the consumption rates we recommend in the HHRAP (see Table 6-1 and Appendix C) are for food that is
both produced and consumed at the exposure location (i.e. at home).
Please Note: these rates do not represent the entire dietary intake of the individual, but only that
portion of the diet produced at home. For example, the beef consumption rate represents only the
amount of beef consumed each day which was raised on the farm property.
TABLE 6-1
MEAN CONSUMPTION RATES" FOR RECOMMENDED EXPOSURE SCENARIOS
(number of servings per week)
Contaminated food
Produce (8 oz servings)
Beef (1/4 Ib servings)
Milk (8 oz servings)
Chicken (1/4 Ib servings)
Eggs (number")
Pork (1/4 Ib servings)
Fish (1/4 Ib servings)
Exposure Scenario
Farmer b
2.8
5.3
29.5
2.8
4.3
2.4
N/A
Farmer
Child"
1.4
0.7
10.5
0.4
0.7
0.4
N/A
Resident
2.3
N/A
N/A
N/A
N/A
N/A
N/A
Resident
Child
1.2
N/A
N/A
N/A
N/A
N/A
N/A
Fisher
2.3
N/A
N/A
N/A
N/A
N/A
5.4
Fisher
Child
1.2
N/A
N/A
N/A
N/A
N/A
0.8
Notes:
Values derived from the U.S. EPA Exposure Factors Handbook (1997).
Values based on consumption rates of a 154 Ib adult and a 33 Ib child.
Values based on an assumed egg weight of 3.0 ounces.
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As described in Section 6.2, exposures calculated using HHRAP methods are intended to represent RME
conditions. Accordingly, the HHRAP recommends default values for exposure parameters that will
result in RME estimates. However, there are likely to be differences between recommended default, and
regional and site-specific exposure parameter values. This may be especially true for the consumption
rates (a general term for intake rates and inhalation rates). In addition to estimates using the
recommended default parameter values, you can refine the your risk assessment by including
supplemental calculations using regional- or site-specific parameter values. We recommend doing this
only if you document the regional- or site-specific parameter values in the risk assessment report. We
recommend providing these supplemental calculations in addition to, and not instead of estimates based
on recommended default exposure parameter values. This will help standardize risk assessment methods,
thereby aiding the ability to compare outputs from different risk assessments. The following subsections
describe exposure pathway-specific considerations regarding consumption rate.
This section gives some of the pertinent history of consumption rates. It also describes the series of steps
we followed to derive the recommended consumption rates found in Table 6-1. Site-specific conditions
might exist such that you need to derive an alternative, or additional consumption rate. For example, you
may need a consumption rate for an additional exposure scenario (e.g. consumption of deer meat for a
hunter scenario). If you need to calculate your own consumption rate(s), we recommend using the
process described below. For transparency and clarity, we recommend identifying all consumption rates
used in the risk assessment. We also recommend clearly identifying and discussing alterations to
recommended default rates, and additional consumption rates, with the permitting authority prior to use.
The amount of daily food consumption varies with age, sex, body weight, and geographic region, and it
also varies within these categories. U.S. EPA (1998c) recommended using values from the 1997
Exposure Factors Handbook (EFH) (U.S. EPA 1997b) to complete the risk assessment process. The
EFH used the 1987-1988 USDA Food Consumption Survey to represent consumption rates for urban and
suburban areas. However, if site-specific information indicates that the population is in a more rural or
agricultural area, U.S. EPA (1990e) recommended using the 1966-67 USDA Food Consumption Survey
to represent the consumption rates of a more agrarian population.
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The 1997 Exposure Factors Handbook (EFH) (U.S. EPA 1997b) performed an analysis of the 1987-1988
USDA National Food Consumption Survey (NFCS). The NFCS collects information over a 7-day period
on the socioeconomic and demographic characteristics of households, and the types, values, and sources
of foods consumed. The following information was taken from the survey:
• whether or not the food product was used in the house that week;
• whether or not the food product used that week was home produced;
• the quantity (mass, such as pounds or kilograms) of food consumed (home produced or
not) in the house that week;
• the number, age, and body weight of individuals in the household; and
• the number of weekly meals consumed by each family member.
All households were surveyed about the same food types, and consumption rates were averaged over the
entire survey population - to calculate what are known in the EFH as the "Per Capita" rates. In addition,
EPA calculated consumption rates for "consumers only:" a rate for only those households which
consumed a particular food stuff during the week the survey was taken. In addition to total consumption
(i.e. for the entire population of consumers), rates were broken out according to various criteria, such as
age of consumer, geographic region, and level of urbanization. Survey participants were also asked if
they operated a farm or ranch, raised animals, or had a home garden, and consumption rates were also
broken out for these sub-groups. We recommend using food consumption rate information (ingestion
rates) from the EFH; specifically, the section regarding home produced food items.
Chapter 13 of the EFH (Intake Rates for Various Home Produced Food Items) lists consumer only
consumption rates of home produced food. For example, Table 13-65 (Consumer Only Intake of Home
grown Root Vegetables (g/kg-day)) lists a mean consumption rate of 1.16 g/kg-day for all households
that consumed root vegetables they produced themselves. From these households, the EFH also breaks
out consumption rates specific to households who farm (e.g. a mean of 2.63 g/kg-day). These farm-
specific rates represent the amount of food that farm families produced themselves that was consumed
during the week the survey was taken. We recommend using these consumption rates to estimate
exposures to the Farmer and Farmer Child scenarios.
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The recommended farm-specific consumption rates, as listed in the EFH, are averaged across all family
members, regardless of age or body weight. They also do not consider losses during preparation and
cooking of the food. Additional work is needed, therefore, to acquire scenario-appropriate actual
consumption rates:
As mentioned above, the EFH provides consumption rates for the entire population of consumers - the
consumer only rate. Depending on data availability, the consumer only rate is also broken out into
various age-specific demographics, covering 0-1 year, 1-2 years, 3-5 years, 6-11 years, 12-19 years,
20-39 years, and 40-69 years. We suggest combining the consumer only rate, age-appropriate
demographic rates, and the sub-population rate (e.g. for a farm family) to derive a scenario-specific
consumption rate (e.g. for an adult Farmer) using equation 6-2:
Equation 6-2
where:
CRscen = Consumption rate for scenario
CRsubpop = Consumption rate of subpopulation
TWAgen = Time-weighted average of age-appropriate subset of consumer only population
CRgen = Consumption rate for consumer only population
Sufficient age-specific data were not always available. For example, there was insufficient data for the
EFH to provide a consumer only poultry consumption rate for the 12-19 age group. In this event we
suggest again using Equation 6-2, to combine the total consumer only population rate, the age-specific
Per Capita rate, and the total Per Capita rate (for poultry, Per Capita values are found in EFH Table
11-11), to generate an age-specific consumer only rate.
See the example derivation below for a demonstration of how we recommend generating scenario-
specific consumption rates. To derive consumption rates for the Farmer and Farmer Child exposure
scenarios, we recommend using the home produced, consumers only consumption rates of households
who farm, for home produced beef, pork, chicken, milk, eggs, vegetables, and fruits.
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Consumption rates of households who farm are not necessarily the most appropriate rates for households
who don't farm. For certain food types, the EFH also breaks out consumption rates of home-produced
foods specific to those households who garden. This combination of subpopulation (i.e. gardeners) and
food source (i.e. home produced) is the closest option available in the EFH to the residential and fishing
scenarios we recommend in the HHRAP. Consumption rates for this subpopulation are available for the
food types included in the produce-related exposure pathways we recommend (i.e. protected produce,
exposed produce, and belowground vegetables). We recommend using these rates to generate produce-
related consumption rates for the Resident, Resident Child, Fisher, and Fisher Child exposure scenarios.
The EFH provides information to account for cooking and post-cooking losses for food products which
are home produced. These data are summarized in Table 6-2. See the example derivation below for a
demonstration of how to use cooking loss data in deriving consumption rates.
TABLE 6-2
COOKING-RELATED WEIGHT LOSSES
FOR VARIOUS HOME-PRODUCED FOODS
Percent Weight
Losses from Preparation of Various Meats
(SOURCE: EFH Table 13-5)
Meat Type
Beef
Pork
Chicken
Milk
Eggs
Fish
Mean Net Cooking
Loss (%)
27
28
32
N/A
N/A
30
Mean Net Post Cooking
Loss (%)
24
36
31
N/A
N/A
11
Percent Weight Losses from Preparation of Various Produce
(SOURCE: EFH Tables 13-6 and 13-7)
Produce Type
Protected Fruits
Protected
Vegetables
Exposed Fruit
Exposed Vegetables
Mean Paring or
Preparation Loss
29
23
21
16
Moisture Content (%)
87
82
85
90
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Example Consumption Rate Derivation: Homegrown Poultry consumption for the Farmer
The Farmer scenario includes an average consumption rate during a 70-year lifetime, from age 7 to 70.
Table 13-55 of the EFH lists the following Farmer-related mean consumption rates of home produced
poultry (g/kg-day):
Total population 1.57
broken out by age group:
ages 6-1 1 ND (5 years)
ages 12-19 ND (8 years)
ages 20-3 9 1.17 (20 years)
ages 40-69 1.51 (30 years)
households who farm 1.54
Please note that though the EFH demographic subset includes years 6-11, the Farmer
scenario begins at age 7, so only 5 years of that consumption rate are used.
In addition, Table 11-11 (Mean Meat Intakes Per Individual in a Day, by Sex and Age (g/day as
consumed) for 1987-1988) lists the following Per Capita consumption rates of poultry (among others):
All individuals 26
broken out by age group:
ages 6-1 1 27
ages 12-19 27
Using the 12-19 age group as an example, equation 6-2 is used to calculate age-specific consumer only
rates as follows:
,2.,9 = 1.57 * 27/26= 1.63 g/kg-day
A time weighted average for an adult Farmer =
(1 63x 5) + (1.63x 8) + (1.17 x 3Q) + (1.5 lx 30)
""" = - 5+8 + 20 +30 -
And, using Equation 6-2:
CRFarmer = 1.54 * 1.43/1.57 = 1.40 g/kg-day
Table 13-5 of the EFH lists cooking and post-cooking losses for poultry of 31% and 32%, respectively.
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Therefore:
1.40 *(1-0.31)*(1-0.32) = 0.66
Poultry consumption rate for the Farmer = 0.66 g/kg-day
Additional consumption rate information is presented in Appendix C as follows: Table C-l-2 (produce);
Table C-l-3 (beef, milk, pork, chicken, and eggs); and Table C-l-4 (fish).
6.2.2.3 Percentage of Contaminated Food
The percentage of food consumed by an individual which is home-grown will affect exposure, because
the HHRAP assumes that only the portion of an individual's dietary intake which is home-grown is
impacted by facility emissions.
We recommend assuming that all food produced at the exposure location - i.e. the farm for the farming
scenarios, and the home garden for the residential and fishing scenarios - is impacted by facility
emissions. Only that portion of the diet produced at home (and therefore exposed to facility emissions) is
of consequence in the risk assessment. As detailed in Section 6.2.2.2, the consumption rates we
recommend represent only the home-produced portion of the diet. Therefore, by using consumption rates
specific to home produced foods, we consider it reasonable to assume that 100% of those home produced
foods are contaminated.
6.2.3 Soil (Ingestion) Exposure Pathway
Soil ingestion, dermal exposure to soil, and inhalation of resuspended dust are potential soil exposure
pathways. For the purpose of RCRA combustion permitting decisions, we recommend considering soil
ingestion. However, we currently only recommend evaluating dermal exposure to soil (see Section 6.3)
and inhalation of resuspended dust (see Section 6.1.1) if site-specific exposure setting characteristics
support evaluating these exposure pathways. Based on air dispersion modeling and deposition of
COPCs, emission concentrations in soil will vary with distance from the source. It's possible to
determine potential routes of exposure by evaluating the way in which the soils in the area are used. Soil
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used for farming or recreation will be involved in pathways of human exposure that differ from those of
soil on roadways or in urban areas.
Children and adults are exposed to COPCs in soil when they consume contaminated soil that has adhered
to their hands. Factors that influence exposure by soil ingestion include soil concentration, the rate of
soil ingestion during the time of exposure, and the length of time spent in the vicinity of contaminated
soil. Soil ingestion rates in children are based on studies that measured the quantities of non-absorbable
tracer minerals in the feces of young children. Ingestion rates for adults are based on assumptions about
exposed surface area and frequency of hand-to-mouth activity. Indoor dust and outdoor soil may both
contribute to the total daily ingestion. Exposure levels are also influenced by the amount of time that the
individual spends in the vicinity of soil exposed to deposition of emitted pollutants.
In addition, some young children—referred to as "pica" children—may intentionally eat soil. As
discussed in U.S. EPA (1989f), the typical medical and scientific use of the term "pica" refers to the
ingestion of non-food items, such as soil, chalk, and crayons. Such behavior is considered a temporary
behavior and a normal part of child development. For risk assessment purposes, pica is typically defined
as "an abnormally high soil ingestion rate" and is believed to be uncommon in the general population
(U.S. EPA 1989f). Agency risk assessment documents don't identify a default "pica" soil ingestion rate
(U.S. EPA 1989e; 1989f; 1991b). Pica behavior is not generally included as part of risk assessments.
If available information indicates that there are children exhibiting pica behavior in the assessment area,
and you determine that these children represent a special subpopulation potentially receiving significant
exposure (see Chapter 4), it may be prudent to include these children in the risk assessment. We
recommend making this evaluation on a case-by-case basis based on site-specific exposure setting
characterization.
6.2.4 Water (Ingestion) Exposure Pathways
Evaluating HHRAP water exposure pathways involves estimating COPC concentrations in drinking
water from surface water bodies or collected precipitation (e.g., cisterns). Contaminants moving through
the water pathways also influence COPC concentrations in fish. Various models are available to estimate
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daily exposures of individuals using these water sources for various purposes, such as fishing and
drinking water.
We recommend using site-specific information to determine which water exposure pathways to evaluate
in the risk assessment. Whether it's collected precipitation, or from a surface water body such as a lake,
farm pond, or city reservoir, the way in which water is used will suggest possible exposure pathways.
For example, using a surface water body as a drinking water source will introduce water ingestion as a
possible exposure pathway. Commercial and/or recreational fishing, with subsequent use offish and
shellfish as a food source, make the food chain an important route of exposure for communities having a
surface water body in the vicinity of a combustor.
U.S. EPA (1998c) recommended varying the water input variables to determine a range of exposures. An
individual that fishes and obtains drinking water from the same water source could represent an average
exposure scenario. A worst-case possibility might involve a person who (1) uses drinking water from a
cistern that collects precipitation, and (2) fishes in a small farm pond.
Because ground-level concentrations of COPCs generally decrease with distance from the source,
important factors in determining the water concentration include:
• the location of the precipitation-collection apparatus,
• surface water body onto which emitted COPCs are deposited, and
• the COPC soil concentration (which affects runoff and leachate concentrations).
In addition, the location and size of the watershed will affect the concentration of COPCs suspended in
runoff.
6.2.4.1 Ingestion of Drinking Water from Surface Water Sources
For evaluating a surface water body as a drinking water source, exposure is affected by the COPC
concentration in the water, the daily amount of water ingested, and the length of time that the receptor
spends in the area serviced by that water supply system. The equations we recommend for estimating the
COPC concentration in a surface water body are discussed in Chapter 5 and Appendix B. These
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equations also consider contributions of COPC loading from the surrounding watershed. We recommend
using the water consumption rates specified in U.S. EPA (1997b) and described in Appendix C.
As in previous Agency guidance (U.S. EPA 1998c), we recommend typically assuming that treatment
processes for drinking water do not alter dissolved COPC concentrations.
6.2.4.2 Ingestion of Drinking Water from Ground Water Sources
For the purpose of RCRA combustion permitting decisions, we don't typically recommend evaluating
exposure from ground water sources used as drinking water. Study of this pathway for combustor
emissions indicates that this isn't a significant exposure pathway (U.S. EPA 1998c). However, COPCs
may - because of special site-specific characteristics - infiltrate into ground water, resulting in COPC
exposure via ingestion when ground water is used as drinking water. This could be because of extremely
shallow aquifers being tapped for drinking water, or a karst environment in which the local surface water
significantly affects the quality of ground water used as a drinking water source. The method developed
to calculate risks from the ground water pathway was originally intended for use in evaluating impacts of
the land disposal of various wastes (U.S. EPA 1998c; 1994q; 2003). We recommend consulting these
reference documents if you intend to evaluate this exposure pathway.
6.2.4.3 Ingestion of Fish
You may find the fish ingestion rates specified in U.S. EPA (1997b) and further described in HHRAP
Appendix C useful for evaluating the fish ingestion pathway. Factors that affect human exposure by
ingestion offish from a surface water body affected by combustion unit emissions include:
• COPC concentrations in the sediment and water column,
• the types of fish and shellfish consumed,
• the ingestion rates for the various fish and shellfish groups, and
• the percent of dietary fish caught in the surface water body affected by the combustor.
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The types of fish consumed will affect exposure, because different types of fish and shellfish take up
COPCs at different rates. For example, fatty fish tend to accumulate organic COPCs more readily than
lean fish. The amount offish consumed also affects exposure, because people who eat large amounts of
fish will tend to have higher exposures. Fish consumption rates vary greatly, depending on geographic
region and social or cultural factors. For example, populations such as Indian tribes, American & Pacific
Islanders, and some immigrant groups are known to have high local fish consumption rates. Because 100
percent of a receptor's dietary fish may not originate from the surface water body near the combustion
facility, the percentage of locally caught fish is also a variable for exposure.
6.3 DERMAL EXPOSURE PATHWAYS
6.3.1 Dermal Exposure to Soil
For the purpose of RCRA combustion permitting decisions, We don't typically recommend evaluating
dermal exposure to COPCs through contact with soil. However, site-specific exposure setting
characteristics may support evaluating this exposure pathway. Therefore, this section discusses dermal
soil exposure.
Available data indicate that the contribution to overall risk from dermal exposure to soils impacted from
hazardous waste combustion facilities is typically small relative to contributions resulting from exposures
via the food chain (U.S. EPA 1995h; 1996g). For example, the risk assessment conducted for the Waste
Technologies Industries, Inc., hazardous waste incinerator in East Liverpool, Ohio, indicated that—for an
adult subsistence farmer in a subarea with high exposures—the risk resulting from soil ingestion and
dermal contact was 50-fold less than the risk from any other exposure pathway and 300-fold less than the
total estimated risk (U.S. EPA 1995h; 1996g).
Humans can be exposed to COPCs by absorption through the skin when it comes into contact with
contaminated soil. Factors that affect dermal exposure include:
• exposed skin surface area;
• contact time;
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• contact amount;
• amount of time spent near the combustion source; and
• fraction of COPCs absorbed through the skin.
In general, an increased dose of COPCs can potentially be absorbed through the skin as the surface area
of the skin is increased. Surface area is affected by age and body weight; for example, children have less
total surface area than adults. The amount of surface area available for exposure to soil is also affected
by the amount of clothing worn. An adult working in the garden in long sleeves and pants will have a
smaller exposed surface than an adult working in shorts and a short-sleeved shirt. For dermal exposure
from soil, the exposed surface area affects the amount of soil that can adhere to exposed skin.
Contact time refers to the duration of time each day that skin is in contact with contaminated soil. As
duration increases, so does the amount of COPCs that can be absorbed. Dermal exposure is also affected
by the amount of time each day spent in the vicinity of the combustion source, where the soil may
contain pollutants emitted from the combustion facility. Indoor dust and outdoor soil may both increase
the daily contact. Consider seasonal exposure might also be appropriate, because regional climate will
influence contact time.
The amount of COPCs that can be absorbed through the skin depends on the chemical properties of the
COPC, properties of the soil matrix, and dermal pharmacokinetics. If a COPC can't be readily absorbed
through the skin, the daily intake of the COPC may be small even if other exposure characteristics (e.g.
contact time) encourage absorption. However, if site-specific conditions suggest that dermal exposure to
soil may contribute significantly to total soil-related exposures, we recommend considering the
assessment methods described in U.S. EPA (2004d).
6.3.2 Dermal Exposure to Water
We don't typically recommend evaluating the dermal water exposure pathway when assessing risk from
hazardous waste combustor emissions. However, if the surface water body affected by combustor
emissions is used frequently for recreational purposes such as swimming and boating, dermal absorption
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of contaminated water becomes another possible route for human exposure. Dermal exposure is affected
by:
• the surface area of exposed skin,
• the COPC concentration in the water,
• the permeability of the skin to the COPC, and
• the length of time that the individual is in contact with the water.
6.4 EXPOSURE FREQUENCY
The HHRAP assumes that the receptors in each recommended exposure scenario are exposed to all of the
scenario-specific exposure pathways 350 days per year (U.S. EPA 1989e; 1991b; 1991d). This
assumption is based on the protective estimate that all receptors spend a maximum of 2 weeks away from
the exposure scenario location selected in Section 4.3.
6.5 EXPOSURE DURATION
Exposure duration is the length of time that a receptor is exposed via a specific exposure pathway. A
receptor is no longer exposed to COPCs via the direct inhalation exposure pathway after an emission
source ceases operation. However, a receptor could be exposed via the indirect exposure pathways for
as long as they remain in the assessment area. We recommend using default RME values to estimate
exposure duration for specified receptors.
As in U.S. EPA (1998c), we recommend assuming that receptors are exposed to the long-term average
COPC soil or water concentrations (and the subsequent COPC plant or animal concentrations) present in
the environment or media following a period of time during which there were continuous hazardous
waste combustor emissions. For existing facilities, U.S. EPA (1990e) assumed that this period of time
can be represented by default time periods of 30, 60, or 100 years. U.S. EPA (1998c) simplified this to
assume that the period could be > 30 years. These values are based on the assumptions that the
hazardous waste combustion unit or the emission source:
1. is already in place,
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2. will continue to be used for the rest of its useful life (estimated to be 30 years), and
3. may be replaced when it reaches the end of its useful life (possibly as long as 60 or 100
years), because it is an integral part of the facility operations.
We consider these assumptions reasonable for a hazardous waste emission source, such as an industrial
boiler burning a continuous stream of facility hazardous waste.
Although a combustor may remain in the same location for 100 years—and a person may have a lifetime
of exposure to emissions from that combustor— data on population mobility (U.S. Bureau of the Census,
1986) indicate that many Americans do not remain in the same area for their 70-year lifetime. An
estimate of the number of years that a person is likely to spend in one area, such as the vicinity of a
combustion facility, can be derived from information about mobility rate and median time in a residence.
In addition to the number of years at a particular location or residence, the amount of time spent at that
location each day directly affects exposure. For example, children that attend day care or adults that
work in a different location for part of the day may be exposed to higher or lower COPC levels.
The exposure duration values we recommend are presented in Table 6-3.
TABLE 6-3
EXPOSURE DURATION VALUES
Recommended Exposure
Scenario Receptor
Child Resident
Adult Resident
Fisher
Fisher Child
Farmer
Farmer Child
Value
6 years
30 years
30 years
6 years
40 years
6 years
Source
U.S. EPA 1990S 1994r
U.S. EPA 1990S 1994r
U.S. EPA 1990f; 1994r
Assumed to be the same
Resident
as the Child
U.S. EPA 19941; 1994r
Assumed to be the same
Resident
as the Child
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6.6 AVERAGING TIME
For noncarcinogenic COPCs, we generally recommend using a value of exposure duration (years-as
specified for each receptor in Section 6.4) x 365 days/year as the averaging time (U.S. EPA 1989e;
199 Id). However, for carcinogenic COPCs—the effects of which may have long latency periods—the
age of the receptor (i.e., child, adult, or elderly) influences the COPC exposure pathway, because the
exposure duration and, therefore, the quantity of exposure, will vary. For carcinogenic COPCs, we
recommend using an averaging time of 70 years.
We recommend evaluating carcinogenic exposures for different receptor ages separately, because the
daily activities of these receptors (and body weights, as described in Section 6.6) vary, including:
• the amounts of food and water consumed;
• the types of food consumed; and
• the amount of exposed skin surface.
Health-based criteria, such as health advisories for drinking water, are also different for children and
adults. As a result, for some exposure pathways, such as soil ingestion, children may have a greater
quantifiable exposure and be at greater risk than adults. Some behaviors, such as mouthing of dirty
objects or direct ingestion of soil, which could also contribute to exposure, are also much more prevalent
in children than adults.
Because quantifying carcinogenic COPC exposure depends on the duration of exposure, the age of the
receptor is important. For risk assessment purposes, the average human lifespan is generally considered
to be 70 years. Childhood represents only about 10 percent of the lifespan (6 years) (U.S. EPA 1998c).
In actual exposure scenarios, individuals may be exposed only during childhood or adulthood. In other
cases, exposure may overlap these periods, such as a child who grows into adulthood and remains in the
same geographical area. Based on the age of the receptor and information on the duration of exposure,
U.S. EPA (1990e) recommended considering risk to three different receptors:
1. a child who grows to an adult and is exposed for his or her entire 70-year lifetime,
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2. a child who grows to an adult and is exposed for only a part of his or her adulthood—a
total of 30 years, and
3. an adult exposed for 16 years.
Because the effects of certain carcinogenic COPCs may have long latency periods—in some instances
approaching the human lifespan—it may be appropriate to estimate daily intake by using the adult value
for body weight and a longer averaging time. In cases where effects have a shorter latency period,
U.S. EPA (1990e) recommended an averaging time period of less than 10 years. However, where
children are known to be at special risk, it may be more appropriate to use this averaging time with a
body weight value for toddlers, infants, or young children. For COPCs classified as carcinogens, we
recommend using a longer averaging time and the adult body weight to calculate the risk resulting from
air or water exposure.
It is significant that childhood is defined differently in the different references. U.S. EPA (1990e)
defines childhood as being from 1 to 7 years old. As in this and other Agency guidance (U.S. EPA
1991b; 1994r; 1998c), we define childhood as having an exposure duration of 6 years. Please note that
some of the data used for input into the various exposure scenario equations in Appendix C was not
available for children, or was available for more restrictive age groups, such as 2-year-olds or 4- to
6-year-olds. In such cases, and as noted in Appendix C where such values are presented,
1. the available data were evaluated to ensure that the presented default values are
sufficient for conducting a risk assessment, and
2. in cases in which the available data were not sufficient, reasonable interpolations of the
available data were possible.
RECOMMENDED INFORMATION FOR THE RISK ASSESSMENT REPORT
Identification of site-specific or alternate default media equations and/or inputs; including
justification and full referencing
Exposure calculations
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Chapter 7
Characterizing Risk and Hazard
What's Covered in Chapter 7:
7.1 Quantitatively Estimating Cancer Risk
7.2 Quantitatively Estimating Noncancer Hazard
7.3 Target Levels
7.4 Estimating Acute Exposure from Direct Inhalation
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak in this chapter is the performer of a risk assessment: the person (or
persons) who will actually put the recommended methods into practice.
The final step of a risk assessment is risk characterization. This involves combining the exposure
quantities generated in Chapter 6, and the toxicity benchmarks available in the HHRAP companion
database, to calculate the excess lifetime cancer risks (risk) and noncancer hazards (hazard) for each of
the pathways and receptors identified in Chapter 4. Risks (and hazards) are then summed for each
receptor, across all applicable exposure pathways, to obtain an estimate of total individual risk and
hazard. Risk characterization also involves documenting the uncertainties and limitations associated with
the rick assessment, as described in Chapter 8.
It is important that risk characterization exhibit the core values
of transparency, clarity, consistency, and reasonableness
(please see the related EPA Information Quality Guidelines
recommendations as discussed in Chapter 1, page 1-11).
Risk from exposure to combustor emissions is the probability that a human receptor will develop cancer,
based on a unique set of exposure, model, and toxicity assumptions. We recommend using the slope or
unit risk factor in risk assessments to estimate the probability of an individual developing cancer as a
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result of exposure to a particular level of a COPC. For example, a risk of 1 x 10~5 is interpreted to mean
that an individual has up to a one in 100,000 chance of developing cancer during their lifetime from the
exposure being evaluated. In contrast, hazard is the potential for developing noncancer health effects as
a result of exposure to COPCs. A hazard is not a probability but, rather, a comparison (calculated as a
ratio) of a receptor's potential exposure relative to a standard exposure level (RfD or RfC). The standard
exposure level is calculated over a similar exposure period and is estimated to pose no appreciable
likelihood of adverse health effects to potential receptors, including special populations (U.S. EPA
1989e).
Risks and hazards here are typically characterized for single scenarios, and are referred to as individual
risks and hazards (U.S. EPA 1989e; 1994g; NC DEHNR 1997). Individual risk and hazard descriptors
are intended to convey information about the potential risks to individuals potentially impacted by
emissions from a facility burning hazardous waste. A risk assessment developed following the
procedures described in Chapters 2 through 8 and Appendixes B and C will provide
quantitative and qualitative estimates of risk and hazard associated with exposure to
COPCs;
• estimates of blood levels associated with exposure to lead;
• evaluation of infant exposure via breast milk to COPCs with appropriate biotransfer
factors1, and
evaluation of acute risk and hazard resulting from direct inhalation.
If a permitting authority feels that you need to consider calculating population risks, we recommend
following the applicable methods described in the U.S. EPA NCEA document, Methodology for
Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (U.S.
EPA 1998c).
Standard rules for rounding apply which will commonly lead to an answer of one significant figure in
both risk and hazard estimates. For presentation purposes, hazard quotients (and hazard indices) and
1 Currently 2,3,7,8-TCDD TEQ and dioxin-like PCBs are the only COPCs with biotransfer factors for the
breastmilk pathway. However, appropriate biotransfer factors for other chemicals may become available and thus
provide the information needed to include them in this pathway evaluation. We suggest consulting Chapter 9
(Breastmilk Pathway) of U.S. EPA (1998c).
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September 2005
cancer risk estimates are usually reported as one significant figure. We recommend rounding only the
final reported results, not the intermediate calculations.
INFORMATION RECOMMENDED FOR RISK ASSESSMENT REPORT
Indicate the scope of the risk assessment (match the level of effort to the scope)
Summarize the major risk conclusions.
Identify key issues (a key issue is critical to properly evaluate the conclusions). For example,
was surrogate or measured emissions data used.
Describe clearly the methods used to determine risk (provide qualitative narration of the
quantitative results).
Summarize the overall strengths and major uncertainties.
7.1 QUANTITATIVELY ESTIMATING CANCER RISK
As described above, risk estimates represent the incremental probability that an individual will develop
cancer over a lifetime as a result of a specific exposure to a carcinogenic chemical (U.S. EPA 1989e). We
recommend calculating these risks as follows:
Inhalation Cancer Risk
where
EC
URF =
Cancer Risk = EC ~URF
Exposure concentration (• g/m3) [see Chapter 6]
Unit risk factor (• g/m3)"1
Equation 7-1
Ingestion Cancer Risk
where
LADD =
CSF =
Cancer Risk = LADD -CSF
Lifetime average daily dose (mg/kg-day)
Cancer slope factor (mg/kg-day)"1
Equation 7-2
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PLEASE NOTE: In the Supplemental Guidance for Assessing Susceptibility from
Early-Life Exposure to Carcinogens (U.S. EPA 2005g), the Agency recommends
estimating inhalation and ingestion cancer risk slightly differently for carcinogens the
Agency determines to cause cancer by a mutagenic Mode of Action (MOA, as defined in
Guidelines for Carcinogen Risk Assessment [U.S. EPA 2005f]). Unfortunately, we haven't
completed our recommendations for how to implement the guidelines set out in U.S. EPA
(2005f; g). We recommend periodically checking the EPA hazardous waste combustion
web site (http://www.epa.gov/epawaste/hazard/tsd/td/combust/index.htm) for updates on our
recommendations.
It's possible for receptors to be exposed to multiple COPCs within a individual exposure pathway. We
recommend estimating the total risk associated with exposure to all COPCs through a single exposure
pathway as follows (U.S. EPA 1989e):
Cancer RiskT = • 'Cancer Riskt Equation 7-3
where
Cancer RiskT = Total cancer risk for a specific exposure pathway
Cancer Riskt = Cancer risk for COPC i for a specific exposure pathway
Receptors might be exposed through a number of exposure pathways (see Table 4-1). We consider it
appropriate to sum risks from multiple exposure pathways for a given receptor. The cumulative risk
posed to a receptor is the sum of total risks from each individual exposure pathway. Express the
cumulative risk as follows:
Cumulative Cancer Risk = • 'Cancer RiskT Equation 7-4
where
Cumulative Cancer Risk= Cumulative cancer risk from multiple exposure
pathways
Cancer RiskT = Cumulative cancer risk for exposure pathway T
In addition to multiple pathways, a receptor might be exposed to emissions from multiple sources (See
Chapter 2 for additional discussion on emission sources). In addition to emission source-specific
risk/hazard estimates (see Chapter 3 regarding source-specific modeling), we recommend summing the
risks from all modeled sources for each receptor at each exposure scenario location. For example, if a
facility operates an incinerator and a boiler that both burn hazardous waste, sum the risks from both units
for each receptor. For fugitive emissions from storage and handling of hazardous waste, add the risk
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associated with fugitive emissions to the risks from the combustion unit for each receptor at each
exposure scenario location.
We present the equations we recommend to estimate dose and risk levels in Appendix C. The HHRAP
companion database presents inhalation URFs and oral CSFs for many potential COPCs. However, for
each risk assessment, we recommend checking the hierarchy of toxicity benchmark and slope factor
resources listed in Appendix A-2, Section A2.6 (Human Health Benchmarks) for updated values. We
suggest using the same hierarchy to acquire toxicity values for COPCs not identified in Appendix A-2.
In the assessment of carcinogenic risk from COPCs, we recommend U.S. EPA-derived or reviewed health
benchmarks (URFs and CSFs). However, for numerous compounds, a complete set of inhalation and oral
EPA-derived health benchmarks are not available. In such cases, we calculated the health benchmarks
presented in the companion database based on available U.S. EPA-derived benchmark values.
If relevant information is not available from these sources, we recommend contacting the appropriate
permitting authority, which may be able to assist in developing the necessary toxicity values. For
example, Minimum Risk Levels published by the Agency for Toxic Substances and Disease Registry
(ATSDR) might be applicable.
7.2 QUANTITATIVELY ESTIMATING NONCANCER HAZARD
Standard risk assessment models assume that, for most chemicals with noncancer effects, the noncancer
effects exhibit a threshold response2,. That is, there is a level of exposure below which no adverse effects
will be observed (U.S. EPA 1989e). The default approaches used by USEPA to assess the potential for
health effects associated with a nonlinear or threshold relationship with exposure as set out in U.S. EPA
(2002; 2005f) involve:
1. Comparing an estimate of ingested exposure (see Chapter 6) to an RfD for oral exposures;
and
Some chemicals don't demonstrate a threshold response. Lead and ozone are two examples of chemicals with
noncancer effects that don't have a threshold below which no adverse effects are observed.
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2. Comparing an estimated chemical-specific air concentration to the RfC for direct
inhalation exposures.
An RfD is a daily oral intake rate that is estimated to pose no appreciable risk of adverse health effects,
even to sensitive populations, over a 70-year lifetime. Similarly, an RfC is an estimated daily
concentration of a chemical in air, the exposure to which over a specific exposure duration poses no
appreciable risk of adverse health effects, even to sensitive populations (U.S. EPA 2002).
The exposure durations assumed for the exposure pathways identified in Table 4-1 range from subchronic
to chronic in relative length. However, we consider it appropriate to use chronic RfDs and RfCs to
evaluate all recommended exposure pathways. The comparisons of oral and inhalation exposure
estimates to RfD and RfC values, described above, are known as hazard quotients (HQ), which are
calculated as follows:
rrn ADD un EC
HQ = Or HQ = Equation 7-5
RfD * RfC
where
HQ = Hazard quotient (unitless)
ADD = Average daily dose (mg/kg-day)
RfD = Reference dose (mg/kg-day)
EC = Exposure air concentration (mg/m3)
RfC = Reference concentration (mg/m3)
Please note that each program office within U.S. EPA determines whatHQ level poses a concern to
exposed individuals. For example, Superfund has determined that an HQ of less than or equal to 1 is
considered health-protective (U.S. EPA 1989e). However, because RfDs and RfCs do not have equal
accuracy or precision, and are not based on the same severity of effect, the level of concern does not
increase linearly as anHQ approaches and exceeds 1 (U.S. EPA 1989e). In addition, noncancer estimates
only identify the exposure level below which adverse effects are unlikely; an RfD or RfC does not say
anything about incremental risk for higher exposures (U.S. EPA 1998c).
Also note that background exposures may be an important consideration in setting HQ levels of concern.
This is because you generally model noncancer effects as thresholds, and biologic systems (including
human receptors) do not distinguish between exposures from regulated versus non-regulated sources. In
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certain cases, a permitting authority may elect to adjust the assessed facility-specific HQ downward, to
account for any exposure that individuals may have from non-assessed sources.
As with carcinogenic chemicals, a receptor might be exposed to multiple chemicals associated with
noncancer health effects. We recommend calculating the total chronic hazard for each exposure pathway
by following the procedures outlined in U.S. EPA (1986e; 1989e; and 2000e). Specifically, the total
chronic hazard attributable to exposure to all COPCs through a single exposure pathway is known as a
hazard index (HI). The HI is calculated as follows:
HI = ' 'HQ, Equation 7-6
where
HI = Hazard index for a specific exposure pathway
HQ, = Hazard quotient for COPC /
This method assumes that the health effects of the various COPCs are additive. This method is a
simplification of the HI concept because it doesn't, at this stage, directly consider the portal of entry
associated with each exposure pathway (i.e. inhalation, or ingestion). This method also doesn't consider
the often unique toxic endpoints and toxicity mechanisms of the various COPCs.
As discussed in Section 7.1 for carcinogenic risks, a receptor might be exposed to COPCs associated with
noncancer health effects through more than one exposure pathway, and from multiple emissions sources.
We recommend estimating the noncancer hazards from each modeled source (including fugitive
emissions) separately, as well as all sources summed for each receptor. We consider it reasonable to
estimate a receptor's total hazard as the sum of the His for each of the exposure pathways chosen for the
receptor. Specifically, a receptor's cumulative hazard is the sum of hazards from each individual
exposure pathway, expressed as follows:
Cumulative HI = • "HI Equation 7-4
where
Cumulative HI = Cumulative hazard index from all scenario-specific exposure
pathways
HI = Hazard index for a specific exposure pathway
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As in U.S. EPA (1989e), we recommend further evaluating a cumulative ///which exceeds the target
hazard level. A cumulative HI can exceed the target hazard level due to either
One or more COPCs with an HQ exceeding the target hazard level, or
The summation of several COPC-specific HQs that are each less than the target hazard
level.
In the former case, you can interpret the presence of at least one COPC-specific hazard greater than the
target hazard level as indicating the potential for noncancer health effects. In the latter case, you need to
perform a detailed analysis to determine whether the potential for noncancer health effects is accurately
estimated by the total HI. This is because the toxicological effects associated with exposure to multiple
chemicals, often through different exposure pathways, may not be additive. The total HI might therefore
overestimate the potential for noncancer health effects.
To address this issue, we recommend summing the COPC-specific hazards according to toxicological
similarity (e.g. the same target organs or systems) (U.S.
EPA 2000e). This process is referred to as segregating the ^^^^^^^^^^^^^^^^^^^^^^^^^^™
HI. It is especially important to consider any differences Summing all ff/s
As stated above, estimating a single HI encompassing
related to exposure route . If any segregated HI exceeds all His across all exposure pathways is a simplification
of the HI concept. However, it may save valuable
the target hazard level, noncancer health effects cannot be resources: if the single HI is not above the target
ruled out. However, if all segregated Hh are less than the hazard level> ^ no furtbf segregation would be
necessary. We recommend this as a first step, and
target hazard level, noncancer health effects are not likely going to the expense of segregating His only if the
single HI falls above the target hazard level.
to result from exposure to the COPCs included in the HI. ^^^^^^^^^^^^^^^^^^^^^^^^^^^
Technically, segregating the ///based only on target organs or systems is a simplification of HI. Ideally,
the ///would also be segregated according to the often unique mechanisms of toxicity of the COPCs.
However, segregating the ///based on mechanisms of toxicity is beyond a screening level or initial risk
evaluation approach (U.S. EPA 2000e).
The HHRAP companion database includes information on target organs and systems that are affected by
each COPC. The database also presents RJDs and RJCs for these same COPCs. If you include COPCs
not identified in Appendix A-2 (and therefore not in the companion database) in the risk assessment, we
recommend obtaining RJDs and RJCs for these compounds using the hierarchy of toxicity benchmark and
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slope factor resources listed in Appendix A-2, Section A2.6 (Human Health Benchmarks). If relevant
information is not available from these sources, we recommend working with the permitting authority to
contact the U.S. EPA National Center for Environmental Assessment (NCEA) office in Cincinnati, Ohio.
NCEA personnel may be able to assist in developing the necessary toxicity values.
In the assessment of noncancer risk from COPCs, we recommend U.S. EPA-derived or reviewed RfDs
and RfCs. However, for numerous compounds, a complete set of inhalation and oral health benchmarks is
not available. If such was the case for COPCs listed in Appendix A-2, we calculated the health
benchmarks presented based on available U.S. EPA-derived benchmarks values. For instance, if the oral
RfD (mg/kg/day) was available and the RfC (mg/m3) was not, we calculated the RfC by multiplying the
RfD by an average human inhalation rate of 20 mVday and dividing by the average human body weight of
70 kg. This conversion is called a route-to-route extrapolation, which assumes that the toxicity of the
given compound is equivalent over all routes of exposure.
Route-to-route extrapolation introduces additional uncertainty into the risk assessment, and there isn't
Agency consensus regarding the appropriateness of its use. This method assumes that the qualitative data
supporting the benchmark value for a certain route also applies to the route in question. For example, if
an RfD is available and you calculate the RfC from that value, you are assuming that the toxicity seen
following oral exposure will be equivalent to toxicity following inhalation exposure. This assumption
could overestimate or underestimate the toxicity of the given compound following inhalation exposure.
Because of the degree of uncertainty involved in using toxicity benchmark values calculated based on
route-to-route extrapolation, we recommend using route-to-route extrapolations for organic compounds
(but not inorganic), and revisiting the appropriateness of applying this extrapolation for individual
chemicals if they are found to be risk drivers. An example might include using route-to-route
extrapolations as the first step in a screening risk assessment, then expending resources evaluating the
appropriateness of only those extrapolations associated with risk drivers. Including this further evaluation
(a qualitative assessment of the toxicity information available for the compound and exposure route ) in
the Uncertainty section of the risk assessment report will enable the risk manager to make an informed
decision concerning the validity of values calculated based on route-to-route extrapolation.
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7.3 TARGET LEVELS
Target levels are risk management-based and set by the permitting authority. Target values are not a
discrete indicator of observed adverse effect. If a risk estimate falls below target levels, a regulatory
authority may, without further investigation, conclude that a proposed action does not present an
unacceptable risk. A risk estimate that exceeds these targets, however, would not, in and of itself,
necessarily indicate that the proposed action is not safe or that it presents an unacceptable risk. Rather, a
risk estimate that exceeds a target value triggers further careful consideration of the underlying scientific
basis for the calculation.
7.4 ESTIMATING ACUTE EXPOSURE FROM DIRECT INHALATION
In addition to long-term chronic effects, we recommend considering short-term or acute effects from
direct inhalation of vapor phase and particle phase COPCs. Short-term emissions don't typically have a
significant impact through the indirect exposure pathways (as compared to impacts from long-term
emissions). Therefore, we recommend evaluating acute effects only through the short-term (maximum 1-
hour) inhalation of vapors and particulates exposure pathway of the acute risk scenario. We give our
recommendations for where and when to evaluate the acute risk scenario in Sections 4.2 and 4.3.
In order to establish acute inhalation exposure criteria (AIEC), we needed to identify and evaluate
1. Existing guidelines for acute inhalation exposure; and
2. Existing approaches for developing acute inhalation exposure levels.
Existing approaches are composed of hierarchical guidelines for acute inhalation exposure, ranked in
order of applicability and technical basis, and all being protective of the general public.
Please Note, hierarchical approaches are needed because criteria values are COPC-
specific, and no single organization or method has developed acute criteria values or
benchmarks for all of the potential COPCs.
7.4.1 Existing Hierarchical Approaches for Acute Inhalation Exposure
Existing guidelines or criteria for evaluating acute inhalation exposure have been or are being developed
by several organizations in the United States, including:
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• Agency for Toxic Substances and Disease Registry (ATSDR 1997);
• American Conference of Governmental Industrial Hygienists (ACGIH 1996);
• American Industrial Hygiene Association (AIHA 1997);
California Environmental Protection Agency (Cal/EPA) (Cal/EPA 1999);
• National Advisory committee (NAC 1997); and
National Institute of Occupational Safety and Health (NIOSH 1994);
• National Research Council Committee on Toxicology (NRC COT 1986; U.S. EPA
1987b);
Occupational Safety and Health Administration (NIOSH 1994);
U.S. Department of Energy, Subcommittee on Consequence Assessment and Protective
Actions (SCAPA) (SCAPA 2001a; 2001b).
U.S. EPA (U.S. EPA 1987b);
Acute inhalation exposure guidelines and criteria are
• Designed to protect a variety of exposure groups, including occupational workers,
military personnel, and the general public,
• Based on varying exposure durations up to 24 hours in length, and
• Intended to protect against a variety of toxicity endpoints ranging from discomfort or
mild adverse health effects to serious, debilitating, and potentially life-threatening effects,
up to and including death.
Hierarchical approaches for establishing acute inhalation exposure levels protective of the general public
have been developed by a variety of organizations and teams of organizations. These organizations
include:
U.S. Department of Defense (DoD 1996);
U.S. Department of Energy (DoE) (SCAPA 1997a; WSRC 1998).
U.S. EPA Region 3 (EPA 1996b);
U.S. EPA Region 10 (U.S. EPA 1996a); and
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Federal Emergency Management Agency, Department of Transportation (DoT), and
U.S. EPA (U.S. EPA 19931).
The acute inhalation exposure guidelines developed by these organizations are generally quite
heterogenous, developed to protect different subpopulations against different effects and apply to various
exposure durations. All the hierarchical approaches listed above except the SCAPA approach needed to
adjust the existing guidelines using safety factors (usually multiples of 10) to account for differences in
exposure group, exposure duration, and toxicity endpoint, to arrive at acute inhalation exposure values
applicable to the general public.
In contrast to the hierarchical approaches developed using safety factors, the DoE's Emergency
Management Advisory Committee's SCAPA developed temporary emergency exposure limits (TEELs)
based on tiered, formula-like statistical analyses between existing guidelines for acute inhalation exposure
and AIHA emergency response planning guidelines (ERPG) (Craig et al. 1995; WSRC 1998). The
methodology is described athttp://www.orau.gov/emi/scapa/files/Method_for_deriving_TEELs.pdf and
available on-line at http://www.atlintl.com/DOE/teels/teel/teeljdf.html. Like ERPGs, TEELs are
multiple-tiered, representing concentrations associated with no effects (TEEL-0), mild, transient effects
(TEEL-1), irreversible or serious effects (TEEL-2), and potentially life-threatening (TEEL-3). DOE
developed TEELs for situations where no other value is available. TEELs do not undergo peer review.
For compounds for which TEEL values could not be developed using this approach, SCAPA developed a
supplementary approach using available toxicity information, primarily (1) lethal dose and concentration
median, and (2) lethal dose and concentration low values (DoE 1997a).
7.4.2 Our Recommended Hierarchical Approach
After reviewing the existing hierarchical approaches, we recommend the following approach. Because of
the daily operations of most combustion units and the potential for upset conditions to sometimes occur
during operations, we consider acute values that address intermittent exposures more appropriate and
more protective than values that are based on the assumption that acute exposures will be one-time only.
When available, we recommend using values from all of the sources that are based on one-hour
exposures.
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1. Cal/EPA Acute RELs - the concentration in air at or below which no adverse health
effects are anticipated in the general population, including sensitive individuals, for a
specified exposure period (Cal/EPA 1999)
(On-Line Address - http://www.oehha.ca.gov/air/pdf/acuterel.pdf)
2. Acute inhalation exposure guidelines (AEGL-1) - "the airborne concentration of a
substance above which it is predicted that the general population, including susceptible
individuals, could experience notable discomfort, irritation, or certain asymptomatic
nonsensory effects. However, the effects are not disabling and are transient and
reversible upon cessation of exposure." (NOAA 2001; U.S. EPA 2001a) (On-Line
Address - http://www.epa.gov/oppt/aegl/)
3. Level 1 emergency planning guidelines (ERPG-1) - "the maximum concentration in air
below which it is believed nearly all individuals could be exposed for up to one hour
without experiencing other than mild transient adverse health effects or perceiving a
clearly defined objectionable odor." (DoE 2001; SCAPA 200Ib)
(On-Line Address - http://www.bnl.gov/emergencyservices/)
4. Temporary emergency exposure limits (TEEL-1) - "the maximum concentration in air
below which it is believed nearly all individuals could be exposed without experiencing
other than mild transient adverse health effects or perceiving a clearly defined odor."
(DoE 2001; SCAPA 200la) (On-Line Address -
http://orise.orau.gov/emi/scapa/files/Method_for_deriving_TEELs.pdf)
5. AEGL-2 values - "the airborne concentration of a substance above which it is predicted
that the general population, including susceptible individuals, could experience
irreversible or other serious, long-lasting adverse health effects or an impaired ability to
escape." AEGL-2 values are to be used only if lower ERPG-1 or TEEL-1 values are not
available. (NOAA 2001; U.S. EPA 2001a) (On-Line Address -
http://www.epa.gov/oppt/aegl/)
The hierarchy is presented in order of preference, from 1 (most preferred) to 5 (least preferred). We
generally recommend the Acute Reference Exposure Levels (Acute RELs) developed by Cal/EPA
(Cal/EPA 1999) as the first choice for acute inhalation values. If no acute REL value is available for a
given COPC, you can work down the list in order. If no AEGL-1 value is available, but an AEGL-2
value is available, select the AEGL-2 as the AIEC only if it's a more protective value (lower in
concentration) than an ERPG-1, or a TEEL-1 value if either of these values is available. If no acute
values are available for a COPC, an acute value can be developed following the toxicity-based approach
used by SCAPA (Tier 5) (DoE 1997a; WSRC 1998). The companion database provides a listing of
AIECs compiled from values currently available following the hierarchical approach presented above.
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Please note that the TEEL-1 values (SCAPA 2001a) are calculated assuming a 15-minute exposure
period. As discussed in Section 3.10, for the purposes of this protocol, we recommend evaluating risks
due to acute exposure based on the highest 1-hour average air concentrations. Therefore, the TEEL-1
values were extrapolated from a 15-minute to a 1-hour exposure basis using a modification to Haber's
Rule developed by ten Berge et al (1986) and used by Cal/EPA to develop acute RELs (Cal/EPA 1999),
as shown below.
C" "T = K Equation 7-8
where
C = Concentration (mg/m3)
n = Constant greater than zero (unitless)
T = Time of exposure (hour)
K = Constant level or severity of response (unitless)
Where available, chemical-specific values for the exponent n were used to make the extrapolations
(Cal/EPA 1999). For chemicals for which a chemical-specific value of n was not available,
extrapolations were made using a value of n = 1, as recommended by OEHHA, because the extrapolations
were all based on an initial exposure period (15-minutes) of less than 1 hour duration (Cal/EPA 1999).
Using the modified form of Haber's Rule allows you to consider contributions by both concentration and
time to the overall severity of effect. However, we highly recommend taking special care interpreting the
extrapolated air concentrations, as they aren't absolutes. For example, chemical-specific values of the
exponent n are sometimes based on a relatively limited set of dose-response data. Also, the majority of
extrapolated TEEL-1 values were calculated using default exponent values and, therefore, are likely to be
even less certain than exponent values based on limited data sets.
The EPA IRIS program is currently developing additional acute reference values that do not exclude
intermittent exposures. When available, we recommend using those values (referred to as Acute
Reference Concentrations [Acute RfCs]) as the first choice, with the Cal/EPA acute RELs second in the
hierarchy.
7.4.3 Characterizing Potential Health Effects from Acute Exposure
We recommend characterizing the potential for adverse health effects from acute exposure to
COPC-specific emissions by comparing the acute air concentration (Cacute) resulting from maximum
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emissions over a 1-hour period to the COPC-specific AIEC (see Appendix C, Table C-4-1). This
comparison is known as the acute hazard quotient (AHQinh). Chapter 3 discusses air dispersion modeling
related to obtain 1-hour maximum values. Appendix B, Table B-6-1 describes how to calculate Cacute.
We recommend using Equation 7-9 to calculate the AHQinh:
AHQ h = ac"te' Equation 7-9
mh AIEC
where
AHQinh = Acute hazard quotient (unitless)
Cacute = Acute air concentration (• g/m3)
0. 001 = Conversion factor (mg/« g)
AIEC = Acute inhalation exposure criteria (mg/m3)
We recommend calculating acute hazard quotients at the selected acute exposure scenario locations (see
Sections 4.2 and 4.3) for COPCs specific to emissions from each source, and from all facility sources
combined. We recommend summing acute hazard quotients from individual chemicals (e.g. acid gases),
if they have similar effects. Setting target levels for evaluating acute hazard quotients is a risk
management decision made by the permitting authority.
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Chapter 8
Interpreting Uncertainty
for Human Health Risk Assessment
What's Covered in Chapter 8:
8.1 Uncertainty and Limitations of the Risk Assessment Process
8.2 Types of Uncertainty
8.3 Qualitative Estimate s of Uncertainty
8.4 Quantitative Estimates of Uncertainty
8.5 Risk Assessment Uncertainty Discussion
This section discusses interpreting uncertainties associated with the risk assessment. The discussion of
uncertainties in Section 8.1 and 8.2 was adopted from the 1996 Risk Assessment Support to the
Development of Technical Standards for Emissions from Combustion Units Burning Hazardous Waste.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak in this chapter is the performer of a risk assessment: the person (or
persons) who will actually put the recommended methods into practice.
8.1 UNCERTAINTY AND LIMITATIONS OF THE RISK ASSESSMENT PROCESS
Uncertainty can be introduced into a health risk assessment at every step of the process outlined in this
document. Uncertainty occurs because risk assessment is a complex process, requiring the integration of
the following:
•• Release of pollutants into the environment
•• Fate and transport of pollutants, in a variety of different and variable environments, by
processes that are often poorly understood or too complex to quantify accurately
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Potential for adverse health effects in humans, as extrapolated from animal studies
Probability of adverse effects in a human population that is highly variable genetically,
and in age, activity level, and lifestyle
Uncertainty is inherent in the process even when using the most accurate data and the most sophisticated
models. The method we recommend in the HHRAP relies on a combination of point values—some
protective and some typical—yielding a point estimate of exposure and risk that falls at an unknown
percentile of the full distributions of exposure and risk. Forthis reason, the degree of conservatism in risk
estimates cannot be known. Therefore, you need a formal uncertainty analysis to determine the degree of
conservatism. Section 8.2 discusses the types of uncertainty and the areas in which uncertainty can be
introduced into an assessment. The remaining Sections discuss methods for qualitatively and
quantitatively addressing uncertainty in risk assessments.
It should also be noted, variability is often used interchangeably with the term "uncertainty," but this is
not strictly correct. "Variability" may be tied to variations in physical and biological processes.
Variability can't be reduced with additional research or information, although it may be known with
greater certainty (for example, the age distribution of a population may be known and represented by the
mean age and its standard deviation). "Uncertainty" is a description of the imperfect knowledge of the
true value of a particular variable, or its real variability in an individual or a group.
In general, uncertainty is reducible by additional information-gathering or analysis activities (that is,
better data or better models), whereas real variability won't change (although it may be more accurately
known) as a result of better or more extensive measurements (Hattis and Burmaster 1994).
8.2 TYPES OF UNCERTAINTY
Finkel (1990) and U.S. EPA (1999f) classified all uncertainty into four types:
1. variable uncertainty,
2. model uncertainty,
3. decision-rule uncertainty, and
4. variability.
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Variable uncertainty and model uncertainty are generally recognized by risk assessors as major sources
of uncertainty. Variable uncertainty occurs when variables appearing in equations cannot be measured
precisely or accurately, because of either (1) equipment limitations, or (2) spatial or temporal variances
between the quantities being measured. Random, or sample, errors are common sources of variable
uncertainty that are especially critical for small sample sizes. It is more difficult to recognize nonrandom,
or systematic, errors that result from the basis for sampling, experimental design, or choice of
assumptions.
Model uncertainty is associated with all models used in all phases of a risk assessment, including:
animal models used as surrogates for testing human carcinogenicity,
the dose-response models used in extrapolations, and
the computer models used to predict the fate and transport of chemicals in the
environment.
Using rodents as surrogates for humans introduces uncertainty into the risk factor because of the
considerable interspecies variability in sensitivity. Computer models are simplifications of reality,
requiring exclusion of some variables that influence predictions but cannot be included in models because
of (1) increased complexity, or (2) a lack of data for these variables. The importance of excluded
variables is generally considered on a case-by-case basis. A specific variable may be important, in terms
of its impacts on uncertainty, in some instances and not in others. A similar problem can occur when a
model that is applicable under average conditions is used for a case in which conditions differ from the
average. Finally, choosing the correct model form is often difficult, because conflicting theories appear to
explain a phenomenon equally well.
The models we recommend in the HHRAP were selected based on scientific policy. We selected the air
dispersion and deposition model, and the indirect exposure models, because they provide the information
you need to conduct indirect assessments, and we consider them state-of-the-science. ISCST3—the air
dispersion model we recommend—has not been widely applied in its present form. Few data are
available on atmospheric deposition rates for chemicals other than criteria pollutants, thereby making it
difficult to select input variables related to deposition, or validate modeled deposition rates. Long-range
transport of pollutants into and out of the study areas wasn't modeled, resulting in an underestimation of
risk attributable to each facility.
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In addition to air dispersion modeling, using other fate and transport models recommended by this
guidance can also result in some uncertainty. For example, the models which estimate COPC
concentrations in waterbodies may be particularly protective for waterbodies located in estuarine
environments with tidal influence. Because tidal influence is not considered in the models presented in
Chapter 5, the resulting dilution of COPC concentrations in water and sediments likely caused by tidal
influence won't be considered in the risk assessment. Thus, the risk assessment results will likely be
more protective for tidally influenced waterbodies than for those waterbodies that aren't tidally
influenced. We recommend that permitting decisions based on risk estimates for estuarine environments
consider this uncertainty. The delineation of this uncertainty may be one area that could be addressed in a
more refined site-specific risk assessment, if warranted.
Decision-rule uncertainty is probably of greatest concern to risk managers. This type of uncertainty
arises, for example, out of the need to balance different social concerns when determining an acceptable
level of risk. The uncertainty associated with risk analysis influences many policy and risk management
decisions. Possibly the most important aspect for the risk estimates is the selection of constituents to be
assessed. Constituents that are identified using the process provided in this document will include
compounds that have the potential to pose the greatest risk to human health through indirect exposure
routes. For example, many PICs are highly lipophilic and tend to bioaccumulate in the food chain,
thereby presenting a potentially high risk through the consumption of contaminated food.
A second area of decision-rule uncertainty includes the use of standard Agency default values in the
analysis. These include inhalation rates, body weight, and lifespan, which are standard default values
used in most Agency risk assessments. Inhalation rate is highly correlated to body weight for adults.
Using a single point estimate for these variables instead of a joint probability distribution ignores a
variability that may influence the results by a factor of up to two or three.
A third area of decision-rule uncertainty is the use of Agency-verified cancer SFs, RfDs, and RfCs. These
health benchmarks are used as single-point estimates throughout the analysis, and uncertainty and
variability are both associated with them. U.S. EPA has developed, however, a process for setting
verified health benchmark values to be used in all Agency risk assessments. This process is used to
account for much of the uncertainty and variability associated with the health benchmarks. With the
exception of the dioxin toxicity equivalency methodology, health benchmarks which can be found on
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IRIS, have been verified through Agency work groups. Estimating the uncertainty in using Agnecy-
verified health benchmarks or the dioxin toxicity equivalency methodology is beyond the scope of the
HHRAP.
8.3 QUALITATIVE ESTIMATES OF UNCERTAINTY
Often, sources of uncertainty in a risk assessment can be identified but not quantified. For example, this
can occur when you know (or suspect) a factor to vary, but have no data (e.g., presence of COPCs without
toxicity data, amount of time that people at a specific site spend outdoors). In such cases, default data
may be available that can be useful in estimating a possible range of values. Uncertainty also often arises
out of a complete lack of data. A process may be so poorly understood that you can't quantify the
uncertainty with any confidence. In addition, some sources of uncertainty (such as uncertainty in theories
used to deduce models) are inherent qualifications reflecting subjective modes of confidence rather than
probabilistic arguments. When you can only present the uncertainty qualitatively, you might consider the
possible direction and orders of magnitude of the potential error.
8.4 QUANTITATIVE ESTIMATES OF UNCERTAINTY
It's also possible to use knowledge of experimental or measurement errors to introduce a degree of
quantitative information into a qualitative presentation of uncertainty. For example, standard laboratory
procedures or field sampling methods may have a known error level that you can use to quantify
uncertainty. In many cases, it's possible to express the uncertainty associated with particular variable
values or estimated risks quantitatively, and further evaluate them with variations of sensitivity analyses.
Finkel (1990) identified a six-step process for producing a quantitative uncertainty estimate:
1. Define the measure of risk (such as deaths, life-years lost, maximum individual risk
(MIR), or population above an "unacceptable" level of risk). More than one measure of
risk may result from a particular risk assessment: however, the uncertainty may be
quantified or reached individually.
2. Specify "risk equations" that present mathematical relationships that express the risk
measure in terms of its components. This step is used to identify the important variables
in the risk estimation process.
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3. Generate an uncertainty distribution for each variable or equation component. These
uncertainty distributions may be generated by using analogy, statistical inference
techniques, expert opinion, or a combination of these.
4. Combine the individual distributions into a composite uncertainty distribution.
5. Recalibrate the uncertainty distributions. Inferential analysis could be used to "tighten"
or "broaden" particular distributions to account for dependencies among the variables and
to truncate the distributions to exclude extreme values.
6. Summarize the output clearly, highlighting the important risk management implications.
Address specific critical factors.
Implication of supporting a point estimate produced without considering
uncertainty
Balance of the costs of under- or over-estimating risks
Unresolved scientific controversies, and their implications for research
When you need a detailed quantitative treatment of uncertainty, statistical methods are generally
considered the most appropriate. We describe two possible approaches to a statistical treatment of
uncertainty with regard to variable values here, though other methods are certainly available. The
methods described here were used in this analysis where appropriate.
The first approach is to use an appropriate statistic to express all variables for which uncertainty is a
major concern. For example, if a value comes from a sample (such as yearly emissions from a stack), the
mean and standard deviation may both be presented. If the sample size is very small, it may be
appropriate to
• give the range of sample values and use a midpoint as a best estimate in the model, or
• use the smallest and largest measured value to obtain two estimates that bound the
expected true value.
Selecting the appropriate statistic depends on the amount of data available and the degree of detail you
need. It's possble to propagate uncertainties by using analytical or numerical methods.
A second approach is to use the probability distributions of major variables to propagate variable value
uncertainties through the equations you use in the risk assessment. You then develop a probability
distribution of expected values for each variable value. These probability distributions are typically
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expressed as either probability density functions (PDF) or cumulative probability density functions
(CPF). The PDF presents the relative probability for discrete variable values, whereas the CPF presents
the cumulative probability that a value is less than or equal to a specific value.
A composite uncertainty distribution is created by combining the individual distributions with the
equations used to calculate the probability of particular adverse health effects and points. In Monte Carlo
simulations, a computer program is used to repeatedly solve the model equations, under different
selections of variable values, to calculate a distribution of exposure (or risk) values. Each time the
equations are calculated, values are randomly sampled from the specified distributions for each variable.
The end result is a distribution of exposure (or risk). These can again be expressed as PDFs or, more
appropriately, as CPFs. The distribution enables you to choose the value corresponding to the
appropriate percentile in the overall distribution. For example, you could select an exposure level or risk
level that corresponds to the 95th percentile of the overall risk distribution rather than a point estimate of
risk that is based on the 95th percentile values for each variable. For more information on how to conduct
a quantitative uncertainty analysis, we refer you to Risk Assessment Guidance for Superfund, Volume III:
Part A (Process for Conducting Probabilistic Risk Assessment), which is located at the web address
www.epa.gov/oswer/riskassessment/rags3a/index.htm.
8.5 RISK ASSESSMENT UNCERTAINTY DISCUSSION
The science of risk assessment is evolving. Where the science-base is incomplete and uncertainties exist,
science policy assumptions must me made. It is important for risk assessments to fully explain the areas
of uncertainty in the assessments and to identify the key assumptions used in conducting the assessments.
Toward that end, one option is to add a table at the end of each section (e.g., stack emissions, air
modeling, exposure assessment, toxicity evaluation, risk characterization) that lists the key assumptions in
that section, the rationale for those assumptions, their effect on estimates of risk (overestimation,
underestimation, neutral), and the magnitude of the effect (high, medium, low). For example, it could
explain that using a particular input variable, such as exit gas temperature, will under- or overestimate
chronic emissions, and the resulting risks and hazards, by a factor of (x). These tables could be used to
evaluate the extent to which you used public health-protective assumptions in the risk assessment. They
could also help determine the nature of the uncertainty analysis to be performed. The assumptions listed
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in the risk characterization section, which synthesizes the data outputs from the exposure and toxicity
analyses, might include the most significant assumptions from each of the previous sections.
Within the HHRAP, we've identified uncertainties and limitations within the discussion of specific
technical issues (e.g., TOE, estimates of emission rates, COPC selection process, quantifying non-detects)
as they are presented in their respective sections. We present the limitations associated with parameter
values and inputs to equations in Appendices A, B, and C, respectively.
As an example discussion, the following summarizes some of the uncertainty involved in the air
dispersion modeling component of the risk assessment process.
Although dispersion modeling is a valuable tool for estimating concentration and deposition impacts, it
has many limitations. The accuracy of the models is limited by
the ability of the model algorithms to depict atmospheric transport and dispersion of
contaminants, and
• the accuracy and validity of the input data.
For example, most refined models require input of representative meteorological data from a single
measuring station. In reality, a release will encounter highly variable meteorological conditions that are
constantly changing as it moves downwind. U.S. EPA's Guideline on Air Quality Models—Revised (U.S.
EPA 1986b, 1993b; Title 51 CFR Appendix W) describes two types of model uncertainty. Inherent
uncertainty involves deviations in concentrations that occur even if all of the model input is accurate.
Reducible uncertainty is associated with the model and the uncertain input values that will affect the
results. Although it is important to accurately represent actual conditions by selecting the right model,
and using accurate and representative input data, all model results are subject to uncertainty.
Nevertheless, models are generally considered reasonably reliable in estimating the magnitude of highest
concentrations resulting from a release, although they may not necessarily be time-and space-specific
(Title 51 CFR Appendix W). When applied properly, air dispersion models are typically accurate to ± 10
to 40 percent and can be used to yield a "best estimate" of air concentrations (Title 51 CFR Appendix W).
As mentioned earlier, uncertainties specific to other technical components of the risk assessment process
(e.g., TOE, quantification of non-detects) are further described in their respective chapters or sections of
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this guidance. For more information on Agency policy and guidelines regarding uncertainty in risk
assessments, please see the U.S. EPA Science Policy Council's Risk Characterization Handbook (U.S.
EPA 2000f, available at http://www.epa.gov/osa/spc/pdfs/rchandbk.pdf).
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Chapter 9
Completing the Risk Assessment Report
and Follow-On Activities
What's Covered in Chapter 9:
9.1 Conclusions
9.2 Activities Following Risk Assessment Completion
Our main purpose in developing the HHRAP was to provide you with the tools you need to efficiently
complete quality, consistent, and defensible risk assessments. You can produce the risk assessment in a
relatively short amount of time, rather than spending years determining which COPCs, exposure
pathways, and receptors to include and evaluate.
It's important to note that final risk assessments might include both human health and ecological
evaluations. In addition to available Agency guidance for conducting ecological risk assessments (U.S.
EPA 1997e) and Volume 63, Number 93, of the Federal Register, we are currently finalizing an
ecological risk assessment guidance document titled U.S. EPA OSWScreening Level Ecological Risk
Assessment Protocol (U.S. EPA 1999a), prepared as a companion to this guidance.
PLEASE NOTE, for the purposes of this guidance, "we" refers to the U.S. EPA OSW.
The HHRAP is written for the benefit of a varied audience, including risk assessors,
regulators, risk managers, and community relations personnel. However, the "you" to
which we speak in this chapter is the performer of a risk assessment: the person (or
persons) who will actually put the recommended methods into practice.
9.1 CONCLUSIONS
We recommend that each risk assessment include a Conclusions section. This section is included
primarily to interpret the results of the risk and hazard characterization in light of the uncertainty
analysis. We recommend that, at a minimum, it present and interpret all risk and hazard results
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exceeding target levels. It might also identify receptors having the greatest risks and hazards, in addition
to COPCs and exposure pathways contributing significantly to these risks and hazards. Finally, the
Conclusions section is a place for you to present and defend your position on whether actual or potential
releases from the facility you studied pose significant risks and hazards to human populations.
9.2 ACTIVITIES FOLLOWING RISK ASSESSMENT COMPLETION
As stated previously, we developed the HHRAP to promote a consistent approach for completing risk
assessments that:
• Encourages the use of appropriate site-specific information early in and throughout the
process;
• Provides a tool for completing quality and defensible risk assessments;
• Minimizes inefficient expenditure of time and resources by suggesting up front a process
to determine which COPCs to consider, which exposure pathways, and receptors the risk
assessment report to include and evaluate, and
• Provides a comprehensive explanation of the procedures and uncertainties involved in
the process.
However, the risk assessment process does not end following the completion, submittal, and approval of
the risk assessment report.
Facilities burning hazardous waste are also responsible for communicating the results of the risk
assessment process to affected members of the community. One reason for our comprehensive
explanation of the procedures and uncertainties involved in the process was to provide the facilities, risk
assessors, and regulators with the tools they need to clearly communicate the procedures, results, and
limitations of the risk assessment process. Communication is an ongoing process.
Finally, completing the risk assessment process involves using
• site-specific environmental data,
• various assumptions, and
• an evolving procedure for estimating risk.
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We expect that facilities will periodically review each of these factors, and update the process with the
latest facility-specific operating and emission information. Updating will help determine whether the
best data and procedures are used to estimate the risk resulting from operating the facility hazardous
waste combustor. The permitting authority might establish the period for this review in the operating
permit. However, we recommend reviewing any significant changes involving newly available data or
risk assessment procedures as they become available, to gauge if they significantly affect the outcome of
the risk assessment process.
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Center for Combustion Science and Engineering R-16
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Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-17
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Human Health Risk Assessment Protocol
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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References September 2005
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-19
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Human Health Risk Assessment Protocol
References September 2005
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-20
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References September 2005
U.S. EPA. 1994e. Guidance Manual for the Integrated Exposure Uptake Biokinetics Model for Lead in
Children. OERR. Washington, D.C. EPA/540/R-93/081. February.
U.S. EPA. 1994f. Draft Exposure Assessment Guidance for RCRA Hazardous Waste Combustion
Facilities. OSWER. EPA-530-R-94-021. April 22.
U.S. EPA. 1994g. Draft Guidance for Performing Screening Level Risk Analyses at Combustion
Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment Guidance for
RCRA Hazardous Waste Combustion Facilities. April 15.
U.S. EPA. 1994L Table 1, "Chemicals Recommended for Identification," and Table 2, "Chemicals for
Potential Identification." Attachment A, Draft Exposure Assessment Guidance for RCRA
Hazardous Waste Combustion Facilities. April 15.
U.S. EPA. 1994i. Draft Revision, Implementation Guidance for Conducting Indirect Exposure Analysis
at RCRA Combustion Units. Attachment, Draft Exposure Assessment Guidance for RCRA
Hazardous Waste Combustion Facilities. April 22.
U.S. EPA. 1994J. Draft Guidance on Trial Burns. Attachment B, Draft Exposure Assessment Guidance
for RCRA Hazardous Waste Combustion Facilities. May 2.
U.S. EPA. 1994k. Estimating Exposure to Dioxin-Like Compounds. Volume I: Executive Summary.
Review Draft. ORD. Washington, D.C. EPA/600/6-88/005Ca. June.
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Occurrence, and Background Exposures. Review Draft. ORD. Washington, D.C.
EPA/600/6-88/005Cb. June.
U.S. EPA. 1994m. Estimating Exposure to Dioxin-Like Compounds. Volume III: Site-Specific
Assessment Procedures. Review Draft. ORD. Washington, D.C. EPA/600/6-88/005Cc. June.
U.S. EPA. 1994n. Draft Revision of Guidance on Trial Burns. Attachment B, Draft Exposure
Assessment Guidance for RCRA Hazardous Waste Combustion Facilities. OSWER. June 2.
U.S. EPA. 1994o. "Memorandum Regarding Revised Interim Soil Lead Guidance for CERCLA Sites
and RCRA Corrective Action Facilities." OSWER Directive 9355.4-12. From Elliot P. Laws,
Assistant Administrator. To Regional Administrators Regions I - X. July 14.
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Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment Guidance for
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EPA/540/R-94/106. OSWER. Washington, D.C. December.
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Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment Guidance for
RCRA Hazardous Waste Combustion Facilities. OERR. OSW. December 14.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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References September 2005
U.S. EPA. 1994s. Estimating Radiogenic Cancer Risks. Office of Radiation and Indoor Air. U.S. EPA.
Washington, DC. EPA 402-R-93-076. June.
U.S. EPA. 1995a. Compilation of Air Pollutant Emission Factors: Volume I, Stationary Point and Area
Sources. Research Triangle Park, North Carolina. 5th Edition. AP-42. January.
U.S. EPA. 1995b. Further Issues for Modeling the Indirect Exposure Impacts from Combustor
Emissions. ORD. Washington, B.C. January 20.
U.S. EPA. 1995c. User's Guide to the Building Profile Input Program. EPA-454/R-93-038. Office of
Air Quality Planning and Standards, Technical Support Division. Research Triangle Park, North
Carolina. February.
U.S. EPA. 1995d. "Regulatory Determination on Cement Kiln Dust; Final Rule." Federal Register.
February 7.
U.S. EPA. 1995e. Review Draft Development of Human Health-Based and Ecologically-Based Exit
Criteria for the Hazardous Waste Identification Project. Volumes I and II. Office of Solid
Waste. March 3.
U.S. EPA. 1995f User's Guide For The Industrial Source Complex Dispersion Models, Volumes I and
II. Office of Air Quality Planning and Standards. Emissions, Monitoring, and Analysis Division.
Research Triangle Park, North Carolina. EPA-454/B-95-003a. September.
U.S. EPA. 1995g. PCRAMMET User's Guide. Office of Air Quality Planning and Standards.
Emissions, Monitoring, and Analysis Division. Research Triangle Park, North Carolina.
October.
U.S. EPA. 1995h. "Waste Technologies Industries Screening Human Health Risk Assessment
(SHHRA): Evaluation of Potential Risk from Exposure to Routine Operating Emissions."
Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.
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Levels. U.S. EPA, Washington, DC. NTIS PB95-216321.
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Administrators, General Counsel, Inspector General, Associate Administrators, regional
Administrators. From Carol M. Browner, Administrator, and Fred Hansen, Deputy
Administrator. U.S. EPA, Washington, DC. October 20.
U.S. EPA. 1995k. Protocol for Equipment Leak Emission Estimates. Office of Air Quality Planning and
Standards. Emissions, Monitoring, and Analysis Division. Research Triangle Park, North
Carolina. EPA/453/R-95-017. November http://www.epa.gov/ttn/chief/efdocs/lks95_ch.pdf
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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Human Health Risk Assessment Protocol
References September 2005
U.S. EPA. 1996c. Review and Comments of EPA 's Peer Review Panel on the Risk Assessment in
Support of a Proposed Rule for Technical Standards for Emissions from Combustion Units
Burning Hazardous Wastes.
U.S. EPA. 1996d. Guidance for Total Organics. Final Report. Radian Corporation. Contract
68-D4-0022, Work Assignment 8. EPA/600/R-96/033. March.
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Federal Register (FR) 17960. Volume 61. Number 79. April 23.
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Clean Air Act Section 112(r)(7)." Federal Register. 61FR31667. Volume 61. Number 120.
June 20.
U.S. EPA. 1996g. Public Participation Record for Screening Risk Assessment for Operation of the
Tooele Chemical Demilitarization Facility at the Tooele Chemical Activity and Resulting Permit
Modification. June 20.
U.S. EPA. 1996h. "Formation of Dioxin-Like PICs During Incineration of Hazardous Wastes."
Memorandum to the Record. Dorothy Canter. June 21.
U.S. EPA. 1996L "Memorandum regarding Johnston Atoll Chemical Agent Disposal System (JACADS)
Risk Assessment Issues." From Dorothy Canter to Patrick Wilson. July 24.
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EPA-454/B-96-002. Office of Air Quality Planning and Standards, Emissions Monitoring and
Analysis Division. Research Triangle Park, North Carolina. August.
U.S. EPA. 1996k. Guideline on Air Quality Models. Title 40 Code of Federal Regulations Part 51,
Appendix W. September.
U.S. EPA. 1996m. Internal Memorandum Regarding JACADS Risk-Related Issues. From Timothy
Fields, Jr., Deputy Assistant Administrator, OSWER. To Julie Anderson, Director, Waste
Management Division. October 2.
U.S. EPA. 1996n. Telefax Memorandum Regarding Evaluation of Acute Inhalation Exposure to Kalama
Chemical Company Upset Incinerator Emissions. From Cathy Massimino, U.S. EPA Region 10.
To David Weeks, U.S. EPA Region 6. December 5.
U.S. EPA. 1996o. "Region 10 Tiered Approach for Developing Acute Inhalation Exposure Values."
Facsimile Transmission from Cathy Massimino, EPA Region 10 to David Weeks, EPA Region 6.
December 16, 1996.
U.S. EPA. 1996p. "Drake Chemical Incinerator Trial Burn Risk Assessment, Volume 1." Prepared by
Roy F. Weston for EPA Region 3. June 21.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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References September 2005
U.S. EPA. 1996q. "PCBs: Cancer Dose-Response Assessment and Application to Environmental
Mixtures." National Center for Environmental Assessment, Office of Research and
Development. EPA/600/P-96/001F. September.
U.S. EPA. 1996r. Recommendations for the Technical Review Workgroup for Lead for an Interim
Approach to Assessing Risks Associated with Adult Exposures to Lead in Soil. Technical Review
Workgroup for Lead. December.
U.S. EPA. 1996s. Mercury Study Report to Congress, Volume III, SAB Review Draft. Office of Air
Quality Planning and Standards and ORD. EPA/452/R-96-001c. April.
U.S. EPA. 1997a. "Development of a Hazardous Waste Incinerator Target Analyte List of Products of
Incomplete Combustion, Draft Final Report". National Risk Management Research Laboratory,
Air Pollution Prevention and Control Division, U.S. EPA. Research Triangle Park, North
Carolina. July 25.
U.S. EPA. 1997b. Exposure Factors Handbook. Office of Research and Development.
EPA/600/P-95/002Fc. August.
U.S. EPA. 1997c. Mercury Study Report to Congress, Volumes I through VIII. Office of Air Quality
Planning and Standards and ORD. EPA/452/R-97-001. December.
U.S. EPA. 1997d. Ecological Risk Assessment Guidance for Superfiind: Process for Designing and
Conducting Ecological Risk Assessments. Interim Final. OSWER, U.S. EPA.
EPA 540-R-97-006. June.
U.S. EPA. 1997e. Notice of Draft Source Category Listing for Section 112(d)(2) Rule making Pursuant
to Section 112(c)(6) Requirements. Federal Register. 62:33625. June 20.
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EPA Region 6 Center for Combustion Science and Engineering.
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Analysis. U.S. EPA, Risk Assessment Forum, Washington, DC. USEPA 630/R-96/012. Feb
1997.
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Office of Research and Development, National Center for Environmental Assessment, U.S. EPA.
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U.S. EPA. 1998c. Methodology for Assessing |