vvEPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/6-88/005CC
June 1994
External Review Draft
Estimating
Exposure to
Dioxln-Like
Compounds
Review
Draft
(Do Not
Cite or
Quote)
Volume III:
Site-Specific
Assessment Procedures
Notice
This document is a preliminary draft. It has not been formally
released by EPA and should not at this stage be construed to
represent Agency policy. It is being circulated for comment on its
technical accuracy and policy implications.
. 120,
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Dioxin
External Review Drafts
Page 9 of 9
Next Stages ip flie Reassessment Process
As described previously, public briefings will be held during the first week of the
public comment period to be followed by formal public hearings in December 1994.
After the dose of the public comment period, the Agency's Science Advisory Board
(SAB) will review the draft documents in public session (early 1995). Following SAB
review, the draft documents will be revised, comments and revisions will be
Incorporated, and final documents will be Issued.
Dat
1 Act1 ng Assistant Administrator
for Research and Development
Billing Coda: 6560-50-P
9
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ecosystems from exposure to dloxlns. Research efforts are focused on the study of
organisms in aquatic food webs to identify the effects of dloxln exposure that are likely
to result in significant population impacts. A report titled, Interim Report on Data and
Methods for the As$es$ment of 2,3,7,8-TetrachlQrQdiQenzc-p-Dloxln (TCDD) Risks to
Aquatic Organisms and Associated Wildlife (EPA/600/R-93/055), was published in
April 1993. This report will serve as a background document for assessing dioxin-
related ecological risks. Ultimately, these data will support the development of aquatic
life criteria which will aid in the Implementation of the Clean Water Act.
As mentioned previously, completion of the health assessment and exposure
documents Involves three phases: Phase 1 Involved drafting state-oMhe-science
chapters and a dose-response model for the health assessment document, expanding
the exposure document to address dloxln related compounds, and conducting peer-
review workshops by panels of experts. This phase has been completed.
Phase 2, preparation of the risk characterization, began during the September
1992 workshops with discussions by the peer-review panels and formulation of points
to be carried forward Wo the risk characterization. Following the September 1993
workshop, this work was completed and was incorporated as Chapter 9 (Volume III)
of the draft hearth assessment document. This phase has been completed.
Phase 3 Is currently underway. It includes making External Review Drafts of
both the health assessment document and the exposure document available for public
review and comment.
8
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09-08-1994 12:24 202 260 8061 TIS P.08
scientific experts from outside the Agency reviewed the draft documents and provided
valuable comments. It also should be noted that outside scientists have been heavily
involved throughout the developmental process of writing and reviewing these draft
documents. With this notice, the External Review Drafts of both draft documents are
being released for a 120-day public review and comment period.
Stage of the Scientific Reassessment of Dioxin
The scientific reassessment of dloxln consists of five activities:
1. Update and revision of the health assessment document for dioxln.
2. Laboratory research in support of the dose-response model.
3. Development of a biologically based dose-response model for dioxin.
4. Update and revision of the dloxln exposure assessment document.
5. Research to characterize ecological risks in aquatic ecosystems.
The first four activities have resulted in two draft documents (the health
assessment document and exposure document) for 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) and related compounds. These companion documents, which form the basis
for the Agency's reassessment of dloxln, have been used In the development of the
risk characterization chapter that follows the health assessment (Chapter 9, Volume
III). The process for developing these documents consisted of three phases which are
outlined in later paragraphs.
The fifth activity, which Is In progress at EPA's Environmental Research
Laboratory in Duluth, Minnesota, Involves characterizing ecological risks In aquatic
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09-08-1994 12:24 202 260 8061 TIS P.O7
to hear and receive public comments and reviews of the proposed plans, and to
receive any current, scientifically relevant Information.
In the Feu of 1992, the Agency convened two peer-review workshops to review
draft documents related to EPA's scientific reassessment of the health effects of dioxin.
The first workshop was held September 10 and 11,1992, to review a draft exposure
assessment titled, Estimating Exposures to Dloxln-Uke Compounds. The second
workshop was held September 22-25, 1992, to review eight chapters of a future draft
Health Assessment Document for ^SJ^-Tetrachlorodlbenzo-p-dioxin (TCDD) and
Related Compounds. Peer-reviewers were also asked to Identify Issues to be
incorporated Into the risk characterization, which was under development.
In the Fall of 1993, a third peer-review workshop was held on September 7 and
8, to review a draft of the revised and expanded Epidemiology and Human Data
Chapter, which also would be part of the future health assessment document. The
revised chapter provided an evaluation of the scientific quality and strength of the
epidemiology data In the evaluation of toxic health effects, both cancer and noncancer,
from exposure to dioxin, with an emphasis on the specific congener, 2,3,7,8-TCDD.
Prior to each workshop, the draft documents or chapters were made available
in keeping with the Agency's continuing commitment to conduct the reassessment of
dioxin In an open and participatory manner, to keep the public Informed of Its
progress, and to encourage public participation in the document development
process. The public also was invited to attend the workshops, to present oral
comments, and/or to submit written comments. At each workshop, a panel of
6
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09-O8-1994 12:23 202 260 8061 TIS P.06
adverse health effects of dioxin in people, of the pathways to human exposure, and of
the toxic effects of dioxin to the environment. The reassessment Is part of the
Agency's goals to Improve the research and science base and to incorporate
Improved research and science into EPA decisions.
History
In 1985 and 1988, the Agency prepared assessments of the human health risks
from environmental exposures to dioxin. Also, In 1988, a draft exposure document
was prepared that presented procedures for conducting site-specific exposure
assessments to dloxln-llke compounds. These assessments were reviewed by the
Agency's Science Advisory Board (SAB). At the time of the 1988 assessments, there
was general agreement within the scientific community that there could be a
substantial improvement over the existing approach to analyzing dose response, but
there was no consensus as to a more biologically defensible methodology. The
Agency was asked to explore the development of such a method. The Agency's
reassessment activities are In response to this request.
Stages In the Reassessment Process That Have Been Completed
The EPA had endeavored to make each phase of the reassessment of dioxin
an open and participatory effort. On November 15,1991, and April 28, 1992, public
meetings were held to discuss the Agency's plans and activities for the reassessment,
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09-06-1994 12:23 202 260 8061 T1S P.05
For the exposure assessment document, send comments to: Dloxln Exposure
Assessment Comments, Technical Information Staff (8601), Office of Health and
Environmental Assessment, U.S. Environmental Protection Agency, 401 M Street,
S.W., Washington, DC 20460.
FOR FURTHER INFORMATION, CONTACT:
For questions on the overall reassessment of dioxin or technical questions on
the health assessment document: William Farland, Office of Health and Environmental
Assessment (8601), Office of Research and Development, U.S. Environmental
Protection Agency, 401 M Street, S.W., Washington, DC 20460; telephone (202) 260-
7315; fax (202) 260-0393.
For technical questions on the exposure assessment: John Schaum, Exposure
Assessment Group (8603), Office of Health and Environmental Assessment, U.S.
Environmental Protection Agency, 401 M Street, S.W., Washington, DC 20460;
telephone (202) 260-8909; fax (202) 260-1722.
SUPPLEMENTARY INFORMATION:
The Scientific Reassessment of Dioxin
In April 1991, EPA announced that It would conduct a scientific reassessment of
the health risks of exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and
chemically similar compounds collectively known as dioxin. The EPA has undertaken
this task in response to emerging scientific knowledge of the biological, human health,
•nd environmental effects of dioxin. Significant advances have occurred In the
scientific understanding of mechanisms of dioxin toxiclty, of the carcinogenic and other
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09-08-1S94 12:22 202 260 8O61 TIS P.04
Agency, 26 W. Martin Luther King Drive, Cincinnati, OH 45268; telephone (513) 569-
7562; fax (513) 569-7566. Please provide your name, mailing address, document title,
and EPA number.
Please note that the two summary volumes also will be made available as
WordPerfect 5.1 files on 3V PC-DOS formatted disks. Please request by document
title and EPA number:
Risk Characterization Chapter (Vol. Ill-Health), EPA/600/BP-92/001ca (disk)
Executive Summary Chapter (Vol. i-Exposure), EPA/600/6-88/005Caa (disk)
The draft documents will be provided for Inspection at the ORD Public
Information Shelf, EPA Headquarters Library, 401 M Street, S.W., Washington, DC
20460, between the hours of 10:00 a.m. and 2:00 p.m., Monday through Friday,
except for Federal holidays, and at all of the EPA Regional and Laboratory libraries.
Submitting Comments
All comments must be in writing. Commenters should submit three copies of
each comment, and If commenting on both documents—the health assessment
document and the exposure assessment-submit separate comments rather than
combined submissions.
For the health assessment document, send comments to: Dioxin Health
Assessment Comments, Technical Information Staff (8601), Office of Health and
Environmental Assessment, U.S. Environmental Protection Agency, 401 M Street,
S.W., Washington, DC 20460.
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09-0-8-1994 12:22 202 260 8061 TIS P.03
ADDRESSES:
Requesting Documents
Due to the large size of both draft documents (each Is over 1,000 pages In
length), the documents will be available as follows:
Health Assessment Document for 2,3,7,8'Tetrachlorodlb9nz&p-dlQxln (TCDD)
and Related Compounds, EPA/600/BP-92/001a, 001 b, 001 c. (Note: The full
document is 3 volumes and approx. 1,100 pages.)
OR
Risk Characterization Chapter, EPA/600/BP-92/001C. (Note: This third volume
of the 3-volume set Integrates health and exposure information on dioxin and
related compounds; approx. 100 pages.)
AND/OR
Estimating Exposure to Dloxln-Llke Compounds, EPA/600/6-88/005Ca, Cb, Cc.
(Note: The full document is 3 volumes and approx. 1,300 pages.)
OR
Executive Summary Chapter of the Exposure Document, EPA/600/6-88/005Ca.
(Note: This first volume of the 3-volume set summarizes the exposure
Information; approx. 100 pages.)
To obtain a paper copy of these draft documents, interested parties should
contact the ORD Publications Center, CERI-FRN, U.S. Environmental Protection
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09-08-1994 12:22 202 260 8061 IIS P.02
Reassessment of 2,3,7,8-Tetrachlorodlbenzo-p-dloxJn
(2,3,7,8-TCDD, dloxln)
AGENCY: U.S. Environmental Protection Agency (EPA)
ACTION: As part of the Agency's reassessment of 2,3,7,8-tetrachlorodibenzo-p-dloxln
(2,3,7,8-TCDD; hereinafter referred to as simply dloxln), two External Review Draft
documents are being made available for a 120-day public review and comment period.
SUMMARY: This notice announces the availability of two External Review Draft
documents for public review and comment:
1. Health Assessment Document for 2,3,7,8-Tetrachlorodibenzo-p*dloxln
(TCDD) and Related Compounds (EPA/600/BP-92/OOla-c)
2. Estimating Exposure to Dloxln-Llke Compounds (EPA/600/6-88/005Ca-c)
During the public comment period, public comment meetings will be convened to take
formal comments on the draft documents. These meetings are being planned for the
first two weeks of December at five locations: Washington, DC; New York, NY/New
Jersey; Chicago, IL; Dallas, TX; and San Francisco, CA. Detailed information will be
provided in a future Federal Register notice.
The draft documents also will be reviewed at a Science Advisory Board meeting
to be held after the public comment period has ended, early next year. Information
about this meeting will be published In a future Federal Register notice.
DATES: The draft documents will be made available on September 13, 1894.
Comments must be postmarked by January 13, 1994.
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EPA/600/6-88/005CC
DO NOT QUOTE OR CITE June 1994
External Review Draft
ESTIMATING EXPOSURE TO DIOXIN-LIKE COMPOUNDS
VOLUME III: Site-Specific Assessment Procedures
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the U.S.
Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. It is being circulated for comment on its technical accuracy and policy
implications.
U.S. Environmental Protection Agency
Region 5, Library (PL-12J)
77 West Jackson Boulevard, 12th Floor
Chicago, IL 60604-3590
Exposure Assessment Group
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Washington, D.C.
/-ry
TOO Printed on Recycled Paper
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DISCLAIMER
This document is an external draft for review purposes only and does not constitute
U.S. Environmental Protection Agency policy. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
Ill-ii
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CONTENTS
Tables viii
Figures xiii
Foreword xv
Preface xvi
Authors, Contributors, and Reviewers xix
1. INTRODUCTION 1-1
1.1. BACKGROUND 1-1
1.2. TOXICITY EQUIVALENCY FACTORS 1-2
1.3. OVERALL COMMENTS ON THE USE OF THE DIOXIN EXPOSURE
DOCUMENT 1-6
1.4. NOTES ON THE USE OF PROCEDURES IN VOLUME III 1-7
REFERENCES FOR CHAPTER 1 1-11
2. ESTIMATING EXPOSURES AND RISKS 2-1
2.1. INTRODUCTION 2-1
2.2. EXPOSURE EQUATION 2-2
2.3. RISK EQUATION 2-4
2.4. PROCEDURE FOR ESTIMATING EXPOSURE 2-7
2.5. STRATEGY FOR DEVISING EXPOSURE SCENARIOS 2-10
2.6. EXPOSURE PATHWAYS AND PARAMETERS 2-13
2.6.1. Soil Ingestion 2-14
2.6.2. Soil Dermal Contact 2-18
2.6.3. Vapor and Dust Inhalation 2-19
2.6.4. Water Ingestion 2-20
2.6.5. Beef and Dairy Product Ingestion 2-20
2.6.6. Fish Ingestion 2-22
2.6.7. Fruits and Vegetables 2-25
REFERENCES FOR CHAPTER 2 2-27
3. EVALUATING ATMOSPHERIC RELEASES OF DIOXIN-LIKE
COMPOUNDS FROM COMBUSTION SOURCES 3-1
3.1. INTRODUCTION 3-1
3.2. ESTIMATING THE EMISSIONS OF DIOXIN-LIKE COMPOUNDS FROM
ANTHROPOGENIC COMBUSTION SOURCES 3-3
3.2.1. A Stragey for Generating Emission Factors 3-4
3.2.2. Use of Homologue Profiles for Estimating
Congener Specific Emission Factors 3-6
3.2.3. Estimation of Emissions of Dioxin-Like
Compounds from the Hypothetical Incinerator 3-20
3.2.4. Estimation of the Vapor Phase/Particle Phase
Partitioning of Emissions of Dioxin-Like
Compounds 3-21
3.2.4.1. Vapor phase/particulate phase
inferences from stack measurements .... 3-23
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CONTENTS (cont'd)
3.2.4.2. Discussion of vapor/particle ratios
derived from stack test methods 3-27
3.2.4.3. Vapor/particle partitioning of PCDD/Fs
from ambient air sampling 3-29
3.2.4.4. Discussion of the vapor/particle
partitioning in ambient air sampling
studies 3-37
3.2.4.5. Theoretical prediction of vapor/particle
partitioning of PCDD/Fs under ambient
conditions 3-38
3.2.4.6. Discussion of vapor/particle
partitioning 3-42
3.2.5. Estimation of the Concentration of Dioxin-Like
Compounds in Incineration Ash 3-44
3.3. AIR DISPERSION/DEPOSITION MODELING OF THE STACK GAS
EMISSIONS OF DIOXIN-LIKE COMPOUNDS 3-45
3.3.1. Basic Principles Used to Estimate Atmospheric
Dispersion/Deposition of Stack Emissions 3-46
3.3.2. Estimation of Dry Surface Deposition Flux 3-47
3.3.3. Estimation of the Particle Size Distribution
in the Stack Emissions 3-51
3.3.4. Estimation of Wet Deposition Flux 3-54
3.3.5. The Requirement to Run the COMPDEP Model Twice . . 3-55
3.4. RESULTS OF AIR DISPERSION MODELING OF CONGENER-SPECIFIC
EMISSIONS FROM THE HYPOTHETICAL ORGANIC WASTE
INCINERATOR 3-58
3.5. REVIEW OF PROCEDURES FOR ESTIMATING SITE-SPECIFIC
IMPACTS FROM A STACK EMISSION SOURCE 3-62
REFERENCES FOR CHAPTER 3 3-70
4. ESTIMATING EXPOSURE MEDIA CONCENTRATIONS 4-1
4.1. INTRODUCTION 4-1
4.2. BACKGROUND FOR SOLUTION ALGORITHMS 4-2
4.3. ALGORITHMS FOR THE "ON-SITE SOIL"
SOURCE CATEGORY 4-7
4.3.1. Surface Water and Sediment Contamination 4-7
4.3.2. Vapor-Phase Air Concentrations 4-27
4.3.3. Particulate-Phase Air Concentrations 4-32
4.3.4. Biota Concentrations 4-35
4.3.4.1. Fish concentrations 4-35
4.3.4.2. Vegetation concentrations 4-48
4.3.4.3. Beef and milk concentrations 4-65
4.4. ALGORITHMS FOR THE "OFF-SITE" SOURCE CATEGORY 4-76
4.4.1. Exposure Site Soil Concentrations 4-78
4.4.2. Off-site Transport of Air-borne Contaminants 4-85
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CONTENTS (cont'd)
4.4.3. Specific Cases of Off-Site Soil Contamination 4-87
4.4.3.1. Landfills receiving ash from
municipal waste incinerators 4-87
4.4.3.2. Land application of sludge from
pulp and paper mills 4-97
4.4.3.3. Sites studied in the National
Dioxin Study 4-100
4.5. ALGORITHMS FOR THE STACK EMISSION
SOURCE CATEGORY 4-102
4.5.1. Steady-State Soil Concentrations 4-104
4.5.2. Surface Water Impacts 4-106
4.6. ALGORITHMS FOR THE EFFLUENT DISCHARGE
SOURCE CATEGORY 4-112
4.6.1 The Simple Dilution Model 4-114
REFERENCES FOR CHAPTER 4 4-1 22
5. DEMONSTRATION OF METHODOLOGY 9-1
5.1. INTRODUCTION 5-1
5.2. STRATEGY FOR DEVISING EXPOSURE SCENARIOS
FOR DEMONSTRATION PURPOSES 5-2
5.3. EXAMPLE EXPOSURE SCENARIOS 5-9
5.4. EXAMPLE COMPOUNDS 5-12
5.5. SOURCE TERMS 5-13
5.6. RESULTS 5-20
5.6.1. Observations Concerning Exposure Media
Concentrations 5-23
5.6.2. Observations Concerning LADD Exposure Estimates .... 5-32
REFERENCES FOR CHAPTER 5 5-45
6. USER CONSIDERATIONS 6-1
6.1. INTRODUCTION 6-1
6.2. CATEGORIZATION OF METHODOLOGY PARAMETERS 6-1
6.3. SENSITIVITY ANALYSIS 6-14
6.3.1. Limitations of the Sensitivity
Analysis Exercises 6-14
6.3.2. Methodology Description and
Parameter Assignments 6-18
6.3.3. Results 6-33
6.3.3.1. Estimation of off-site air
concentrations in the vapor phase 6-34
6.3.3.2. Estimation of off-site air
concentrations in the paniculate phase 6-34
6.3.3.3. Estimation of soil erosion impacts
to nearby sites of exposure 6-37
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CONTENTS (cont'd)
6.3.3.4. Estimation of soil erosion impacts
to nearby surface water bodies 6-41
6.3.3.5. Estimation of fish tissue concentrations 6-43
6.3.3.6. Estimations of on-site air concentrations
in the vapor phase 6-44
6.3.3.7. Estimation of on-site air concentrations
in the particulate phase 6-45
6.3.3.8. Vapor-phase transfers and particle phase
depositions to above ground vegetations 6-47
6.3.3.9. Estimation of below ground vegetation
concentrations 6-54
6.3.3.10. Beef fat concentration estimation 6-58
6.3.3.11. Vegetable/fruit and beef/milk concentrations
resulting from stack emissions 6-62
6.3.3.12. Water and fish concentrations resulting
from effluent discharges 6-65
6.3.3.13. Water and fish concentrations resulting
from stack emission 6-67
6.3.4. Key Trends from the Sensitivity Analysis Testing 6-70
6.4. MASS BALANCE CONSIDERATIONS 6-72
REFERENCES FOR CHAPTER 6 6-78
7. UNCERTAINTY 7-1
7.1. INTRODUCTION 7-1
7.2. AN EVALUATION OF THE ALGORITHMS USED TO
ESTIMATE EXPOSURE MEDIA CONCENTRATIONS 7-3
7.2.1. Uncertainaties and Variabilities with Chemical-Specific
Parameters and Assumptions 7-4
7.2.2. A Discussion of Uncertainty Issues Associated With Use
of COMPDEP for Transport and Dispersion of Stack
Emitted Contaminants 7-8
7.2.3. Comparing Model Estimations of Exposure and Environmental
Media With Those Found in the Literature 7-11
7.2.3.1. The impact to soils of point source releases
of dioxin-like compounds 7-12
7.2.3.2. Soil concentrations and concurrent
concentrations in bottom sediment and fish . . . 7-16
7.2.3.3. Other bottom sediment concentration data . . . 7-29
7.2.3.4. Data on water concentrations of dioxin-like
compounds 7-31
7.2.3.5 Data on fish concentrations in the literature . . . 7-32
7.2.3.6. Impact of pulp and paper mill effluent discharges
on fish tissue concentrations 7-36
7.2.3.7. Examination of observed air concentrations . . . 7-48
7.2.3.8. Impacts of contaminated soils to vegetations . . 7-51
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CONTENTS (cont'd)
7.2.3.9. A validation exercise for the beef
bioconcentration algorithm 7-61
7.2.3.9.1. Air and beef concentrations 7-62
7.2.3.9.2. Summary of algorithms, key
assumptions, and parameter
values 7-67
7.2.3.9.3. Results and discussion 7-70
7.2.3.9.4. Conclusions 7-78
7.2.3.10. Comparison of modeled beef and milk
concentrations with concentrations found . . . 7-81
7.2.4. Alternate Modeling Approaches for Estimating Environmental
and Exposure Media Concentrations 7-84
7.2.4.1. An alternate approach for estimating bottom
sediment concentrations from watershed soil
concentrations 7-84
7.2.4.2. An alternate modeling approach for estimating
water concentrations given a steady input load
from overland sources 7-85
7.2.4.3. Estimating fish tissue concentrations based on
water column concentrations rather than bottom
sediment concentrations 7-88
7.2.4.4. Other modeling approaches and considerations for
air concentrations resulting from soil
volatilization 7-94
7.2.4.5. Alternate models for estimating plant
concentrations from soil concentrations 7-98
7.2.4.6. Alternate modeling approaches for estimating
beef and milk concentrations 7-101
7.3. UNCERTAINTIES ASSOCIATED WITH EXPOSURE PATHWAYS .. 7-108
7.3.1. Lifetime, Body Weights, and Exposure Durations 7-109
7.3.2. Soil Ingestion Exposure 7-110
7.3.3. Soil Dermal Contact Pathway 7-114
7.3.4. Water Ingestion 7-117
7.3.5. Fish Ingestion Exposure 7-119
7.3.6. Vapor and Particle Phase Inhalation Exposure 7-124
7.3.7. Fruit and Vegetable Ingestion 7-129
7.3.8. Beef and Milk Ingestion 7-133
7.4. USE OF MONTE CARLO TECHNIQUES FOR ASSESSING EXPOSURE
TO DIOXIN-LIKE COMPOUNDS 7-139
REFERENCES FOR CHAPTER 7 7-146
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TABLES
1-1 Toxicity equivalency factors (TEF) for CDDs and CDFs 1-3
1-2 Dioxin-Like PCBs 1-4
1-3 Nomenclature for dioxin-like compounds 1-5
2-1 Summary of exposure pathway parameters selected for the
demonstration scenarios of Chapter 5 2-15
2-2 Fish consumption estimates from the USDA 1977-78 National Food
Consumption Survey (consumptions were recorded for three day
periods; N = 36249; units are grams/day/person; SF = shellfish) . . . 2-23
3-1 The number of dioxin-like and total congeners within
dioxin, furan, and coplanar PCB homologue groups 3-8
3-2 Emission factors and average emissions used for the
hypothetical incinerator 3-22
3-3 Percent distribution of dioxins and furans between vapor
phase (V) and particulate phase (P) as interpreted by various
stack sampling methods 3-24
3-4 Percent distribution of dioxins and furans between vapor
phase (V) and particulate phase (P) in ambient air as observed
in ambient air sampling studies 3-31
3-5 Fractions of dioxins and furans calculated to partition
to particles in various classifications of ambient air using
the method of Bidleman (1988), Junge (1977), and Whitby (1978) . . 3-42
3-6 Factors that influence the dry deposition removal
rate in the atmosphere 3-50
3-7 A summary of dry deposition velocities for particles 3-52
3-8 Typical particle size distribution in particulate
emissions from incineration 3-53
3-9 Wet deposition scavenging coefficients per particle
diameter category (micrometers), expressed per second
of time 3-57
3-10 Emissions of PCDD/Fs (g/sec) from the hypothetical incinerator 3-59
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TABLES (cont'd)
3-11 Modeling parameters used in the COMPDEP modeling of
PCDD/F emissions from the hypothetical incinerator 3-61
3-12 Predicted annual average vapor-phase concentrations of
PCDD/Fs (g/m3) 3-63
3-13 Predicted annual average particle-phase air concentrations
of PCDD/Fs (g/m3) 3-64
3-14 Predicted total (vapor + particle) ambient air concentrations
of PCDD/Fs (g/m3) 3-65
3-15 Predicted annual dry deposition fluxes of particle-bound
PCDD/Fs (g/m2-yr) 3-66
3-16 Prediced annual wet deposition fluxes of particle-bound
PCDD/Fs (g/m2-yr) 3-67
3-17 Predicted total (dry + wet) deposition fluxes of
particle-bound PCDD/Fs (g/m2-yr) 3-68
4-1 Available Biota to Sediment Accumulation Factors, BSAF,
for dioxin-like compounds 4-41
4-2 Available Biota to Sediment Accumulation Factors, BSAF,
for PCBs 4-46
4-3 Ratios of dioxins and furans in milk fat (MF) and body fat (BF)
to concentrations in diets of farm animals 4-67
4-4 Ratios of PCBs in milk fat (MF) and body fat (BF) to concentrations
in diets of lactating cows 4-69
4-5 Ranges of concentrations of PCDDs, PCDFs, and PCBs in municipal
waste combustor ash (results in ng/g or ppb) 4-91
5-1 Environmental fate parameters for the three example compounds
demonstrated for the soil contamination source categories and
the effluent discharge source category 5-14
5-2 Key source terms and fate parameters for 2,3,7,8-TCDD and for
individual dioxin and furan congeners with non-zero TEFs
for the demonstration of the stack emission source category 5-15
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TABLES (cont'd)
5-3 Summary of key source terms for the six exposure scenarios
and the example compounds 5-16
5-4 Exposure media concentrations estimated for all scenarios
and pathways 5-21
5-5 Lifetime average daily dose (LADD) estimates for all scenarios
and exposure pathways (all results in mg/kg-day) 5-24
5-6 Percent contribution of the different exposure pathways
within each exposure scenario 5-35
5-7 Exposures to low soil concentrations of 2,3,7,8-TCDD assuming
lifetime exposure durations and unlimited contact with impacted
media, compared with exposures assuming limited durations and
limited contact 5-38
5-8 Comparison of exposure pathway contributions to total daily
exposure as estimated in example Scenario #2 and in Travis and
Hattemer-Frey (1991) 5-41
6-1 Parameters used to estimate exposure media concentrations
for this assessment 6-2
6-2 Contribution of above ground vegetation concentrations of
2,3,7,8-TCDD from air-to-leaf transfers and particulate
depositions 6-52
6-3 Results of sensitivity test of modeling vapor/particle partitioning
for volatilized residues (note:soil concentration equals 1 ppt
in tests below) 6-55
7-1 Summary of off-site soil contamination from Tier and 2 sites of
the National Dioxin Study 7-15
7-2 Description of soil, sediment, and fish sampling program of
dioxin-like compounds undertaken by the Connecticut Department
of Environmental Protection 7-19
7-3 Frequency of nondetects and detection limits for soil,
and fish, for three congeners in the Connecticut Department
of Environmental Protection data set 7-23
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TABLES (cont'd)
7-4 Results for Connecticut Department of Environmental Protection
sampling, including soil, sediment and fish concentrations, and
key concentration ratios of sediment to soil and the Biota
Sediment Accumulation Factor (BSAF) ratio 7-24
7-5 Model parameters and results for effluent discharge model
validation testing 7-40
7-6 Summary of plant concentration versus soil concentration data for
2,3,7,8-TCDD 7-52
7-7 Observed air and beef concentrations, and fate parameters
for individual dioxin and furan congeners 7-66
7-8 Model parameters used for all dioxin-like congeners 7-68
7-9 Results of validation exercise showing observed and
predicted concentrations of dioxin-like compounds in whole
beef 7-71
7-10 Comparison of concentrations of dioxin-like compounds found
in hay in a rural setting with model predictions of grass
concentrations 7-73
7-11 Calibration exercise showing improvements in grass and beef
concentrations when the fraction sorbed parameter, 0, drops
minutely below 1.00 for OCDD and OCDF 7-74
7-12 Comparison of concentrations of dioxin-like compounds found in
soils described as "rural" or "background" with model predictions
of soil concentrations 7-76
7-13 Uncertainties associated with the lifetime, body weight,
and exposure duration parameters 7-111
7-14 Uncertainties associated with the soil ingestion pathway 7-115
7-15 Uncertainties associated with the dermal exposure pathway 7-118
7-16 Uncertainties associated with the water ingestion pathway 7-120
7-17 Uncertainties associated with the fish ingestion pathway 7-124
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TABLES (cont'd)
7-18 Uncertainties and sensitivities associated with estimating
vapor and particle-phase air concentrations from
contaminated soils 7-130
7-19 Uncertainties associated with vegetable and fruit ingestion
exposure algorithms 7-134
7-20 Uncertainties associated with beef and milk ingestion
exposure algorithms 7-140
7-21 Distributions for a Monte Carlo exercise which developed
soil cleanup levels at residential and industrial sites 7-142
7-22 Summary of Monte Carlo distributions used in a fish
consumption assessment 7-143
7-23 Summary of Monte Carlo distributions used in a food
chain study 7-145
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FIGURES
2-1 Roadmap for assessing exposure and risk to dioxin
and dioxin-like compounds 2-8
3-1 Homologue profile emission factors for source categories
of dioxin-like compound release 3-9
4-1 Diagram of the fate, transport, and transfer relationships
for the on-site source category 4-8
4-2 Diagram of the fate, transport, and transfer relationships
for the off-site source category 4-9
4-3 Diagram of the fate, transport, and transfer relationships
for the stack emission source category 4-10
4-4 Diagram of the fate, transport, and transfer relationships
for the effluent discharge source category 4-11
4-5 Watershed delivery ratio, SDW, as a function of
watershed size 4-24
6-1 Results of sensitivity analysis of algorithms estimating exposure
site vapor phase air concentrations resulting from off-site
soil contamination 6-35
6-2 Results of sensitivity analysis of algorithms estimating exposure
site particle phase air concentrations resulting from off-site
soil contamination 6-36
6-3 Results of sensitivity analysis of algorithms estimating exposure
site soil concentrations resulting from erosion from off-site
soil contamination 6-38
6-4 Results of sensitivity analysis of algorithms estimating surface
water and bottom sediment concentrations resulting from a site of
soil contamination 6-42
6-5 Results of sensitivity analysis of algorithms estimating fish
tissue concentrations given bottom sediment concentrations 6-45
6-6 Results of sensitivity analysis of algorithms estimating on-site
vapor phase air concentrations from on-site soil contamination 6-46
6-7 Results of sensitivity analysis of algorithms estimating on-site
particle phase air concentrations from on-site soil contamination .... 6-47
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FIGURES (cont'd)
6-8 Results of sensitivity analysis of algorithms estimating above ground
vegetations concentrations due to vapor phase transfers 6-49
6-9 Results of sensitivity analysis of algorithms estimating above ground
vegetation concentrations resulting from particle phase depositions . . 6-50
6-10 Results of sensitivity analysis of algorithms estimating below
ground vegetation concentrations resulting from soil to root
transfers 6-59
6-11 Results of sensitivity analysis of algorithms estimating beef
fat concentrations resulting from soil contamination 6-60
6-12 Results of sensitivity analysis of algorithms estimating
above and below ground vegetation, and beef and milk fat
concentrations resulting from stack emissions 6-63
6-13 Results of sensitivity analysis of algorithms estimating
surface water and fish concentrations resulting from effluent
discharge 6-66
6-14 Results of sensitivity analysis of algorithms estimating
surface water and fish concentrations resulting from stack
emissions 6-68
7-1 Schematic of effluent discharge model showing all parameter
inputs and observed fish concentrations 7-38
7-2 Comparison of predicted and observed fish tissue concentrations
for validation of the effluent discharge model 7-46
7-3 Overview of model to predict beef concentrations from air
concentrations 7-62
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FOREWORD
The Exposure Assessment Group (EAG) within the Office of Health and
Environmental Assessment of EPA's Office of Research and Development has three main
functions: (1) to conduct exposure assessments, (2) to review assessments and related
documents, and (3) to develop guidelines for exposure assessments. The activities under
each of these functions are supported by and respond to the needs of the various EPA
program offices. In relation to the third function, EAG sponsors projects aimed at
developing or refining techniques used in exposure assessments.
The purpose of this document is to present and evaluate methods for conducting
site-specific assessments of exposure to dioxin-like compounds. It is the third in a three
volume set addressing these compounds. The first volume provides an overall executive
summary and the second volume describes the properties, sources, environmental levels,
and background exposures to dioxin-like compounds. The document is intended to be used
as a companion to the health reassessment of dioxin-like compounds that the Agency is
publishing concurrently. It is hoped that these documents will improve the accuracy and
validity of risk assessments involving this important family of compounds.
Michael A. Callahan
Director
Exposure Assessment Group
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PREFACE
In April 1991, the U.S. Environmental Protection Agency (EPA) announced that it
would conduct a scientific reassessment of the health risks of exposure to 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) and chemically similar compounds collectively known as
dioxin. The EPA has undertaken this task in response to emerging scientific knowledge of
the biological, human health, and environmental effects of dioxin. Significant advances
have occurred in the scientific understanding of mechanisms of dioxin toxicity, of the
carcinogenic and other adverse health effects of dioxin in people, of the pathways to human
exposure, and of the toxic effects of dioxin to the environment.
In 1985 and 1988, the Agency prepared assessments of the human health risks from
environmental exposures to dioxin. Also, in 1988, a draft exposure document was prepared
that presented procedures for conducting site-specific exposure assessments to dioxin-like
compounds. These assessments were reviewed by the Agency's Science Advisory Board
(SAB). At the time of the 1988 assessments, there was general agreement within the
scientific community that there could be a substantial improvement over the existing
approach to analyzing dose response, but there was no consensus as to a more biologically
defensible methodology. The Agency was asked to explore the development of such a
method. The current reassessment activities are in response to this request.
The scientific reassessment of dioxin consists of five activities:
1. Update and revision of the health assessment document for dioxin.
2. Laboratory research in support of the dose-response model.
3. Development of a biologically based dose-response model for dioxin.
4. Update and revision of the dioxin exposure assessment document.
5. Research to characterize ecological risks in aquatic ecosystems.
The first four activities have resulted in two draft documents (the health assessment
document and exposure document) for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and
related compounds. These companion documents, which form the basis for the Agency's
reassessment of dioxin, have been used in the development of the risk characterization
chapter that follows the health assessment. The process for developing these documents
consisted of three phases which are outlined in later paragraphs.
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The fifth activity, which is in progress at EPA's Environmental Research Laboratory in
Duluth, Minnesota, involves characterizing ecological risks in aquatic ecosystems from
exposure to dioxins. Research efforts are focused on the study of organisms in aquatic
food webs to identify the effects of dioxin exposure that are likely to result in significant
population impacts. A report titled, Interim Report on Data and Methods for the
Assessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) Risks to Aquatic Organisms and
Associated Wildlife (EPA/600/R-93/055), was published in April 1993. This report will
serve as a background document for assessing dioxin-related ecological risks. Ultimately,
these data will support the development of aquatic life criteria which will aid in the
implementation of the Clean Water Act.
The EPA had endeavored to make each phase of the current reassessment of dioxin
an open and participatory effort. On November 15, 1991, and April 28, 1992, public
meetings were held to inform the public of the Agency's plans and activities for the
reassessment, to hear and receive public comments and reviews of the proposed plans, and
to receive any current, scientifically relevant information.
In the Fall of 1992, the Agency convened two peer-review workshops to review
draft documents related to EPA's scientific reassessment of the health effects of dioxin.
The first workshop was held September 10 and 11, 1992, to review a draft exposure
assessment titled. Estimating Exposures to Dioxin-Like Compounds. The second workshop
was held September 22-25, 1992, to review eight chapters of a future draft Health
Assessment Document for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and Related
Compounds. Peer-reviewers were also asked to identify issues to be incorporated into the
risk characterization, which was under development.
In the Fall of 1993, a third peer-review workshop was held on September 7 and 8,
1993, to review a draft of the revised and expanded Epidemiology and Human Data
Chapter, which also would be part of the future health assessment document. The revised
chapter provided an evaluation of the scientific quality and strength of the epidemiology
data in the evaluation of toxic health effects, both cancer and noncancer, from exposure to
dioxin, with an emphasis on the specific congener, 2,3,7,8-TCDD.
As mentioned previously, completion of the health assessment and exposure
documents involves three phases: Phase 1 involved drafting state-of-the-science chapters
and a dose-response model for the health assessment document, expanding the exposure
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document to address dioxin related compounds, and conducting peer review workshops by
panels of experts. This phase has been completed.
Phase 2, preparation of the risk characterization, began during the September 1992
workshops with discussions by the peer-review panels and formulation of points to be
carried forward into the risk characterization. Following the September 1993 workshop,
this work was completed and was incorporated as Chapter 9 of the draft health assessment
document. This phase has been completed.
Phase 3 is currently underway. It includes making External Review Drafts of both
the health assessment document and the exposure document available for public review and
comment.
Following the public comment period, the Agency's Science Advisory Board (SAB)
will review the draft documents in public session. Assuming that public and SAB comments
are positive, the draft documents will be revised, and final documents will be issued.
Estimating Exposures to Dioxin-Like Compounds has been prepared by the Exposure
Assessment Group of the Office of Health and Environmental Assessment, Office of
Research and Development, which is responsible for the report's scientific accuracy and
conclusions. A comprehensive search of the scientific literature for this document varies
somewhat by chapter but is, in general, complete through January 1994.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The Exposure Assessment Group (BAG) within EPA's Office of Health and
Environmental Assessment was responsible for the preparation of this document. General
support was provided by Versar Inc. under EPA Contract Number 68-DO-0101. Dr. William
Farland, as overall Director of the Dioxin Reassessment, provided policy guidance and
technical comments. Matthew Lorber of EAG served as EPA task manager (as well as
contributing author) providing overall direction and coordination of the production effort.
AUTHORS
Primary authors of each chapter are listed below in alphabetical order.
David H. Cleverly Chapters 3, 7
U.S. Environmental Protection Agency
Washington, DC
Matthew Lorber Chapter 1-7
U.S. Environmental Protection Agency
Washington, DC
John L. Schaum Chapters 1, 2
U.S. Environmental Protection Agency
Washington, DC
Paul White Chapter 7
U.S. Environmental Protection Agency
Washington, DC
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CONTRIBUTORS AND REVIEWERS
An earlier draft of this exposure document was reviewed by the Science Advisory
Board in 1988. A revised draft was issued in August 1992 and was reviewed by a panel of
experts at a peer-review workshop held September 10 and 11, 1992. Members of the Peer
Review Panel for this workshop were as follows:
M. Judith Charles, Ph.D.
University of North Carolina
Chapel Hill, NC
Dennis Paustenbach, Ph.D.
ChemRisk - A McLaren/Hart Group
Alameda, CA
Ray Clement, Ph.D.
Ontario Ministry of the Environment
Quebec, Canada
Richard Dennison, Ph.D.
Environmental Defense Fund
Washington, DC
Richard Reitz, Ph.D.
Dow Chemical
Midland, Ml
In addition, the following experts outside of EPA have reviewed and/or contributed to this
document:
Michael Bolger
US Food and Drug Administration
Washington, DC
James Falco, Ph.D.
Battelle Northwest
Richland, WA
Heidelore Fiedler, Ph.D.
University of Bayreuth
Federal Republic of Germany
Charles Fredette
Connecticut Department of Environmental Protection
Hartford, CT
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George Fries, Ph.D
United States Department of Agriculture
Beltsville Agricultural Research Center
Beltsville, MD
Laura Green, Ph.D, D.A.B.T
Cambridge Environmental, Inc.
Cambridge, MA
Dale Hattis, Ph.D.
Clark University
Worcester, MA
Steven Hinton, Ph.D., P.E.
National Council of the Paper Industry for Air and Stream
Improvement
Tufts University
Medford, MA
Kay Jones
Zephyr Consulting
Seattle, WA
George Lew
California Air Resources Board
Sacremento, CA
Thomas E. McKone, Ph.D.
Lawrence Livermore National Laboratory
Livermore, CA
Derek Muir, Ph.D
Freshwater Institute
Department of Fisheries and Oceans
Winnipeg, MB, Canada
Marvin Norcross, Ph.D.
Food Safety Inspection Service, USDA
Washington, DC
Vlado Ozvacic, Ph.D.
Ministry of the Environment
Toronto, ON, Canada
Thomas Parkerton, Ph.D
Manhattan College
Riverdale, NY
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Christopher Rappe, Ph.D.
University of Umea
Institute of Environmental Chemistry
Umea, Sweden
Curtis C. Travis, Ph.D.
Oak Ridge National Laboratory
Oak Ridge, TN
Thomas 0. Tiernan, Ph.D.
Wright State University-
Dayton, OH
Thomas Umbreit, Ph.D.
Agency for Toxic Substances and Disease Registry
Atlanta, GA
G.R. Barrie Webster, Ph.D.
University of Manitoba
Winnipeg, Canada
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The following individuals within EPA have reviewed and/or contributed to this document:
OFFICE
REVIEWERS/CONTRIBUTORS
Office of Research and Development
Frank Black
Brian Gullett
Joel McCrady
Philip Cook
Donna Schwede
Bill Petersen
James Kilgroe
Office of Air and Radiation
Pam Brodowicz
Thomas Lahre
Phil Lorang
Dennis Pagano
Dallas Safriet
Joseph Wood
George Streit
Anne Pope
Walter Stevenson
Jim Crowder
Joe Somers
Office of Pollution, Pesticides and Toxic
Substances
Joe Cotruvo
Steven Funk
Pat Jennings
Leonard Keifer
Robert Lipnick
Tom Murray
Office of Water
Ryan Childs
Mark Morris
Edward Ohanian
Al Rubin
Maria Gomez Taylor
Office of General Counsel
Chuck Elkins
Office of Policy, Planning and Evaluation
Dwain Winters
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1. INTRODUCTION
1.1. BACKGROUND
In May of 1991, the Environmental Protection Agency (EPA) announced a scientific
reassessment of the human health and exposure issues concerning dioxin and dioxin-like
compounds (56 FR 50903). This reassessment has resulted in two reports: a health
reassessment document (EPA, 1994), and Estimating Exposure to Dioxin-Like Compounds
[this three-volume report], which expands upon a 1988 draft exposure report titled,
Estimating Exposure to 2,3,7,8-TCDD (EPA, 1988). The health and exposure
reassessment documents can be used together to assess potential health risks from
exposure to dioxin-like compounds. In a related area, EPA has also discussed the data and
methods for evaluating risks to aquatic life from 2,3,7,8-tetrachlorodibenzo-p-dioxin
(2,3,7,8-TCDD) (EPA, 1993).
The purpose of the exposure portion of the dioxin reassessment is to describe the
causes and magnitude of background exposures, and provide site-specific procedures for
evaluating the incremental exposures due to specific sources of dioxin-like compounds.
In September of 1992, EPA convened workshops to review the first public drafts of
the health (EPA, 1992a) and the exposure documents (EPA, 1992b). The current draft of
the exposure document incorporates changes as a result of that workshop as well as other
review comments.
The exposure document is presented in three volumes. Following is a summary of
the material contained in each of the three volumes:
Volume I - Executive Summary
This volume includes summaries of findings from Volumes II and III. It also includes
a unique section on research needs and recommendations for dioxin-like
compounds.
Volume II - Properties, Sources, Environmental Levels, and Background Exposures
This volume presents and evaluates information on the physical-chemical
properties, environmental fate, sources, environmental levels, and background
human exposures to dioxin-like compounds. It summarizes and evaluates relevant
information obtained from published literature searches, EPA program offices and
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other Federal agencies, and published literature provided by peer reviewers of
previous versions of this document. The data contained in this volume is current
through 1993 with some new information published in early 1994.
Volume III - Site-Specific Assessment Procedures
This volume presents procedures for evaluating the incremental impact from
sources of dioxin release into the environment. The sources covered include
contaminated soils, stack emissions, and point discharges into surface water. This
volume includes sections on: exposure parameters and exposure scenario
development; stack emissions and atmospheric transport modeling; aquatic and
terrestrial soil, sediment, and food chain modeling; demonstration of methodologies;
and uncertainty evaluations including exercises on sensitivity analysis and model
validation, review of Monte Carlo assessments conducted for dioxin-like
compounds, and other discussions. The data contained in this volume is current
through 1993 with some new information published in early 1994.
1.2. TOXICITY EQUIVALENCY FACTORS
Dioxin-like compounds are defined to include those compounds with nonzero
Toxicity Equivalency Factor (TEF) values as defined in a 1989 international scheme, I-
TEFs/89. This procedure was developed under the auspices of the North Atlantic Treaty
Organization's Committee on Challenges of Modern Society (NATO-CCMS, 1988a; 1988b)
to promote international consistency in addressing contamination involving CDDs and
CDFs. EPA has adopted the l-TEFs/89 as an interim procedure for assessing the risks
associated with exposures to complex mixtures of CDDs and CDFs (EPA, 1989). As
shown in Table 1-1, this TEF scheme assigns nonzero values to all chlorinated dibenzo-p-
dioxins (CDDs) and chlorinated dibenzofurans (CDFs) with chlorines substituted in the
2,3,7,8 positions. Additionally, the analogous brominated compounds (BDDs and BDFs)
and certain polychlorinated biphenyls (PCBs, see Table 1-2) have recently been identified
as having dioxin-like toxicity (EPA, 1994) and thus are also included in the definition of
dioxin-like compounds. However, EPA has not assigned TEF values for BDDs, BDFs, and
PCBs. In the case of PCBs, research on the applicability of the TEF approach is ongoing
but there is not yet any formal EPA policy. The nomenclature adopted here for purposes
of describing these compounds is summarized in Table 1-3.
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Table 1-1. Toxicity Equivalency Factors (TEF) for CDDs and CDFs.
Compound TEF
Mono-, Di-, and Tri-CDDs 0
2,3,7,8-TCDD 1
Other TCDDs 0
2,3,7,8-PeCDD 0.5
Other PeCDDs 0
2,3,7,8-HxCDD 0.1
Other HxCDDs 0
2,3,7,8-HpCDD 0.01
Other HpCDDs 0
OCDD 0.001
Mono-, Di-, and Tri-CDFs 0
2,3,7,8-TCDF 0.1
Other TCDFs 0
1,2,3,7,8-PeCDF 0.05
2,3,4,7,8-PeCDF 0.5
Other PeCDFs 0
2,3,7,8-HxCDF 0.1
Other HxCDFs 0
2,3,7,8-HpCDF 0.01
Other HpCDFs 0
OCDF 0.001
Source: EPA, 1989.
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Table 1-2. Dioxin-Like PCBs.
IUPAC No.
Congener
77
81
105
114
118
126
156
157
167
169
189
3,3',4,4'-tetra PCB
3,4,4',5-tetra PCB
2,3,3',4,4'-penta PCB
2,3,4,4',5-penta PCB
2,3',4,4',5-penta PCB
3,3',4,4',5-penta PCB
2,3,3',4,4',5-hexa PCB
2,3,3',4,4',5'-hexa PCB
2,3',4,4',5,5'-hexa PCB
3,3',4,4',5,5'-hexa PCB
2,3,3',4,4',5,5'-hepta PCB
Source: EPA, 1992a.
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Table 1-3. Nomenclature for dioxin-like compounds.
Term/Symbol
Definition
Congener
Homologue
Isomer
Specific
congener
D
F
M
D
Tr
T
Pe
Hx
Hp
O
CDD
CDF
PCB
2378
Any one particular member of the same chemical family; e.g., there are 75
congeners of chlorinated dibenzo-p-dioxins.
Group of structurally related chemicals that have the same degree of chlorination.
For example, there are eight homologues of CDDs, monochlorinated through
octochlorinated.
Substances that belong to the same homologous class. For example, there are 22
isomers that constitute the homologues of TCDDs.
Denoted by unique chemical notation. For example, 2,4,8,9-
tetrachlorodibenzofuran is referred to as 2,4,8,9-TCDF.
Symbol for homologous class: dibenzo-p-dioxin
Symbol for homologous class: dibenzofuran
Symbol for mono, i.e., one halogen substitution
Symbol for di, i.e., two halogen substitution
Symbol for tri, i.e., three halogen substitution
Symbol for tetra, i.e., four halogen substitution
Symbol for penta, i.e., five halogen substitution
Symbol for hexa, i.e., six halogen substitution
Symbol for hepta, i.e., seven halogen substitution
Symbol for octa, i.e., eight halogen substitution
Chlorinated dibenzo-p-dioxins, halogens substituted in any position
Chlorinated dibenzofurans, halogens substituted in any position
Polychlorinated biphenyls
Halogen substitutions in the 2,3,7,8 positions
Source: EPA, 1989.
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The procedure relates the toxicity of 210 structurally related individual CDD and
CDF congeners and is based on a limited data base of m vivo and ]n vitro toxicity testing.
By relating the toxicity of the 209 CDDs and CDFs to the highly-studied 2,3,7,8-TCDD,
the approach simplifies the assessment of risks involving exposures to mixtures of CDDs
and CDFs (EPA, 1989).
In general, the assessment of the human health risk to a mixture of CDDs and
CDFs, using the TEF procedure, involves the following steps (EPA, 1989):
1. Analytical determination of the CDDs and CDFs in the sample.
2. Multiplication of congener concentrations in the sample by the TEFs in Table
1-1 to express the concentration in terms of 2,3,7,8-TCDD equivalents
(TEQs).
3. Summation of the products in Step 2 to obtain the total TEQs in the sample.
4. Determination of human exposure to the mixture in question, expressed in
terms of TEQs.
5. Combination of exposure from step 4 with toxicity information on 2,3,7,8-
TCDD to estimate risks associated with the mixture.
Samples of this calculation for several environmental mixtures are provided in EPA
(1989). Also, this procedure is demonstrated in Volume III of this assessment in the
context of the demonstration of the stack emission source category. The seventeen
dioxin-like congeners are individually modeled from stack to exposure site. TEQ
concentrations are estimated given predictions of individual congener concentrations using
Steps 2 and 3 above.
1.3. OVERALL COMMENTS ON THE USE OF THE DIOXIN EXPOSURE DOCUMENT
Users of the dioxin exposure document should recognize the following:
1. This document does not present detailed procedures for evaluating multiple sources of
release. However, it can be used in two ways to address this issue. Incremental impacts
estimated with procedures in Volume III can be compared to background exposure
estimates which are presented in Volume II. This would be a way of comparing the
incremental impact of a specific source to an individual's total exposure. If the releases
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from multiple sources behave independently, it is possible to model them individually and
then add the impacts. For example, if several stack emission sources are identified and
their emissions quantified, and it is desired to evaluate the impact of all sources
simultaneously, then it may be possible to model each stack emission source individually
and then sum the concentrations and depositions at points of interest in the surrounding
area.
2. The procedures and estimates presented in this three-volume exposure document best
serve as general examples for evaluating exposures to dioxin-like compounds, rather than
specific assessments. This document was not generated for purposes of supporting any
specific regulation. Rather, it is intended to be a general information source which Agency
programs can adopt or modify as needed for their individual purposes. For example, the
demonstration scenarios of Volume III were not crafted as Agency policy on "high end" or
"central tendency" scenarios for evaluating land contamination, stack emissions, or
effluent discharges. Rather, they were designed to illustrate the site-specific
methodologies in Volume III.
3. The understanding of the exposure to dioxin-like compounds continues to expand.
Despite being one of the most studied groups of organic enivronmental contaminants, new
information is generated almost daily about dioxin-like compounds. This document is
considered to be current through 1993, with some information published early in 1994
included as well. Section IV of Volume I, Executive Summary, discusses research needs
for dioxin exposure evaluation.
1.4. NOTES ON THE USE OF PROCEDURES IN VOLUME III
Numerous parameter values are used in this document and it is important to
understand their degree of "endorsement" by EPA. The parameters can be divided into the
following four classes for purposes of addressing this issue:
1) First Order Defaults: As defaults, these parameters are independent of site specific
characteristics and can be used for any assessment. Also, as first order defaults, it is felt
that the values selected for the demonstration scenarios carry a sufficient weight of
evidence from current literature such that these values are recommended for other
assessments. Several of the chemical specific parameters, such as the Henry's Constant,
H, and the organic carbon partition coefficient, Koc, fall into this category. The qualifier
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above, "current literature", indicates that new information could lead to changes in these
values.
2) Second Order Defaults: Like the above category, these parameters are judged to be
independent of site specific characteristics. However, unlike the above category, the
current scientific weight of evidence is judged insufficient to describe values selected for
demonstration purposes as first order defaults. Parameters of principal note in this
category are the bioconcentration parameters specific to the chemicals, such the Biota
Sediment Accumulation Factor, or BSAF. This parameter translates a bottom sediment
concentration to a fish tissue concentration. The science is evolving for this parameter,
including thought on the extent to which BSAFs generated for one species at one site can
be generalized to other sites and/or species, the differences in BSAF between column and
bottom feeders, the differences between past and ongoing contamination, and so on.
Users should carefully review the justification for the SOD values selected for the
demonstration scenarios before using the same values.
3) Site Specific: These parameters should or can be assigned values based on site-
specific information. The information provided on their assignment for the demonstration
of methodologies in this document can be useful where site specific information is
unavailable. A key class of site specific are the source strength terms - the soil
concentrations, effluent discharge rates, and stack emission rates. Others include physical
properties (organic carbon contents of soil and sediment, climate variables, areas,
distances, and volumes) and parameters for bioconcentration algorithms (yields of
vegetations, cattle raising practices, fish lipid contents).
4) Exposure Parameters: The exposure parameters have not been categorized as have
the contaminant fate and transport/transfer parameters. Assignment of these values are
critical as Lifetime Average Daily Dose (LADD) estimates are linearly related to parameter
assignments - doubling exposure duration assumptions double LADDs, and so on. Some
exposure parameters are appropriately described as first order defaults. These include:
lifetime, body weights, water ingestion rates, inhalation rates, and an exposure duration
for a childhood pattern of soil ingestion. All of the other exposure parameters are better
described as either second order defaults or site specific parameters. All exposure
parameters were developed based on information and recommendations in EPA's Exposure
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Factors Handbook (EPA, 1989b) and Dermal Exposure Assessment: Principles and
Applications (EPA, 1992c).
The end products of the exposure assessment procedures presented in this
document are estimates of potential dose expressed in mg of dioxin-like compound/kg-day.
The procedures for converting these dose estimates to risk estimates are provided in a
companion document on health assessment which EPA is publishing concurrently and
which addresses the same compounds. It is also noted that EPA has recently focused on
the indirect impacts from combustor emissions (EPA, 1993b). Much of the information in
that document, such as procedures for estimating stack emissions, dispersion/deposition
modeling, and fate, transport, and food chain impact modeling, coincides with information
in this document.
The scope of each chapter in Volume III is summarized below.
Chapter 2, Estimating Exposure and Risks, presents overall framework for
conducting exposure assessments. It provides procedures for identifying exposure
pathways, estimating contact rates and resulting exposure levels. Approaches for defining
exposure scenarios are presented. Procedures for using the Toxicity Equivalency Factors
in exposure assessments are discussed here.
Chapter 3, Evaluating Atmospheric Releases of Dioxin-Like Compounds from
Combustion Sources, provides procedures to estimate the emission rates of dioxin-like
compounds from combustion processes and further atmospheric transport modeling
procedures from the stack to the surrounding land surface. This chapter describes and
demonstrates the use of the COMPDEP model on a hypothetical incinerator and lists the
associated atmospheric dispersion and deposition estimates from that model exercise.
Chapter 4, Estimating Exposure Media Concentrations, provides procedures for
estimating concentrations of the dioxin-like compounds in exposure media (soil, air, water,
biota) resulting from soil contamination, effluent discharges, and stack emissions.
Chapter 5, Demonstration of Methodology, develops hypothetical scenarios and
generates exposure estimates to demonstrate the methodologies of this document.
Chapter 6, User Considerations, discusses key issues for users of the
methodologies. All model parameters are listed and categorized according to the scheme
noted above. Sensitivity analysis is conducted on the algorithms estimating exposure
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media concentrations. An exercise on estimating the releases from a bounded area of soil
contamination is presented. The purpose of this exercise is to determine whether a
reservoir of soil contamination would be depleted prior to an assumed duration of
exposure.
Chapter 7, Uncertainty, discusses the sources and possible magnitude of
uncertainty in the exposure assessment procedures. Uncertainty and variability of fate and
transport, and exposure parameters, are discussed. Modeled exposure media
concentrations are compared with concentrations that have been found in the literature,
and alternate modeling approaches are demonstrated and compared with modeling
approaches discussed in Chapter 5 and demonstrated in Chapter 6.
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REFERENCES FOR CHAPTER 1
NATO/CCMS (North Atlantic Treaty Organization, Committee on the Challenges of Modern
Society). (1988a) International toxicity equivalency factor (I-TEF) method of risk
assessment for complex mixtures of dioxins and related compounds. Report No.
176.
NATO/CCMS (North Atlantic Treaty Organization, Committee on the Challenges of Modern
Society). (1988b) Scientific basis for the development of international toxicity
equivalency (I-TEF) factor method of risk assessment for complex mixtures of
dioxins and related compounds. Report No. 178.
U.S. Environmental Protection Agency. (1988) Estimating exposure to 2,3,7,8-TCDD.
U.S. Environmental Protection Agency, Office of Health and Environmental
Assessment, Washington, DC; EPA/600/6-88/005A.
U.S. Environmental Protection Agency. (1989) Interim procedures for estimating risks
associated with exposures to mixtures of chlorinated dibenzo-p-dioxins and
-dibenzofurans (CDDs and CDFs) and 1989 update. U.S. Environmental Protection
Agency, Risk Assessment Forum, Washington, DC; EPA/625/3-89/016.
U.S. Environmental Protection Agency. (1992a) Health reassessment of dioxin-like
compounds, Chapters 1-8. U.S. Environmental Protection Agency, Office of Health
and Environmental Assessment, Washington, DC. EPA/600/AP-92/001a through
EPA/600/AP-92/001h. August 1992 Workshop Review Draft.
U.S. Environmental Protection Agency. (1992b) Estimating Exposure to Dioxin-Like
Compounds. U.S. Environmental Protection Agency, Office of Health and
Environmental Assessment, Washington, DC. EPA/600/6-88/005B. August 1992
Workshop Review Draft.
U.S. Environmental Protection Agency. (1993) Interim Report on Data and Methods for
Assessment of 2,3,7,8-Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and
Associated Wildlife. Environmental Research Laboratory, Duluth, MN, Office of
Research and Development, U.S. Environmental Protection Agency. EPA/600/R-
93/055. March, 1993.
U.S. Environmental Protection Agency. (1994) Health Assessment for 2,3,7,8-TCDD and
Related Compounds. Public Review Draft. EPA/600/EP-92/001.
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2. ESTIMATING EXPOSURES AND RISKS
2.1. INTRODUCTION
In this chapter, the framework for assessing exposure and risk to 2,3,7,8-TCDD
and related dioxin-like compounds will be described. Section 2.2 introduces the exposure
equation and its key terms. Section 2.3 describes how risk is estimated given estimates
of exposure. It also discusses the use of toxicity equivalency factors. Section 2.4
provides the overview of the procedures used in this document, and provides a roadmap
for finding pertinent information in other chapters of the document. Section 2.5 describes
the development of exposure scenarios for this assessment. Section 2.6 describes the
exposure parameters chosen for the exposure pathways of this assessment.
The development of exposure assessment methods, scenarios and associated
parameter values raises many issues which are generic to all chemicals. In order to keep
the scope of this document reasonable, the decision was made to focus on issues specific
to dioxin-like compounds and to avoid evaluating generic issues. Thus, priority is given to
addressing issues such as fish bioconcentration, dermal absorption, degradation, and other
chemical/physical properties of these compounds. The approach used to address generic
issues such as soil ingestion rates, inhalation rates and other behavior parameters is based
on previously published Agency documents, primarily the Exposure Factors Handbook
(EPA, 1989a). Another generic issue which has been raised in connection with this
document is the use of Monte Carlo procedures to define exposure scenarios. These
procedures require distributions for the input parameters used in the assessment. Such
distributions have not been established by the Agency. Decisions on the use and definition
of such distributions affect assessments of all chemicals and cut across all Agency
programs. Thus, it is not appropriate to establish such polices in this document.
However, individuals outside of the Agency have published assessments which applied
Monte Carlo procedures to problems involving dioxin-like compounds. In recognition of the
high interest in this area, a general description of this technique and summaries of
assessments which applied it to dioxin-like compounds are included in Chapter 7 of this
Volume.
The Agency does have efforts underway to evaluate these generic issues. For
example, the Office of Health and Environmental Assessment (OHEA) is in the process of
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revising the Exposure Factors Handbook and plans to hold public review meetings in 1993.
In addition, OHEA is developing a guidance document on generating exposure scenarios
which will be issued for review in 1993. Several offices have projects specific to Monte
Carlo:
• Office of Health and Environmental Assessment - A Workshop on approaches to
evaluating uncertainty (including the use of Monte Carlo) was held in 1992.
• Office of Policy, Planning and Evaluation - A workshop on using Monte Carlo
methods is scheduled for 1993.
• Office of Pollution Prevention and Toxics - A handbook on the use of Monte
Carlo is being developed and is scheduled for publication in 1993.
Readers interested in generic Monte Carlo procedures are best served in these forums.
2.2. EXPOSURE EQUATION
This document describes procedures for conducting exposure assessments to
estimate either potential or internal dose. A potential dose is defined as a daily amount of
contaminant inhaled, ingested, or otherwise coming in contact with outer surfaces of the
body, averaged over an individual's body weight and lifetime. An internal dose is defined
as the amount of the potential dose which is absorbed into the body (EPA, 1991). Section
2.3 below discusses the relevancy of this distinction for dioxin-like compounds.
The general equation used to estimate potential dose normalized over bodyweight
and lifetime is as follows:
Lifetime Average Daily Dose (LADD) = (exposure media concentration x
contact rate x contact fraction x exposure duration ) /
(body weight x lifetime) (2-1)
This procedure is used to estimate dose in the form needed to assess cancer risks. Each
of the terms in this exposure equation is discussed briefly below:
• Exposure media concentrations: These include the concentrations in soil for
dermal contact and soil ingestion exposure pathways, in vapor and
particulate phase in air for inhalation exposure pathways, in water for a
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water ingestion pathway, and in food products such as fish, fruits and
vegetables, and beef and milk, for food ingestion pathways. The
concentrations used should represent a temporal average over the time of
exposure. Chapter 4 provides procedures for estimating exposure media
concentrations.
Contact rate: These include the ingestion rates, inhalation rates, and soil
contact rates for the exposure pathways. These quantities are generally the
total amount of food ingested, air inhaled, etc. Only a portion of this
material may be contaminated. The next term, the contact fraction, which is
1.0 or less, reduces the total contact rate to the rate specific to the
contaminated media.
Contact fraction: As noted, this term describes the distribution of total
contact between contaminated and uncontaminated media. For example, a
contact fraction of 0.8 for inhalation means that 80% of the air inhaled over
the exposure period contains dioxin-like compounds in vapor form or sorbed
to air-borne particulates. The contact fractions for the exposure pathways
of air inhalation and water ingestion are related to the time individuals spend
at home. Other pathways such as fish ingestion or ingestion of home grown
foods are not related to time at home. Similarly, contact fractions for
individuals exposed at work places relate largely to time spent at the work
place. EPA (1989a) discusses several time use studies which can be used to
make assumptions about time spent at home (and outdoors at the home
environment) versus time spent away from home. Generally, these time use
studies asked participants to keep 24 hour diaries of all activities. Studies
summarized were national in scope, involved large numbers of individuals,
cross-sections of populations in terms of age and other factors, and up to 87
categories of activities. Results from different studies consistently indicate
that the average adult spends between 68 to 73% of time at the home
environment.
Exposure duration: This is the overall time period of exposure. Values of 9
years and 20 years are used in the example scenarios described in Chapter
5. The value of 9 years corresponds to the average time spent at one
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residence (EPA, 1989a), and was used as an exposure duration for a non-
farming family living in a rural setting. Twenty years was used as the
exposure duration for farming families in a rural setting. Another exposure
duration demonstrated in Chapter 5 is one associated with a childhood
pattern of soil ingestion. The exposure duration in this case is 5 years.
• Body weight: For all the pathways, the human adult body weight of 70 kg
is assumed. This value represents the United States population average.
The body weight for child soil ingestion is 17 kg (EPA, 1989a).
• Lifetime: Following traditional assumptions, the average adult lifetime
assumed throughout this document is 70 years. Even though actuarial data
indicate that the United States average lifetime now exceeds 70 years, this
convention is used to be consistent with other Agency assessments of
exposure and risk.
2.3. RISK EQUATION
Although estimation of risk is technically beyond the scope of an exposure
assessment, the exposure assessor needs some background understanding in this area.
The primary source of information on the health risks of the dioxin-related compounds is
the Health Reassessment that EPA is publishing concurrently with this document (EPA,
1993). However some general considerations for using exposure estimates in support of
cancer risk assessments are summarized here. The usual procedure used to calculate an
upper-limit incremental cancer risk is as follows:
R = 1 - e-*'d* gi* d (2-2)
when q/d < 10~3 and where q/ is the 95% upper confidence limit of the linearized
cancer slope factor of the dose-response function (expressed in inverse units of the dose
quantity, typically kg-day/mg) and d is the dose (typically in mg/kg-day). The dose is
generally equal to the potential dose given above in Equation (2-1). The slope factor, q/,
for 2,3,7,8-TCDD has been previously estimated by EPA as 0.156 kg-d/ng. The derivation
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of this factor is described in EPA (1984a) and further background is provided in EPA
(1981). The Agency is currently reevaluating this slope factor and the reader should
consult the companion Health Reassessment (EPA, 1994) for the current policy.
EPA derived the 1984 slope factor for 2,3,7,8-TCDD from animal feeding studies
on the basis of potential (i.e., administered) dose. Thus, for purposes of consistency,
when using this slope factor to estimate risk to humans, the exposure assessor should
provide the dose estimate as a potential dose. This point raises issues specific to the
various pathways.
The absorption which occurred during the animal experiments EPA used to derive
the 1984 slope factor for 2,3,7,8-TCDD was estimated to be 55% (Farland, 1987). The
review of literature on bioavailability in Appendix C of Volume 2 of this assessment
indicates that the gut absorption of 2,3,7,8-TCDD in humans when the vehicle is soil is
20-40% of potential dose. Fries and Marrow (1975) found that 50-60% of the 2,3,7,8-
TCDD was absorbed by rats from feed. Rose, et al. (1976) estimated that 86% of
2,3,7,8-TCDD in a mixture of acetone and corn oil fed by gavage to rats was absorbed.
EPA (1984), using animal data and information on fate of particles in the respiratory
system, estimated that the fraction of 2,3,7,8-TCDD absorbed into the body ranges from
0.25 to 0.29. What this discussion indicates is that the absorption for human ingestion
and inhalation pathways might range from 20-80% of potential dose, which compares to
55% found in the laboratory experiments. If no adjustment were made to potential dose
estimates, then human risk estimates might be overestimated (when absorption is in the
20% range) or underestimated (in the 80% range). This discrepancy is not felt to be large
enough or certain enough to warrant an absorption correction factor.
The rate of absorption of vapor-phase 2,3,7,8-TCDD into the lungs has not been
studied, but it seems reasonable to assume that the absorption in the vapor phase should
exceed that of absorption from bound 2,3,7,8-TCDD on particulates, probably above 50%.
There is also an unknown uncertainty introduced when assuming that the q^ * developed
from a feeding study can be used for an inhalation pathway. Thus, it is unclear what
adjustment is needed to account for differences between the feeding study and a vapor-
phase inhalation exposure. Accordingly, it is recommended that assessors not attempt
any such adjustments at this point, but fully acknowledge the uncertainty.
Estimating risks associated with dermal exposure introduces several issues to
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consider. First, use of an oral dose-response function may not be applicable to the dermal
route. Second, dermal absorption of dioxin-like compounds from soil appears to be much
lower than that which occurred in the dose-response feeding studies. EPA (1992a)
indicates that 0.1 - 3% of 2,3,7,8-TCDD may be dermally absorbed from soil, which is
significantly less than the 55% absorption found in the laboratory feeding experiments. It
is assumed for this assessment that an absorption fraction of 0.03 (3%) applies to
2,3,7,8-TCDD as well as the other dioxin-like compounds. Specifically, this assessment
estimates the total amount of compounds applied to skin and then reduces it by 97% to
estimate the absorbed dose. This is the only pathway in which an absorption fraction is
used to adjust a dose. Because of this adjustment, an additional adjustment to the risk
equation, Equation (2-2) above, is needed when estimating risk from dermal exposure in a
manner consistent with other exposure pathways: the slope factor should be multiplied by
(100%) / (55%), or about 2, to convert it to an absorbed basis. Finally, the assessor
should acknowledge that considerable uncertainty is introduced by applying an oral based
dose-response function to dermal exposure.
Another set of issues facing the exposure/risk assessor is how to estimate
exposure to mixtures of dioxin-like compounds with differing slope factors. EPA (1989b)
has proposed a procedure to address this issue, which is to adjust the risk estimate using a
"toxicity equivalency factor", commonly referred to as TEF. The TEF for a congener of
interest is the cancer potency of that congener divided by the cancer potency of 2,3,7,8-
TCDD. As shown in Table 1-1 in Chapter 1, the TEF for 2,3,7,8-TCDD is 1 and all other
dioxin-like compounds have TEFs less than 1 . The combined risk resulting from exposure
to a mixture of dioxin-like compounds can be computed using the TEFs and assuming that
the risks are additive:
(2-3)
where q^ is the cancer slope factor for 2,3,7,8-TCDD (kg-day/mg), TEFj is the toxicity
equivalency factor for dioxin-like compound i, dj is the potential dose for dioxin-like
compound i (mg/kg-day), and n is the total number of dioxin-like compounds to which an
individual is exposed.
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2.4. PROCEDURE FOR ESTIMATING EXPOSURE
Section 2.2 described the exposure equation as it applies to dioxin and dioxin-like
compounds. Before making exposure estimates, the assessor needs to gain a more
complete understanding of the exposure setting and be prepared to estimate exposure
media concentrations. The purpose of this section is to provide guidance for the
procedures followed in this assessment to define such settings and estimate exposure
media concentrations. The approach used here is termed the exposure scenario approach.
Brief descriptions of the steps and associated document chapters are presented below and
summarized in Figure 2-1.
Step 1. Identify Source
Three principal sources are addressed in this document. The first, identified as
"soil", is called a source in that the starting point of the assessment is soil contamination.
Of course, the ultimate source for soil contamination is some unidentified cause for the soil
to become contaminated. For exposure and risk assessment purposes, the cause for
contamination is not relevant except to assume that the cause is not ongoing and that the
impact of the "initial" levels is what is being evaluated. The soil source is further
characterized as off-site or on-site. Off-site implies that the soil contamination is located
some distance from the site of exposure. The site of exposure could be a residence or
farm, and the site of contamination could be a landfill, for example. On-site implies that
the soil contamination is on the site of exposure. The second principal source is called
"stack emissions." Unlike the soil source, the contamination is assumed to be on-going.
Stack emissions in particulate form are assumed to deposit onto the soils and vegetations
of the site of exposure, and emissions in vapor form result in air-borne concentrations
which transfer into vegetations at sites of exposure. It is noted that individuals working at
the site where stack emissions occur are also exposed. The procedures in this document
only apply to residents who are not associated with the site where stack emissions occur.
The third principal source is called "effluent discharges". Such discharges represent point
source inputs to surface water bodies. Like the stack emission source, impacts to surface
water bodies are assumed to be ongoing during the period of exposure. Unlike either of
the above two sources, only the impacts to water and fish are considered for this source
category.
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STEPS
Step 1. Identify Sources
A. Soil, on and off-site
B. Stack emissions
C. Effluent Discharges
Step 2. Estimate Release Rates
A. Volatilization, erosion, etc.
B. Stack emissions
C. Effluent discharges
Step 3. Estimate Exposure Point Concentrations
A. Transport, bioaccumulation, etc.
B. Atmospheric dispersion, deposition, etc.
Step 4. Characterize Exposed Individuals and
Exposure Patterns
A. Contact rates, exposure durations
Step 5. Put It Together in Terms of Exposure
Scenarios
A. Scenario concept expanded
B. Demonstration with scenarios
Step 6. Estimate Exposure and Risk
A. Equations and background
B. Results for example scenarios
Step 7. Assess Uncertainty
A. Parameter uncertainty/variability,
validity of media concentrations,
other models
B. Sensitivity analysis, parameter
discussions
DOCUMENT CHAPTERS
Chapter 4, Volume II
Chapter 3, Volume III
Chapter 3, Volume II
Chapter 3, Volume II
Chapter 4, Volume III
Chapter 3, Volume III
Chapter 3, Volume II
Chapter 4, Volume III
Chapter 3, Volume II
Chapter 4, Volume
Chapter 3, Volume
This Chapter
This Chapter
Chapter 5, Volume
This Chapter
Chapter 5, Volume III
Chapter 7, Volume III
Chapter 6, Volume III
Figure 2-1. Roadmap for assessing exposure and risk to dioxin and dioxin-like compounds.
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Step 2. Estimate Release Rates
Estimating the release of contaminants from the initial source is the first step
towards estimating the concentration in the exposure media. Releases from soil
contamination include volatilization, and wind and soil erosion. Chapter 4 on estimating
exposure media concentrations describes fate and transport modeling procedures for
estimating soil releases. Stack emissions and effluent discharges are point source releases
into the environment. Background on stack emissions including details on modeling from
the stack to a site of exposure are provided in Chapter 3.
Step 3. Estimate Exposure Point Concentrations
Contaminants released from soils, emitted from stacks, or discharged into surface
waters move through the environment to points where human exposure may occur.
Contaminated soil that is near but not at the site of exposure is assumed to slowly erode
and contaminate the exposure site soil, but to a level lower than the level at the
contaminated site. The only time when the source concentrations equal the exposure
concentrations is for the soil pathways, soil ingestion and dermal contact, when the soil
contamination is on-site. Chapter 3 describes the use of the COMPDEP Model used to
estimate dispersion of stack plumes to arrive at air-borne concentrations at the site of
exposure as well as deposition rates of stack emitted particulates. Chapter 4 describes
how soil and vegetation concentrations are estimated given particulate deposition rates,
and also how release rates from soil initially contaminated translate to exposure point
concentrations. Chapter 4 also describes a simple dilution model which translates effluent
discharges into surface water and fish tissue concentrations.
Step 4. Characterize Exposed Individuals and Exposure Patterns
The patterns of exposure are described in Sections 2.5 and 2.6 of this Chapter.
Exposed individuals in the scenarios of this assessment are individuals who are exposed in
their home environments. They are adult residents who also recreationally fish, have a
home garden, farm, and are children ages 2-6 for the soil ingestion pathway. Each of
these pathways are evaluated separately. Since it is unlikely that single individuals would
experience all of these pathways, the exposures across pathways are not added.
Exposure pathways evaluated, which have generally been alluded to in discussions above,
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include inhalation, ingestion, and soil dermal contact. Each pathway has the set of
parameters including contact rates, contact fractions, body weights, and lifetime. These
parameters were defined earlier in Section 2.2.
Step 5. Put It Together in Terms of Exposure Scenarios
A common framework for assessing exposure is with the use of "settings" and
"scenarios." Settings are the physical aspects of an exposure area and the scenario
characterizes the behavior of the population in the setting and determines the severity of
the exposure. A wide range of exposures are possible depending on behavior pattern
assumptions. An exposure scenario framework offers the opportunity to vary any number
of assumptions and parameters to demonstrate the impact of changes to exposure and risk
estimates. Exposure estimates for six example scenarios are demonstrated in Chapter 5.
Step 6. Estimate Exposure and Risk
Section 2.2 described the basic equation that estimates exposure for every
assumed pathway in an exposure scenario. Chapter 5 demonstrates the methodology on
six example scenarios, which includes the generation of exposure estimates for ten
different exposure pathways and three different dioxin-like compounds.
Step 7. Assess Uncertainty
Chapter 7 provides a discussion on the validity of exposure media concentration
estimation, and possible sources of uncertainty associated with this methodology. These
uncertainties should be considered when applying this procedures to a particular site.
Chapter 6 on User Considerations includes discussions on other pertinent topics such as
sensitivity of model results to parameter selection, and judgements on use of the
parameters selected for the demonstration scenarios for other applications.
2.5. STRATEGY FOR DEVISING EXPOSURE SCENARIOS
EPA (1992b) states, "In exposure scenario evaluation, the assessor attempts to
determine the concentrations of chemicals in a medium or location and link this information
with the time that individuals or populations contact the chemical. The set of assumptions
about how this contact takes place is an exposure scenario." These assumptions can be
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made many different ways producing a wide variety of scenarios and associated exposure
levels. The number of people exposed at different levels form a distribution of exposures.
Ideally assessors would develop this entire distribution to fully describe the exposed
population. Such distributions could be defined using Monte Carlo techniques if sufficient
input data are available. However, as discussed in Section 2.1 above, generic issues
surrounding use of Monte Carlo are not evaluated here. Discussions of how other
assessors have applied Monte Carlo to problems involving dioxin-like compounds are
presented in Chapter 7. Since the necessary information for developing a population
distribution is rarely available, EPA (1992b) recommends developing a central and high end
scenario to provide some idea of the possible range of exposure levels. This section will
illustrate this procedure as applied to the dioxin-like compounds. In addition, this section
identifies the exposure pathways which are relevant to these compounds, and provides
background and justification for the exposure parameters which were selected for the
demonstrations in Chapter 5.
For any physical setting, a wide variety of exposure scenarios are possible. The
range of exposure levels results from a number of different factors including individual
behavior patterns, proximity of individuals to the source of contamination, the fate
characteristics of the contaminant, and others. In order to illustrate the possible range, the
assessor should try to characterize a central and high end scenario. The general strategy
recommended here for defining these scenarios is to first identify and quantify the source
of contamination. Next, the assessor should determine the geographic area that is
impacted by this source. The contaminant levels are likely to vary widely over this area.
Select locations of interest within this area such as the location of the nearest exposed
individual or most heavily populated area. For each of these locations, identify behavior
patterns which characterize central and high end exposure patterns. Central scenarios
correspond to average or median levels and high end scenarios are defined as levels above
the 90th percentile but within the actual range of exposure levels (EPA, 1992b).
Statistical data are rarely available to precisely define such scenarios. Instead
judgement is usually required to identify behavior patterns meeting these criteria. For
example, most rural areas probably include both farming and nonfarming residents.
Farmers who grow or raise much of their own food could be selected to represent the high
end scenario and those living in typical residential areas could represent the central
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scenario. Alternatively, if more detail is desired, central and high end scenarios could be
defined for both segments of the population, i.e., farmers and residents. For each
scenario, determine relevant exposure pathways and assign values for exposure
parameters such as contact rate, exposure duration, and so on, which represent a central
and/or high end pattern for the type of receptor. Finally, compute the associated exposure
level. The resulting range of exposure levels for each location can be used to illustrate the
possible range of exposures.
Reference has been made in this chapter to the example scenarios found in Chapter
5, Demonstration of Methodology. Four "source categories", categories of contamination
sources described in Chapter 4, are demonstrated in Chapter 5. The on-site soil and stack
emission sources are assumed to expose a relatively large population in a rural area
containing residences and farms. For these sources, both central and high end scenarios
are defined in the manner outlined above. Specifically, a central scenario is based on
typical behavior at a residence and the high end is based on a farm family that raises a
portion of its own food. For the other two sources, off-site soil and effluent discharges,
only one scenario each will be defined and demonstrated. The off-site soil source category
will be demonstrated with a high end scenario - a farm is located near the site of
contamination. Soil on the farm becomes impacted through the process of soil erosion.
Other individuals within a community can also be impacted by a site of high soil
contamination. Such individuals would include those visiting or trespassing on the site,
volatilized residues can reach their residences, they may obtain water and fish from a
nearby impacted water body, and so on. As such, alternate scenarios demonstrating the
impact of a site of soil contamination could be developed. For the sake of brevity, and
also considering that those residing nearest the contaminated are most impacted, only a
high end scenario is developed for the off-site soil source category. The effluent discharge
source category is unique in that only the pathways of water ingestion and fish ingestion
are considered. For this category, fish and water ingestion patterns will be those adopted
for the central scenarios. Again, other patterns of fish and water ingestion could be
evaluated for this source category. As a matter of brevity again, only central patterns of
behavior with regard to fish and water ingestion are demonstrated.
The methodologies used to estimate exposure media concentrations are described
in Chapter 4 as screening level in their technical sophistication, but site specific in their
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application. Defining populations that are typical of central and/or high end exposures is
clearly a site specific exercises. Assessors need to make the kinds of assumptions
discussed here for their own source and populations of concern. Many acceptable ways
could be used to define central and high end scenarios. The approach used here was done
for demonstration purposes only. On the other hand, the example scenarios in Chapter 5
were carefully crafted to be plausible and meaningful, considering key factors such as
source strength, fate and transport parameterization, exposure parameters, and selection
of exposure pathways. For example, a beef and milk exposure pathway is demonstrated
only for the high end scenarios. Farmers raise cattle for beef and dairy products and are
assumed to obtain a portion of their intake of these products from their own farm,
whereas non-farming residents are assumed not to be exposed to contaminated farm
products.
Key source strength terms were carefully developed and defined. Key source
strength terms include soil concentrations, effluent discharge rates, and stack emission
rates. For the demonstration of methodologies of this assessment, concentrations of
dioxin-like compounds in contaminated soils for the off-site soil source category were set
at 1 ppb, which was a typical concentration of 2,3,7,8-TCDD found in Superfund-like sites
studied in the National Dioxin Study (EPA, 1987). Concentrations in soil used for the on-
site source category were characterized as typical of background levels and assigned a
value of 1 ppt, three orders of magnitude lower than the 1 ppb for off-site soil
contamination. Researchers investigating concentrations of 2,3,7,8-TCDD in
"background" or "rural" settings have typically found it in the ppt range or not detected it
(with a detection limit generally less than 1 ppt). Introductory sections of Chapter 5
provide a more complete description of the example scenarios.
2.6. EXPOSURE PATHWAYS AND PARAMETERS
The dioxin-like compounds have been found primarily in air, soil, sediment and biota
and to a lesser extent in water. Thus, the most likely exposure pathways are:
• Ingestion of soil, water, beef, dairy products, fish, fruit, and vegetables
• Dermal contact with soil
• Inhalation of particulates and vapors.
The following sections describe the selection of central and high end exposure parameters
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for these pathways. Table 2-1 summarizes all the exposure parameters selected to
represent the central and high end demonstration scenarios of Chapter 5.
2.6.1. Soil Ingestion
Soil ingestion occurs commonly among children during activities such as mouthing
of toys and other objects, nonsanitary eating habits, and inadvertent hand-to-mouth
transfers. In addition to normal soil ingestion activities, some individuals exhibit behavior
known as pica which involves intentional soil ingestion. Soil ingestion rates associated
with pica are probably much higher. No measured values for pica patterns have been
reported in the literature, though EPA (1989a) reports that other assessments have
assumed values such as 5 and 10 g/day. This document considers only normal soil
ingestion among children.
To a lesser extent, soil ingestion also occurs among adults from activities such as
hand-to-mouth transfer when eating sandwiches or smoking. However, the data to
estimate the adult rate of soil ingestion is essentially unavailable, so adult soil ingestion is
not demonstrated in this assessment. Paustenbach (1987) and Sheenan et al. (1991)
have suggested calculating exposures for this pathway (as well as dermal contact and
inhalation) separately over three to four age periods to reflect major changes in body
weight, surface area and inhalation rates. In general, exposure assessments can be
refined by estimating exposures separately over each year of age that is of interest and
summing to get the total. Age specific data for body weight, surface area and inhalation
rate are presented in EPA (1989a and 1992b). These procedures are not presented here,
but readers interested in refining exposure estimates are encouraged to check the above
references for further guidance.
Based on the review of literature, particularly the studies of Binder et al. (1986) and
Clausing et al. (1986), the following values for soil ingestion were suggested in EPA
(1989a): average soil ingestion in the population of young normal children (under the age
of 7) is estimated at approximately 0.1 to 0.2 g/d. An upper-range ingestion estimate
among children with a higher tendency to ingest soil materials, although not a pica pattern,
could be as high as 1 g/d. However, a value of 0.8 g/day is recommended for high end
exposure estimates. The values of 0.2 g/d and 0.8 g/d were the values adopted for the
central and high end exposure scenarios in Chapter 5.
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Table 2-1. Summary of exposure pathway parameters selected for the demonstration
scenarios of Chapter 5.
Pathway
description
Soil Ingestion
Central
High End
Contact
rates
200 mg/d
800 mg/d
Contact
fractions
1.0
1.0
Comments
Only pathway specific to an
age-range; 2- to 6-year-old
children
Soil Dermal Contact
Central
High End
0.2 mg/cm2-event
5000 cm2
40 events/yr
1.0 mg/cm2-event
1000cm2
350 events/yr
1.0
1.0
Dermal absorption fraction: 0.03
Vapor/Dust Inhalation
Central
High End
20 m3/day
20 m3/day
0.75
0.90
Water Ingestion
Central
High End
1.4 L/day
2.0 L/day
0.75
0.90
Unlike other pathways, daily
contact is not assumed;
approach instead estimates
contact in terms of contact/
event * events/yr; both soil
pathways distinguish typical and
high end contact by contact
rate rather than contact
fraction; high end based on
behavior of farmers; typical
pattern based on non-farming
adults; a "dermal absorption
fraction" reduces the amount
contacting the body to an
amount absorbed by the body.
Indoor/outdoor air quality
assumed equal; contact fraction
can be used to reflect a
different assumption; central
contact fraction is an average
time-at-home based on time use
surveys
2.0 L/day evaluated as 90th
percentile value in EPA (1989a),
who instead recommend 1.4
L/day as an average
consumption rate; like inhalation
pathways, 0.75 contact fraction
is an average time-at-home
estimate
(continued on the following page)
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Table 2-1. (continued)
Pathway
description
Contact
rates
Contact
fractions
Comments
Beef Fat Ingestion
Central
High End
Milk Fat Ingestion
Central
High End
Fish Ingestion
Central
High End
NA
22 g/day
NA
10.5 g/day
1.2 g/day
4.1 g/day
Fruit Ingestion
Central
Above ground unprotected 88 g/day
Below ground unprotected 0 g/day
High End
Above ground unprotected 88 g/day
Below ground unprotected 0 g/day
NA Central scenario includes adults
0.44 who do not home produce their
beef supply; 22 g/day beef fat
assumes 100 g/day average
beef consumption and 22% fat
NA Like beef ingestion, central
0.40 scenarios do not include home
milk production; 10.5 g/day milk
fat assumes 300 g/day whole
milk and 3.5% fat
1.00 Unlike other exposure
1.00 pathways, contact rate is rate
of contaminated fish - hence
contact fraction is 1.00; 1.2
g/day based on 3 recreationally
caught fish meals/yr; 4.1 g/day
based on 10 recreationally
caught fish meals/yr
0.20 200 g/day is average total fruit
consumption rate; 44%
assumed above ground and
0.30 unprotected - no below ground
unprotected fruit; contact
fraction of 0.20 based on a
range of rates of 0.09 to 0.33
for wide variety of fruits (EPA,
1989a)
(continued on the following page)
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Table 2-1. (continued)
Pathway Contact
description rates
Vegetable Ingestion
Typical
Above ground unprotected 76 g/day
Below ground unprotected 28 g/day
High End
Above ground unprotected 76 g/day
Below ground unprotected 28 g/day
Contact
fractions Comments
0.25 140 g/day is average total
vegetable consumption rate;
54% assumed above ground
0.40 and unprotected, 20% assumed
below ground and unprotected;
central contact fraction of 0.25
based on a range of rates of
0.04 to 0.75 rates for wide
variety of vegetables (EPA,
1989a)
Exposure Duration: A duration of 9 years was assumed for the "residence" setting based on
mobility data showing the average time in one residence was 9 years (EPA, 1989a). A duration of
20 years was evaluated as the 90th percentile of time in one residence, and was selected for the
"farm" settings, which assumes that farming families live in a given residence longer than a non-
farming family. These values apply to all pathways with no variation, except for soil ingestion,
which was demonstrated only for children and was at 5 years.
Body Weight/Lifetime: The standard assumptions of a 70 kg adult and 70 years lifetime were
assumed for all pathways, except that of soil ingestion. In that case, a body weight of 17 kg was
used.
A number of investigators have suggested that lower values are more appropriate
for soil ingestion rates. These suggestions are being evaluated by the Office of Health and
Environmental Assessment in connection with the revisions to the Exposure Factors
Handbook (EPA, 1989a). To date, a final position has not yet been reached. As discussed
in Section 2.1, it was decided to not independently evaluate such generic issues in this
document. Thus, the soil ingestion rates adopted here reflect those previously accepted
by the Agency, but should be updated if and when the Agency adopts new values. Note
that the general need to update values for exposure factors as new information becomes
available applies to all factors. It has been emphasized in this discussion on soil ingestion
just because it appears that changes are most imminent here.
For the soil ingestion pathway, contact fraction refers to the portion of ingestion soil
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which is contaminated. For the residential setting, the assumption is made here that all
soil ingestion by children occurs in and around the home, and that all the soil at the home
is contaminated. Thus, a value of 1 has been adopted in the example scenarios presented
in Chapter 5. If the soil contact occurs primarily outdoors, climatic factors such as snow
cover, frozen soil, rain, etc. can substantially limit contact and ingestion of soil. In
situations where the contaminated area is located remote from where children live, and
children have some access to these areas (if the areas are parks or playgrounds, e.g.),
lower fractions would be appropriate.
2.6.2. Soil Dermal Contact
The total annual dermal contact, expressed in mg/yr, is the product of three terms:
the contact rate per soil contact event, the surface area of contact, and the number of
dermal contact events per year. EPA (1992a) recommends the following ranges for these
terms:
• Contact rate: 0.2 to 1.0 mg/cm2-event
• Adult surface area: 5000 to 5800 cm2
• Event frequency: 40 to 350 events/year.
An event frequency and contact rate near the upper end of these ranges may be
appropriate for an high end exposure activity pattern such as farming, where the individual
more often comes in contact with soil and may be exposed to fugitive dust emissions
while they work. An event frequency of 40 and 350, and a contact rate of 0.2 and 1.0
mg/cm2-event, are assumed for the central and high end exposure scenarios in Chapter 5.
However, the exposed surface area of 5000 cm2 may be reduced for farmers. This area
corresponds to 25% of the total body area and apparel such as short sleeves, shoes,
socks and short pants. Farmers working in the field are likely to wear long pants at least, if
not also long sleeves. Although EPA (1992a) indicates that clothing is not always
effective in preventing dermal contact, it seems reasonable that a value of 1000 cm2 (5%
of total body area) representing hands, neck, and face might be more appropriate in a
farming scenario. Values of 5000 and 1000 cm2 were selected for the central and high
end scenarios in Chapter 5.
The considerations for contact fraction are similar to those for soil ingestion; i.e.,
that all contact occurs with contaminated soil at the residence or farm site. Accordingly a
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value of 1 was selected for the example scenarios presented in Chapter 5.
One further adjustment was made for this exposure pathway. The contact as
estimated above is the amount of soil which contacts the body. EPA (1992a) indicates
that only a small percent of strongly hydrophobic organic compounds such as 2,3,7,8-
TCDD are absorbed into the body from soil dermal contact. The "dermal absorption
fraction" recommended for 2,3,7,8-TCDD in EPA (1992a) was 0.001 (0.1%) to 0.03
(3%). EPA (1992a) recommends using the upper end of this range for application to other
dioxin-like compounds as a conservative assumption until these compounds have been
tested. An absorption fraction of 0.03 was used for the three compounds demonstrated in
Chapter 5. The dermal contact exposure pathway was the only one in which such an
absorption fraction was used.
2.6.3. Vapor and Dust Inhalation
EPA (1989a) describes derivation of the commonly used ventilation rates of 20 and
23 m3/day. As noted in that reference, these values assume 16 hours of light activity and
8 hours of resting. Other recommendations in that reference are a rate of 30 m3/day for
high end exposures, and to derive specific ventilation rates (EPA (1989a) gives information
to do so) for specific activity patterns. The example scenarios of this assessment all use
20 m3/day.
An additional assumption needs to be made for the vapor and dust inhalation
pathways. This pertains to an assumption concerning the differences in air quality
between indoor and outdoor conditions. Algorithms for both particulate and vapor-phase
air-borne concentrations of contaminants are specific to outdoor air. Hawley (1985)
assumed, based on several other studies in which measurements were made, that the
concentration of suspended particulate matter in indoor air is equal to 75% of that outside.
Also, his report stated that most household dust is outdoor dust that is transported into
the house, and that only a small percentage is developed from sources within. He then
concluded that 80% of the indoor dust is identical in contaminant content to outdoor soil.
Refinements to the concentration of contaminants on indoor versus outdoor dust should
have a minor effect on exposure estimates. A similar trend is assumed for air-borne vapor
phase concentrations. For this reason, differences between indoor and outdoor
concentrations are not specifically considered, or equivalently, no distinctions are made for
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outdoor and indoor air quality.
The contact fraction for this pathway is equal to the fraction of total inhaled air
which is contaminated. Thus it relates largely to percent of time spent in the
contaminated area. For the example exposure scenarios presented in Chapter 5, the
contact fraction corresponds to percent time at home. EPA (1989a) suggests a range of
0.75 to 1.0; the lower value will be adopted as the central value used in the residence
setting. The value selected for high end scenarios will be 0.90 instead of 1.00,
recognizing that 1.00 is more likely a bounding rather than a high end estimate.
2.6.4. Water Ingestion
The water ingestion rate of 2 L/day is traditionally assumed for exposure through
drinking water. However, EPA (1989a), after review of several literature sources,
concludes that 2.0 L/day may be more appropriately described as a 90% value, or a value
for high end exposure estimates. For this reason, a water ingestion rate of 2 L/day is
assumed only for the high end exposure estimates. Since the high end scenario includes a
farm and the farming family, it is also argued that farm labor requirements justify the
higher rate of water ingestion. EPA (19899a) recommends a rate of 1.4 L/day as
representative of average adult drinking water consumption. This is the rate used for
central scenarios in Chapter 5. The difference in central and high end tendencies is also
modeled using the contact fraction. Again, this fraction is based on the time spent at
home. The value of 0.75 is used to model the central estimate, for the residence setting,
and the value of 0.90 is used to model the high end estimate, for the farm setting.
2.6.5. Beef and Dairy Product Ingestion
If contaminated beef or dairy products from one source are marketed along with
uncontaminated products from many sources, only a small percent of the product
consumed by an individual may be contaminated. The potential effects of such "market
dilution" of beef and dairy products on human exposure are discussed briefly in EPA
(1984a), at more length by Fries (1986), and at much greater length in EPA (1985) for the
particular case of cattle production in Missouri. Aspects of the beef industry in this region
specifically noted in EPA (1985) as important to estimating exposure were type of activity
within the industry (e.g. cow-calf production, "backgrounding" - preparing calves for
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feedlots, feeding for slaughter), replacement rates as a function of activity, fractions of
cattle fed to maturity outside contaminated areas before slaughter, and slaughter
categories and rates relative to national figures. EPA (1984a and 1985) concluded that
dilution will vary widely between different marketing areas. EPA (1984a and 1985) and
Fries (1986) noted that the subpopulations most likely to receive high exposures are beef
producers, dairy farmers, and their direct consumers. The residents in the central
exposure scenario in Chapter 5 are not assumed to be producers or direct consumers of
farm products, and for this reason, the central estimate scenarios do not include a beef
and milk pathway. The high end farming scenarios do have these pathways, meaning that
the farmers home slaughter for beef and also obtain a portion of their milk from their
lactating cattle.
Average consumption rates and fat content data for beef and dairy products are
described in EPA (1989a). Summary information presented in that reference comes
principally from a U.S. Department of Agriculture Nationwide Food Consumption Survey
(NFCS) conducted in 1977-1978 (described in EPA, 1984b), with additional information
added by Fries (1986). The NFCS covered intake of 3,735 possible food items by 30,770
individuals characterized by age, sex, geographic location, and season of the year. The
average beef fat consumption rates listed in EPA (1989a) ranges from 14.9 to 26.0 g per
70-kg person/day, with a single high consumption estimate of 30.6 g per 70-kg
person/day. Based on this information, EPA (1989a) recommends using a beef fat
consumption rate of 22 g/day (based on an arithmetic mean from studies summarized in
EPA (1989a) of 100 g/day whole beef and 22% fat content). This may underestimate the
amounts eaten by households who home slaughter; i.e., the availability of beef by farming
families raising beef for slaughter might lead to consumption habits for beef that exceed
those of the general population. Milk fat consumption from all dairy products ranges from
18.8 to 43 g per 70-kg person/day. Considering fresh milk only, the milk fat consumption
is reported as 8.9 to 10.7 g per 70-kg person/day in various studies summarized in EPA
(1989a), with a single high consumption estimate of 35 g per 70-kg person/day. An
arithmetic mean milk fat consumption rate of 10.5 g/day is derived in EPA (1989a) (this
assumes 300 g/day whole milk and 3.5% fat). This may also underestimate the
consumption rate of farming families who consume milk supplied by their own cattle. The
rate of beef and milk fat consumption assumed for the high end farming scenarios in
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Chapter 5 are 22 and 10.5 g/day, respectively.
Consumption rates of beef and milk are expressed in terms of fat ingested per day
because dioxin-like compounds tend to partition strongly toward lipids. It is assumed that
virtually all of such compounds will be found in the fat portion of milk or beef. Further,
the algorithms to estimate concentrations in these food products estimated fat
concentrations and not whole product concentrations.
EPA (1989a) also reports on another survey of 900 rural farm households (USDA,
1966), including some where the farm's beef and dairy cattle supply a portion of the
household's beef and milk. In these situations, the average percent of homegrown beef
and milk (dairy products) is 44% and 40%, respectively. Contact fractions of 0.44 and
0.40 were used in this assessment for the high end farming scenarios. Lacking better
information, EPA (1989a) recommends a contact fraction for beef and dairy of 75% if the
intent is to estimate high end estimates for a farmer who uses a portion of his farm's
products.
2.6.6. Fish Ingestion
EPA (1989a) concludes that consumption rate data from two studies, that of Puffer
(1981) and Pierce, et al. (1981) are most appropriate for estimating consumption rates for
recreational fishing from large water bodies. The recommended 50th percentile
consumption rate, or typical rate, for this subpopulation is 30 g/day, and the 90th
percentile rate is 140 g/day. Table 2-2 contains ingestion rates for freshwater and
estuarine fish and shellfish. These are based on an analysis of the results of the USDA
1977-78 National Food Consumption Survey. If using these data, the assessor should
consider the following points:
1) The survey was conducted over a three day period. Thus, it does not represent
long term behavior patterns which is the interest of exposure assessments used to support
analysis of chronic health effects. This problem introduces uncertainty into the estimates
of medians (50th percentile) and other percentiles. It can provide appropriate estimates of
the average.
2) Because most of the persons surveyed did not eat fish or shellfish during the
survey period, the 50th percentile values are zero. The mean values are more appropriate
to use as central tendency estimates of fish and shellfish consumption over a lifetime.
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Table 2-2. Fish consumption estimates from the USDA 1977-78 National Food Consumption Survey (consumptions were
recorded for three day periods; N = 36249; units are grams/day/person; SF = Shellfish.).
Fresh Fresh Estuarine Estuarine
Estimate No SF With SF No SF With SF
Mean 1.64 1.64 2.50 4.27
50th % 0.00 0.00 0.00 0.00
90th % 0.00 0.00 4.73 9.80
95th % 5.29 5.29 14.50 28.35
99th % 38.00 38.00 56.00 80.00
Marine Marine Total Total
No SF With SF No SF With SF
7.72 8.23 11.85 14.15
0.00 0.00 0.00 0.00
28.33 30.00 42.07 51.04
48.37 51.17 66.15 75.60
93.33 97.07 128.00 146.17
Source: USDA (1983).
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However, these averages are on a per capita basis, ie. averaged across all survey
participants (including fish eaters and nonfish eaters). The average fish consumed by fish
eaters is probably a more relevant estimate of central exposures. This value would be
higher than the per capita average.
3) These data represent total ingestion rates of store-bought fish. Obviously, what
is of interest for a site specific survey is the amount of fish consumed from waters within
the study area. Assuming local surveys are not available EPA (1989b) recommends
approaching this problem by using judgement to estimate the number of fish meals (100 to
200 g) per year that a person may reasonably consume from the water body of concern.
By comparing these judgement based values to the national survey data the assessor can
make some evaluation of their reasonableness. If evidence exists that subsistence fishing
occurs in the area of interest, then even higher levels than those given in Table 2-1 may be
warranted. Wolfe and Walker (1987) found subsistence fish ingestion rates up to 300 g/d
in a study conducted in Alaska.
For smaller water bodies, EPA (1989a) recommends that site-specific information be
obtained via surveys of local fisherman to obtain the most appropriate fish consumption
information for site-specific assessments. Alternately, EPA (1989a) recommends using
judgement regarding how many fish meals per year an individual could obtain from the
contaminated waters and assuming meal sizes of 100 to 200 g. Consumption of
commercial fish (at restaurants or from markets) raises market dilution issues analogous to
those described earlier for beef and milk. For this reason, exposed individuals in both the
central and high end scenarios in Chapter 5 are assumed to obtain their contaminated fish
intake from a nearby contaminated stream or pond; other fish they may consume is not
considered in this assessment.
The examples used in this assessment assume that the contaminated waters are
small lakes or streams which are occasionally fished on a recreational basis. Further it is
assumed that an individual could eat 3 to 10 meals per year from the contaminated
waters. Assuming an average meal size of 150 g, this translates to 450 to 1500 g/year or
an average of 1.2 to 4.1 g/day. The central estimate for the example scenarios in Chapter
5 will therefore be 1.2 g/day, and the high end will be 4.1 g/day. Since these fish
ingestion rates are rates of ingestion of contaminated fish, the contact fraction would
be 1.
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2.6.7. Fruits and Vegetables
EPA (1989a) estimated ingestion rates for individuals who have home gardens and
hence grow a portion of their fruit and vegetable intake. Their approach was to review the
literature and derive average intake rates for all individuals, whether or not they have a
home garden, and considering a variety of different fruits and vegetables. A typical and
high end exposed individual had the same total ingestion rates. Their exposure was
distinguished by the contact fractions; high end exposed individuals grew a larger
proportion of their intake in their home gardens.
The average amounts of fruit and vegetable consumption are 200 and 140 g/day,
respectively. These total ingestion rates are further refined considering two factors
pertinent to estimation of concentration of dioxin-like compounds: whether the vegetation
is grown below (carrots, e.g.) or above ground (tomatoes), and whether the edible portion
is protected (citrus) or unprotected (apples). Chapter 4 discusses distinct procedures for
estimating vegetative concentrations for below and above ground vegetation. Also, both
algorithms assume that inner portions of vegetation are largely unimpacted, whereas outer
portions of both above and below ground vegetation are impacted (see Chapter 4 for
further detail on these algorithms and assumptions). Therefore, for fruits or vegetables
which are protected, it can be assumed that there will be no exposure since the outer
portions are not eaten. Results from a food consumption survey, such as that from Pao,
et al. (1982), can be used to determine percent of total fruit/vegetable intake which is
below/above ground and which is protected/unprotected. Such an exercise was
undertaken using data from Pao, et al. (1982) summarized in EPA (1989a) to arrive at the
following percentages:
I. Fruits II. Vegetables
Above Below Above Below
Protected 56% 0 25% 1%
Unprotected 44% 0 54% 20%
As seen, it was found that there are no fruit grown underground, and there was a fairly
similar proportion of protected and unprotected fruit. Fruits considered protected for this
exercise included oranges, grapefruits, and cantaloupe; unprotected fruits included apples,
peaches, pears, and strawberries. It is noted that this is clearly not a complete inventory,
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but only those fruits from the survey of Pao as summarized in EPA (1989a). Similarly,
these percentages are not being recommended as general values for other site-specific
assessments. For this assessment, it will be assumed that a total ingestion rate of
unprotected above ground fruit is 88 g/day (0.44*200 g/day), and that there is no
ingestion of unprotected under ground fruit. Vegetables above ground and unprotected
include: cabbage, cucumbers (including cucumbers as pickles), lettuce, tomatoes, broccoli,
spinach, string beans, and squash. Above ground protected vegetables include: corn, lima
beans, and peas (several kinds). Below ground unprotected vegetables included potatoes
and carrots; mature onions were considered below ground and protected. Assumed for
this assessment are ingestion rates of unprotected above ground vegetables of 76 g/day
(0.54*140 g/day) and unprotected below ground vegetables of 28 g/day (0.20*140
g/day).
These ingestion rates are defined as total ingestion rates of unprotected
above/below ground fruits/vegetables. Only a portion of these are homegrown. Data
summarized in EPA (1989a) shows that the fraction of vegetables consumed that are
homegrown ranges from 0.04 to 0.75, depending on type. The overall average of the data
is 0.25, which is recommended as a contact fraction for the average home gardener. The
recommendation for the high end exposure was 0.40. These contact fractions were
adopted as the central and high fractions for the example scenarios in Chapter 5. Similar
data for fruits show a homegrown range of 0.09 to 0.33, with an average of 0.20, which
is the central estimate used in Chapter 5. EPA (1989a) recommends a high end value of
0.30, which is the value used for the high end exposure scenarios.
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REFERENCES FOR CHAPTER 2
Binder, S.; Sokal, D.; Maughn, D. (1986) The use of tracer elements in estimating the
amount of soil ingested by young children. Arch. Environ. Health 41: 341-345.
Clausing, P.; Brunekreff, B.; Van Wijen, J.H. (1987) A method for estimating soil
ingestion by children. Int. Arch. Occupational Environ. Health 59: 73-82.
Farland, W.H. (1987) Memorandum titled, "Absorption fraction when calculating upper-
limit risks due to dioxin exposure", dated September 2, 1987, to Michael Callahan,
Exposure Assessment Group, Washington, DC. from William Farland, U.S.
Environmental Protection Agency, Office of Health and Environmental Assessment,
Washington, D.C.
Fries, G.F. (1986) Assessment of potential residues in foods derived from animals
exposed to TCDD-contaminated soil. Presented at 6th international symposium on
chlorinated dioxins and related compounds; September; Fukuoka, Japan.
Fries, G.F.; Marrow, G.S. (1975) Retention and excretion of 2,3,7,8-tetrachlorodibenzo-
p-dioxin (TCDD) by rats. J. Agric. Food Chem. 23: 265-269.
Hawley, J.K. (1985) Assessment of health risk from exposure to contaminated soil. Risk
Analysis 5(4): 289-302.
Pao, E.M., K.H. Fleming, P.M. Guenther, et al. (1982) Foods commonly eaten by
individuals: amount per day and per eating occasion. U.S. Department of
Agriculture. Home Economics Report No. 44.
Paustenbach, D.J. (1987) Assessing the potential human health hazards of dioxin
contaminated soils. Commun. Toxicol. 1:185-220.
Pierce, R.S.; Noviello, D.T.; Rogers, S.H. (1981) Commencement Bay seafood
consumption report. Preliminary report. Tacoma, WA: Tacoma-Pierce County
Health Department.
Puffer, H. (1981) Consumption rates of potentially hazardous marine fish caught in the
metropolitan Los Angeles area. EPA Grant #R807 120010.
Rose, J.Q.; Ramsey, J.C.; Wentzler, T.H. (1976) The fate of 2,3,7,8-tertrachloridbenzo-
p-dioxin following single and repeated oral doses to the rat. Toxicol. Appl.
Pharmacol. 36: 209-226.
Sheenan, P.J.; Meyer, D.M.; Sauer, M.M.; Paustenbach, D.J. (1991) Assessment of the
human health risks posed by exposure to chromium-contaminated soils. J. Toxicol.
Environ. Health 34: 161-201.
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U.S. Department of Agriculture. (1983) Food Consumption: Households in the U.S.,
Seasons and Year 197-1978. Consumer Nutrition Division, Hyattsville, MD. MFCS
1977-78. Report No. H-6. USDA Nationwide Food Consumption Survey.
U.S. Department of Agriculture. (1966) Household food consumption survey 1965-1966.
Report 12. Food Consumption of households in the U.S., Seasons and years 1965-
1966. United States Department of Agriculture, Washington, D.C. U.S.
Government Printing Office.
U.S. Environmental Protection Agency. (1981) Risk assessment on (2,4,5-
trichlorophenoxy)acetic acid (2,4,5-T), (2,4,5-trichlorophenoxy)propionic acid
(silvex), and the 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Office of Health and
Environmental Assessment. EPA-600/6-81-003. NTIS PBS 1-234825.
U.S. Environmental Protection Agency. (1984a) Risk Analysis of TCDD Contaminated
Soil. Office of Health and Environmental Assessment. EPA-600/8-84-031.
U.S. Environmental Protection Agency. (1984b) Stochastic processes applied to risk
analysis of TCDD contaminated: a case study. Internal report dated May 31, 1984;
Exposure Assessment Group, Office of Health and Environmental Assessment,
Office of Research and Development, U.S. Environmental Protection Agency.
U.S. Environmental Protection Agency. (1985) Dioxin Transport From Contaminated Sites
to Exposure Locations: A Methodology for Calculating Conversion Factors. Office
of Health and Environmental Assessment, EPA-600/8-85-012. NTIS PB85-214310.
U.S. Environmental Protection Agency. (1987) National Dioxin Study. Office of Solid
Waste and Emergency Response. EPA/530-SW-87-025. August, 1987.
U.S. Environmental Protection Agency. (1989a) Exposure Factors Handbook. Office of
Health and Environmental Assessment, EPA/600/8-89/043. July, 1989.
U.S. Environmental Protection Agency. (1989b) Interim procedures for estimating risks
associated with exposures to mixtures of chlorinated dibenzo-p-dioxins and
-dibenzofurans (CDDs and CDFs) and 1989 update. Risk Assessment Forum,
Washington, DC; EPA/625/3-89/016.
U.S. Environmental Protection Agency. (1992a) Dermal Exposure Assessment: Principals
and Applications. Office of Health and Environmental Assessment EPA/600/8-
91/011B.
U.S. Environmental Protection Agency. (1992b) Guidelines for exposure assessment.
Office of Health and Environmental Assessment. EPA/600-Z-92/001. published in
Federal Register, May 29, 1992, p. 22888-22938.
U.S. Environmental Protection Agency. (1992c) Environmental Equity. Reducing Risk for
all Communities. Vol II. Office of Policy Analysis. EPA 230-R-92-008A.
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U.S. Environmental Protection Agency. (1994) Health Assessment for 2,3,7,8-TCDD and
Related Compounds. External Review Draft. EPA/600/BP-92/001a-c.
Wolfe, R.J.; Walker, R.J. (1987) Subsistence economica in Alaska: productivity,
geography and developmental impacts. Arctic Anthropology 24(2):56-81.
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3. EVALUATING ATMOSPHERIC RELEASES OF DIOXIN-LIKE COMPOUNDS FROM
COMBUSTION SOURCES
3.1. INTRODUCTION
Since the late 1970's, it has become well established that the combustion of
certain fuels containing both organic material and chlorides can form polychlorinated
dibenzo-p-dioxins (CDDs) and poylchlorinated dibenzofurans (CDFs). This discovery has
prompted world-wide research to identify combustion sources, to characterize the
conditions favoring the formation of CDDs and CDFs within the combustion process, and
to characterize the emission of dioxin-like compounds to the air from the stack of the
process.
The purpose of this chapter is to provide site-specific procedures for evaluating the
emission of dioxin-like compounds from stationary combustion sources. The first step is
to characterize stack emissions in terms of mass of TCDD/F congener released, and then
to partition that release into a vapor and a particle phase. Using atmospheric transport
modeling, these releases are translated to ambient air vapor and particle phase
concentrations, and wet and dry paniculate deposition amounts, in the vicinity of the
release. This chapter demonstrates these procedures on a hypothetical incinerator using
an air dispersion model called COMPDEP. A second purpose of this chapter, therefore, is
to provide the background and justification for the model inputs and key parameters for
COMPDEP. The final results for this model simulation are vapor and particle phase
concentrations, and particulate deposition amounts of the specific dioxin-like congeners,
which are then used for the demonstration of the stack emission source category in
Chapter 5 of this Volume.
This chapter is structured as follows:
• Section 3.2 describes the generation of congener specific emission factors.
These factors are defined as the mass of congener emitted per mass of feed material
combusted. Subsections within Section 3.2 discuss: 1) a heirachy of preferred options for
generating such emission factors, starting with site-specific stack testing for specific
congeners and ending with engineering evaluations when no other data is available, 2) an
approach to estimating congener-specific emission factors if homologue group emission
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factors are all that is available, including a presentation of source category homologue
group profiles generated with limited data, 3) the emission factors for the example
incinerator demonstrated in Chapter 5, and assuming a feed rate into the example
incinerator, emissions expressed on a mass per time basis (which is required for transport
modeling), 4) partitioning of emissions into a vapor and a particle phase for atmospheric
transport modeling, and 5) a procedure to estimate the mass released and concentrations
for a related emission of a combustor, that of ash. It is noted that a similar discussion on
emission factors is presented in Chapter 3 on Sources in Volume II of this assessment.
However, the Volume II discussion derives national average emission factors for various
combustor types as a basis for estimating total annual releases to the air in the United
States from each combustion source category in comparative units of grams of Toxic
Equivalent (TEQs) emissions per year.
• Section 3.3 describes a general air modelling procedure for evaluating the fate
and transport of dioxin-like compounds emitted from stacks. The discussion presents a
general review of dispersion theory, a general review of dry particle deposition, and a
general review of the wet deposition algorithm employed in this analysis. EPA's
COMPDEP air dispersion and deposition model is reviewed. Wherever pertinent. Section
3.3 describes the assumptions and parameter values that were used in the demonstration
of methodologies in Chapter 5 of this Volume.
• Sections 3.2. and 3.3. summarized input data and assumptions (emission rates,
vapor/particle partitioning assumptions, etc.) that were made for the demonstration of the
methodologies for evaluating stack emissions in Chapter 5 of this Volume. Section 3.4
supplies all other key assumptions for the stack emission demonstration, such as stack
height and exit temperatures, meteorological data, and others. This Section also provides
the final results from the COMPDEP modeling, including vapor phase air concentrations at
various distances in the predominant wind direction, and dry and wet deposition fluxes,
also at various distances in the predominant wind direction.
• Section 3.5 closes out the chapter by summarizing critical aspects for making
site specific evaluations of stack emission sources.
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3.2. ESTIMATING THE EMISSIONS OF DIOXIN-LIKE COMPOUNDS FROM
ANTHROPOGENIC COMBUSTION SOURCES
Estimating the emission factor is the first step in assessing a specific stack
emission source of dioxin-like compound release. For this assessment, an "emission
factor" is defined as the total mass (in vapor and particulate forms) of dioxin-like
compound emitted per mass of feed material combusted.
This assessment recommends the generation of emission factors for individual
dioxin-like congeners for a site-specific assessment. Chapter 3 on Sources in Volume II of
this assessment also derives emission factors. However, the Volume II discussion derives
national average emission factors for TEQ emissions for various combustor types as a
basis for estimating total annual releases to the air in the United States in comparative
units of grams TEQ per year.
In the past, EPA converted the concentrations of TCDD/F mixtures into an
equivalent concentration of 2,3,7,8-TCDD (Cleverly, et al., 1989, 1991; EPA, 1987a;
Mukerjee and Cleverly, 1987) when deriving an emission factor. The fate, transport, and
transfer parameters of 2,3,7,8-TCDD were applied to model the environmental fate of this
TEQ mixture. This perspective has been changed in the following procedure. In order to
increase the level of accuracy, the air dispersion modeling was done separately for each of
the toxic congeners. Only at the interface to human exposure, e.g., ingestion, inhalation,
dermal absorption, etc., are the individual congeners recombined and converted into the
toxic equivalence of 2,3,7,8-TCDD to be factored into the quantitative risk assessment. It
is recommended that a site-specific assessment, or even an assessment of a source
category, be based on congener-specific emissions rather than TEQ emissions.
Section 3.2.1 presents a strategy for development of emission factors for
conducting a site-specific assessment. Section 3.2.2 describes an approach to estimating
congener-specific emission factors when all that is available are homologue group emission
factors. Section 3.2.3 summarizes the emission factors for the hypothetical incinerator
demonstrated in Chapter 5. Section 3.2.4 presents an in-depth evaluation of the
partitioning of emissions between a vapor and a particle phase for further atmospheric
transport modeling. This discussion includes subsections on measurements of partitioning
at the stack, measurements of partitioning in ambient air, and the theoretical approach
used in this assessment for vapor/particle partitioning. Section 3.3.5 closes this section
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on emission factors by describing procedures to estimate the mass of ash (fly and bottom)
*»
produced and the concentration of dioxin-like compounds on ash.
3.2.1. A Strategy for Generating Emission Factors
The following is a heirarchal listing of data collection options for emission factors:
A. For facilities that are built and operational, it is preferred that direct stack
measurements be used, using EPA recommended congener-specific stack monitoring and
analytical protocols. Stack monitoring provides concentrations and mass release rates of
the pollutant, actual volume of stack gas and temperature. Care should be taken to ensure
that the emissions characterization reflects a wide range of operating conditions and also
accounts for deterioration in emissions output of the facility over its useful life.
Procedures to convert data expressed in concentrations or mass release rates to an
emission factor are as follows:
1. Test data of emissions are first placed into common units of measurement.
English units are converted into metric, and the concentration term (mass of
pollutant per unit volume of combustion gas emitted from the stack) should be
corrected to the standard temperature and pressure on a dry gas basis, and
standard percent carbon dioxide or oxygen within the combustion gas (e.g.,12%
C02). These adjustments may be necessary if more than one test occurred for
stack emissions.
2. The next step involves converting the mass emission concentration of the
specific dioxin-like congener in units of nanograms per normal cubic meter (at
standard temperature and pressure) of combustion gas corrected to 12% carbon
dioxide into an equivalent emission factor in units of grams of pollutant emitted
from the stack per kilogram of combustable material or feed (g CDD and CDF/kg
feed) that was incinerated at the facility during the duration of stack sampling. This
is done as follows:
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where:
Ef = emission factor, //g/kg
Cfg = concentration in flue gas, ng/dscm
Vfg = volume of combustion gas/unit of time, dscm/hr
Mw = mass of waste incinerated/unit of time, kg/hr
0.001 = units conversion factor
3. As a final step, the average emission factor of each congener is derived by
summing the emission factors and dividing by the number of data points used. The
average should represent an approximation of long-term emissions. Many air
dispersion models require emission factors in units of amount of the pollutant
emitted per second of time. Therefore the average emission factor must be
adjusted accordingly by adjusting the units in Equation (3-1) to a time-scale of one
second.
B. For facilities that have been constructed, but not yet operational, or are in the planning
stages of development, the following procedure is recommended:
1. Collect and review stack test reports which have measured the emissions of
specific dioxin-like congeners from facilities that are most similar in technology,
design, operation, capacity, fuel, waste feed composition, and pollution control as
the facility under consideration.
2. Determine if the stack test reports used EPA recommended stack monitoring
and analytical protocols specific to dioxin-like compounds and discard those data
not in conformance.
3. When combining data from a test results of a number of facilities, care should
be taken to convert emissions, process feed rates and stack gas parameters to
consistent units of measurement.
4. Ranges and average values should be developed for purposes of exposure
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analysis.
C. If no congener-specific data exists for a specific facility or similar facilities, then use
homologue profile emissions from similar facilities. Steps 1-4 in B above pertain here as
well. Estimating congener-specific emission factors given homologue emission factors is
described in Section 3.2.2 below.
D. If no data exist relevant to a specific facility, then the Compilation of Air Pollution
Emission Factors (EPA, 1985; and subsequent updates), should be used. This compilation
was put together and is periodically updated by EPA's Office of Air Quality Planning and
Standards (OAQPS), and is commonly referred to as AP-42. Care should be taken to
select emission factors which were developed for technologies that best match the facility
under consideration. The basic limitation of these of these data is the fact that emission
factors are not usually reflective of specific emission control equipment.
OAQPS's AP-42 document provides TCDD/F emission factors for municipal waste
combustors, sewage sludge incinerators, and medical waste incinerators. At this time,
emissions from hazardous waste incinerators are not addressed in AP-42. Emission
factors presented in AP-42 are designed for estimating emissions from a large number of
sources over a wide area. They are averages of values determined at one or more
individual facilities. The individual values which are used to develop the average may vary
considerably. The use of AP-42 emission factors to estimate emissions from any one
facility should be done with great care.
E. In the absence of suitable AP-42 emission factors, clearly documented engineering
evaluations may be used. Documentation should include copies of emission test reports
used to derive the emission estimates, any assumptions made and the rationale for the
conclusions that were made.
3.2.2. Use of Homologue Profiles for Estimating Congener Specific Emission Factors
This section describes emission factors for homologue groups of dioxin-like
compounds from various stack emission sources. These emission factors are described in
units of pg homologue emitted/kg feed material combusted. These are presented for
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comparative purposes only, and should not be interpreted as representative of the sources
described. Most of the profiles are based on very limited data generated under limited
emission controls.
When only homologue emission factors are available, then rough estimates of
congener specific factors can be made. First, an equal probability of occurrence of the
specific congener is assumed based on relative proportionality. For example, 2,3,7,8-
TCDD is one congener out of 22 possible congeners in the TCDD homologue. Therefore,
the probability of occurrence is assumed to be the ratio of 1/22 or 0.045. Multiplication of
a total TCDD emission factor by 0.045 gives an approximation of the emission factor for
2,3,7,8-TCDD. Table 3-1 lists the number of dioxin-like congeners within a homologue
group and the total number of congeners within that homologue group.
The use of this procedure may be the only way to evaluate source or site-specific
emissions of dioxin-like polychlorinated biphenyls (PCBs), known as the coplanar PCBs.
No congener specific test data of coplanar PCBs from incinerators or combustion sources
could be found for this assessment. The data that are available usually have reported
PCBs as the sum total of all PCBs present in the sample without further speciation of toxic
congeners or congener groups (EPA, 1987b). The greatest level of detail in any test report
is a further breakdown of total PCBs into homologue groups, e.g., mono - deca-
chlorobiphenyl.
Figure 3-1 displays the homologue profiles for 11 specific source categories.
Following now are brief summaries of the reference materials for these homologue profiles.
Again, it is emphasized that these homologue profiles are not offered as source
generalities; they are mostly generated on a small number of different facilities not
including a range of emission controls. It is not known whether tested facilities represent
the average, or are higher or lower than typical facilities in each source category.
1. Municipal Solid Waste Incineration (MSWI): Municipal incinerators can be
classified into four general design categories: mass burn, modular, refuse-derive fuel
(RDF), and fluidized-bed combustors (EPA, 1991). Figure 3-1 depicts the homologue
profile of TCDD/Fs from MSWIs. It was constructed by merging emissions data from ten
modern mass burn technologies (EPA, 1983; EPA, 1988a; Knisley, et al., 1986; Seelinger,
et al., 1986; EPA, 1988b; Marklund, et al., 1985; Siebert, et al., 1991; Entropy, 1987).
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Table 3-1. The number of dioxin-like and total congeners within dioxin, furan, and
coplanar PCB homologue groups.
n: number of N: number of
Homologue Group dioxin-like congeners total congeners 1/N
1 . Dioxins
Tetra-CDD
Penta-CDD
Hexa-CDD
Hepta-CDD
Octa-CDD
2. Furans
Tetra-CDF
Penta-CDF
Hexa-CDF
Hepta-CDF
Octa-CDF
3. Coplanar PCBs
Tetra-Chloro PCBs
Penta-Chloro PCBs
Hexa-Chloro PCBs
Hepta-Chloro PCBs
1
1
3
1
1
1
2
4
2
1
2
4
4
1
22
14
10
2
1
38
28
16
4
1
42
46
42
24
0.022
0.071
0.100
0.500
1.000
0.026
0.036
0.063
0.250
1.000
0.024
0.022
0.024
0.042
2. Hazardous Waste Incineration: Hazardous waste incinerators have not been
extensively evaluated for stack emissions of dioxin-like compounds. Only a few reports
appear in the published literature from which homologue emission factors can be
developed (NATO, 1988; EPA, 1992), and these do not give complete inventories of
emissions. Therefore, homologue emission factors were estimated based on a series of
emission tests at a rotary kiln waste incinerator in Biebesheim, German (EPA, 1992).
Figure 3-1 depicts the homologue profile for this single hazardous waste incinerator.
3. Drum and Barrel Reclamation Furnace: Dioxin-like compounds were measured
by EPA in the stack gas emissions of a drum and barrel reclamation furnace as part of the
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Municipal Waste Incinerators
e.0e-01
Hazardous Waste Incinerators
3.06-01
Figure 3-1. Homologue emission factors for source categories of
dioxin-like compound release.
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Drum and Barrel Reclamation Furnaces
6.0e-01
5.00-01
Hospital Waste Incinerators
Figure 3-1. (cont'd)
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Wire Rec
e.0e-01
Tire Incineration
TJ
$
E
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Wood-Fired Boiler
Ł
I
Secondary Copper Smelts
Figure 3-1. (cont'd)
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Kraft Black Liquor Boilers
Q.
•3
Sewage Sludge
Figure 3-1. (cont'd)
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Carbon Regeneration Furnaces
Figure 3-1. (cont'd)
National Dioxin Study of Combustion Sources conducted in 1986 (EPA, 1987d). The
tested facility was judged by EPA to be typical of the industry. These plants operate a
furnace to prepare used steel 55-gallon drums for cleaning to base metal. The cleaned
drums are repaired, repainted, relined and sold for reuse. The used drums processed at the
tested facility were from the petroleum and chemical industry. The drum burning process
subjected the used drums to an elevated temperature in a tunnel furnace for a sufficient
time so that the paint, interior linings, and previous contents were burned or disintegrated.
The furnace was fired by auxiliary fuel. Used drums were loaded onto a conveyor that
moved at a fixed feed rate. As the drums passed through the preheat and ignition zone of
the furnace, additional contents of the drums drained into the furnace ash trough. A drag
conveyor moved these sludges and ashes to a collection pit. The drums were air cooled as
they exited the furnace. Exhaust gases from the burning furnace were drawn through a
breaching fan to a high-temperature afterburner. The homologue profile for drum and
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barrel reclamation furnaces, shown in Figure 3-1, was developed from EPA stack tests of
this operation (EPA, 1987d).
4. Medical Waste Incinerators: The State of California Air Resources Board
(CARS) has stack tested a number of hospital waste incinerators in southern California
(CARB, 1990). Congener-specific emissions of PCDD/Fs were measured in the stack gas
emissions of 7 facilities. Figure 3-1 displays the homologue profile constructed from the
average emission of three facilities tested by CARB identified as facilities A - C in their
summary overview of emissions (CARB, 1990).
5. Scrap Electric Wire Incineration: Dioxin-like compounds emitted to the air from
scrap electric wire incineration were measured from a facility during EPA's National Dioxin
Study of combustion sources (EPA, 1987d). The objective of wire incineration is to
remove the insulating material and reclaim the metal (e.g., copper, silver, and gold)
comprising the electric wire, hence these facilities are sometimes referred to as wire
reclamation incinerators. The reclaimed metal is then sold to a secondary metal smelter.
The tested facility was judged by EPA to be typical of this industry. Insulated wire and
other metal-bearing scrap material were fed to a combustion unit where incineration of the
material was assisted by the combustion of natural gas. The estimated temperature during
combustion was 650° C, and combustion transpired in both a primary and secondary
chamber. The tested facility was equipped with a high temperature afterburner to further
destroy organic compounds entrained in the combustion gases prior to discharge to the air
from the stack. Figure 3-1 displays the homologue distribution developed from this single
facility.
6. Automobile Tire Incineration: Homologue emissions factors shown in Figure
3-1 were developed from an automobile tire incinerator stack tested by the State of
California Air Resources Board (CARB, 1991). The facility consists of two excess air
furnaces equipped with steam boilers to recovery the energy from the heat of combustion.
Discarded whole tires are fed to the incineration units at a rate of 3000 kg/hr. The
furnaces are equipped to burn natural gas as auxiliary fuel. The steam produced from the
boilers is used to drive electrical turbine generators to produce 14.4 megawatts of
electricity. The facility is equipped with a dry acid gas scrubber and fabric filter for the
control of emissions prior to exiting the stack. These devices are capable of greater than
95% reduction and control of dioxin-like compounds prior to discharge from the stack.
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7. Industrial Wood-Burning Facilities: The homologue profile shown in Figure 3-1
for this source category was developed from measurements of stack emissions from an
industrial wood-burning furnace (EPA, 1987d). The tested facility was judged by EPA as
being typical of these combustion technologies. The facility was located at a lumber
products plant that manufactures overlay panels and other lumber wood products. The
wood-fired boiler tested was a three-cell dutch oven equipped with a waste heat boiler.
During normal operation, the furnace is 100% fired with scrap wood from the lumber
plant. The feed wood is typically a mixture of bar, hogged wood, and green and dry planar
shavings. For the stack test from which the homologue profile was developed, the feed
was mostly wood from fir and hemlock. Nearly all the wood fed to the lumber plant had
been stored in sea water adjacent to the facility, and therefore had a significant
concentration of inorganic chloride. The scrap wood fed to the boiler had not been treated
with chemical preservatives, such as pentachlorophenol. The wood was fed to the boiler
by a screw conveyor that dumps the feed into a pile in the primary combustion chamber.
The furnace was operated at air in 50% excess of stoichiometric requirements. Boilers
captured the heat of combustion and transfered the heat into steam for co-generation of
energy at the plant. The exhaust gases from the boiler passed through a cyclone and
fabric filter prior to discharge from the stack. The facility was equipped with a cyclone
and fabric filter to control emissions. Emissions testing at this facility demonstrated that
the fabric filter was reducing dioxin emissions by about 90% (EPA, 1987d).
8. Metal Reclamation Plants: Metal reclamation plants are secondary metal
smelting facilities which include secondary copper smelters, secondary aluminum smelters,
secondary magnesium smelters, and secondary ferrous smelters. The only complete
information with regard to the potential stack emission of dioxin-like compounds is from a
secondary copper smelter tested by EPA during the National Dioxin Study (EPA, 1987d).
The homologue profile shown in Figure 3-1 was developed from this facility. The tested
facility was a secondary copper smelter that recovers copper and precious metals from
copper and iron-bearing scrap. The copper and iron-bearing scrap was fed to a blast
furnace, which produced a mixture of slag and black copper. The blast furnace was a
batch-fed cupola furnace. Four to five tons of metal-bearing scrap were fed to the furnace
per charge, with materials typically being charged 10 to 12 times per hour. Coke was
used to fuel the furnace, which represented 14% (by wt) of the total feed. During the
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dioxin stack tests, the feed consisted of electronic telephone scrap and other plastic scrap,
brass and copper shot, iron-bearing copper scrap, precious metals, copper bearing
residues, refinery by-products, converter furnace slag, anode furnace slag, and metallic
floor cleaning material. Oxygen enriched combustion air for combustion of the coke was
blown up through the bottom of the furnace. At the top of the blast furnace were four
natural gas-fired afterburners to aid in completing combustion of the exhaust gases.
Particulate emissions were controlled by fabric filters, and the flue gas then was
discharged into a common stack.
9. Kraft Black Liquor Recovery Boilers: EPA stack tested three kraft black liquor
recovery boilers for the emission of dioxin in conjunction with the National Dioxin Study
(EPA, 1987d). The three sites were judged by EPA to be typical of Kraft black liquor
recovery boilers, and the homologue profile shown in Figure 3-1 was derived from these
three sites. These sources are associated with the production of pulp in the making of
paper using the Kraft process. In this process, wood chips are cooked in large vertical
vessels called digesters at elevated temperatures and pressures in an aqueous solution of
sodium hydroxide and sodium sulfide (Someshwar and Pinkerton, 1992). Wood is broken
down into two phases: a soluble phase containing primarily lignin, and an insoluble phase
containing the pulp. The spent liquor (called black liquor) from the digester contains
sodium sulfate and sodium sulfide that the industry finds beneficial in recovering for reuse
in the Kraft process. In the recovery of black liquor chemicals, weak black liquor is first
concentrated in multiple-effect evaporators to about 65% solids. The concentrated black
liquor also contains 0.5% - 4% weight chlorides (EPA, 1987d). Recovery of beneficial
chemicals is accomplished through combustion in a Kraft black liquor recovery furnace.
The concentrated black liquor derived from the pulping process is sprayed into a furnace
equipped with a heat recovery boiler. The bulk of the inorganic molten smelt that forms in
the bottom of the furnace contains sodium carbonate and sodium sulfide in a ratio of about
3:1 (Someshwar and Pinkerton, 1992). The combustion gas is usually passed through an
electrostatic precipitator that collects particulate matter prior to being vented out the
stack. The particulate matter can be processed to further recover and recycle sodium
sulfate.
10. Sewage Sludge Incineration: EPA has conducted stack emission testing for
dioxin from sewage sludge incineration at three multiple-hearth sewage sludge incinerators
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(EPA, 1987d). The homologue profile shown in Figure 3-1 was developed from tests on
these three incinerators. Multiple hearth incinerators are the dominant technology in use
in the United States today for the incineration of sewage sludge.
11. Granular activated carbon regeneration furnaces: Granular activated carbon
(GAC) is an adsorbent that is widely used in the control of pollutants in wastewater
discharged from chemical and pharmaceutical industries, and in the treatment of finished
drinking water at water treatment plants. Industrial manufacture of activated carbon is
mostly obtained from the heat treatment of nut shells and coal under pyrolytic conditions
(Buonicore, 1992). The properties of GAC make it ideal for adsorbing and controlling
vaporous organic and inorganic chemicals entrained in combustion plasmas, as well as
soluble organic contaminants in industrial effluents and drinking water. The high ratio of
surface area to particle weight (e.g, 600 - 1600 m2/g), combined with the extremely small
pore diameter of the particles (e.g., 15-25 A) increases the adsorption characteristics
(Buonicore, 1992). GAC will eventually become saturated and the adsorption properties
will significantly degrade. When this occurs, the GAC usually must be replaced and
discarded, which significantly increases the costs of pollution control. The introduction of
carbon reactivation furnace technology in the mid 1980's created a method involving the
thermal treatment of used GAC to thermolytically desorb the synthetic compounds and
restore the adsorption properties for reuse (Lykins, et al., 1987).
The used GAC can contain compounds that are precursors to the formation of
PCDD/Fs during the thermal treatment process. EPA measured precursor compounds in
spent GAC used as a feed material to a carbon reactivation furnace tested during the
National Dioxin Study (EPA, 1987d). The total chlorobenzene content of the GAC ranged
from 150 ppb to 6,630 ppb. Trichlorobenzene was the most prevalent species present,
with smaller quantities of di- and tetra-chorobenzenes detected. Total halogenated
organics were measured to be about 150 ppm.
EPA has stack tested two GAC reactivation furnaces for the emission of dioxin
(EPA, 1987d; Lykins, et al., 1987). The homologue profile shown in Figure 3-1 was
developed from the tests at these two facilities. One facility was an industrial carbon
reactivation plant, and the second facility was used to restore GAC at a municipal drinking
water plant. The industrial carbon regeneration plant processed 36,000 kg/day of spent
GAC used in the treatment of industrial wastewater effluents. Spent carbon was
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reactivated in a multiple-hearth furnace, cooled in a water quench, and stored and shipped
back to primary chemical manufacturing facilities for reuse. The furnace fired natural gas,
and consisted of seven hearths arranged vertically in series. The hearth temperatures
ranged from 480° C to 1000° C. The spent GAC contained about 40% weight moisture.
The used GAC was fed to the top hearth. In the furnace, the spent carbon was dried and
the organics adsorbed onto the carbon particles were volatilized and burned in the heated
combustion atmosphere. The regenerated carbon dropped from the bottom hearth of the
furnace to a quench tank to reduce the temperature. Air pollutant emissions were
controlled by an afterburner, a sodium spray cooler, and a fabric filter. Temperatures in
the afterburner were about 930° C.
The second GAC reactivation facility tested by EPA consisted of a fluidized-bed
furnace located at a municipal drinking water treatment plant (Lykins, et al., 1987). The
furnace was divided into three sections: a combustion chamber, a reactivation section and
a dryer section. The combustion section was fired by natural gas, and consisted of a
stoichiometrically balanced stream of fuel and oxygen. These expanding gases of
combustion provided heat, and suspended and fluidized the carbon. Temperatures of
combustion were about 1,000° C. The reactivation section outside the combustion
chamber allowed for the complete volatilization of the heated GAC. Off-gasses from the
reactivation/combustion section were directed through an acid gas scrubber and high-
temperature afterburner prior to discharge from a stack.
Another combustion process for which emissions data were sought was coal
combustion in electric power generating facilities (utility boilers). Currently there is
conflicting and extremely limited data on emission of dioxin-like compounds from coal-fired
utility boilers (NATO,1988). The few published results of stack testing and monitoring of
emissions from facilities in the United States have shown that dioxin has not been
detected in stack gas emissions (NATO, 1988). Therefore, a homologue profile for this
source category was not developed. The federal Clean Air Act requires an assessment of
stack emissions of toxic air contaminants, including CDDs and CDFs, from coal-fired utility
boilers. The EPA is currently collaborating with the U.S. Department of Energy in stack
sampling seven facilities for CDD and CDF emissions. These results will be included in the
final version of this document.
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A homologue profile also could not be developed for a industrial category of
potential concern, portland cement kilns. The database on stack emissions of dioxin from
these units is just now becoming available. Volume 2, Chapter 3 of this assessment
reviews current emissions inventories for cement kilns burning and not burning hazardous
waste as auxiliary fuel in the production of cement clinker. In evaluating the TEQ of the
mixture of CDDs and CDFs discharged from the stack of individual facilities, it became
apparent that there was no consistent pattern to the relationship of total CDDs and CDFs
to the estimated dioxin TEQ. For example, the ratio of total PCDD/Fs to the TEQ ranged
from about a factor of 5:1 to a factor of 1000:1. A lower ratio reflects a skewing towards
penta and tetra-chlorinated congeners in the distribution, and a higher ratio reflects a
greater proportion of hexa, hepta, and octa chlorinated congeners in the emissions. Until
more information becomes available from stack testing additional sources, a homologue
profile of this industry will not be derived from the existing data.
3.2.3. Estimation of Emissions of Dioxin-Like Compounds from the Hypothetical
Incinerator
The emission factors for the dioxin-like compounds from the stack of the
hypothetical waste incinerator were derived from actual stack monitoring and emissions
testing of an incinerator burning a complex mixture of organic waste. The concentrations
of the specific PCDD/F congeners in units of nanograms per dry standard cubic meter (at
20° C; 1 atm.; 7% 02) were available, as were the volume of gas escaping from the stack
and feed rates for the material being combusted during the stack tests. Using procedures
described in Section 3.2.1, this data was converted to emission factors. Such factors for
three test runs are shown in Table 3-2. The fourth column is the average of these
emission factors converted to g/sec units, which are the appropriate units for the
application of the COMPDEP model. The conversion assumed a constant feed rate of 200
metric tons of feed material per day (further details on the hypothetical incinerator are
found in Section 3.5). Human exposures to the coplanar PCBs emitted from a combustion
source is not demonstrated in Chapter 5. Therefore, an estimation of congener-specific
emission factors of coplanar PCBs for the hypothetical incinerator are not provided.
In order to put the emissions from the hypothetical waste incinerator into
perspective, they can be compared with emissions from other incineration sources that are
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similarly controlled, e.g., equipped with scrubbers and/or fabric filters.
Such air pollution control devices can reduce the amount of dioxin that is formed within
the system by >99% prior to the release from the stack. In this comparison, emissions
from the following types of incineration processes were used (CARS, 1990; EPA, 1993):
medical waste incineration; hazardous waste incineration; sewage sludge incineration; and
municipal solid waste incineration. For comparisons, all emissions factors are expressed in
units of nanograms TCDD-TEQ (Toxic Equivalent) emitted from the stack per kg of waste
combusted, and are presented as ranges in measurements (minimum to maximum). This
should not be confused as typical of the incineration source category, but specific only to
sources having scrubbers and/or fabric filters. Volume 2, Chapter 3 of this assessment
gives an overview of dioxin emissions from incineration technologies equipped with a
variety of pollution control systems. The emissions from the hypothetical incinerator is
ranked with the other types of waste incinerators that are well controlled with some
combination of a scrubber device and/or a fabric filter, as follows:
1. Medical waste incineration: 25 - 200 ng TEQ/kg waste combusted.
2. Hazardous waste incineration: 0.18 - 119 ng TEQ/kg waste combusted.
3. Hypothetical waste incinerator: 4.5 ng TEQ/kg waste combusted.
4. Municipal solid waste incineration: 0.05 - 3 ng TEQ/kg waste combusted.
5. Sewage sludge incineration: 0.002 - 0.03 ng TEQ/kg sludge combusted.
From these comparisons it appears that the TCDD-TEQ emission factor derived for
the hypothetical incinerator lies well within the range of emission factors developed from
measured incineration sources burning a diversity of waste material, but employing similar
air pollution control technology. The hypothetical incinerator was arbitrarily assigned a
waste combustion rate of 200,000 kg waste/day. This charging rate conforms to a large
medical waste incinerator, an average hazardous waste facility, and moderate sewage
sludge and municipal waste incinerators.
3.2.4. Estimation of the Vapor Phase/Particle Phase Partitioning of Emissions of Dioxin-
Like Compounds
The first step in the air modeling is the partitioning of total emissions into a vapor
and a particle state. This section will review data on partitioning at the point of stack
emission, in ambient air, and a theoretical approach to estimating the partitioning of dioxin-
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Table 3-2. Emission factors and average emissions used for the hypothetical incinerator.
Emission Factors
Congener Test 1 Test 2 Test 3 Emissions
2378-TCDD
Other TCDD
12378-PeCDD
Other PeCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
Other HxCDD
1234678-HpCDD
Other HpCDD
OCDD
2378-TCDF
Other TCDF
12378-PeCDF
23478-PeCDF
Other PeCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
0.052
0.826
0.148
1.390
0.104
0.157
0.148
2.440
2.350
4.040
4.260
3.300
20.00
0.435
0.243
6.280
0.478
0.478
0.357
0.243
1.490
0.243
0.391
241.0
1.570
ny/*y
0.031
0.870
0.056
0.322
0.165
0.187
0.165
0.670
0.957
1.650
1.390
2.390
15.70
0.165
0.139
4.480
0.365
0.343
0.165
0.117
0.313
0.565
0.096
2.380
0.478
0.037
0.913
0.048
0.783
0.056
0.130
0.117
1.040
0.957
2.170
3.130
2.170
14.30
0.226
0.122
3.480
0.357
0.313
0.226
0.074
0.943
0.696
0.165
2.180
0.971
y/oco
9.3E-11
2.0E-9
1.9E-10
1.9E-9
2.5E-10
3.6E-10
3.3E-9
3.2E-9
3.3E-9
6.0E-9
6.7E-9
6.0E-9
3.8E-8
6.3E-10
3.9E-10
1.1 E-8
9.2E-10
8.7E-10
5.7E-10
3.3E-10
2.1E-9
1.2E-9
5.0E-10
5.4E-9
2.2E-9
like compounds in ambient air. The true vapor/particle partitioning of dioxin under different
conditions has not been directly measured, and therefore, is usually implied from these
limited data or by theoretical means.
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3.2.4.1. Vapor phase/particulate phase inferences from stack measurements
While the available literature is weak in this area, various investigators have made
inferences on the vapor phase/particulate phase (V/P) partitioning from in-the-stack
sampling of PCDD/F emissions from combustion sources. Sampling systems which have
been used basically consist of a paniculate filter followed by a section designed to
condense vapors in impinger glassware surrounded by an ice bath, and a resinous material
suitable for absorbing vapor phase compounds. Depending on where the congener is
distributed within the component parts of the sampling apparatus, the investigator reports
the fraction associated with particulate, and the fraction found in the vapor absorbing
material. In order to collect sufficient mass of particulate for accurate analytical
determination of the concentration of the recovered congener at sub-part per trillion levels
of detection, it is often necessary to sample in stack for periods of four hours or longer.
This introduces the possibility of movement of the collected dioxin sample from one part of
the sampling train to another through adsorption, desorption, particulate blow-off, or other
such phenomena as the sampling train continues to be exposed to the hot combustion
gases. No real-time sampling method currently exists to instantaneously measure the
concentration and physical state of the various PCDD/F congeners in the fluid turbulence
of the hot combustion plasma characteristic of gases from combustion traveling up a
cylindrical stack. For these reasons, V/P partitioning based on stack test data is highly
uncertain. Additional laboratory research is needed that is specifically directed at
identifying the physical state partitioning of individual PCDD/F congeners at the exit to the
stack under varying temperature profiles and conditions of particulate loading and acid gas
concentration. Table 3-3 is a summary of the percent distribution of PCDD/Fs between
the vapor-phase (V) and the particulate phase (P) as interpreted by various stack sampling
techniques employed in the measurement of the compounds during incinerator operations.
Cavallaro, et al. (1982) performed a series of stack tests on six municipal solid
waste (MSW) incinerators in Italy. He was one of the first investigators to interpret the
V/P ratio from where the PCDD/F segregated with the sampling train, e.g., the particulate
filter and resinous trap. From these data, the percent distribution of congener groups were
estimated. Cavallaro observed that the PCDD/F emissions from the stack of the tested
incinerators seemed to predominate in vapor phase. He attributed this to the possibility
that the relatively high temperatures of the combustion gases during sampling (700 to
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Table 3-3. Percent distribution of dioxins and furans between vapor-phase (V) and
particulate-phase (P) as interpreted by various stack sampling methods.
Cavallaro, et al.
1982
Cavallaro, et al.
1982
Cavallaro, et al.
1982
Cavallaro, et al.
1982
Cavallaro, et al.
1982
Cavallaro, et al.
1982
Benfenati, et al.
1986
Tiernan, et al.
1984
Tiernan, et al.
1984
Tiernan, et al.
1982
Tiernan, et al.
1982
Clement, et al.
1985
Clement, et al.
1985
Clement, et al.
1985
Hagenmaier, et al.
1986
Battelle 1988
EPA 1 990a
Radian 1986
Average
V/P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
4-CDD
95
5
9
91
99
1
85
15
97
3
100
0
94
6
75
25
95
5
91
9
17
83
98
2
84
16
100
0
62
38
90
10
56
44
16
84
76
24
5-CDD
91
9
38
62
99
1
92
8
90
10
99
1
NR
NR
68
32
90
10
91
9
22
78
92
8
55
45
99
1
42
58
NR
NR
42
58
16
84
70
30
6-CDD
94
6
69
31
99
1
99
1
63
37
99
1
NR
NR
67
33
88
12
89
11
45
55
96
4
54
46
98
2
25
75
NR
NR
30
70
16
84
71
29
7-CDD
99
1
57
43
99
1
98
2
82
18
100
0
NR
NR
55
45
85
15
77
23
84
16
93
7
72
28
93
7
20
80
NR
NR
26
74
20
80
73
27
8-CDD
89
11
14
86
99
1
99
1
59
42
99
1
NR
NR
64
36
98
2
56
44
85
15
73
27
20
80
95
5
20
80
NR
NR
18
82
16
84
63
37
4-CDF
NR
NR
59
41
99
1
99
1
80
20
100
0
NR
NR
75
25
86
14
92
8
7
93
97
3
95
5
100
0
68
32
NR
NR
62
38
16
84
76
24
5-CDF
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
70
30
98
2
89
11
10
90
96
4
73
27
100
0
55
45
NR
NR
56
44
14
84
66
34
6-CDF
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
64
36
89
11
91
9
22
78
98
2
70
30
99
1
40
60
NR
NR
45
55
17
83
64
36
7-CDF
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
69
31
88
12
72
28
63
37
98
2
52
48
99
1
25
75
NR
NR
37
63
16
84
62
38
8-CDF
96
4
60
40
99
1
99
1
97
3
100
0
NR
NR
86
14
98
2
65
35
77
23
94
6
68
32
98
2
0
100
NR
NR
21
79
17
83
73
27
NR = not reported; NRs not counted in average estimations
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4/94
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900° C) may have promoted desorption of PCDD/Fs from particulate, although the
sampling probe was kept at a constant 150° C.
Benfenati, et al. (1986) describes the stack testing of a modular MSW incinerator in
Italy having a combustion capacity of 1500 kg/hour. The purposes of the study were to
analyze the concentration of TCDD and TCDF at various points of the incineration process,
to estimate the vapor phase versus the particulate phase partitioning at various sampling
points corresponding to changes in temperatures, and to estimate the TCDD/TCDF control
efficiency of the pollution control device (an electrostatic precipitator). Comparisons were
made between the distribution of TCDD/TCDF after the secondary furnace in a region
where combustion gas temperature was about 330° C, and the distribution at the stack
where combustion gas temperature was 230° C. Benfenati observed that approximately
85% of the TCDD was in the vapor phase at the exit to the furnace, and approximately
95% of the TCDD was in the vapor phase at the stack. It was concluded that most of the
TCDD predominated in vapor phase at the point of release from the stack at the reported
temperature of 230° C. However, Benfenati could not exclude the possibility that the
TCDD was adsorbed onto ultra fine, submicron aerosol particles.
Tiernan, et al. (1984) reported on the distribution of PCDD/Fs recovered in the
stack sampling apparatus (EPA Modified Method 5) following the stack testing of a mass
burn MSW incinerator operating in Japan. In the Modified Method 5 procedure, the
sampling probe is maintained at a temperature of 120°C while the stack gases are
isokinetically sampled. The facility was equipped with a dry scrubber combined with a
fabric filter as the primary pollution control device. Tiernan observed congener-specific
variability in the V/P partitioning inferred from the sampling method. However, greater
than 55% of the PCDD/Fs were estimated to be in vapor phase at the point of release to
the stack. In an earlier stack test (Tiernan, et al., 1982) of an MSW incinerator equipped
with an electrostatic precipitator, Tiernan found that 45% to 89% of the PCDD/Fs were
associated with particulate.
Clement, et al. (1985) stack tested a mass burn MSW incinerator operational in
Canada for the emission of PCDD/Fs. Three 24-hour stack samples were taken using the
EPA Modified Method 5 train with a stack temperature of 230 - 250° C. The components
of the sampling train were analyzed separately. Clement observed that more than 95% of
the total PCDD/Fs detected in the sampling train samples was found in the impingers used
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to condense vapor phase organic pollutants. Interpretation of this is difficult. However, it
is implied from these data that most of the PCDD/Fs prevailed in vapor phase.
Hagenmaier, et. al (1986) conducted field tests of two different stack test methods
for the accuracy, precision, and comparability of PCDD/F measurements. Both instruments
were similarly constructed with a glass fiber filter for the capture of particulate-bound
contaminants, a series of water or ice-cooled impingers to promote condensation of vapor
phase contaminants, followed by an absorbing material to trap vapor phase pollutants.
Eight parallel stack sampling experiments were carried out over a three week period using
the sampling trains known as the German simple dilution method and the EPA Modified
Method 5. Although the two methods reported quite similar total concentrations of
PCDD/Fs, the distribution of PCDD/Fs between the heated glass filter, and ice-cooled
impingers and the sorbent trap were remarkably different. In one train, referred to as Train
A by Hagenmaier, the temperature in the filter housing was 140° C, and in the second
train. Train B, the temperature was 90° C. The stack gas temperature in both cases was
230° C. Hagenmaier found that the percentage of PCDD/Fs in the glass fiber filter was
markedly greater in Train B than in Train A. Up to 93% of the PCDDs and 90% of the
PCDFs were detected in the particulate filter in Train B. By comparison, 73% and 58% of
PCDDs and PCDFs, respectively, were detected in the particulate filter in Train A.
Although Hagenmaier's data is used in Table 3-3, Hagenmaier theorized that this
difference in the distribution of PCDD/Fs in the two sampling trains was due to the
differences in the temperature of the glass fiber filter housing.
EPA (1990a) conducted a field validation study for the EPA stack testing Method
23 (the Modified Method 5) for the collection and retention efficiency of PCDD/Fs. A
carbon-13 labelled congener was metered into the sampling probe just preceding the glass
fiber filter using a dynamic spiking system. The validation procedure involved the
isokinetic sampling in the stack of a large mass burn MSW incinerator. Sampling in situ in
the stack while using a dynamic spiking system demonstrated that most of the isotope
was recovered in the filter trap and front half of the sampling train designed to capture
particulate, and a lower amount was recovered in the XAD resin designed to capture vapor
phase organic compounds. In the particular tests in which the overall percent recovery of
the dynamic spike were found to be acceptable, the XAD resin and condenser contained
about 49% of the isotope, and 51 % was associated with carbonaceous particulate.
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3.2.4.2. Discussion of vapor/particle ratios derived from stack testing methods
It is apparent that the stack sampling method gives inconclusive and contradictory
evidence of the V/P partitioning of PCDD/Fs at the stack of incinerators. Although most of
the researchers report finding the greatest quantity of PCDD/Fs captured within the
resinous material having the physical/chemical properties of absorbing vapor phase organic
compounds, a few studies have reported the opposite. What is unusual about the V/P
distributions in Table 3-3 is the lack of complete consistency despite the similarity of
sampling method. Although the stack gas temperatures may vary, the probe and housing
to the sampling instrument is usually kept at a standard temperature while traversing the
hot flue gas. A more consistent pattern of V/P should have emerged.
Hagenmaier, et al. (1986) has postulated that, depending on the temperature of the
glass fiber particulate filter housing, the PCDD/Fs might desorb (volatilize) from paniculate
matter trapped in the filter during the 4 hours of sampling time required of the stack
sampling method. Therefore, Hagenmaier does not believe that the distribution of
PCDD/Fs between the particulate filter, the condensing impingers, and the vapor absorber
gives a true indication of the V/P partitioning of these compounds at the stack.
Tests also have been devised by the EPA (1990a) to study the effect a change in
temperature of the glass fiber filter housing might have on the distribution of PCDD/Fs in
the sampling train. During the sampling period, two sampling trains were used: one inlet
to the electrostatic precipitator (ESP), and the other placed near the outlet to the ESP.
Temperatures of the filter housing were raised from the standard 120° C to 215° C in
both sampling trains. In agreement with the observations of Hagenmaier, et al. (1986), an
increase in temperature generally resulted in a change in the distribution of the recovered
13-C labelled PCDD/F congeners. However, the temperature effect was most apparent
within the sampling train inlet to the ESP. In the inlet sampling train, the higher filter box
temperature increased the relative percentage of PCDD/Fs trapped in the impingers and
XAD-2 resin. An amount estimated to be in the vapor phase, based on the segregation of
the compounds within the component parts of inlet sampling train, is as follows (with a
range listed from low to high temperature): TCDD = 20 - 55% vapor; HxCDD = 10 -
30% vapor; OCDD = 5 - 18% vapor; HxCDF = 18 - 58% vapor; OCDF = 5 - 18%
vapor. In the outlet sampling train (characteristic of stack emissions), this dramatic
shifting of the congeners from the filter to the XAD-2 did not occur with an increase in
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temperature. Interpretation of the vapor phase partitioning in the outlet sampling train
from low to high temperatures was as follows: TCDD = 90 - 95% vapor; HxCDD = 85 -
90% vapor; HxCDF = 90 - 95% vapor; OCDD = 75 - 90% vapor; OCDF = 78 - 90%
vapor. Both these interpretations were developed using a 500 ng PCDD/F spiked
congener. Notice that the vapor phase to particle phase ratio is significantly different
between the inlet and outlet sampling trains: in the inlet train most of the PCDD/F
congeners seemed to predominate in the particle phase at the standard temperature of the
filter housing, whereas in the outlet train most of the PCDD/F congeners seemed to
predominate in the vapor phase, as interpreted by the distribution within the apparatus.
The temperature-dependent partitioning has recently been observed by Janssens, et
al. (1992) during field validation studies involving the sampling of operating incinerators in
Belgium. Janssens observed that the fraction of PCDD/Fs collected in the heated portion
of the paniculate glass filter (temperatures in the range of 250 to 300° C) showed an
expected partitioning according to the vapor pressures of the compounds. It was found
that a very low proportion of the PCDD/Fs were found in the particle phase; nearly all the
compounds were detected in the vapor phase. Moreover, Janssens observed that higher
temperatures seemed to favor the vaporous state of the lower chlorinated congeners
(compounds having one to five chlorines on the aromatic ring), and the paniculate phase
for higher chlorinated congeners (five to eight chlorines). This agrees well with the
decrease in vapor pressures that occurs with an increase in chlorination, and an increase in
vapor pressure that occurs with a decrease in chlorination of PCDD/Fs. Adding to the
theory of Hagenmaier, et al. (1986), Janssens believed that either the sampling apparatus
was giving a true distribution of the V/P ratio of individual congeners, or that a significant
portion of the congeners were reversibly sorbed onto paniculate surfaces and could be
eluded to vapor phase by the passage of the volume of sampled combustion gas over a
lengthy time interval, neither of which could be proven by his study.
Benfenati, et al. (1986) has suggested that what may be reported as vapor phase
may actually consist of nucleated aerosol particles having diameters less than 0.1
micrometers. The impingers in the sampling method are located a few centimeters behind
the heated particulate glass fiber filter, and are bathed in an ice bath. The dramatic
reduction in temperature within the impinger glassware may cause sublimation from vapor
phase to nucleation of aerosol particles. Downstream of the impingers is the vapor
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absorbing material, usually XAD-2 resin. Although this has been shown to be an excellent
trap for semi-volatile organic compounds, the retention of submicron size particles with
PCDD/Fs adsorbed onto the surfaces, or absorbed into the interior spaces, cannot be ruled
out or excluded as a possible explanation for investigators reporting a preponderance of
concentration both in the impingers and the vapor trap.
Complicating any meaningful interpretation of the data is the long duration of
sampling time required in the stack measurement method. In order to reach a sub-ppt
level of detection of PCDD/Fs for reliable quantification of specific congeners, sampling
proceeds until approximately a five gram mass of particulate is gathered in the particulate
filter. This may require in situ placement of the sampling apparatus such that samples are
taken isokinetically, and the stack interior diameter is traversed for four or more hours.
Thus the sampling instrument is continuously exposed to the hot gas plasma over a long
sampling moment. In addition the hot gases also contain precursor compounds, chlorides,
oxides of sulfur and HCI which may have an effect on the success of accurately sampling
PCDD/Fs. Although Janssens, et al. (1992), Hagenmaier, et al. (1986), and EPA (1990a)
have all but excluded the possibility that sampling under these conditions creates results
by producing PCDD/Fs or destroying PCDD/Fs somewhere within the sampling train, the
possibility that the method creates an illusion of the true V/P ratio cannot be excluded.
The above discussions have indicated the variability in the data and the uncertainty
with the stack results of vapor/particle partitioning. For these reasons, these data will not
be used to infer the V/P distribution of PCDD/Fs at the point of release from the stack.
3.2.4.3. Vapor/particle partitioning of PCDD/Fs from ambient air samp/ing
The measurement of PCDD/Fs in air under ambient conditions has only been
achieved since the late 1980's. Although there may be some variations, the ambient air
sampler usually consists of a glass fiber filter followed by a polyurethane foam (PDF) plug
some distance downstream in the direction of air flow. Typically, these are active
samplers utilizing electric pumps to regulate the flow of sampling air to some
predetermined rate, usually in the range of 300 - 400 cubic meters of air over a 24-hour
sampling period. Unlike the stack sampling method, the particulate filter is not artificially
heated, nor is there a condensing component where the sampled air is quickly cooled in
order to force condensation of vapor phase semi-volatile organics. Hence this sampling
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scheme should be more useful to the interpretation of the vapor phase/particle phase
partitioning of PCDDs/Fs under ambient conditions. Such observations are made on the
basis of the segregation of the dioxin congener in the filter versus the PDF plug. The PDF
plug has been verified to efficiently trap vapor phase semi-volatile pesticides and organic
species (Wagel, et al., 1989), and the glass fiber filter uses filter paper with porosities to
0.1 microns to collect particulate matter. Because sampling of PCDD/Fs is not
instantaneous (i.e. real time measurement), but requires 24-hour air sampling to assure a
level of detection of about 0.03 pg/m3, the interpretation of the V/P ratio should be
construed as operationally defined rather than a direct empirical observation. Under this
assumption, the V/P ratio is relative and not absolute. The following is a review of
ambient air sampling providing sufficient information of the relative V/P partitioning of
PCDD/F congeners at ambient conditions. Table 3-4 provides a summary of the V/P ratio
inferred from these reports.
Oehme, et al. (1986) first described a method sensitive enough for the congener-
specific measurement of PCDD/Fs at 0.1 pg/m3 levels of detection in ambient air. Such
low levels of detection introduced the possibility of taking ambient air samples in the
vicinity of known combustion sources of PCDD/Fs to reliably establish an association with
sack emissions. Oehme tested the performance and reliability of an ambient air sampler
consisting of a glass fiber filter followed by a polyurethane foam plug. Ambient air was
sampled over a predetermined period after first spiking the filter with a known
concentration of 13C12 labelled PCDD/F standards. This experiment was designed to
determine the percent of the initial spiked labelled standard that could be recovered from
the sampler after sampling 1000 m3 of ambient air. The percent recovery of the standard
was a measure of the collection and retention efficiency of the sampler. After collecting a
sample, the particulate filter and the PUF plugs were extracted and analyzed separately.
This was done in order to establish the particle phase and vapor phase partitioning of the
PCDD/F congeners. Oehme demonstrated that the sampling method was capable of a high
degree of reliability in sampling sub-part per trillion concentrations of PCDD/Fs as indicated
by highly satisfactory recovery of the isotopically labelled standards in the apparatus, e.g.,
88 - 102% recoveries. From the results of separately analyzing the filter and the PUF,
Oehme postulated on the typical distribution of PCDD/Fs between vapor and particles in
ambient air. They suggested that TCDF and PeCDF were mainly present in the vapor
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Table 3-4. Percent distribution of dioxins and furans between vapor phase (V) and
particulate phase (P) in ambient air as observed in ambient air sampling studies.
Source
Harless & Lewis
19921
Harless & Lewis
19921
Eitzer & Hites
1989
Eitzer & Hites
1989
Eitzer & Hites
1989
Eitzer & Hites
1989
Eitzer & Hites
1989
Hunt & Maisel
1990
Hunt & Maisel
1990;T = 18°C
Bobet, et al.
1990
Bobet, et al.
1990
Hites 1991
T = 3°C
Hites 1991
T = 18°C
Hites 1991
T = 28°C
Hunt & Maisel
1992
Average
V/P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
4-CDD
79
21
97
3
87
13
ND
ND
ND
ND
ND
ND
100
0
NR
NR
NR
NR
ND
ND
ND
ND
60
40
92
8
95
5
ND
ND
87
13
5-CDD
80
20
95
5
67
33
67
33
80
20
67
33
33
67
NR
NR
NR
NR
100
0
ND
ND
13
87
72
28
87
13
ND
ND
69
31
6-CDD
76
24
88
12
22
78
17
83
33
67
20
80
40
60
8
92
0
100
35
65
0
100
0
100
55
45
55
45
0
100
30
70
7-CDD
30
70
36
64
3
97
0
100
4
96
0
100
4
96
0
100
0
100
18
82
0
100
0
100
12
88
40
60
0
100
10
90
8-CDD
15
85
10
90
NR
NR
0
100
0
100
0
100
2
98
22
78
0
100
0
100
0
100
0
100
0
100
0
100
0
100
4
96
4-CDF
77
23
93
7
91
9
98
2
88
12
88
12
100
0
86
14
95
5
80
20
ND
ND
0
100
ND
ND
ND
ND
96
4
83
17
5-CDF
74
26
88
12
63
37
73
27
67
33
57
43
70
30
58
42
57
43
29
71
ND
ND
40
60
72
28
100
0
56
44
65
35
6-CDF
71
29
85
15
27
73
45
55
23
77
30
70
33
67
27
73
0
100
0
100
ND
ND
12
88
70
30
62
38
0
100
35
65
7-CDF
41
59
57
43
8
92
20
80
5
95
0
100
0
100
0
100
0
100
0
100
ND
ND
0
100
7
93
22
78
0
100
11
89
8-ODF
6
94
9
91
NR
NR
0
100
0
100
0
100
0
100
0
100
NR
NR
0
100
ND
ND
2
98
0
100
2
98
ND
ND
2
98
1 V/P based on percent recoveries of isotopically labeled PCDD/F in filter or PDF and corrected to 100% with
author's permission.
NR = not reported; ND = not detected; NR and NDs not included in average estimations
phase, and HxCDD, HxCDF as well as the less volatile isomers of HpCDF, HpCDD, OCDF,
and OCDD, were mainly present in the particle phase. Oehme took over 60 ambient air
samples with this device in rural, suburban, and urban areas of Europe.
Eitzer and Hites (1989) reported on the measurement of PCDD/Fs in the ambient
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atmosphere of Bloomington, Indiana while using a similarly configured ambient air sampling
method, the General Metals Works PS-1 sampler. Ambient air is drawn through a glass
fiber filter followed by a polyurethane foam plug (PDF). This was a long-term study
designed to investigate the daily and seasonal variability of the compounds in the ambient
air as measured at a single location, and to examine the vapor-phase, particulate-phase
partitioning of the chlorinated congeners under ambient conditions. Samples were taken at
four different sites over a 2-3 day sampling period until 1500 to 2400 m3 of ambient air
volume had passed through the apparatus. Sampling was conducted monthly from
August, 1985 through July, 1986. The quantitative method produced a limit of detection
of the individual chlorinated congeners in the range of ~ 1 femtogram/m3. Eitzer and Hites
(1989) operationally defined the vapor-phase/particle-bound phase of the chlorinated
congeners as any compounds found in the PDF plug and the glass fiber filter, respectively.
The V/P ratio was subject to certain restrictions of the sampling method, which the
authors identified as: 1. Particles smaller than 0.1 microns would pass through the filter
paper of the glass fiber particulate filter and be absorbed into the polyurothane foam; 2.
Diurnal temperature variation could cause particle-bound PCDD/Fs collected and retained in
the filter to vaporize and be "blown-off" to the PDF plug by the passage of the sampled air
stream; 3. At these relatively large sampling volumes of ambient air, it is possible that
some breakthrough on the PDF plug occurs, and a portion of the PCDD/F sample is lost.
The investigators were able to rule-out the latter condition through the addition of a XAD-2
resin trap after the PUF. This was one of the first reports on the congener-specific V/P
partitioning in the ambient air under variable average ambient temperatures. Although they
could find no seasonal effect on the total concentrations of PCDD/Fs, seasonal change in
temperature did affect the V/P ratio. It was noted that during the warm summer months
the V/P ratio was as great as 2:1, and during the cold winter months the V/P ratio could
be <0.5. Thus, at warm temperatures most of the lower chlorinated congeners, e.g.,
mono through penta-chlorinated PCDD/Fs, were mostly found in the vapor phase and the
hexa - octachlorinated congeners were mostly particulate-bound. The colder winter
temperatures produced the effect of more eventually splitting the V/P distribution of the
lower chlorinated species. The higher chlorinated congeners, e.g., hexa-, hepta-, and octa-
PCDD/Fs, mostly were found to be particle-bound at both the warm and cold
temperatures. These quantitative results of the V/P ratio of individual congeners at three
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ambient air temperatures (3° C, 16 - 20° C, and >28° C) was again reported by Hites
(1991), as shown in Table 3-8. Through these analyses, Eitzer and Hites (1989) and Hites
(1991) found two dependant variables controlled the V/P ratio in ambient air: 1. the
ambient air temperature; and 2. the vapor pressures of the PCDD/F congeners. The
authors concluded that because the lower chlorinated compounds have higher vapor
pressures, they will be found mostly in the vapor phase, and because the higher
chlorinated congeners have lower vapor pressures, they will prevail in the ambient air
bound to paniculate matter.
Wagel, et al. (1989) reported on the performance of the General Metals Works PS-1
sampler for the collection and retention of PCDD/Fs while sampling ambient air. This
sampler configuration consists of a quartz glass fiber filter followed by a polyurethane
foam (PUF) plug, and the investigators added an XAD-2 resin cartridge after the PDF. The
addition of the XAD was a check on whether breakthrough of any PCDD/F congeners
occurred from the PUF during sampling. The PS-1 is the sampler most often used in the
U.S. to quantify PCDD/Fs in air under ambient conditions. The protocol of this research
was to use two samplers co-located. The particulate filter of one sampler was spiked with
13C12-labelled PCDD/F congeners while the second sampler was used to provide
background measurements of native (non-labelled) PCDD/Fs. Both units were operated to
sample ambient air for 24-hours. The average ambient temperature during the sampling
period was 24° C. Following the sampling the filter and PUF were removed and extracted
according to published procedures (Wagel, et al., 1989). Performance of the PS-1 sampler
was reported as percent recovery of the labelled standards initially spiked onto the
particulate filter. The percent recovery was calculated by subtracting the background
contributions from the total detected spike concentration and dividing by the concentration
of the labelled standard initially added to the filter. The percent recoveries were reported
in a range of from 85% to 124%, with an average recovery of 102%. This indicated a
high degree of reliability in collecting and retaining PCDD/Fs in the sampler during the 24-
hr sampling period.
A second series of experiments were conducted to investigate the distribution of
PCDD/Fs within the sampling apparatus, e.g., the particulate filter versus the PUF plug, by
extracting and analyzing the filter and PUF separately. Subject to the caveats previously
discussed, the investigators made observations regarding the V/P ratio of PCDD/F
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congeners. It was observed that PCDD/Fs having 7-8 chlorines were mostly detected in
the paniculate filter, and lower chlorinated species were mostly detected in the PUF.
Wagel, et al. (1989) suggested that it was possible that the lower chlorinated congeners
volatilized from the paniculate filter (somewhat affected by the rate of flow of the sampled
air volume), and then were retained by the PUF. Furthermore, Wagel, et al. (1989) warned
that if results of separately analyzing the filter and PUF are used to derive a vapor phase
and particle phase partitioning of the PCDD/Fs under ambient conditions, then this may
give erroneously high estimates of the amount present in vapor phase.
Harless and Lewis (1992) have quantitatively evaluated the performance of the
General Metals Works PS-1 sampler for the trace-level measurement of PCDD/Fs in
ambient air, adding to the growing evidence that results are actual measurements and not
an artifact of the sampling method. In this study, three samplers were used in the same
general vicinity, and were operated for a 24-hour period until an air volume of 350 - 400
m3 had passed through the system. The quartz glass fiber paniculate filters of two of the
samplers were then spiked with 13 C12 labeled PCDD/F congener with a known
concentration after the 24-hour sampling period. The three samplers were then operated
another 24-hours. The samplers were then shut down, and the filters and PUF plugs were
removed and extracted and analyzed for PCDD/Fs separately according to prescribed
procedures. A separate series of experiments involved precleaning the glass fiber
paniculate filters, and adding the isotopically labelled PCDD/F spike to the filter prior to
sampling for seven days until about 2660 m3 of ambient air had been sampled. Results of
this study confirmed the accuracy and reliability of the PS-1 sampler for collecting and
retaining PCDD/Fs at sub-ppt concentrations in ambient air. Performance was defined as
the percent of the initial concentration of the labelled isotope recovered in the sampling
apparatus following the operation over the predetermined sampling period. The average
efficiency of recovery of the 0.8 ng 13C12-1,2,3,4,-TCDD isotope that was spiked onto the
filter prior to sampling was 91 %, and similar efficiencies were observed for the recovery of
the other labeled PCDD/Fs. Additionally, Harless and Lewis (1992) used the spiking
system to observe the distribution of PCDD/Fs in the filter and the PUF after sampling 400
m3 of ambient air. It was observed that most of the hepta-, and octa-CDD/Fs were
retained by the glass fiber filter, indicating that these compounds were primarily
particulate-bound, and most of the tetra-, penta-, and hexa-CDD/Fs volatilized and were
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collected by the PUF plug. When partitioning was observed on a congener-specific basis,
significant differences were observed in the V/P ratio, as shown in Table 3-4.
Hunt and Maisel (1990) reported on the ambient air measurement of PCDD/Fs in a
northeastern U.S. urban coastal environment during the fall and winter seasons. Isomer-
specific sampling was conducted with the General Metal Works PS-1 sampler in and
around Bridgeport, Connecticut from November, 1987 through January, 1988. Nine
sampling sessions consisting of a total of 43 ambient air samples were taken in this study.
Each sampling session was conducted either over a 24-hour or 72-hour period until about
350 m3 and 600 m3 of air volume had passed through the sampler. Hunt and Maisel
(1990) reported on the typical vapor phase/particle bound partitioning of individual
congeners during cold ambient air temperatures. The V/P ratio was based on the results of
separately analyzing the PUF plugs and the glass fiber particulate filters for the presence of
PCDD/Fs. From these data, the investigators concluded that greater than 92% of all the
congeners of PCDD/Fs were particulate bound (operationally defined as detected in the
particulate filter). The 2,3,7,8-TCDD isomer was not detected in any of the 43 collected
samples (reported limit of detection was 5-20 fg/m3). The particulate bound distribution
(reported as a percent of the detected concentration) for some of the other congeners
were as follows: 2,3,7,8-TCDF = 93%; 1,2,3,7,8-PeCDF = 94%; 2,3,4,7,8-PeCDF =
99%; 1,2,3,4,7,8-HxCDF = 97%; 1,2,3,4,6,7,8-HpCDF = 100%; 1,2,3,6,7,8-HxCDD =
96%; and the 1,2,3,4,6,7,8-HpCDD = 92%. The vapor phase/ particle bound distribution
observed in this study is probably controlled by the cold January temperatures from which
these observations were derived (average temperature = -5° C).
At a later date, Hunt and Maisel (1992) conducted ambient air monitoring of
PCDD/Fs in multiple locations in the warm climate of southern California for the State of
California Air Resources Board (CARS). Ambient air samplers, e.g., the General Metal
Works PS-1 sampler, were primarily placed in areas of high population density that
contained known combustion sources of PCDD/Fs, but sites were also sampled that were
considered removed from the influences of any local sources. The purpose of the study
was to evaluate the congener-specific spacial distribution of PCDD/Fs in ambient air near
environmental sources of the compounds, and in remote locations, in order to provide a
baseline to evaluate population exposures within the region. Monitoring sites were
established at eight locations in the South Coast Air Basin in and around the city of Los
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Angeles. Nine discrete sample sets were collected from December, 1987 through March,
1989. The authors defined a sample set as consisting of five to seven stations at which
one or two co-located samplers were operated. Microscale meteorological data was
collected during sampling to include wind speed, wind direction, barometric pressure, and
temperature. One sampling site was chosen to investigate the distribution of PCDD/Fs in
ambient air where average ambient temperatures ranged from 1 6-20°C. This was done by
the usual procedure of separately analyzing the filter and the PUF and making the
assumption that what is detected in the glass fiber filter is particulate bound, and what is
trapped in the PUF is in vapor phase. The authors noted that under these conditions, the
V/P partitioning is operationally defined by the ambient air sampling system, and therefore
may not be a true indication of the partitioning in the atmosphere. The majority of the
hexa through octa CDD/F congeners were detected in the filter, and the authors observed
that they were mainly associated with particulate matter. The authors found these
observations were consistent with the V/P ratio observed by Eitzer and Hites (1989) in
warm climate conditions. In addition, the authors noted that these observations give
further evidence that vapor pressures of the specific PCDD/F compounds and ambient air
temperatures strongly influence the V/P partitioning. Therefore the tetra- and penta-
CDD/Fs are expected to predominate in vapor phase during warm seasons. However,
during the cold temperatures of the winter season these congeners are expected to be
primarily associated with particulate matter in the ambient air.
Bobet, et. al. (1990) reported the results of an ambient air monitoring network
operated by Environment Canada to temporally measure PCDD/Fs in the ambient air in
southwestern Ontario, Canada. The intent of the study was to monitor possible
environmental impacts of a large refuse-derived fuel municipal waste combustor
operational in the City of Detroit, Michigan. The ambient air monitoring network consisted
on two stations, one in Windsor, Ontario, and the other located in the Walpol Island Indian
Reservation 18 km to the northeast of Windsor. The former site was considered in an
urban area near the expected point of maximum impact from the stack emissions from the
MWC, and the other site was considered rural, and away from the influence of any
stationary combustion source. PCDD/F samples were collected once every 24 days using
an high-volume ambient air sampler consisting of a Teflon-coated glass fiber particulate
filter and a PUF adsorbent trap. Ambient air was sampled over a 24-hour period from July,
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1987 to August, 1988 with a total sample volume of 800 - 1000 m3 of air. From August
on, the samplers were operated over a 48-hour period, and 1 600 - 2000 m3 of air passed
through the sampler. Mean total concentrations of PCDD/Fs were compared between the
urban and rural sites, and Bobet observed that concentrations measured at the urban site
were 4 -20 times greater than at the rural site. Additionally, the V/P partitioning of
PCDD/Fs (as operationally defined by detection in the PDF verses detection in the filter)
was investigated at both sampling stations. Bobet stated that the V/P may be influenced
by "blow-off" of particulate from the filter to the PUF, and/or the passage of particulate
matter <0.1 microns from the filter to the PUF, and if this is the case, then the vapor
phase partitioning may be too great as interpreted by the method. Under these
circumstances the authors suggested that the V/P partitioning should be considered as
roughly representative of the vapor/particulate phases in the ambient air. On a total
concentration basis, and on a total of 12 separate ambient air samples, the investigators
found the following average percent vapor phase versus percent particle phase partitioning
of the PCDD/F homologues at the Windsor, Ontario station: TCDD = not detected;
PeCDD = 100% V/ 0% P; HxCDD = 35% V/ 65% P; HpCDD = 18% V/ 82% P; OCDD
= 0% V/100% P; TCDF = 80% V/20% P; PeCDF = 29% V/ 71% P; HxCDF = 0%
V/100% P; HpCDF = 0% V/ 100% P; OCDF = 0% V/ 100% P. At the rural Walpole
Island station, no TCDD, PeCDD or TCDF - OCDF were detected in any of the 5 separate
ambient air samples. All of the detected HxCDD, HpCDD and OCDD was found in the
particulate filter indicating a V/P distribution of 0% V/ 100% P for these compounds. The
authors did not report the average ambient air temperature at the two stations.
3.2.4.4. Discussion of the vapor/particle partitioning in ambient air sampling
studies
The studies that have been reviewed here indicate the following:
• The high-volume ambient air sampler consisting of a glass fiber particulate filter
and polyurethane foam absorbent trap is a reliable method for the collection and retention
of PCDDs/Fs in ambient air.
• Current analytical methods assure detection limits, on a congener specific basis,
of about 0.03 pg/m3.
• Experiments involving the recovery of isotopically labelled PCDD/Fs within the
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sampler after 24-hours operation indicate that the sampler does not create artifacts
representative of either sample losses or the synthesis of dioxin.
• Because the sampler is not artificially heated or cooled, but is allowed to operate
at existing ambient air temperatures during sampling sessions, the method can be used to
imply the vapor phase and particle bound partitioning of PCDD/Fs in ambient air. This is
accomplished by separately extracting and analyzing the glass fiber filter and the
polyurethane foam for the presence of PCDD/F congeners.
• However, the V/P ratio interpreted from these results is operationally defined.
This will only give an approximate indication of the V/P ratio since mass transfer between
the particulate filter and the vapor trap cannot be ruled out. The particulate filter paper
porosity is > 0.1 microns, and therefore it is possible that aerosol particles with diameters
< 0.1 microns will pass through the filter and be trapped in the polyurethane foam plug.
If this is the case, then the percent observed in vapor phase will be overestimated. The
method involves ambient air sampling at a relatively high sample volume, around 300-400
m3 of air, over a 24-hour period. It is possible that PCDD/Fs that are not sorbed to
particulate matter captured in the filter may be volatilized by subtle changes in ambient
temperature, and that PCDD/F in the vapor phase may be carried with the sampling air
flow to the PUF sorbent trap. If this were to occur, then the interpretation of the percent
of the PCDD/Fs partitioned to the vapor phase would be an over estimate. Unfortunately
there are no empirical data that have demonstrated that any of these effects may actually
occur.
3.2.4.5. Theoretical prediction of vapor/particle partitioning of PCDD/Fs under
ambient conditions
Bidleman (1988) offers a theoretical construct for estimating the vapor
phase/particle bound partitioning of PCDD/Fs in ambient air. Bidleman presents the theory
that a portion of the semivolatile compounds found in ambient air are freely exchangeable
between the vapor and particle phases. Bidleman defines a second portion, the
nonexchangeable fraction, as the quantity that is strongly and irreversibly adsorbed to
particulate matter, and is not at equilibrium with a corresponding vapor phase. Bidleman
cites an earlier model by Junge (1977), a theoretical model based on adsorption theory,
which mathematically described the exchangeable fraction of the semivolatile organic
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compound adsorbed to aerosol particles as a function of solute saturation vapor pressure
and total surface area of atmospheric aerosol particles available for adsorption. This is
given by:
= c S? (3-2)
P + c ST
where:
0 = adsorbed fraction, unitless
c = constant developed by Junge, atm-cm
ST = total surface area of atmospheric aerosols in relation to total volume
of air, cm2/cm3
p = solute saturation vapor pressure, atm"1
Although Junge treated the term 'c' in Equation (3-2) as a constant, e.g., c = 1.7 E-4 atm-
cm, Bidleman notes that it actually is variable and quite dependent on the chemical's
sorbate molecular weight, the surface concentration of the chemical on aerosol particles
(assuming monolayer coverage), and the difference between the heat of desorption from
the surface of a particle and the heat of vaporization of the liquid-phase sorbate.
Bidleman (1988) poses the question as to whether it is the chemical's sub-cooled
liquid vapor pressure (P|) or the chemical's crystalline solid vapor pressure (Ps) that
ultimately controls the rate of adsorption to aerosol particles. P| and Ps are related
according to Equation (3-3) developed by Bidleman (1988). The sub-cooled liquid vapor
pressure is estimated by extrapolating below the melting point of the compound.
where:
ln «, .
-1
Ps = crystalline solid vapor pressure, atm
PI = liquid sub-cooled vapor pressure, atm"1
ASf = entropy of fusion, atm-m3/mole-deg K
R = universal gas constant, atm-m3/mole-deg K
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Tm = melting point, K
T = ambient air temperature, K
Bidleman notes that a satisfactory estimate of ASf/R observed in other treatments of this
subject is 6.79. This can be substituted for ASf/R in Equation (3-3), and used as a
constant. Bidleman argues that the use of P| in Junge's equation, the sub-cooled liquid
vapor pressure, makes the most accurate estimation of the vapor phase/particle bound
partitioning of semi-volatile organic compounds in ambient air. To support his argument,
Bidleman gives the example of the comparison of the application of the crystalline solid
(Ps) versus the sub-cooled liquid (P,) vapor pressure of TCDD in Junge's equation to
estimate the V/P partitioning at an ambient air temperature of 20°C in an urban air shed.
The Ps predicts a V/P partitioning of 0%/100%, whereas the P, predicts a V/P of
20%/80%. Bidleman then compares these predictions against the V/P partitioning of
TCDD as observed by Eitzer and Hites (1986) from the sampling of ambient air for
PCDD/Fs in Bloomington, Indiana. His conclusions are that the prediction of the V/P ratio
using the sub-cooled liquid vapor pressure of TCDD best fits the observed partitioning
interpreted from directly measuring PCDD/Fs in ambient air, e.g., most tetra-, and penta-
CDD congeners are prevalent in the vapor phase, and the higher chlorinated congeners are
mainly particle bound (as operationally defined by the sampling method).
Calculations of 0 (the fraction that is bound to particulate) from Equation (3-2) can
be made on a congener-specific basis for the PCDD/Fs. The estimate of 1.7 E-4 atm-cm
for the value c can be assumed from the work of Junge (1977) as cited by Bidleman
(1988). The sub-cooled vapor pressures can be converted from the crystalline solid vapor
pressures of the specific congeners found in Volume 2, Chapter 2, of this assessment, by
applying Equation (3-3). The melting points of the specific congeners are also referenced
in Chapter 2, Volume 2. Bidleman (1988) provides estimates of average total surface
f\ *$
areas of aerosol particles relative to average total volume of air (crrr/crrr), the term ST in
Equation (3-2), citing a study by Whitby (1978). In addition, Whitby estimated the
average total volume of aerosol particles per volume of air (Vt = cm3 particles/cm3 of air).
Whitby's (1978) calculations varied according to the density of aerosol particles in the
ambient air in different air sheds. These were as follows (units of ST of cm2/cm3, VT of
cm3/cm3):
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Clean continental background • Average background
ST = 4.2 E-7 ST = 1.5E-6
VT = 6.5E-12 VT = 3.0E-11
• Background plus local sources • Urban
ST = 3.5 E-6 ST = 1.1 E-5
VT = 4.3E-11 VT = 7.0E-11
Bidleman noted that if the average particle density of aerosol particles suspended in urban
air is assumed to be 1.4 grams/cm3, then the surface area of the average urban aerosol
particles is 11 m2/g, and the average total suspended particulate is 98 /yg/m3, following
the calculations of Whitby. This was regarded as being in close agreement to the average
monitored total suspended particulate of 79 //g/m3 for 46 cities surveyed in the United
States in 1976. Therefore, Whitby's values for ST were judged by Bidleman to be
adequate for purposes of calculating and estimating 0. If these assumptions are applied to
the variables in Equation (3-2), the value for 0, the fraction of the PCDD/F congener
reversibly adsorbed to particles in ambient air, can be calculated. Table 3-5 shows the
calculated fraction of the congener that is bound to particles suspended in ambient air over
a range of air shed classifications.
The values of the fraction of PCDD/Fs adsorbed to particulate in the ambient air can
be compared against measurements of partitioning of the PCDD/Fs in ambient air as a
simple means of validating these calculations. Bidleman (1988) compared theoretical
predictions of the fraction of TCDD partitioning to suspended particulate in urban air to the
measurements taken by Eitzer and Hites (1986) in an urban setting as a way of
qualitatively addressing the "reasonableness" of his calculations. He found that the
model's estimation of the fraction of PCDD/Fs bound to particles (based primarily on
adsorption theory and using the sub-cooled liquid vapor pressure of 2,3,7,8-TCDD) did
agree with the ambient measurements of Eitzer and Hites (1986) at 20°C. Therefore the
model was judged to be reasonable in it's estimation of the physical state partitioning of
PCDD/Fs in ambient air.
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Table 3-5. Fraction of dioxins and furans calculated to partition to particles in various
classifications of ambient air using the method of Bidleman (1988), Junge (1977), and
Whitby (1978).
Ambient Air Classification:
Clean Background +
Continental Background Local Sources
Urban
2378-TCDD
12378-PeCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-CDF
12378-PeCDF
23478-PeCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
0.09
0.26
0.60
0.76
0.86
0.85
0.98
0.05
0.14
0.22
0.66
0.64
0.50
0.63
0.73
0.83
0.98
0.26
0.55
0.84
0.92
0.96
0.95
0.99
0.15
0.38
0.50
0.87
0.86
0.78
0.86
0.91
0.94
0.99
0.45
0.74
0.93
0.96
0.98
0.98
1.00
0.29
0.58
0.70
0.94
0.94
0.89
0.93
0.96
0.98
1.00
0.72
0.90
0.98
0.99
0.99
0.99
1.00
0.57
0.82
0.88
0.98
0.98
0.96
0.98
0.99
0.99
1.00
3.2.4.6. Discussion of vapor/particle partitioning
This subsection has reviewed stack testing data, ambient air sampling data, and
theory rooted in basic physical chemistry that either imply, directly deduce or theoretically
calculate the V/P partitioning in the ambient air. From this review it is generally concluded
that:
1. The stack test methods in use today to monitor and measure the concentration
of PCDD/Fs emitted to the air from combustion sources do not provide a credible basis for
assuming the vapor phase and particle bound partitioning at the point of release. There is
no consistent pattern to the interpretation of V/P based on where the PCDD/F segregates
in the instrument, e.g., the glass fiber filter or the XAD resin. Factors that may contribute
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to this are: the relatively long residence time spent traversing the stack interior; the probe
to the instrument is inserted into a relatively hostile environment of the hot combustion
gas; the static temperature of the particulate filter caused by heating the particulate filter
housing; the fact that located between the particulate trap and the vapor trap is a
condensing section consisting of glass tubing surrounded by an ice bath.
2. On the other hand, the ambient air sampling methods do give an approximate
indication of the V/P ratio that seems to be responsive to changes in temperature, and
degree of chlorination of the PCDD/Fs. This is in accordance with what would be
expected from their individual vapor pressures. There is no artificial heating or cooling of
any component of the sampler. The sampler is exposed to actual temperature, pressure,
and humidity of the ambient air. This removes the possibility that the vapor phase-particle
bound partitioning, operationally defined as the compound segregating to the particulate
trap and vapor trap, is actually an artifact induced by artificial heating and cooling within
the system. Therefore the methods present a realistic picture of partitioning under variable
ambient conditions. However, the method has certain limitations that currently prevent
deriving a true measurement of V/P partitioning in the ambient air. Among these
limitations are:
a. The glass fiber filter designed to capture and retain particulate matter has filter
pours down to 0.1 //m diameter. Particles less than this diameter will pass through the
filter and be retained in the polyurethane foam vapor trap downstream. If this is the case,
the amount of PCDD/Fs observed to be particle bound would be underestimated, and the
amount observed to be in vapor phase would be overestimated.
b. The relatively high sampling volume passed through the system (200 to 400 m3
of air per 24 hours) may redistribute the more volatile congeners from the filter to the
absorbent trap by a process known as 'blow-off.
3. Until sampling methods are improved and modified such that they give results
that indicate the true V/P ratio of PCDD/Fs in ambient air, the theoretical construct
described by Bidleman (1988; and detailed above) is used to calculate the V/P ratio for
purposes of air dispersion and deposition modeling of emissions from the hypothetical case
demonstrated in Chapter 5. Key advantages to the theoretical approach are that the
theoretical construct relies on current adsorption theory, considers the molecular weight
and the degree of halogenation of the congeners, uses the boiling points and vapor
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pressures of the congeners, and uses the availability of surface area for adsorption of
atmospheric particles that correspond to a variety of ambient air shed classifications
having variable particulate matter densities.
3.2.5. Estimation of the Concentration of Dioxin-Like Compounds in Incineration Ash
The ash that is collected by the particulate matter control device preceding the
stack is known conventionally as fly ash. Fly ash is the airborne combustion residue from
burning the fuel. Bottom ash is the ash residue that results from the combustion of the
organic solids within the combustion chamber, and usually is collected below a grate
system used to convey combustible fuels into the fire zone, or is collected at the bottom
of the combustion chamber. In general, there are many factors that may influence the
formation of particulate matter known as fly ash from the incineration of organic wastes.
Among these factors are: the heating value of the incinerated material (BTU/kg), the
percent moisture in the fuel, the furnace temperature and combustion efficiency, and the
efficiency of particulate matter capture by the air pollution control device (Brunner, 1984;
OTA, 1989). Fly ash, and not the bottom ash, contains most, if not all, the dioxin-like
congeners. This can be explained by the synthesis of dioxin that occurs on the reactive
surface of fly ash. Therefore, the following estimation of the ash generation rate, and the
concentration of dioxin-like compounds in the ash particles, will focus solely on fly ash to
the exclusion of bottom ash. Because bottom ash is mostly free of these contaminants,
and is about 10 to 100 times the mass of fly ash, the mixing of fly ash with bottom ash
will dilute the concentration of dioxin by about a factor of 10 - 100.
Estimation of the mass of fly ash generated, and concentration of dioxin-like
compounds can be determined by the following (if no actual data exists):
1. Determine the mass of fly ash generated per day at the facility. This can be
estimated from the percent control of particulate matter (PM) of the air pollution control
device (APCD) installed at the facility. For example, if a combustor emits 0.5 kg of
particulate matter per hour of operation, then 1 2 kg of PM is released from the stack in
one day. If PM is controlled by 99%, then this rate of emission represents one percent of
the fly ash generated by the combustion process. The amount of fly ash that is collected
by the APCD would be 100 times the amount emitted, or 1200 kg/day.
2. Estimate the congener-specific concentration of PCDD/Fs contained in the
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collected fly ash. This is done by assuming that what is prevented from exiting the stack
is contained in the fly ash collected by the pollution control device. If, for example, 10
picograms PCDD/F is emitted per gram of PM from the facility per day, and the APCD
reduces emissions by 99%, then 100 times more PCDD/F concentration, or 1000
picograms PCDD/F per gram fly ash, would be in the collected fly ash. If the
concentration of dioxin in emitted fly ash and the percent control of dioxin are known,
then the concentration of dioxin in the mass of collected fly ash can be estimated. It is
important to make such estimations in order to evaluate the potential environmental impact
of ash management practices before the operation of the facility, and to select appropriate
disposal practices to preclude future adverse conditions from arising.
3. Now estimate total mass, including fly and bottom ash, and final
concentrations. If bottom ash mass is estimated at ten times fly ash, than the total ash
generated in this example would be 1200 + 1200*10 = 13,200 kg/day. If fly and
bottom ash were mixed for disposal, which is common, than the average concentration of
the total ash would be one-tenth that estimated for fly ash.
The hypothetical example in Chapter 5 does not assess impacts associated with
ash disposal. Section 4.4.3.1 of Chapter 4 describes procedures for estimating impacts
from ash disposal given ash concentrations and mass generated.
3.3. AIR DISPERSION/DEPOSITION MODELING OF THE STACK GAS EMISSIONS OF
DIOXIN-LIKE COMPOUNDS
It has been customary for EPA to use air dispersion/deposition models to estimate
the atmospheric transport, the deposition flux, and the ambient air concentrations of
specific pollutants attributable to smokestack emissions from an industrial combustion
source. Air dispersion models are mathematical constructs that approximate the physical
and chemical processes occurring in the atmosphere that directly influence the dispersion
of gaseous and particulate emissions from smokestacks of stationary combustion sources.
These models are computer programs encompassing a series of partial differential and
algebraic equations to calculate the dispersion and deposition of the emissions.
Concentration and deposition isopleths of the pollutants discharged from the stack are
computed at specified distances from the smokestack. These quantities are used to
estimate the magnitude of potential exposures to the human receptor.
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Numerous dispersion/deposition models have been developed. This document
focuses on the COMPDEP model. COMPDEP was first described in EPA (1990b). Recent
revisions to the computer code were made, and this newer version was used to generate
the results for the hypothetical incinerator of this assessment. A principal change that
was made allows the user to define 10 particle size categories. Earlier versions allowed
only 3 size categories. Use of COMPDEP is this assessment is not intended to imply that
COMPDEP is the only acceptable model to use in the analysis of ambient air
concentrations, and wet and dry deposition.
Subsection 3.3.1 below presents an overview of the dispersion and deposition
algorithms in the COMPDEP model. Subsection 3.3.2 discusses dry deposition fluxes,
including pertinent assumptions made in the application of the COMPDEP model for the
hypothetical combustor demonstrated in Chapter 5. Subsection 3.3.3 discusses particle
size distributions for emitted particles. Subsection 3.3.4 discusses wet deposition, again
noting key assumptions for the hypothetical combustor. Subsection 3.3.5. closes the
section with guidance indicating that the COMPDEP model should be run twice for
assessments - once to model particle fate and transport, and once for the vapor phase.
3.3.1. Basic Physical Principles Used to Estimate Atmospheric Dispersion/Deposition of
Stack Emissions
Air dispersion/deposition models use the basic physical processes of advection,
turbulent diffusion, and removal to estimate the atmospheric transport, resulting ambient
air concentration, and settling of particles. Advection describes the physical movement of
the air pollutants by the horizontal movement of wind. Turbulent diffusion is the
"spreading" of the emissions plume with distance from the stack due to multi-directional
fluctuations in air movement. Removal refers to mechanisms which remove emissions
from the atmosphere. This can be caused by the force of gravity exerted on the particle
mass, Brownian movement of aerosol particles, and scavenging of particles. Scavenging
is the removal of particles or vapors by precipitation.
COMPDEP contains modifications of the Industrial Source Complex model (Short-
Term version), and COMPLEX I to incorporate algorithms to estimate dispersion, and
resulting ambient air concentrations and wet and dry deposition flux. COMPLEX I is a
second level screening model applicable to stationary combustion sources located in
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complex and rolling topography (EPA, 1986a). The COMPDEP model was developed by
EPA to provide estimates of air concentrations and deposition rates of the stack emissions
of contaminants from industrial sources located in varied terrain (e.g., from simple to
complex terrain). Simple and complex terrain are defined as topogragraphy that is either
below or above the effective stack height of the source (Turner, 1986). To account for
pollutant deposition, the concentration algorithms in COMPLEX 1 were replaced with those
from the Multiple Point Source Algorithm with Terrain Adjustments Including Deposition
and Sedimentation (MPTER-DS) model (Rao and Sutterfield, 1982). The MPTER-DS
algorithms incorporate the gradient transfer theory described by Rao (1981), and are
extensions of the traditional Gaussian plume algorithms. The dispersion algorithms
contained in the Industrial Source Complex, Short-term version (ISCST), have been
incorporated in COMPDEP to analyze ground-level receptors located below the height of
the emission plume (EPA, 1986b). COMPDEP uses the generalized Briggs (1975, 1979)
equation to estimate plume-rise and downwind dispersion as a function of wind speed and
atmospheric stability. A wind-profile exponent law is used to adjust the observed mean
wind speed from the measurement height to the emission height for the plume rise and
pollutant concentration calculations. The Pasquill-Gifford curves are used to calculate
lateral and vertical plume spread (EPA, 1986a). These curves are based on Pasquill's
definitions of atmospheric stability classes, e.g., extremely unstable, moderately unstable,
slightly unstable, neutral, slightly stable, and moderately stable, that correspond to various
intensities of solar radiation and wind speeds (Seinfeld, 1986). The incorporation of these
two basic models into COMPDEP permits analysis of a source located in all types of
terrain.
3.3.2. Estimation of Dry Surface Deposition Flux
Dry deposition is one removal process that is simulated by the COMPDEP model.
Dry deposition refers to the transfer of airborne particulate matter to the earth's surface
(including water, soil, and vegetation) whereby it is removed from the atmosphere. The
deposition of vapor-phase contaminants is not considered in the COMPDEP model. The
general processes controlling the transfer of particulate from some height above the
surface through the surface layer down to the immediate vicinity of the surface are the
forces of gravity and turbulent diffusion (Seinfeld, 1986), followed by diffusion through
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the laminar sub-layer (defined as a thickness of 10'1 to 10'2 cm) to the surface. The rate at
which contaminants sorbed to atmospheric particulates are removed by the physical forces
of gravity, atmospheric turbulence, and diffusion is termed the "deposition flux" (Kapahi,
1991), and is mathematically represented by Fd. The deposition flux, Fd, is a function of
the concentration of the chemical contaminant on particulate, C0, and the settling velocity
of the contaminated particles can be defined by:
fd = Vd C0 (3-4)
where:
Fd = dry deposition flux of contaminants sorbed to particles, /yg/m2-sec
Vd = the particulate settling velocity, m/sec
C0 = concentration of pollutant on settling particles, //g/m3
In general. Chamberlain and Chadwick (1953) first defined the settling velocity, Vd, as the
quotient of the deposition flux, Fd, divided by the airborne concentration, Co:
(3-5)
Sehmel (1980) noted that the value for Fd in Equation (3-5) has a minus sign because the
downward flux is negative, whereas the deposition velocity is positive. By this
relationship, Chamberlain and Chadwick (1953) first introduced the concept of plume
depletion: as the plume emission is dispersed with downwind distance from the stack,
the deposition flux decreases with distance from the source.
The basic dynamics in the physics of modeling dry deposition have not changed
significantly since Sehmel's (1980) comprehensive scientific review. The factors that
most influence the predicted deposition flux can be divided as being either meteorological
influences, or the influences of the properties of the pollutant under analysis.
Meteorological influences include the friction velocity, represented as //e, and the
aerodynamic surface roughness, represented as z0. These terms are used to describe the
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wind speed profile above the Earth's surface. In most cases, the analyst uses a graphical
procedure to determine values for //, and z0. If the logarithm of wind speed is plotted for
near neutral atmospheric stability as a function of height from the surface, then the values
for the constant z0 is fitted to a straight line on a semi-logarithmic scale. This can be
described mathematically by Equation (3-6). In most cases, the friction velocity is a
percentage of the wind speed.
In ( °) (3-6)
z
0
where:
fj = the measured wind speed, cm/sec
//. = the friction velocity, cm/sec
z = the measured height above the surface, cm
z0 = surface roughness length, cm
A = von Karman's constant, approx. = 0.4
As a general rule, particles greater than 30 micrometers (//m) in diameter will be removed
from the atmosphere primarily by the force of gravity, whereas particles less than 30 //m
will be removed primarily by atmospheric turbulence. The deposition flux for the smaller
particles is influenced by many factors, including: the distribution of particles by diameter
and density; assumptions of atmospheric turbulence; the friction of the ground surface and
the height of the stack release of emissions. Deposition flux is also affected by the
partitioning properties of the pollutant. These properties will determine how much of the
pollutant is sorbed to the particle and how much is in the vapor phase. A detailed list of
the many factors that can affect dry deposition is shown in Table 3-6.
The COMPDEP estimates dry deposition flux based on empirical associations
developed by Sehmel (1980) and Sehmel and Hodgson (1978) relating the deposition flux
to the deposition velocity of particles. The downward motion represented by deposition
velocity is controlled by the gravitational settling velocity, atmospheric resistance, surface
resistance and the atmospheric surface friction layer. This model assumes that a fraction
of the particulate comes into contact with the ground surface by the combined processes
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Table 3-6. Factors that influence the dry deposition removal rate in the atmosphere.
Micrometeorological
Variables
Aerodynamic roughness
Mass transfer of
Particles
Gases
Heat transfer
Momentum transfer
Atmospheric stability
Diffusion
Friction velocity
Inversion layer
Pollutant concentration
Relative humidity
Seasonal variation
Solar radiation
Surface heating
Temperature
Terrain effects
Turbulence
Wind velocity
Zero plane
displacement effect
Mass transfer of
Particles
Gases
Heat transfer
Momentum transfer
Characteristics of
Particles
Agglomeration
Diameter
Diffusion effects
Brownian
Eddy
Particle
Momentum
Heat
Electrostatic effects
Attraction
Repulsion
Gravity settling
Hygroscopicity
Impaction
Interception
Momentum
Physical properties
Resuspension
Solubility
Thermophoresis
Characteristics of
Gases
Chemical Activity
Diffusion effects
Brownian
Eddy
Partial pressure in
equilibrium with
the surface
Solubility
Surface
Variables
Accommodation:
Exudates
Trichomes
Pubescence
Wax
Biotic surface
Canopy growth
Dormant
Expanding
Senescent
Canopy structure
Areal density
Bark
Bole
Leaves
Porosity
Soils
Stem
Type
Electrostatic
properties
Water
Pollutant
penetration of
canopy
Source: Adapted from Sehmel (1980).
of gravitational settling, atmospheric turbulence, and Brownian diffusion. The COMPDEP
model contains enhancements to calculate dry deposition flux using a computerized routine
developed by the State of California Air Resources Board (CARB, 1986). The CARS
algorithms represent Sehmel's (1980) empirical relationships for transfer resistances as a
function of particle size, density, surface roughness, and friction velocity. In the CARB
model, integrated resistances to mass transfer are computed within two layers. In the first
layer, which extends from one centimeter to one meter above the surface, atmospheric
turbulence dominates mass transfer. In the second layer, which lies within one centimeter
of the surface, the resistance to mass transfer is derived from particle deposition
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measurements that were taken in a wind tunnel over various surfaces using mono-
dispersed particles (Sehmel, 1980; Sehmel and Hodgson (1978).
Despite what is currently known about the physical and chemical processes that
influence the final deposition flux of particles released from a stationary combustion
source, a more thorough understanding of the influence of particle size on deposition
velocity is needed. In Sehmel's (1980) review of settling velocities corresponding to
particle diameter it was noted that the range of values spanned several orders of
magnitude. This complicates efforts to make generalizations of Vd by particle diameter for
air modelling purposes. Although dry particle deposition velocities have been estimated
from both field studies and laboratory experiments, derived velocities are limited and
highly uncertain. This is due largely to the complex and variable array of factors that can
influence the rate of deposition (as depicted in Table 3-6).
In the general classification of particles, particles < 2.5 micrometers (/ym) in
diameter are referred to a "fine particles", and those > 2.5 /ym are "coarse particles".
Sehmel (1980) offers the most current review of dry deposition settling velocities for a
variety of depositing materials having a broad range of particle diameters. This summary
appears in Table 3-7.
For the example application of the COMPDEP model in Chapter 5, particles less
than 2 jjm were represented by a 1 /ym size and were calculated by COMPDEP to deposit
at a velocity of about 0.007 cm/sec. Particles between 2 and 10 //m were represented by
a 6.78 //m size and were calculated to deposit at a velocity of about 0.3 cm/sec. Finally,
particles greater than 10 //m were represented by a 20 //m size and were calculated to
deposit at a velocity of 2.5 cm/sec, although the variable ambient air temperature resulted
in more variable calculations. The derivation of these particle size representations is given
in the next section.
3.3.3. Estimation of the Particle Size Distribution in the Stack Emissions
Certain inferences must be made concerning the distribution of particulate
differentiated on the basis of particle diameter before the COMPDEP program can predict
deposition flux of the dioxin-like congeners. The diameters of small particles comprising
particulate matter in stack emissions are usually measured in units of one millionth of a
meter (micrometer, commonly called micron, abbreviated by the letters/ym. Unfortunately,
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Table 3-7. A summary of dry deposition velocities for particles.
Depositing
Material
Particles
Pollen
Natural
aerosol
Pb Auto exhaust
Particle D
Diameter (//m)
0.03-30
20
32-35
90-100
1-10
Deposition
Surface
Grassland
Grassland
Grassland
Grassland
Grass shard
Deposition
Velocity
(cm/s)
1 0'3-40
4.5
9.9
20
0.8
Source: Sehmel (1980).
few studies describe the distribution of particulate matter entrained in the emissions from
various combustion technologies broken down and fractionated by particle diameter. The
distribution of particulate matter by particle diameter will differ from one combustion
process to another, and is greatly dependent on such factors as: 1) the efficiency of
various air pollution control devices, 2) the composition of the feed/fuel, 3) the design of
the combustion chamber, 4) the amount of air used to sustain combustion, and 5) the
temperature of combustion. Table 3-8 gives an example of a particle diameter distribution
as measured at a stack on an incinerator. This example distribution will be assumed for
the hypothetical incinerator.
Although the COMPDEP model can simulate up to 10 particle size categories, only
three particle sizes are assumed for the model runs of the demonstration in this
assessment. These three sizes are generalized from the data in Table 3-8:
• Category 1: < 2 jjm • Category 2: > 2 to < 10 /ym • Category 3: > 10 /ym
After selecting the particle size distribution, it is necessary to calculate the mass emission
rate of the particulate-bound congeners of PCDDs/Fs by particle size category. This is
accomplished by calculating the proportion of surface area (available for adsorption of
PCDD/Fs) for a given particle diameter. The ratio of the surface area to volume is
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Table 3-8. Generalized particle size distribution (/vm), and proportion of available surface
area, in particulate emissions from incineration.
Particle
Diameter
Orni)8
>15.0
12.5
8.1
5.5
3.6
2.0
1.1
0.7
<0.7
Particle
Radius
(//m)
7.50
6.25
4.05
2.75
1.80
1.00
0.55
0.40
0.40
Surface
Area/
Volume
0.400
0.480
0.741
1.091
1.667
3.000
5.455
7.500
^.500
Fraction of
Total
Weight
0.128
0.105
0.104
0.073
0.103
0.105
0.082
0.076
0.224
Proportion
Available
Surface
Area
0.0512
0.0504
0.0771
0.0796
0.1717
0.3150
0.4473
0.5700
1.6800
Fraction
of Total
Surface
Area
0.0149
0.0146
0.0224
0.0231
0.0499
0.0915
0.1290
0.1656
0.4880
Total surface area: 3.4423 jum2
Notes: a. Geometric mean diameter in a distribution. Distribution from EPA (1980).
proportional to the ratio of the surface area to weight for a particle with a given radius.
Multiplying this proportion times the weight fraction of particles of a specific diameter
gives a value that approximates the amount of surface area available for chemical
adsorption. The surface area to volume ratio can be described as follows:
(a) Assume aerodynamic spherical particles.
(b) Specific surface area of a spherical particle with radius,r:
S = 4m-2
(c) Volume of spherical particle with radius, r:
V = 4/3 m3
(d) The ratio of surface area to volume is:
S/V = 4 m21 (4/3 m3)
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S/V = 3/r
Dividing the surface area for each particle category by the total available surface area for
all particles gives an estimation of the fraction of total area on any size particle.
Multiplication of the emission rate of the dioxin-like congener times the fraction of
available surface area will estimate the emission rate of the pollutant per particle size. The
fraction of total surface area was computed for the three particle size categories. The
fraction of total surface areas for the ranges of particle diameters are summed with each
particle size category to represent a single fraction of total surface area for the given
particle size category, as follows:
• Paniculate category 1: fraction of total surface area = 0.875
• Paniculate category 2: fraction of total surface area = 0.095
• Paniculate category 3: fraction of total surface area = 0.030
Thus by these assumptions, 87.5% of the emission rate of the dioxin-like congener is
calculated to be associated with particles less than < 2//m in diameter, 9.5% of the
emission is associated with the particle size of > 2 to < 10 //m, and only 3% of the
emission is associated with particles greater than 10//m. To assist in deposition modeling
of the emissions from the hypothetical incinerator, the particle size distribution is further
simplified by assuming a median particle diameter to represent each broad particle size
category, as follows:
• Paniculate category 1 = 1 //m particle diameter
• Paniculate category 2 = 6.78 //m particle diameter
• Paniculate category 3 = 20 //m particle diameter
3.3.4. Estimation of Wet Deposition Flux
Wet deposition occurs by precipitation (rain, hail, snow) physically washing out the
chemically contaminated paniculate and vapors from the atmosphere. Vapor scavenging is
not yet well understood and is not addressed in the COMPDEP model. The remainder of
this discussion refers only to the wet deposition of particles.
Wet deposition flux depends primarily on the fraction of the time precipitation
occurs and the fraction of material removed by precipitation per unit of time by particle
size. Based on these relationships, scavenging coefficients were developed by Cramer
(1986) for varying types and intensities of precipitation relative to different particle
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diameters by incorporating the observations of Radke, et al. (1980) in a study of
scavenging of aerosol particles by precipitation. The principal assumptions made in
computing wet deposition flux are: (1) The intensity of precipitation is constant over the
entire path between the source and the receptor; (2) The precipitation originates at a level
above the top of the emission plume so that the precipitation passes vertically through the
entire plume; (3) The flux is computed on the bases of fraction of the hour precipitation
occurs as determined by hourly precipitation measurements compiled by the National
Weather Service. The remaining fraction (1-f) is subject only to dry deposition processes.
Thus no dry deposition occurs during hours of steady precipitation, and dry deposition
occurs between the periods of precipitation. Wet deposition flux is estimated using
Equation (3-7), in which case the total weight of material in settling category n results
from the washout by rain at a distance x downwind from the stack release:
WDep(n) = f
exp (-1/2 (-Ł)
(3-7)
where:
WDep(n)
f
K
Q
°v
u(h)
x
y
wet deposition flux, g/m2-sec
fraction of time precipitation occurs (fraction of hour)
fraction of material removed per unit time in the nth
particle size category and jth precipitation intensity category,
also known as the scavenging coefficient, sec"1
fraction of total source material in the nth particle size
category
units conversion factor
emission rate, g/sec
standard deviation of crosswind deposition distribution, m
wind speed at stack height, m/sec
downwind distance from source to receptor, m
crosswind distance from source to receptor, m
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The relationship between the scavenging coefficient, A(n,j), and the particle size and
precipitation intensity was derived from the review of wet deposition studies of aerosol
particles by Cramer (1986). Table 3-9 displays the scavenging coefficients assigned to the
generalized particle size categories and precipitation events used for computing estimates
of wet deposition in the application of the COMPDEP for the demonstration scenarios in
Chapter 5. The value of f in Equation (3-7) is applied to each hour of reported precipitation
as determined from the National Weather Service meteorological observations. In
computing the dry deposition which occurs between the periods of precipitation, a factor
of (1-f) was used to estimate the fraction of the material that is subject to dry deposition.
The same scavenging coefficient was used for both rainfall and snowfall.
3.3.5. The Requirement to Run the COMPDEP Model Twice
In order to provide estimates of ambient air concentrations of vapor-phase and
particle-phase dioxins, combined with estimates of wet/dry particle deposition flux, it is
necessary to run the COMPDEP model twice. Both model runs should assume a "unit
emissions release rate", e.g., 1 g/s. Results from these unit runs can easily be
transformed to final outputs given assumptions on emissions in vapor and particle forms.
Two assumptions are required, as outlined above. One is the total emission rate of the
compound, in units of mass/time (g/sec), and the second is the vapor/particle partitioning
of this total emission. The two runs are:
• Run 1: To estimate vapor-phase concentration of the contaminant in ambient air.
COMPDEP should be run with the wet/dry deposition switches turned to the "off"
position. This is to isolate the ambient air concentration of the contaminant in vapor-phase
from the calculation of wet and dry particle deposition flux. This inactivates a plume
depletion equation that subtracts out losses in ambient air concentration due to particle
deposition. What is left are the Gaussian dispersion algorithms.
With the "unitized" emission rate, one can reconstruct the actual predicted ambient
air concentration (fjg/m3) of vapors by multiplying the "actual" vapor-phase emission rate
(g/s) by the "unitized" modeling result. For example, let the actual stack gas emission rate
of total (vapor plus particle components) contaminant be 1x10'5 g/s, and the V/P ratio
(expected under ambient conditions) be 60%V/40%P. Then the "actual" emission rate of
the vapor-phase portion of the contaminant is calculated to be 6x10"6 g/s (1x10~5 g/s *
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Table 3-9. Wet deposition scavenging coefficients per particle diameter category
(micrometers), expressed per second of time.
Particle
Precipitation
Intensity
Heavy
Moderate
Light
1
5
2
< 2
.46
.60
.20
E-3
E-3
E-4
Diameter Cateaorv (urn)
> 2
4
8
1
and
.64
.93
.80
< 10
E-3
E-4
E-4
9
9
9
>
.69
.69
.69
10
E-3
E-3
E-3
Notes: Light = trace to 0.10 in/hr; Moderate = 0.11 to 0.30 in/hr;
Heavy = > 0.30 in/hr
0.6). If the "unitized" ambient air concentration at the ground-level receptor is estimated
by the COMPDEP model to be 1x10"8//g/m3 (i.e., this concentration is predicted with a unit
emission rate of 1 g/s), then the "actual" predicted air concentration at that receptor can
be estimated as:
(6x10'8g/s + 1 g/s) * 1x10-8yug/m3 = 6x10-u/yg/m3
• Run 2: To estimate wet and dry particle deposition flux, and the ambient air
concentration of the contaminant that is particle-bound.
COMPDEP should be run with the wet/dry particle deposition switches turned to the
"on" position, and using a "unit emission rate" of 1 g/s. This second run is considered a
simulation of particle-bound contaminant only. Outputs of this run include unitized
deposition rale and unitized ambient air concentrations of particles.
Like tlie vapor-phase run, the "actual" deposition flux (g/m~2 -yr) and "actual"
particle-phase airborne concentrations can then be determined by multiplying the "actual"
emission rate (g/s) of the particle-bound portion of the total contaminant emissions by the
"unitized" modeling result at the ground receptor. For example, let the "actual" emission
rate of the particle-bound portion of the contaminant be 4x10"6 g/s, and the "unitized" dry
deposition flux at the ground receptor be 1x10~5 g/m2-yr. Then the "actual" predicted dry
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deposition flux is 4x10'11 g/m2 (4x10'8 g/s + 1 g/s * 1x10'5 g/m2-yr). Using this same
procedure, this second run provides the airborne concentration of contaminants bound to
particles (//g/m3).
Inhalation exposures are estimated as the sum of vapor and particle phase
concentrations. Air-to-plant transfers require the vapor phase concentrations for vapor
transfers and the particle-phase depositions. The air-to-soil algorithm requires particle
phase depositions.
3.4. RESULTS OF THE AIR DISPERSION MODELING OF CONGENER-SPECIFIC
EMISSIONS FROM THE HYPOTHETICAL ORGANIC WASTE INCINERATOR
The preceding subsections have presented general procedures for conducting air
modeling of the emissions of dioxin-like compounds from the stack to the ground, starting
with estimation of emission factors, vapor/particle partitioning at the stack, and proceeding
to atmospheric dispersion and deposition using EPA's COMPDEP model. Where
appropriate, previous subsections have also included discussion on the assumptions and
the selection of parameters for the hypothetical incinerator which is demonstrated in
Chapter 5. For example, Section 3.2.3 provided the emission factors that were used in
this demonstration. This section will provide all other details of the hypothetical
incinerator and provide the final results of the COMPDEP model simulations.
To reiterate, the purpose of the hypothetical construct is to help readers understand
how to apply these principles to the air dispersion modeling and analysis of dioxin
emissions from the source. Therefore, generalizations should not be made on the basis of
this example regarding the magnitude of the emissions release and associated
environmental impact.
A completely hypothetical incinerator was devised to serve as the example.
Accordingly, a hypothetical, but realistic, incineration technology, facility size, stack
height, and geographical location was selected. The hypothetical incineration facility has
an assumed total daily capacity of 200 metric tons of organic waste materials. The
emission rates of specific congeners of PCDD/Fs were derived from the stack testing and
monitoring of emissions from a modern incinerator of this size. These emissions are
expressed in units of g/sec, and are shown for the hypothetical incinerator in Table 3-10.
In constructing the hypothetical case, the following was defined: stack height;
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Table 3-10. Emission of PCDD/Fs (g/sec) from the hypothetical incinerator.
Congener
2378-TCDD
Other TCDD
12378-PeCDD
Other PeCDD
123478-HxCDD
123789-HxCDD
123678-HxCDD
Other HxCDD
1234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
12378-PeCDF
23478-PeCDF
Other PeCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
Emissions
grams/sec
9.23E-11
2.00E-09
1.93E-10
1.91E-09
2.50E-10
3.63E-10
3.30E-10
3.19E-09
3.27E-09
6.03E-09
6.73E-09
6.03E-09
3.83E-08
3.87E-10
6.33E-10
1 .09E-08
9.20E-10
8.70E-10
5.73E-10
3.33E-10
2.10E-09
1.15E-09
5.00E-10
5.35E-09
2.23E-09
Vapor
grams/sec
5.08E-1 1
1.10E-09
5.02E-11
4.97E-10
1.75E-11
7.26E-12
1.32E-11
1.28E-10
6.54E-11
1.21E-10
6.73E-11
4.28E-09
2.72E-08
1.63E-10
1.90E-10
3.92E-09
5.52E-11
5.22E-11
6.30E-11
2.33E-11
1.47E-10
4.60E-11
1 .OOE-1 1
1.61E-10
2.23E-11
Particulate
grams/sec
4.15E-11
9.00E-10
1.43E-10
1.41E-09
2.33E-10
3.56E-10
3.17E-10
3.06E-09
3.20E-09
5.91E-09
6.66E-09
1.75E-09
1.11E-08
2.24E-10
4.43E-10
6.98E-09
8.65E-10
8.18E-10
5.10E-10
3.10E-10
1 .95E-09
1.10E-09
4.90E-10
5.19E-09
2.21E-09
V/P
Ratio
.557.45
.557.45
.267.74
.267.74
.07/.93
.02/.98
.04/.96
.04/.96
.02/.98
.02/.98
.01 /.99
.71/.29
.71/.29
.427.58
.30/.70
.367.64
.067.94
.067.94
.117.89
.077.93
.077.93
.047.96
.027.98
.037.97
.017.99
stack diameter; exit velocity of the gaseous emissions from the stack; and temperature of
the exhaust gases characteristic of incineration facilities of this size. In order to access
historical meteorological data for air modeling purposes, the hypothetical facility was
located in a specific geographical area having specific meteorological conditions. To
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simplify the ambient air modeling and deposition, the hypothetical organic waste
incinerator was assumed to exist in a simple terrain setting (e.g., flat terrain). By
definition, simple terrain refers to an area where the terrain features are all lower in
elevation than the top of the stack of the stationary source under analysis.
The dispersion and deposition computations performed by the COMPDEP model
require data on wind speed, wind direction, wind profile above the surface, and hourly
precipitation data. When performing a regulatory analysis, e.g., to set air quality permit
conditions, EPA's Guideline on Air Quality Models (EPA, 1986a) recommends the use of
five consecutive years of representative meteorological data. However, in this example
analysis only one year of meteorological data was used as compiled at the Denver-
Stapleton International Airport by the National Weather Service (NWS), because this was
not intended as a regulatory analysis. Hourly measurements of wind speed, wind
direction, temperature, and precipitation were used as a basis of computing annual average
ground-level concentrations of dioxin in ambient air, and as a basis for the estimation of
the dry and wet deposition flux. The Pasquill-Gifford (P-G) stability categories, were used
as defined in the Modeling Guidelines. The specifications of stability categories depending
on wind speed, cloud cover and mixing heights were established by Pasquill (1961), and
later modified by Turner (1964). Reference is made here to Tables 9-3 and 9-4 on pages
9-21,22 of the Modeling Guidelines which gives a method for estimating P-G Stability
Categories for daytime and nighttime conditions based on surface roughness and the wind
speed profiles distributed in the United States.
To summarize, inputs for the COMPDEP model included hourly meteorological data,
source characteristics and receptor features. Hourly meteorological data requirements are
the mean wind speed, the direction from which the wind is blowing, the wind-profile
exponent, the ambient air temperature, the Pasquill stability category, the vertical potential
temperature gradient with height, the mixing layer height, and the frequency distribution of
hourly precipitation. Source input data requirements included the congener-specific mass
emission rate partitioned by vapor and particulate; the physical stack measurements, e.g.,
diameter, base elevation of the stack, and exit velocity and temperature of the stack gas,
and settling parameters for particulate matter for both dry and wet deposition. Table 3-10
is a review of the congener-specific emissions data, and Table 3-11 is a review of the
modeling parameters used in the air quality modeling of the hypothetical incinerator
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Table 3-11. Modeling parameters used in the COMPDEP modeling of PCDD/F emissions
from the hypothetical incinerator.
Technology: Modeled after an actual organic waste incinerator
Rate of organic waste combustion: 200 metric tons per day
Air Pollution Control System:
Stack Height:
Stack Diameter:
Anemometer Height:
Terrain:
Assumed to consist of dry scrubber combined with a baghouse
(fabric filters) resulting in 99% reduction and control of CDDs/CDFs.
30.48 m
1.52 m
10m
Assumed simple
Stack Temp.:
Stack Exit V:
Source elevation:
Z min.
400° Kelvin
8.9 m s°
0 m
10 m
Modeled Options:
Terrain adjustments
Stack downwash
Gradual plume rise
Buoyancy induced dispersion
Gravitational settling/deposition
Wet Deposition
Calm winds processing option
Building wake effects
Exponents for power law wind increase with height:
Yes
No
Yes
Yes
Yes
Yes
Yes
No
0.07
0.07
0.10
0.15
0.35
0.55
Particle size categories for dry/wet deposition analysis:
Category 1: s2 fjm represented by 1.0//m diameter
Category 2: >2-s10//m represented by 6.78 //m
Category 3: >10//m represented by 20.0 fjm
Particle density: 1.4 g cm'3
Fraction of CDD/CDF particle bound emission by particle size category:
Category 1: 0.88
Category 2: 0.09
Category 3: 0.03
Example of dry deposition settling velocities predicted by the CARS algorithm:
1.0 fjm:
6.78 fjm
20 urn:
Wet deposition scavenging coefficients:
National Weather Service data:
Grid system:
7.11 E-3 cm s'1
2.87 E-1 cm s'1
2.47 cm s'1
Table 3-9
Denver-Stapleton Airport; 1989
Polar every 22.5°
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The output of the COMPDEP model for both surface deposition and ambient air
impacts is a concentration array for 160 ground-level receptors around the incinerator,
e.g., 10 receptor points along each of the 16 wind directions every 22.5° on the polar
azimuth. Vapor and particle phase concentrations are in units of grams per cubic meter of
air (g/m3), and particle-bound depositions are in units of grams per square meter of surface
area per year (g/m2-yr). Results for both ambient air and surface deposition were
estimated at concentric radial distances from the incinerator of 0.2, 0.5, 0.8, 0.9, 1.0,
2.0, 5.0, 10, 20, 30, 40, and 50 kilometers. The maximum annual average ground-level
vapor and particle-phase air concentrations of all modeled congeners is estimated to occur
900 meters from the center of the stack. Tables 3-12, 3-13, and 3-14 display the annual
average vapor-phase, particle-phase, and total (vapor + particle) air concentrations of
dioxin-like congeners at various distances in the direction of the maximum impact. Tables
3-15, 3-16, and 3-17 display the dry, wet, and total (dry + wet) deposition fluxes of dioxin-
like compounds at various distances in the direction of maximum impact. The maximum
annual average dry deposition flux occurs 800 meters from the center of the stack,
although there is no significant difference from the 900 m distance where the maximum
annual average ambient air concentration occurs. The maximum annual average wet
deposition occurs 200 meters from the center of the stack, which is what is expected
from the algorithm (refer to subsection 3.3.4. Estimation of Wet Deposition Flux).
3.5. REVIEW OF PROCEDURES FOR ESTIMATING SITE-SPECIFIC IMPACTS FROM A
STACK EMISSION SOURCE
This chapter has detailed a procedure for evaluating site-specific impacts from stack
emission sources. For purposes of demonstration, a hypothetical incinerator was defined,
and using the COMPDEP model, estimates of vapor-phase concentrations and particle
phase depositions at points around the stack were made. Three major points for
estimating impacts of dioxin-like compounds using the COMPDEP or other models are as
follows:
1. Characterize the emissions on a congener-specific basis: Although much of the
information available on stack emission sources in on a TEQ or a homologue group basis,
and not a congener-specific basis, the approach in this assessment, and the
recommendation made here, is to conduct site-specific assessments using specific
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Table 3-12. Predicted annual average vapor-phase concentrations of PCDD/Fs (g/m3).
Downwind
distance, km
2378-TCDD
Other TCDD
1 2378-PeCDD
Other PeCDO
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
Other HxCDD
1 234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
1 2378-PeCDF
23478- PeCDF
Other PeCDF
1 23478-HxCDF
1 23678-HxCDF
1 23789-HxCDF
234678-HxCDF
Other HxCDF
1 234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
0.2
4.68E-19
1.01E-17
4.63E-19
4.58E-18
1.61E-19
6.69E-20
1.22E-19
1.18E-18
6.03E-19
1.11E-18
6.20E-19
3.95E-17
2.51E-16
1.50E-18
1.75E-18
3.62E-17
5.09E-19
4.81E-19
5.81E-19
2.15E-19
1.36E-18
4.24E-19
9.22E-20
1.48E-18
2.06E-19
0.5
7.57E-18
1.64E-16
7.48E-18
7.40E-17
2.61E-18
1.08E-18
1.97E-18
1.90E-17
9.75E-18
1.80E-17
1.00E-17
6.38E-16
4.05E-15
2.42E-17
2.83E-17
5.85E-16
8.23E-18
7.78E-18
9.40E-18
3.48E-18
2.19E-17
6.86E-18
1.49E-18
2.39E-17
3.32E-18
0.8
9.85E-18
2.13E-16
9.73E-18
9.63E-17
3.40E-18
1.41E-18
2.56E-18
2.48E-17
1.27E-17
2.34E-17
1.31E-17
8.31 E-1 6
5.28E-15
3.15E-17
3.68E-17
7.61 E-1 6
1.07E-17
1.01 E-1 7
1.22E-17
4.52E-18
2.85E-17
8.92E-18
1.94E-18
3.11E-17
4.33E-18
0.9
9.91E-18
2.15E-16
9.80E-18
9.69E-17
3.42E-18
1.42E-18
2.58E-18
2.49E-17
1.28E-17
2.35E-17
1.31 E-1 7
8.36E-16
5.31E-15
3.17E-17
3.71E-17
7.66E-16
1.08E-17
1.02E-17
1.23E-17
4.55E-18
2.87E-17
8.98E-18
1.95E-18
3.13E-17
4.35E-18
1
9.81E-18
2.13E-16
9.69E-18
9.59E-17
3.38E-18
1.40E-18
2.55E-18
2.47E-17
1.26E-17
2.33E-17
1.30E-17
8.27E-16
5.25E-15
3.14E-17
3.67E-17
7.58E-16
1.07E-17
1.01 E-1 7
1.22E-17
4.50E-18
2.84E-17
8.89E-18
1.93E-18
3.10E-17
4.31 E-1 8
2
6.53E-18
1.42E-16
6.46E-18
6.39E-17
2.25E-18
9.34E-19
1.70E-18
1.64E-17
8.42E-18
1.55E-17
8.66E-1 8
5.51 E-1 6
3.50E-15
2.09E-17
2.44E-17
5.05E-16
7.10E-18
6.72E-18
8. 11 E-1 8
3.00E-18
1.89E-17
5.92E-18
1.29E-18
2.07E-17
2.87E-18
5
2.69E-18
5.82E-17
2.66E-18
2.63E-17
9.27E-19
3.84E-19
6.99E-19
6.76E-18
3.46E-18
6.39E-18
3.56E-18
2.27E-16
1.44E-15
8.61 E-1 8
1.01 E-1 7
2.08E-16
2.92E-18
2.76E-18
3.34E-18
1.23E-18
7.78E-18
2.44E-18
5.30E-19
8.50E-18
1.18E-18
10
1.18E-18
2.56E-17
1.17E-18
1.16E-17
4.07E-19
1.69E-19
3.07E-19
2.97E-18
1.52E-18
2.81 E-18
1.57E-18
9.96E-17
6.33E-16
3.78E-18
4.42E-18
9.13E-17
1.28E-18
1.21E-18
1.47E-18
5.42E-19
3.42E-18
1.07E-18
2.33E-19
3.73E-18
5.19E-19
20
4.93E-19
1.07E-17
4.87E-19
4.82E-18
1.70E-19
7.05E-20
1.28E-19
1.24E-18
6.35E-19
1.17E-18
6.54E-19
4.16E-17
2.64E-16
1.58E-18
1.84E-18
3.81 E-1 7
5.36E-19
5.07E-19
6.12E-19
2.26E-19
1.43E-18
4.47E-19
9.71 E- 20
1.56E-18
2.17E-19
30
2.94E-19
8.74E-18
1.39E-18
1.37E-17
2.26E-18
3.46E-1 8
3.08E-18
2.97E-17
3.11E-17
5.74E-17
6.47E-17
1.70E-17
1.08E-16
2.18E-18
4.30E-18
6.78E-17
8.40E-18
7.94E-18
4.95E-18
3.01 E-1 8
1.90E-17
1.07E-17
4.76E-18
5.04E-17
2.14E-17
40
2.05E-19
4.45E-18
2.03E-19
2.01 E-1 8
7.08E-20
2.94E-20
5.34E-20
5.16E-19
2.65E-19
4.88E-19
2.72E-19
1.73E-17
1.10E-16
6.58E-19
7.69E-19
1.59E-17
2.23E-19
2.11E-19
2.55E-19
9.43E-20
5.95E-19
1.86E-19
4.05E-20
6.50E-19
9.02E-20
50
1.56E-19
3.38E-18
1.54E-19
1.53E-18
5.38E-20
2.23E-20
4.06E-20
3.92E-19
2.01 E-1 9
3.71E-19
2.07E-19
1.32E-17
8.36E-17
5.00E-19
5.84E-19
1.21E-17
1.70E-19
1.61 E-1 9
1.94E-19
7.17E-20
4.52E-19
1.41 E-1 9
3.08E-20
4.94E-19
6.86E-20
3-63
4/94
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-13. Predicted annual average particle-phase air concentrations of PCDD/Fs (g/m3).
Downwind
distance, km
2378-TCDD
Other TCDD
1 2378-PeCDD
Other PeCDD
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
Other HxCDD
1 234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
1 2378-PeCDF
23478- PeCDF
Other PeCDF
1 23478-HxCDF
1 23678-HxCDF
1 23789-HxCDF
234678-HxCDF
Other HxCDF
1 234678-HpCDF
1 234789-HpCDF
Other HpCDF
OCDF
0.2
2.27E-22
4.92E-21
7.81 E-22
7.73E-21
1.27E-21
1.94E-21
1.73E-21
1.67E-20
1.75E-20
3.23E-20
3.64E-20
9.56E-21
6.07E-20
1.23E-21
2.42E-21
3.81 E-20
4.73E-21
4.47E-21
2.79E-21
1.69E-21
1 .07E-20
6.04E-21
2.68E-21
2.84E-20
1.21E-20
0.5
6.18E-18
1.34E-16
2.13E-17
2.1OE-16
3.46E-17
5.30E-17
4.72E-17
4.56E-16
4.77E-16
8.8OE-16
9.92E-16
2.6OE-16
1.65E-15
3.34E-17
6.6OE-17
1.04E-15
1.29E-16
1.22E-16
7.59E-17
4.61 E-1 7
2.91E-16
1.64E-16
7.30E-17
7.73E-16
3.29E-16
0.8
8.03E-18
1.74E-16
2.76E-17
2.73E-16
4.50E-17
6.88E-17
6.13E-17
5.92E-16
6.20E-16
1.14E-15
1.29E-15
3.38E-16
2.15E-15
4.34E-17
8.57E-17
1.35E-15
1.67E-16
1.58E-16
9.86E-17
5.99E-17
3.78E-16
2.14E-16
9.48E-17
1.00E-15
4.27E-16
0.9
8.08E-18
1.75E-16
2.78E-17
2.75E-16
4.52E-17
6.92E-17
6.16E-17
5.96E-16
6.23E-16
1.15E-15
1.30E-15
3.40E-1 6
2.16E-15
4.37E-17
8.62E-17
1.36E-15
1.68E-16
1.59E-16
9.92E-17
6.02E-17
3.80E-16
2.15E-16
9.53E-17
1.01 E-1 5
4.29E-16
1
8.OOE-18
1.73E-16
2.75E-17
2.72E-16
4.48E-17
6.85E-17
6.10E-17
5.90E-16
6.17E-16
1.14E-15
1.28E-15
3.37E-16
2.14E-15
4.32E-17
8.53E-17
1.34E-15
1.66E-16
1.57E-16
9.82E-17
5.96E-17
3.76E-16
2.13E-16
9.43E-17
9.99E-16
4.25E-16
2
5.31 E-1 8
1.15E-16
1.83E-17
1.81 E-1 6
2.97E-17
4.55E-17
4.05E-17
3.91E-16
4.10E-16
7.55E-16
8.51E-16
2.23E-16
1.42E-15
2.87E-17
5.66E-17
8.92E-16
1.11E-16
1.05E-16
6.52E-17
3.96E-17
2.50E-1 6
1.41 E-1 6
6.26E-17
6.63E-16
2.82E-16
5
2.16E-18
4.68E-17
7.43E-18
7.35E-17
1.21E-17
1.85E-17
1.65E-17
1.59E-16
1.67E-16
3.07E-16
3.46E-16
9.09E-17
5.77E-16
1.17E-17
2.30E-17
3.63E-16
4.50E-17
4.25E-17
2.65E-17
1.61E-17
1.02E-16
5.74E-17
2.55E-17
2.70E-16
1.15E-16
10
9.32E-19
2.02E-17
3.20E-18
3.17E-17
5.22E-18
7.98E-18
7.11E-18
6.87E-17
7.19E-17
1.33E-16
1.50E-16
3.92E-17
2.49E-16
5.04E-18
9.94E-18
1.57E-16
1.94E-17
1.84E-17
1.14E-17
6.95E-18
4.38E-17
2.48E-17
1.10E-17
1.16E-16
4.95E-17
20
3.78E-19
8.20E-18
1.30E-18
1.29E-17
2.12E-18
3.24E-18
2.89E-18
2.79E-17
2.92E-17
5.38E-17
6.07E-17
1.59E-17
1.01 E-1 6
2.04E-18
4.04E-18
6.35E-17
7.88E-18
7.45E-18
4.64E-18
2.82E-18
1.78E-17
1.01 E-1 7
4.46E-18
4.73E-17
2.01E-17
30
2.21E-19
4.79E-18
7.60E-19
7.52E-18
1.24E-18
1.89E-18
1.69E-18
1.63E-17
1.71E-17
3.15E-17
3.55E-17
9. 31 E-1 8
5.91E-17
1.20E-18
2.36E-18
3.71 E-1 7
4.60E-18
4.35E-18
2.72E-18
1.65E-18
1.04E-17
5.88E-18
2.61 E-1 8
2.76E-17
1.18E-17
40
1.52E-19
3.30E-18
5.23E-19
5.18E-18
8.52E-19
1.30E-18
1.16E-18
1.12E-17
1.17E-17
2.17E-17
2.44E-17
6.41E-18
4.07E-17
8.22E-19
1.62E-18
2.56E-17
3.17E-18
3.00E-18
1.87E-18
1.13E-18
7.16E-18
4.05E-18
1.80E-18
1.90E-17
8.09E-18
50
1.14E-19
2.47E-18
3.92E-19
3.88E-18
6.38E-19
9.77E-19
8.70E-19
8.41 E-1 8
8.8OE-18
1.62E-17
1.83E-17
4.8OE-18
3.05E-17
6.16E-19
1.22E-18
1.92E-17
2.37E-18
2.25E-18
1.40E-18
8.50E-19
5.36E-18
3.03E-18
1.35E-18
1.43E-17
6.06E-18
3-64
4/94
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-14. Predicted total (vapor + particle) ambient air concentrations of PCDD/s (g/m3).
Downwind
distance, km
2378-TCDD
Other TCDD
1 2378-PeCDD
Other PeCDD
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
Other HxCDD
1 234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
1 2378-PeCDF
23478- PeCDF
Other PeCDF
1 23478-HxCDF
1 23678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1 234678-HpCDF
1 234789-HpCDF
Other HpCDF
OCDF
0.2
4.68E-19
1.01E-17
4.63E-19
4.59E-18
1.63E-19
6.89E-20
1.23E-19
1.19E-18
6.20E-19
1.14E-18
6.57E-19
3.95E-17
2.51E-16
1.50E-18
1.75E-18
3.62E-17
5.14E-19
4.86E-19
5.84E-19
2.17E-19
1.37E-18
4.30E-19
9.49E-20
1.51E-18
2.18E-19
0.5
1.38E-17
2.98E-16
2.87E-17
2.84E-16
3.72E-17
5.41 E-1 7
4.91E-17
4.75E-16
4.87E-16
8.98E-16
1.00E-15
8.99E-16
5.71 E-1 5
5.77E-17
9.43E-17
1.62E-15
1.37E-16
1.30E-16
8.53E-17
4.96E-17
3.13E-16
1.71E-16
7.45E-17
7.97E-16
3.32E-16
0.8
1.79E-17
3.87E-16
3.74E-17
3.70E-16
4.84E-17
7.02E-17
6.38E-1 7
6.17E-16
6.32E-16
1.17E-15
1.30E-15
1.17E-15
7.42E-15
7.49E-17
1.23E-16
2.11E-15
1.78E-16
1.68E-16
1.11E-16
6.44E-1 7
4.06E-16
2.22E-16
9.67E-17
1.03E-15
4.31E-16
0.9
1.80E-17
3.90E-16
3.76E-17
3.72E-16
4.86E-17
7.06E-17
6.42E-17
6.21E-16
6.36E-16
1.17E-15
1.31E-15
1.18E-15
7.47E-15
7.54E-17
1.23E-16
2.12E-15
1.79E-16
1.69E-16
1.11E-16
6.48E-17
4.09E-16
2.24E-16
9.73E-17
1.04E-15
4.34E-16
1
1.78E-17
3.86E-16
3.72E-17
3.68E-16
4.81E-17
6.99E-17
6.35E-17
6.14E-16
6.30E-16
1.16E-15
1.30E-15
1.16E-15
7.39E-15
7.46E-17
1.22E-16
2.10E-15
1.77E-16
1.68E-16
1.10E-16
6.41 E-1 7
4.04E-16
2.21E-16
9.63E-17
1.03E-15
4.29E-16
2
1.18E-17
2.57E-16
2.47E-17
2.45E-16
3.20E-17
4.64E-17
4.22E-17
4.08E-16
4.18E-16
7.71E-16
8.60E-16
7.74E-16
4.92E-15
4.96E-17
8.11E-17
1 .40E-1 5
1.18E-16
1.11E-16
7.33E-17
4.26E-17
2.69E-16
1.47E-16
6.39E-17
6.84E-16
2.85E-16
5
4.85E-1 8
1.05E-16
1.01 E-1 7
9.98E-17
1.30E-17
1.89E-17
1.72E-17
1.66E-16
1.70E-16
3.14E-16
3.50E-16
3.18E-16
2.02E-15
2.03E-17
3.31E-17
5.70E-16
4.79E-17
4.53E-17
2.99E-17
1.73E-17
1.09E-16
5.98E-17
2.60E-17
2.78E-16
1.16E-16
10
2.11E-18
4.58E-17
4.37E-18
4.33E-17
5.62E-18
8.15E-18
7.42E-18
7.17E-17
7.34E-17
1.35E-16
1.51 E-1 6
1.39E-16
8.82E-16
8.82E-18
1.44E-17
2.48E-16
2.07E-17
1.96E-17
1.29E-17
7.49E-18
4.72E-17
2.58E-17
1.12E-17
1.20E-16
5.01E-17
20
8.71E-19
1.89E-17
1.79E-18
1.77E-17
2.29E-18
3.31E-18
3.01 E-1 8
2.91E-17
2.98E-17
5.50E-17
6.13E-17
5.75E-17
3.65E-16
3.62E-18
5.88E-18
1.O2E-16
8.41E-18
7.96E-18
5.26E-18
3.05E-18
1.92E-17
1.05E-17
4.56E-18
4.88E-17
2.03E-17
30
5.15E-19
1.35E-17
2.15E-18
2.13E-17
3.50E-18
5.35E-18
4.76E-18
4.60E-17
4.82E-17
8.89E-17
1.00E-16
2.63E-17
1.67E-16
3.38E-18
6.66E-18
1.05E-16
1.30E-17
1.23E-17
7.67E-18
4.66E-18
2.94E-17
1.66E-17
7.37E-18
7.80E-17
3.32E-17
40
3.58E-19
7.75E-18
7.26E-19
7.19E-18
9.23E-19
1.33E-18
1.21E-18
1.17E-17
1.20E-17
2.21E-17
2.47E-17
2.37E-17
1.51E-16
1.48E-18
2.39E-18
4.14E-17
3.39E-18
3.21E-18
2.12E-18
1.23E-18
7.75E-18
4.23E-18
1.84E-18
1.97E-17
8.18E-18
50
2.70E-19
5.85E-18
5.46E-19
5.41 E-1 8
6.92E-19
9.99E-19
9.11E-19
8.80E-18
9.OOE-18
1.66E-17
1.85E-17
1.80E-17
1.14E-16
1.12E-18
1.80E-18
3.12E-17
2.54E-18
2.41E-18
1.59E-18
9.22E-19
5.81 E-1 8
3.17E-18
1.38E-18
1.47E-17
6.13E-18
3-65
4/94
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-15. Predicted annual dry deposition fluxes of particle-bound PCDD/Fs (g/m2-yr).
Downwind
distance, km
2378-TCDD
Other TCDD
1 2378-PeCDD
Other PeCDD
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
Other HxCDD
1 234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
1 2378-PeCDF
23478-PeCDF
Other PeCDF
1 23478-HxCDF
1 23678-HxCDF
1 23789-HxCDF
234678-HxCDF
Other HxCDF
1 234678-HpCDF
1 234789-HpCDF
Other HpCDF
OCDF
0.2
3.20E-14
6.93E-13
1.10E-13
1.09E-12
1.79E-13
2.74E-13
2.44E-13
2.36E-12
2.47E-12
4.55E-12
5.13E-12
1.35E-12
8.55E-12
1.73E-13
3.41 E-1 3
5.37E-12
6.66E-13
6.30E-13
3.93E-13
2.38E-13
1.50E-12
8.50E-13
3.77E-13
3.99E-12
1 .70E-1 2
0.5
4.38E-13
9.49E-12
1.51E-12
1.49E-11
2.45E-12
3.75E-12
3.34E-12
3.23E-11
3.38E-1 1
6.23E-11
7.02E-11
1.84E-11
1.17E-10
2.37E-12
4.67E-12
7.35E-11
9.11E-12
8.62E-12
5.38E-12
3.26E-12
2.06E-11
1.16E-11
5.16E-12
5.47E-1 1
2.33E-11
0.8
5.30E-13
1.15E-11
1.82E-12
1.80E-11
2.97E-12
4.54E-12
4.05E-12
3.91E-11
4.09E-11
7.55E-11
8.51E-11
2.23E-11
1.42E-10
2.87E-12
5.66E-12
8.91E-11
1.10E-11
1.04E-11
6.51E-12
3.95E-12
2.49E-11
1.41E-11
6.26E-12
6.63E-11
2.82E-11
0.9
5.23E-13
1.13E-11
1.80E-12
1.78E-11
2.93E-12
4.48E-12
3.99E-12
3.86E-11
4.03E-1 1
7.44E-11
8.39E-11
2.20E-11
1.40E-10
2.83E-12
5.58E-12
8.78E-1 1
1.09E-11
1.03E-11
6.42E-12
3.90E-12
2.46E-1 1
1.39E-11
6.17E-12
6.53E-11
2.78E-11
1
5.08E-13
1.10E-11
1.75E-12
1.73E-11
2.84E-12
4.35E-12
3.87E-12
3.75E-11
3.92E-11
7.23E-11
8.15E-11
2.14E-11
1.36E-10
2.75E-12
5.42E-12
8.53E-11
1.06E-11
1.00E-11
6.24E-12
3.79E-12
2.39E-11
1.35E-11
5.99E-12
6.35E-1 1
2.70E-11
2
3.04E-13
6.59E-12
1.05E-12
1.04E-11
1.70E-12
2.61 E-1 2
2.32E-12
2.24E-11
2.35E-11
4.33E-1 1
4.88E-11
1.28E-11
8.14E-11
1.64E-12
3.25E-12
5.11E-11
6.33E-12
5.99E-12
3.74E-12
2.27E-12
1.43E-11
8.09E-12
3.59E-12
3.80E-11
1.62E-11
5
1.10E-13
2.38E-12
3.78E-13
3.74E-12
6.15E-13
9.41E-13
8.38E-13
8.10E-12
8.47E-12
1.56E-11
1.76E-11
4.62E-12
2.94E-1 1
5.93E-13
1.17E-12
1.84E-11
2.29E-12
2.16E-12
1.35E-12
8.19E-13
5.16E-12
2.92E-12
1.30E-12
1.37E-11
5.84E-12
1O
4.29E-14
9.31 E-1 3
1.48E-13
1 .46E-1 2
2.4OE-13
3.68E-13
3.28E-13
3.17E-12
3.31 E-1 2
6.11E-12
6.89E-1 2
1.81 E-1 2
1.15E-11
2.32E-13
4.58E-13
7.21E-12
8.94E-13
8.46E-13
5.27E-13
3.20E-13
2.02E-12
1.14E-12
5.07E-13
5.37E-12
2.28E-12
2O
1.53E-14
3.31 E-1 3
5.25E-14
5.19E-13
8.54E-14
1.31 E-1 3
1.16E-13
1.13E-12
1.18E-12
2.17E-12
2.45E-12
6.42E-13
4.08E-1 2
8.25E-14
1.63E-13
2.56E-1 2
3.18E-13
3.00E-13
1.87E-13
1.14E-13
7.18E-13
4.06E-13
1.8OE-13
1.91E-12
8.11E-13
30
8.18E-15
1 .77E-1 3
2.81E-14
2.78E-13
4.58E-14
7.01E-14
6.24E-14
6.03E-13
6.31 E-1 3
1.16E-12
1.31 E-1 2
3.44E-13
2.19E-12
4.42E-14
8.73E-14
1.37E-12
1.70E-13
1.61E-13
1.00E-13
6.10E-14
3.85E-13
2.17E-13
9.65E-14
1.02E-12
4.35E-13
40
5.28E-15
1.14E-13
1.82E-14
1.80E-13
2.96E-14
4.52E-14
4.03E-14
3.89E-13
4.07E-13
7.51E-13
8.47E-13
2.22E-13
1.41E-12
2.85E-14
5.63E-14
8.87E-13
1.10E-13
1.04E-13
6.48E-14
3.94E-14
2.48E-13
1.40E-13
6.23E-14
6.60E-13
2.81E-13
50
3.79E-15
8.21E-14
1.30E-14
1.29E-13
2.12E-14
3.25E-14
2.89E-14
2.80E-13
2.92E-13
5.39E-13
6.08E-13
1.60E-13
1.01E-12
2.05E-14
4.O4E-14
6.37E-13
7.89E-14
7.46E-14
4.65E-14
2.83E-14
1.78E-13
1.01 E-1 3
4.47E-14
4.74E-13
2.01E-13
3-66
4/94
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DRAFT-DO NOT QUOTE OR CITE
Table 3-16. Predicted annual wet deposition fluxes of particle-bound PCDDs/Fs (g/m2-yr).
Downwind
distance, km
2378-TCDD
Other TCOD
1 2378-PeCDD
Other PeCDD
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
Other HxCDD
1 234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
1 2378-PeCDF
23478-PeCDF
Other PeCDF
1 23478-HxCDF
1 23678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1 234678-HpCDF
1 234789-HpCDF
Other HpCDF
OCDF
0.2
4.33E-12
9.38E-1 1
1.49E-11
1.47E-10
2.42E-1 1
3.71 E-11
3.30E-11
3.19E-10
3.34E-10
6.16E-10
6.94E-10
1.82E-10
1 . 1 6E-09
2.34E-11
4.62E-11
7.27E-10
9.01E-11
8.52E-11
5.31E-11
3.23E-11
2.04E-10
1.15E-10
5.11E-11
5.41E-10
2.30E-10
0.5
6.85E-13
1.48E-11
2.36E-12
2.33E-11
3.83E-12
5.87E-12
5.22E-12
5.05E-11
5.28E-11
9.74E-1 1
1.10E-10
2.88E-11
1.83E-10
3.70E-12
7.31 E-1 2
1.15E-10
1.43E-11
1.35E-11
8.41 E-1 2
5.11E-12
3.22E-11
1.82E-11
8.08E-12
8.56E-1 1
3.64E-1 1
0.8
3.78E-13
8.20E-12
1.30E-12
1.29E-11
2.12E-12
3.24E-12
2.89E-12
2.79E-11
2.92E-11
5.38E-1 1
6.07E-1 1
1.59E-11
1.01 E-10
2.04E-12
4.04E-12
6.35E-11
7.88E-12
7.45E-12
4.64E-12
2.82E-12
1.78E-11
1.01 E-11
4.46E-12
4.73E-11
2.01 E-11
0.9
3.26E-13
7.06E-12
1.12E-12
1.11E-11
1.82E-12
2.79E-12
2.49E-1 2
2.40E-1 1
2.51E-11
4.64E-1 1
5.23E-11
1.37E-11
8.72E-11
1.76E-12
3.48E-12
5.47E-11
6.79E-12
6.42E-1 2
4.0OE-12
2.43E-12
1.53E-11
8.66E-12
3.85E-1 2
4.07E-1 1
1.73E-11
1
2.85E-13
6.18E-12
9.81 E-1 3
9.71E-12
1.60E-12
2.44E-12
2.18E-12
2.10E-11
2.20E-11
4.06E-1 1
4.58E-1 1
1.20E-11
7.63E-1 1
1.54E-12
3.04E-12
4.79E-1 1
5.94E-12
5.62E-12
3.50E-12
2.13E-12
1.34E-11
7.59E-12
3.37E-12
3.57E-1 1
1.52E-11
2
1.18E-13
2.57E-12
4.07E-13
4.03E-12
6.63E-13
1.01E-12
9.04E-13
8.73E-12
9.14E-12
1.69E-11
1.90E-11
4.99E-12
3.17E-11
6.40E-13
1.26E-12
1.99E-11
2.47E-12
2.33E-12
1.45E-12
8.83E-13
5.57E-12
3.15E-12
1.40E-12
1.48E-11
6.30E-12
5
3.42E-14
7.42E-13
1.18E-13
1.16E-12
1.92E-13
2.93E-13
2.61 E-1 3
2.52E-12
2.64E-12
4.87E-12
5.49E-12
1.44E-12
9.15E-12
1.85E-13
3.65E-13
5.75E-12
7.13E-13
6.74E-13
4.20E-13
2.55E-13
1.61E-12
9.10E-13
4.O4E-1 3
4.28E-12
1.82E-12
10
1.14E-14
2.47E-1 3
3.92E-14
3.88E-13
6.38E-14
9.77E-14
8.70E-14
8.41 E-1 3
8.8OE-13
1.62E-12
1.83E-12
4.8OE-13
3.05E-12
6.16E-14
1.22E-13
1.92E-12
2.37E-13
2.25E-13
1.4OE-13
8.50E-14
5.36E-13
3.03E-13
1.35E-13
1.43E-12
6.O6E-13
20
3.O1E-15
6.52E-14
1.03E-14
1.O2E-13
1.68E-14
2.58E-14
2.3OE-14
2.22E-13
2.32E-13
4.28E-13
4.83E-1 3
1.27E-13
8.O5E-13
1.63E-14
3.21E-14
5.O5E-13
6.27E-14
5.93E-14
3.70E-14
2.24E-14
1.42E-13
8.OOE-14
3.55E-14
3.76E-13
1.6OE-13
30
1.22E-15
2.63E-14
4.18E-15
4.14E-14
6.8OE-15
1.O4E-14
9.27E-15
8.96E-14
9.38E-14
1.73E-13
1.95E-13
5.12E-14
3.25E-13
6.57E-15
1.3OE-14
2.O4E-13
2.53E-14
2.39E-14
1.49E-14
9.06E-15
5.71E-14
3.23E-14
1.43E-14
1.52E-13
6.46E-14
40
6.02E-16
1.31E-14
2.07E-15
2.05E-14
3.37E-15
5.16E-15
4.59E-15
4.44E-14
4.65E-14
8.57E-14
9.66E-14
2.54E-14
1.61E-13
3.25E-15
6.42E-15
1.01 E-1 3
1.25E-14
1.19E-14
7.39E-15
4.49E-15
2.83E-14
1.60E-14
7.11E-15
7.52E-14
3.20E-14
50
3.37E-16
7.30E-15
1.16E-15
1.15E-14
1.89E-15
2.89E-15
2.57E-15
2.48E-14
2.60E-14
4.79E-14
5.40E-14
1.42E-14
9.01E-14
1.82E-15
3.59E-15
5.66E-14
7.01 E-1 5
6.63E-15
4.14E-15
2.51E-15
1.58E-14
8.95E-15
3.97E-15
4.21E-14
1.79E-14
3-67
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Table 3-17. Predicted total (dry + wet) deposition fluxes of particle-bound PCDD/Fs (g/m2-yr).
Downwind
distance, km
2378-TCDD
Other TCDD
1 2378-PeCDD
Other PeCDD
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
Other HxCDD
1 234678-HpCDD
Other HpCDF
OCDD
2378-TCDF
Other TCDF
1 2378-PeCDF
23478-PeCDF
Other PeCDF
1 23478-HxCDF
1 23678-HxCDF
1 23789-HxCDF
234678-HxCDF
Other HxCDF
1 234678-HpCDF
1 234789-HpCDF
Other HpCDF
OCDF
0.2
4.33E-12
9.38E-11
1 .49E-1 1
1.47E-10
2.42E-11
3.71E-11
3.30E-1 1
3.19E-10
3.34E-10
6.16E-10
6.94E-10
1.82E-10
1.16E-09
2.34E-1 1
4.62E-11
7.27E-10
9.01 E-11
8.52E-11
5.31E-11
3.23E-11
2.04E-10
1.15E-10
5.T1E-11
5.41E-10
2.30E-10
0.5
1.12E-12
2.43E-1 1
3.86E-12
3.82E-11
6.28E-12
9.62E-12
8.56E-12
8.28E-11
8.66E-11
1.6OE-10
1.80E-10
4.73E-11
3.00E-10
6.07E-12
1.20E-11
1.89E-10
2.34E-1 1
2.21E-11
1.38E-11
8.37E-12
5.28E-11
2.98E-1 1
1.32E-11
1.40E-10
5.97E-11
0.8
9.08E-13
1.97E-11
3.12E-12
3.09E-11
5.08E-12
7.78E-12
6.93E-12
6.70E-11
7.01 E-11
1.29E-10
1.46E-10
3.82E-11
2.43E-10
4.91 E-1 2
9.69E-12
1.53E-10
1.89E-11
1.79E-11
1.12E-11
6.77E-12
4.27E-11
2.41 E-11
1.07E-11
1.13E-10
4.83E-1 1
0.9
8.49E-13
1.84E-11
2.92E-12
2.89E-11
4.75E-12
7.27E-12
6.48E-12
6.26E-11
6.55E-11
1.21E-10
1.36E-10
3.57E-11
2.27E-10
4.59E-12
9.06E-12
1.43E-10
1.77E-11
1.67E-11
1.04E-11
6.33E-12
3.99E-1 1
2.26E-11
1.00E-11
1.06E-10
4.51E-11
1
7.93E-13
1.72E-11
2.73E-12
2.70E-11
4.44E-12
6.79E-12
6.05E-12
5.85E-11
6.12E-11
1.13E-10
1.27E-10
3.34E-11
2.12E-10
4.29E-12
8.46E-12
1.33E-10
1.65E-11
1.56E-11
9.74E-12
5.92E-12
3.73E-11
2.11E-11
9.36E-12
9.91E-11
4.22E-11
2
4.23E-13
9.16E-12
1.45E-12
1.44E-11
2.37E-12
3.62E-12
3.23E-12
3.12E-11
3.26E-11
6.02E-1 1
6.78E-11
1.78E-11
1.13E-10
2.29E-12
4.51E-12
7.10E-11
8.80E-12
8.33E-12
5.19E-12
3.15E-12
1.99E-11
1.12E-11
4.99E-12
5.28E-11
2.25E-11
5
1.44E-13
3.12E-12
4.95E-13
4.90E-12
8.06E-13
1.23E-12
1.10E-12
1.06E-11
1.11E-11
2.05E-11
2.31E-11
6.06E-12
3.85E-1 1
7.78E-13
1.54E-12
2.42E-11
3.00E-12
2.84E-12
1.77E-12
1.07E-12
6.77E-12
3.83E-12
1.70E-12
1.80E-11
7.66E-12
10
5.43E-14
1.18E-12
1.87E-13
1.85E-12
3.04E-13
4.65E-13
4.14E-13
4.01 E-1 2
4.19E-12
7.73E-12
8.71 E-1 2
2.29E-12
1.45E-11
2.94E-13
5.80E-13
9.12E-12
1.13E-12
1.07E-12
6.67E-13
4.05E-13
2.55E-12
1.44E-12
6.41 E-1 3
6.79E-12
2.89E-12
20
1.83E-14
3.96E-13
6.28E-14
6.22E-13
1.02E-13
1.56E-13
1.39E-13
1.35E-12
1.41E-12
2.60E-12
2.93E-12
7.69E-13
4.89E-12
9.87E-14
1.95E-13
3.07E-12
3.80E-13
3.60E-13
2.24E-13
1.36E-13
8.59E-13
4.86E-13
2.16E-13
2.28E-12
9.71E-13
30
9.4OE-15
2.04E-13
3.23E-14
3.20E-13
5.26E-14
8.05E-14
7.17E-14
6.93E-13
7.25E-13
1.34E-12
1.51E-12
3.96E-13
2.51E-12
5.08E-14
1.00E-13
1.58E-12
1.96E-13
1.85E-13
1.15E-13
7.01E-14
4.42E-13
2.50E-13
1.11E-13
1.17E-12
4.99E-13
40
5.88E-15
1.27E-13
2.02E-14
2.00E-13
3.29E-14
5.04E-14
4.49E-14
4.34E-13
4.54E-13
8.37E-13
9.43E-13
2.48E-13
1.57E-12
3.18E-14
6.27E-14
9.88E-13
1.22E-13
1.16E-13
7.22E-14
4.39E-14
2.77E-13
1.56E-13
6.94E-14
7.35E-13
3.13E-13
50
4.13E-15
8.94E-14
1.42E-14
1.40E-13
2.31 E-1 4
3.54E-14
3.15E-14
3.04E-13
3.18E-13
5.87E-13
6.62E-13
1.74E-13
1.10E-12
2.23E-14
4.40E-14
6.93E-13
8.59E-14
8.13E-14
5.07E-14
3.08E-14
1.94E-13
1.10E-13
4.87E-14
5.16E-13
2.19E-13
3-68
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congener emissions. This is because fate and transport parameters, and
bioconcentration/biotransfer parameters, are different for the various congeners.
Assuming one set of such parameters for TEQ emissions can lead to a different estimated
exposure media TEQ concentration than assuming congener-specific parameters and then,
given estimated congener-specific concentrations, calculating TEQ exposure media
concentrations with the TEF scheme. Emission factors were used in this assessment
todescribe source and site-specific emissions. These are defined as the mass of
contaminant emitted per mass of feed material combusted. Procedures to convert other
emission data, such as mass per time emitted or concentration emitted, are presented.
2. Estimate the vapor/particle partitioning for atmospheric transport and deposition
modeling: Vapors are dispersed assuming Gaussian plume dispersion algorithms, and
particles are transported and deposited via wet and dry deposition. The principal output of
the atmospheric transport model, COMPDEP, used for further exposure analysis are the
vapor and particle phase concentrations, and the wet and dry deposition totals at sites of
exposure. There is some thought that the partitioning between the vapor and particle
phases at the stack differs from the partitioning in ambient air. Such a difference might be
due to the difference in temperature at the stack versus temperature of ambient air. If so,
then deposition and dispersion trends in the close vicinity of the stack may differ from
such trends further from the stack. Currently the data to support such a hypothesis is
lacking; the earlier review of stack vapor/particle partitioning was inconclusive. Also,
modeling approaches for such differences are unavailable. Instead, the approach in this
chapter is to assume one partitioning scheme (separate V/P partitioning for individual
congeners) for atmospheric transport and dispersion modeling. The scheme adopted in
this assessment is based on a theoretical approach described by Bidleman (1988).
3. Conduct atmospheric dispersion and deposition modeling: The COMPDEP model
is used in this assessment to estimate vapor and particle-phase concentrations, and wet
and dry deposition totals for points around the stack emission source. Key inputs are
vapor phase and particulate phase emission rates (rather than emission factor units,
atmospheric transport models require emission rates in units of mass/time, or g/sec), stack
descriptors (stack height, exit temperature, etc.), atmospheric transport parameters
(particle size distributions, dry deposition velocity), meteorological data (hourly rainfall,
windspeeds, etc.), and terrain descriptions. Procedures to translate the final model
3-69 4/94
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DRAFT-DO NOT QUOTE OR CITE
outputs of concentrations and deposition fluxes into exposure media concentrations is
given in Chapter 4, Section 4.5.
3-70 4/94
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REFERENCES FOR CHAPTER 3
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Bidleman, T.F. (1988). Atmospheric processes. Wet and dry deposition of organic
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Cavallaro, A.; Luciani, L., Ceroni, G.; Rocchi, I., Invernizzi, G; Gorni, A. (1982). Summary
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incineration in a Lime Kiln: Rockwell Lime Co. EPA-600/S2-84-132. November,
1984.
Eitzer, B.D.; Hites, R.A. (1986). Concentrations of dioxins and dibenzofurans in the
atmosphere. Int. J. Environ. Anal. Chem., 27: pp. 215-230.
Eitzer, B.D.; Hites, R.A. (1989). Polychlorinated dibenzo-p-dioxins and dibenzofurans in
the ambient atmosphere of Bloomington, Indiana. Environ. Sci. Technol. 23, pp
1389-1395.
Entropy. (1987). Stationary source sampling report. Signal Resco Pinnellas County
resource recovery facility, St. Petersburg, FL. EEI, REF. #5286-8, Entropy
Environmental Inc., Research Triangle Park, NC. September, 1987.
Hagenmaier, H., Kraft, M.; Jager, W.; Mayer, U.; Lutzke, K.; Siegel, D. (1986).
Comparison of various sampling methods for PCDDs and PCDFs in stack gas.
Chemosphere 15:9-12, pp 1187-1192.
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Harless, R.L.; Lewis, R.G. (1992). Evaluation of a sampling and analysis method for
determination of polyhalogenated dibenzo-p-dioxins and dibenzofurans in ambient
air. Chemosphere 25: 7-10, pp 1317-1322.
Hites, R.A. (1991). Atmospheric transport and deposition of polychlorinated dibenzo-p-
dioxins and dibenzofurnas. Prepared for the U.S. Environmental Protection Agency,
Methods Research Branch, Atmospheric Research and Exposure Assessment
Laboratory, Office of Research and Development, Research Triangle Park, NC.
EPA/600/3-91/002.
Hunt, G.T.; Maisel, B.E. (1990). Atmospheric PCDD/PCDFs in wintertime in a
northeastern U.S. urban coastal environment. Chemosphere 20: 10-11, pp 1455-
1462.
Hunt, G.T., Maisel, B.E. (1992). Atmospheric concentrations of PCDDs/PCDFs in
Southern California. J. Air waste Manage. Assoc. 42, pp 672-680.
Janssens, J.J.; Daelemans, F.F.; Schepens, P.J.C. (1992). Sampling incinerator effluents
for PCDDs and PCDFs: A critical evaluation of existing sampling procedures.
Chemosphere 25:7-10, 1323-1332.
Junge, C.E. (1977). In: Suffet, I.H, ed. Fate of pollutants in the air and water
environments. Part I. Wiley, New York , NY, pp 7-26, as cited in: Bidleman, T.F.
(1988).
Kapahi, R. (1991). "Modeling the Dispersion of Toxic Air Pollutants Emitted From
Municipal Solid Waste Incinerators", pp. 67-82 in Hattemer-Frey, H.; Travis, C.,
eds., Health Effects of Municipal Waste Incineration, CRC Press, Boston, MA.
Knisley, D.R.; Jamgochian, C.G.; Gergen, W.P., Holder, D.J. (1986). Draft emissions test
report dioxin/furans and total organic chlorides emission testing. Saugus resource
recovery facility, Saugus, MA. Prepared by Radian Corporation for Office of Air
Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC, DCN No.
86-223,015-05. October 2, 1986.
Lykins, B.W.; Clark, R.; Cleverly, D.H. (1987). Polychlorinated dioxin and furan discharge
during carbon reactivation. J. Environ. Eng. 114:2, pp 316-330.
Marklund, S.; Kjeller, L.O.; Hansson, M.; Tysklind, M.; Rappe, C. (1985). Determination of
PCDDs and PCDFs in incineration samples and pyrolytic products. Presented at
American Chemical Society National meeting. Chlorinated dioxins and dibenzofurans
in the total environment, Miami, Fl,April, 1985.
Mukerjee, D.; Cleverly, D.H. (1987). Risk from exposure to polychlorinated dibenzo-p-
dioxins and dibenzofurans emitted from municipal incinerators. Waste Management
and Research 5:269-283.
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NATO (1988). Pilot study on international information exchange on dioxins and related
compounds: Emissions of dioxins and related compounds from combustion and
incineration sources. North Atlantic Treaty Organization, Committee on the
Challenges of Modern Society. Report #172, August, 1988.
Oehme, M.; Mano, S.; Mikalsen, A. (1986). Quantitative method for the determination of
femtogram amounts of polychlorinated dibenzo-p-dioxins and dibenzofurans in
outdoor air. Chemosphere 15:5, pp 607-617.
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Technology Assessment, U.S.Congress, Washington, DC.
Pasquill, F. (1961). The estimation of the dispersion of windborne material. Meteorology
90:33-49.
Peters, J.A. (1983). Evaluation of Hazardous Waste Incineration in Cement Kilns at San
Juan Cement Co. prepared for, Incineration Research Branch, Industrial Research
Laboratory, U.S. Environmental Protection Agency, Cincinnari, OH.
Radian. (1986). Memorandum from Dennis Knisley, Radian Corp., to Joe Aldina, Rust
International Corp; subject: Dioxin/furan testing at the Saugus resource recovery
facility, October 7, 1986.
Radke, L.F., Hobbs, P.V.; Eltgroth, M.W. (1980). Scavenging of aerosol particles by
precipitation. Journal of Applied Meteorology 19:715-722.
Rao, K.S.; Sutterfield, L. (1982). MPTER-DS. The MPTER model including deposition and
sedimentation. U.S. EPA, Research Triangle Park, NC. EPA 600/8-82/024.
Seelinger, R.; Hahn, J.; VonDemfange, H.P.; Zurlinden, R.A. (1986). Environmental test
report, Ogden Martin Systems of Tulsa, Inc., Compliance with Tulsa City/County
health department and U.S. EPA permits. September 9, 1986.
Sehmel, G.A. (1980). Particle and gas dry deposition: A review. Atmospheric Environ.
14, pp 983-1011.
Sehmel, G.A.; Hodgson, W.H. (1978). A model for predicting dry deposition of particles
and gases to environmental surfaces. Battelle, Pacific Northwest Laboratory,
Richland, WA. PNL-SA-6721.
Seinfeld, J.H. (1986). Atmospheric chemistry and physics of air pollution. New York,NY.,
John Wiley and Sons.
Siebert, P.C.; Alston-Gulden, D.; Jones, K.H. (1991). An analysis of worldwide resource
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Someshwar, A.V.; Pinkerton, J.E. (1992). Wood Processing Industry. In: Buonicore, A.;
Davis, W., eds.. Air Pollution Engineering Manual. Air and Waste Management
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D.F.; Ferguson, G.L.; Everson, C.B. (1982). Development and application of
analytical methodology for assessment of selected toxic components of combustion
effluents. Prepared by Wright State University, Brehm Lab, Dayton, OH., for Scott
Environmental Technology, Plusteadville, PA. EPA contract # 68-02-3496. May,
1982.
Tiernan, T.O.; Garrett, J.H.; Vanness, G.F.; Bultman, S.; Hinders, J.D.; Everson, C.;
Wagel, J.W.; Taylor, M.L. (1984). The results of analyses of combustion products
collected during tests of a refuse-fired incinerator located in Tsushima, Japan for
polychlorinated dibenzodioxins and dibenzofurnas, selected metals and
fluorides/chlorides. Prepared by Wright State University, Dayton, OH., for Cooper
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U.S. Environmental Protection Agency. (1986b). Industrial source complex (ISC) dispersion
model user's guide-second edition. Office of Air Quality Planning and Standards,
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with municipal waste combustion emissions. Office of Solid Waste and Emergency
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Washington,DC., EPA/530-SW-87-021g, September, 1987.
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combustors.Office of Solid Waste and Emergency Response, Office of Air and
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021 b, June, 1987.
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sources. Engineering analysis report. Office of Air Quality Planning and Standards,
Research Triangle Park, NC, EPA-450/4-84-014h, September,1987.
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Associated with Indirect Exposure to Combustor Emissions. Office of Health and
Environmental Assessment. EPA/600/6-90/003. January, 1990.
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U.S. Environmental Protection Agency (1993). Locating and estimating air emissions from
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L.A. (1989) Assessment of ambient air sampling techniques for collecting airborne
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367.
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4. ESTIMATING EXPOSURE MEDIA CONCENTRATIONS
4.1. INTRODUCTION
The purpose of this chapter is twofold. First, it describes the algorithms used to
determine exposure media concentrations of the dioxin-like compounds. Discussion of the
algorithms are structured around four "source categories." These categories roughly
translate to beginning points, or origins, of contamination. The source categories are also
the basis for the example scenarios described in Chapter 5. Second, it provides
information about all the model parameters and justification for the values selected for the
demonstration of methodologies in Chapter 5. Parameter discussions appear immediately
following descriptions of modeling methodologies.
Section 4.2 provides an introduction to the type of modeling used in this
assessment. Section 4.3 describes the algorithms used for the first source category,
on-site soil, where the contaminants occur in surface soils, and this contamination source
and subsequent exposure occur at the same site. The second source category, described
in Section 4.4, is termed off-site soil. The contaminated soil is remote from the exposure,
such as in a landfill impacting a nearby residence. Section 4.5 describes algorithms to
determine exposure media concentrations resulting from stack emissions, the third source
category. Chapter 3 laid the groundwork for this section by describing the use of air
dispersion/deposition models as applied to a point source to generate two key quantities:
air-borne contaminant vapor phase concentrations at a site of exposure, and particulate
phase deposition rates. Section 4.5 describes how modeled concentrations and
depositions translate to soil, vegetative, and water concentrations. Section 4.6 concludes
the chapter with a discussion of algorithms specific to the fourth source category, point-
source effluent discharges into surface water bodies.
Algorithms are presented which estimate exposure media concentrations for: 1)
surface soils, 2) surface water impacts: suspended and bottom sediment and dissolved
phase concentrations, 3) air including the vapor phase and in particulate form, and 4) biota
including beef, milk, fruit and vegetables, and fish.
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4.2. BACKGROUND FOR SOLUTION ALGORITHMS
Literally hundreds of fate and transport models have been published which differ
widely in their technical sophistication, level of spatial or temporal resolution, need for site
specific parameterization, and so on. This makes selection of the most appropriate one for
any particular situation very difficult. EPA has published model selection criteria
documents (EPA, 1987b; EPA, 1988d) and a software system (Integrated Model
Evaluation System, IMES, Version 2.01, 1992, Office of Health and Environmental
Assessment, Office of Research and Development, U.S EPA) to help assessors with model
selection.
Relatively simple, screening level models are used to model fate, transport, and
transfer of dioxin-like compounds from the source to the exposure media in this
assessment. Simple assumptions are often made in order to arrive at the desired result,
which is long-term average exposure media concentrations. Perhaps the most critical of
the assumptions made is the assumption that the source strength remains constant
throughout the period of exposure: the initial soil concentration of dioxin-like compound
remains the same for that exposure period, and stack emissions and effluent discharges
remain steady throughout this period. It is important to understand that EPA is not
endorsing the algorithms of this assessment as the best ones for use in all dioxin
assessments. They are suggested as reasonable starting points for site-specific or general
assessments, and as will be discussed shortly, most multi-media exposure modeling has
included similar screening level approaches. The assumptions behind models are described
carefully throughout this chapter. If these assumptions do not apply to a particular
situation, or where assessors require more spatial or temporal resolution, more complex
models should be selected. References to other models are made in this and other
sections throughout the chapter.
Finally, it cannot be overemphasized that measured concentrations are generally
more reliable than modeled ones. Assessors should use measured concentrations if
available and if such measurements can be considered spatially and temporally
representative for the exposed populations.
The first examples of similar multimedia compartment modeling were probably the
"fugacity" models proposed by Mackay (1979) and Mackay and Paterson (1981, 1982).
Fugacity in this context is defined as the tendency for a chemical to escape from one
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environmental media compartment into another. The fugacity of a chemical present in an
environmental media compartment is modeled using common fate and transport
parameters such as octanol water partition coefficients, Henry's Constants, water
solubilities, and so on. The fugacity concept is based on the fact that at equilibrium, equal
fugacities are established in all compartments of a system. Examples of fugacity
modeling include the transfer of nonionic organic chemicals between the atmosphere and
surface water (Mackay, et al., 1986), between the atmosphere and plants (Riederer,
1990), and for food chain modeling (Travis and Hattemer-Frey, 1987). A definitive text on
multimedia compartment modeling using the fugacity approach has recently been
published (Mackay, 1991). One possible drawback for the fugacity approach applied to
the types of source categories discussed in this assessment is that it does not consider
spatial variability of concentrations within a compartment. For example, air concentrations
vary depending on the distance from a source of air emissions, such as a stack or a site of
soil contamination. The fugacity approach would typically treat air as a single
compartment with a uniform concentration.
The transfer of contaminants between compartments and multimedia modeling
approaches have been extensively studied at the National Center for Intermedia Transport
at the University of California, Los Angeles. Their multimedia compartment model, MCM
(Cohen and Ryan, 1985), provides several useful algorithms for intermedia transfer factors
that would have application for dioxin-like compounds. More recently, this group has
introduced the spatial multimedia compartment model (Cohen, et al., 1990), which allows
for non-uniformity in some compartments. Such a model would be more suitable for the
types of source categories of this assessment, since there is non-uniformity within a
compartment as noted above in the air compartment example.
An early approach which merged simplistic multimedia modeling with human
exposure was termed the exposure commitment method, developed by Bennett (1981).
An exposure commitment is defined as a contaminant concentration in human tissue.
Exposure commitments are calculated from transfer factors that are estimated as the ratios
of the steady-state concentrations of a contaminant in adjoining compartments of an
exposure pathway. An example of adjoining compartments is air to plants to livestock to
diet. This method has been applied to both PCBs (Bennett, 1983) and 2,3,7,8-TCDD
(Jones and Bennett, 1989). These applications have required measured concentrations of
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the contaminants in different compartments in order to estimate the transfer factors. The
retrospective nature of this approach limits its usefulness for general applications.
One of the early multimedia models which also had human exposure as the
endpoint, but did not require retrospective data, was the GEOTOX model (McKone and
Layton, 1986). This model had air (vapor and particle phases), water (surface and ground
water, including bottom sediments of surface water bodies), soil (soil gas, water, and solid
subcompartments), and biomass (eggs, milk, meat, fish, and vegetation including food
crops) compartments. The most recent evolution of this model can be found in McKone
and Daniels (1991).
Multimedia modeling approaches have been extensively used to evaluate the
exposure to dioxins. Paustenbach, et al. (1 992) evaluated the exposure and risk to
humans from residential and industrial soil contamination by 2,3,7,8-TCDD. Simple
models were used to estimate the concentrations of 2,3,7,8-TCDD in air-borne suspended
particulates and fish that reside in nearby streams impacted by the contaminated soil.
Together with concentrations in contaminated soil, Paustenbach evaluated human
exposures via soil ingestion, dermal contact, particulate inhalation, and fish consumption.
They also used Monte Carlo techniques on exposure parameters (in contrast to fate and
transport parameters) to determine a range of residential and industrial soil concentrations
that would result in a specified risk level. The risk level chosen for their demonstration
was 10"5, which was determined by multiplication of the Lifetime Average Daily Doses
(LADDs in mg/kg-day) and the cancer slope factor for 2,3,7,8-TCDD of 9700
(mg/kg-day)"1 derived by Keenan, et al. (1991). Residential soil concentrations less than
20 ppb did not pose a lifetime cancer risk greater than 10"5. For industrial sites,
concentrations in soil that could pose a 10~5 risk ranged between 131 and 582 ppb,
depending on the amount of time the industrial worker spend outdoors under typical
exposure conditions.
Travis and Hattemer-Frey (1991) evaluated human exposure to 2,3,7,8-TCDD from
a broader perspective. The principal assumption of the Fugacity Food Chain model used
for Travis' human exposure assessment is that atmospheric concentrations of 2,3,7,8-
TCDD can be empirically linked to water, soil, and vegetative concentrations, which in turn
are linked to agricultural produce, meat, milk, eggs, and fish concentrations. Simple
models for atmospheric depositions onto plants, air-to-leaf transfers of vapor phase
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2,3,7,8-TCDD onto plants, transfers to cattle beef and milk, and other models, are
presented. They also compared their model predictions of exposure media concentrations
to literature values, and concluded that their approaches resulted in concentrations
comparable to those found in the literature. This effort by Travis and Hattemer-Frey is
examined in more detail in Section 5.6 of Chapter 5.
Exposure to 2,3,7,8-TCDD using simplistic multimedia models has also been
assessed for specific sources. Goeden and Smith (1989) evaluated the impact to fish and
subsequent human exposure by consumption of fish to dioxins and furans emitted by a
resource-recovery facility. Surface water sediment concentrations in a lake were
estimated as a simple weighted average of concentrations on three kinds of particles
entering the lake: soil via erosion whose concentration was estimated given contaminated
particle depositions onto soil (and considering mixing and soil half-lives), deposition of
background uncontaminated suspended particulates directly onto the lake, and direct
deposition of contaminated particles onto the lake. Fries and Paustenbach (1990) also
evaluated the impact of incinerator emissions of 2,3,7,8-TCDD, but they evaluated human
exposure via consumption of food crops, meat, and milk. EPA (1990d) used a simple
dilution model to evaluate the impact of pulp and paper mill effluent discharges of 2,3,7,8-
TCDD and 2,3,7,8-TCDF into surface water bodies.
This is only a cursory summary of the wealth of multimedia modeling approaches
that are available, and the application of such modeling approaches for evaluating human
exposure to 2,3,7,8-TCDD. While there are many similarities and differences among the
approaches, they all share one characteristic in common - they have all been described as
"screening level models". Without attempting a definition of the qualifier, "screening
level", such a qualifier for these models seems to imply the following types of common
features: assumptions of equilibrium and/or steady state conditions between
compartments, lack of substantial (if any) spatial or temporal resolution, the use of
biotransfer or bioconcentration concepts which simply relate an environmental
concentration (air or water concentration, e.g.) to a biomass concentration (plant or fish
concentrations), and so on. A counterpoint to screening level models might be what are
termed "site-specific" or "mechanistic" models. Such models are more theoretically
sophisticated, contain more spatial and temporal resolution, attempt to simulate actual
mechanisms of fate and transport rather than depend on empirical relationships developed
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from data, could involve complex food chain approaches to model biomass concentrations
(to counter the simple biotransfer or bioconcentration approaches), and generally are highly
parameterized requiring site-specific data that is often not readily available.
Because of the complexity of the multimedia environment, modeling of contaminant
fate in such an environment has tended to remain simple. However, there are complex
models which can be applied to smaller subsets of the multimedia environment, and which
have been applied to assessments of dioxin-like compounds. One example is the
COMPDEP model, which was used in this assessment to evaluate the impact of stack
emissions of dioxin-like compounds. That model allows for complexities of terrain, varying
weather patterns, vapor/particle partitioning, etc., to be considered. That model is further
described in Chapter 3. Another example of more complex modeling was the use of the
WASP4 model in a comprehensive evaluation of bioaccumulation of 2,3,7,8-TCDD in Lake
Ontario (EPA, 1990b). That application required a substantial amount of site-specific
parameterization.
With the exception of the COMPDEP model, the models used for this assessment
are better described as screening level rather than mechanistic or site-specific. Many of
the algorithms used are the same or very similar to the ones found in references above.
Except for the effluent discharge source category, which uses a non-spatially resolved
dilution model for surface water impacts, the algorithms do consider spatial differences
between the source and site of impact or site of exposure. For example, the algorithm
estimating surface water impacts from a site of soil contamination, while simple in its
framework, does incorporate the following: the area of the site that is contaminated, the
area of the watershed which drains into the water body, the erosion rates of the site of
contamination as well as the rest of the watershed, the proximity of the site to the water
body, the concentration of the contaminant at the site of contamination as well within the
watershed other than the contaminated site, the lipid content of the fish, and the organic
carbon fractions of the suspended and bottom sediments of the water body. Assignments
for all these parameters impact water and fish concentrations, and it is certainly arguable
that they are all site-specific parameters. From this perspective, it could be argued that
most of the algorithms of this assessment are generally screening level in their theoretical
sophistication, but site specific in their application.
Sections in other chapters of this volume address key issues relating to the use and
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credibility of the algorithms described in this chapter. Chapter 5, which demonstrates the
methodology, makes observations concerning exposure media concentrations in Section
5.6.1. Chapter 6, on User Considerations for use of the models and algorithms of this
assessment, discusses categorization of model parameters and conducts sensitivity
analysis exercises on key fate, transport, and transfer algorithms. Chapter 7 on
Uncertainty also has critical discussions including: when possible, comparison of exposure
media concentration estimations of 2,3,7,8-TCDD made in the demonstration scenarios
with literature values, comparison with alternate modeling approaches, and general
discussions of parameter assignment uncertainty and algorithm uncertainties.
Figures 4-1 through 4-4 are flow diagrams showing interim compartment
concentrations modeled, and principal processes modeled and assumptions made in the
intermedia transfer. Sections 4.3. through 4.7 describe the algorithms for the four source
categories considered in this assessment, and background and assignment of parameters
for the demonstration scenarios of Chapter 5.
4.3. ALGORITHMS FOR THE "ON-SITE SOIL" SOURCE CATEGORY
As earlier noted, the contamination and exposure occur at the same site for this
source category. The contamination is assumed to originate at the soil surface. As such,
the soil itself is the exposure media for the dermal contact and soil ingestion pathways.
Sections 4.3.1 through 4.3.4 describe the algorithms for estimating concentrations of the
dioxin-like compounds in: bottom sediment, suspended solids, and in the dissolved phase
in the water column of surface water bodies (4.3.1), in the air in the vapor phase (4.3.2)
and particulate phase (4.3.3), and in biota including fish (4.3.4.1), home-grown vegetables
and fruit (4.3.4.2), and beef and milk (4.3.4.3).
4.3.1. Surface Water and Sediment Contamination
The principal assumption in the algorithm estimating the impact to surface water
and surface water sediments (suspended and bottom sediments) from an area of
contaminated soil is that such an impact is correlated to surface soil concentrations at that
site as well as surface soil concentrations within a larger area draining into the water body.
This drainage area is commonly referred to as a watershed. Further, the impact to the
water body is assumed to be uniform. This tends to be more realistic for smaller water
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Wind erosion
Volatilization
Near-field dispersion
ON-SITE
SOILS
Above-ground
Vegetables and
Fruits
Air:
Vapor and Particulate
Phases
Wet + dry particle deposition
Particle washoff
Air-to-leaf vapor phase
transfers
Beef and
Milk
Pasture Grass
Cattle Feed
Bioconccentration
Beef and dairy cattle diet assumptions
Erosion
Watershed
dilution
Enrichment
Below-ground
Vegetables and
Fruits
So/7 to root transfers
Steady state between 3 compartments
Suspended
Solids
Bottom
Sediments
Dissolved
Phase
Equilibrium
partitioning
Organic carbon normalized
concentrations are equal
Biota-Sediment
Accumulation
Factor
Figure 4-1. Diagram of the fate, transport, and transfer relationships for the on-site source category.
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Wind erosion
Volatilization
Far-field dispersion
OFF-SITE
SOILS
Air:
Vapor and Particulate>
Phases
Above-ground
Vegetables and
Fruits
Wet + dry particle deposition
Particle washoff
Air-to-leaf vapor phase
transfers
Bloaccumulation
Beef & dairy cattle dief
Erosion
Watershed
dilution
Enrichment
Pasture Grass
Cattle Feed
Soil-to-root transfers
\1 Steady state between 3 compartments
Below-ground >.
Vegetables and )
Fruits ./
^—<
( Dissolved >v
V. Phase )
/Suspended
\Solids
Bottom
Sediments
Equilibrium
partitioning
Organic carbon normalized
concentrations are equal
Biota Sediment
Accumulation
Factor
Figure 4-2. Diagram of the fate, transport, and transfer relationships for the off-site source category.
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Dispersion/deposition
modeling
Vapor/particle
partitioning
Emission factors
Deposition
modeling
Soil mixing
Dissipation
Vapor, Particle-phase
Concentrations
Dry and Wet Depositions
STACK
EMISSIONS
Deposition
modeling
Deposition
modeling
Soil mixing
Dissipation
Erosion
Sediment delivery
Enrichment
Above-ground
Vegetables and
Fruits
Particle washoff
Air-to-leaf vapor phase transfers
Bioaccumulaton
Beef & dairy cattle diet
assumptions
Pasture Grass
Cattle Feed
Soil-to-root transfers
Mass balance maintained
Steady state between 3 compartments
Below-ground
Vegetables and
Fruits
Suspended
Solids
Bottom
Sediments
Equilibrium
partitioning
Organic carbon normalize
concentrations are equal
Biota-Sediment
Accumulation
Factor
Figure 4-3. Diagram of the fate, transport, and transfer relationships for the stack emission source category.
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EFFLUENT
DISCHARGE
Simple
Dilution
Total Water
Concentration
Equilibrium
Partitioning
Suspended
Solids
Dissolved
Phase
Biota Suspended Solids
Accumulation Factor
Figure 4-4. Diagram of the fate, transport, and transfer relationships for the effluent discharge source category.
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bodies as compared to large river systems. Other key assumptions in the surface water
impact algorithm are:
• Soil erosion estimates, coupled with sediment delivery ratios, can be used to
describe the impact of a contaminated site relative to other soils in the watershed which
contribute sediments to the water body;
• The sorption of dioxin-like compounds onto surface soil, suspended solids and
bottom sediments is principally a function of the contaminant's organic carbon partition
coefficient, Koc, and the organic carbon content of soils and sediments;
• The concentration of contaminants in soil eroding from a site are initially higher
than the concentrations at the site itself - it is "enriched" with contaminants. This
enrichment occurs because some processes of transport, such as wind erosion or soil
erosion, favor lighter soils (silts and clays) which have higher surface area to volume ratios
(more binding sites) as well as higher organic matter contents on the average (which also
favors more binding of organic chemicals). Other processes such as volatilization or
degradation may counteract the enrichment noted at the edge of a site - concentrations on
soil entering a water body may be less than those leaving the site;
• The concentration of contaminants in sediment suspended in the water column
exceeds the concentration in bottom sediments. Similar reasoning as the above
enrichment argument applies: particulates which remain in suspension tend to be lighter
and more enriched with organic matter as compared to particulates which settle to the
bottom of water bodies. It should be noted that suspended solids, in this algorithm, are
simply a reservoir into which dioxin-like compounds sorb; more complex models consider
sorption onto more than one reservoir of suspended materials including suspended
particulates and dissolved organic matter;
• Suspended and bottom sediments originate principally as soil erosion; a mass
balance is maintained such that a part of the soil reaching the water body through erosion
remains as suspended particulates, and a part settles to bottom sediments.
• A steady state is achieved between concentrations in the dissolved phase in the
water column, concentrations in the sorbed phase in the water column, and concentrations
in bottom sediments;
• Volatilization out of the water body or degradation of residues in the water body
are not modeled. Neglecting these dissipation processes has the net effect of
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overestimating water body impact. On the other hand, bottom sediment resuspension is
not modeled. Not modeling resuspension would have a net effect of underestimating
water column impacts; and
• Estimating the average impact to the water body, rather than a localized impact
which may be the case if the contaminated soil is very near the water body, is suitable for
purposes of this assessment procedure.
Concentrations in bottom sediment are desired because fish concentrations are
estimated as a function of bottom sediment concentrations (see Section 4.3.4.1).
Concentrations in suspended solids are desired because they are used to estimate bottom
sediment concentrations, and dissolved phase concentrations are needed for estimating
drinking water exposures.
The solution begins with the mass balance statement:
The mass of contaminant An amount which remains as dissolved in
entering the water body = the water column + An amount which remains
sorbed to suspended materials + An amount which
remains sorbed to particles settling to the bottom
This can be described mathematically as:
C^ER^ = C^ V^ + C^ M^ + Csed M^ (4-1)
where:
CSwb = concentration on soil entering water body, mg/kg
ERW = total watershed annual soil erosion, kg/yr
CWat = dissolved-phase concentration in water column, mg/L
Vwat = water body annual volume, L/yr
= concentration on suspended sediment, mg/kg
= mass of suspended sediment introduced per year, kg/yr
CSed = concentration on sediment settling to bottom, mg/kg
Msed = mass of bottom sediment introduced per year, kg/yr
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Other equations based on assumptions stated above and needed for this solution are:
1) mass balance of soil is maintained:
(4-2)
Msed = (1 -f,)ER* (4-4)
where:
fs = fraction of annual erosion remaining as suspended materials, unitless
2) equilibrium between sorbed and dissolved phases is maintained; suspended sediments
are enriched in comparison to bottom sediments:
oc
- r sej (4-6)
- ^ssed ^ D'
where:
soil-water partition coefficient for contaminant in suspended
sediment, L/kg
= fraction organic carbon in suspended sediment, unitless
OCsed = fraction organic carbon in bottom sediment, unitless
Now, Equations (4-2) through (4-6) can be substituted into the right hand side of Equation
(4-1) so that this side can be function a one concentration, Cssed, and one erosion amount,
ERW. Factoring out Cssed then gives:
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'-(^-fs)E^} (4-7)
i
The bracketed quantity in the right hand side of Equation (4-7) can be termed 0, so that
Cssed can be solved as (Cswb ERw)/0. Now, the numerator in this term can be expanded to
describe contaminant contributions by a site of contamination and contaminant
contributions by the rest of the watershed. Included in this solution is the assumption
made above that soils eroding into water bodies are "enriched":
CswhERw = C,SL,A,ESD. + Cw SLW ( Aw - As) E SDW (4-8)
where:
^swb = concentration on soil entering water body, mg/kg
ERW = total watershed erosion, kg/yr
Cc = contaminated site soil concentration of dioxin-like compound, mg/kg
o
E = enrichment ratio, unitless
SLS = unit soil loss from contaminated site area, kg/ha-yr
A- = area of contaminated site, ha
o
SDS = sediment delivery ratio for soil eroding from contaminated site to
water body, unitless
Cw = average concentration of dioxin-like compound in effective area of
watershed not including contaminated site, mg/kg
SLW = average unit soil loss for land area within watershed not including
contaminated site, kg/ha-yr
Aw = effective drainage area of watershed; the area contributing sediment
which mixes with the sediment originating from As, ha
SDW = sediment delivery ratio for watershed, unitless
Finally, the right hand side of Equation (4-8) can be termed, p, and the concentration in
suspended sediment, Cssed, is equal to pl$. All the terms in p/0 are input parameters or
can be solved as a function of input parameters. Other water body concentration terms,
Cwat and Csed, can now be solved using Equations (4-5) and (4-6). Note that this solution
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is most applicable to small water bodies to ponds and streams. The differences in the two
water systems can be expressed in the parameters, effective watershed area, Aw, water
body volume, Vwat, and organic carbon contents of suspended solids and bottom
sediments, OCssed and OCsed. Guidance on these terms and assignment of values for the
demonstration scenarios in Chapter 5 is now given.
• Ca and Cw: These are concentrations of dioxin-like compounds in the
contaminated site soil, Cs, and the average within the effective area of the watershed, Cw.
The contaminated site concentrations drive the concentrations assumed for most
exposures, and is a principal user input (for the on-site source category, the contaminated
site is also the site of exposure). The simplest assumption for Cw is that it is 0.0.
However, examination of soil data from around the world shows that, where researchers
have measured concentration in what they described as "background" or "rural" settings,
soil concentrations of PCDDs and PCDFs are in the non-detect to low ng/kg (ppt) range.
Example Scenarios 1 and 2 in Chapter 5 demonstrate the on-site source category. For
these example scenarios, Cw and Cs are both initialized at 10~6 mg/kg (1 ppt) for all
example compounds representing low concentrations that might be possible for basin-wide
areas.
• E: Enrichment refers to the fact that erosion favors the lighter soil particles,
which have higher surface area to volume ratios and are higher in organic matter content.
Therefore, concentrations of organic contaminants, which are a function of organic carbon
content of sorbing media, would be expected to be higher in eroded soil as compared to in-
situ soil. While enrichment is best ascertained with sampling or site-specific expertise,
generally it has been assigned values in the range of 1 to 5 for organic matter,
phosphorous, and other soil-bound constituents of concern (EPA, 1977). The enrichment
ratio would be expected to be higher in sandy soils as compared to silty or loamy soils
because the finer silt particles which erode from a soil generally characterized as sandy are
more a deviation from the norm compared to silt particles which erode from a soil
generally characterized as silty or loamy. The example scenarios in Chapter 5 modeled
mid-range agricultural loam soils (as modeled with organic carbon fractions, soil loss
parameters as discussed below, etc.). The enrichment ratio will therefore be assigned a
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value of 3.0 in all circumstances.
• SL8 and SLW: These are the unit soil loss, in kg/ha, from the exposure site
and the average from the effective land area draining into the surface water body. In the
simplest case, the unit losses can be considered equal. In the most complicated solution,
the effective drainage area can be broken up into "source areas", where each source area
can be unique in terms of the erosion potential, concentration of contaminant, and so on.
The total contribution equals the sum of contributions from each source area, as:
ICj*SLj*Aj*Ej*SDj for the right hand side of Equation (4-8) for j number of source areas
not including the exposure site. For direct input into Equation (4-8), the terms Cj( SLj( A(,
Ej, and SDj, should be determined and Cw, SLW, Ew, and SDW should be estimated as
weighted averages over all source areas. A.-. The effective drainage area, Aw, would be
the sum of all source areas, Aj.
For the example scenarios in Chapter 5 demonstrating the on-site source
categories, SLS and SLW are assumed equal. This generally assumes that erosion
parameters for the site of exposure mirror the averages for the drainage area. Also, the
enrichment ratio, E, is assumed to be constant for all watershed soils. For the off-site soil
source category, the site of contamination is assumed to have different erosion
characteristics. The following is offered as general guidance and background for
estimation of unit soil losses in this assessment.
The unit soil loss is commonly estimated using the Universal Soil Loss Equation.
This empirical equation estimates the amount of soil eroding from the edge of a field
(Wischmeier and Smith, 1965):
SL = R K LS C P (4'9)
where:
SL = average annual soil loss, Eng. tons/acre-year
R = rainfall/runoff erosivity index, t-ft/ac-yr
K = soil erodibility factor, t/ac-(unit of RF)
LS = topographical factor, unitless
C = cover and management practice, unitless
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P = supporting practices factor, unitless.
Several references are available to evaluate USLE factors for agricultural and
non-agricultural settings (EPA, 1977; USDA, 1974; Wischmeier, 1972; Novotny and
Chesters, 1981). For this assessment, values for these terms will based on assumptions
about contaminated sites and rural soils. Justification and assumptions are given below.
It should be noted that more sophisticated models are available for estimating erosion rates
(i.e., CREAMS as described in Knisel, 1980), and should be considered in actual
site-specific assessments.
• Rainfall/erosivity index, R: The R term represents the influence of
precipitation on erosion, and is derived from data on the frequency and intensity of storms.
This value is typically derived on a storm-by-storm basis, but it has been compiled
regionally for the development of average annual values (EPA, 1977). Annual values
range from < 50 for the arid western United States to > 300 for the Southeast. The
value used in this assessment will be 160, which is typical of rainfall patterns seen in
much of the midwestern United States.
• Soil erodibility, K: The soil erodibility factor reflects the influence of soil
properties on erosion, with values ranging from <0.05 for non-erodible sandy soils to
>0.50 for highly erodible silty soils. The value used in this assessment will be 0.30,
which is typical of, for example, sandy or silty loam soils with 2% - 4% organic matter
contents.
• Length-slope factor, LS: The topographic factor reflects the influence of
slope steepness and length of the field in the direction of the erosion. Steeper slopes and
longer lengths lead to higher LS values, with a range of 0.1 for slopes < 1.0% and lengths
< 100 ft to > 2.0 for slopes generally >10%. The two key considerations for its
assignment, therefore, are the size of the field for which erosion estimates are being made,
and the slope of that field. The example scenarios in Chapter 5 had field sizes of 0.4 ha (1
ac) for a rural residence, 4 ha (10 ac) for a small rural farm, and 10 ha (25 ac) for an off-
site contamination site. Guidance for use of the Universal Soil Loss Equation stops short
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of defining appropriate sizes of field for which unit estimates are to be derived, except that
the USLE was developed for agricultural "fields" where cover, slope, soil type, etc. are
assumed to be uniform. For purposes of estimating erosion losses in this assessment, a
field of 4 ha for estimating the LS factor will be used. In a rural watershed with
agricultural and non-agricultural settings, this would be a reasonable average area of
uniformity. If square shaped, a 4 ha area translates to a side length of 200 m. For
purposes of assignment of the LS factor, it will be assumed that the contaminated site has
a 2% slope. EPA (1977) (and other references as noted above) show nomagraphs giving
the LS factor as a function of slope length and slope. With a 200 meter slope length and a
2% slope, the LS factor is approximately 0.20. This factor will be used for all soil loss
estimates required in this estimates.
• Support practice factor, P: The support practice factor reflects the use of
surface conditioning, dikes, or other methods to control runoff/erosion. P can be no
greater than 1.0. However, values less than 1.0 should only be assigned when specific
practices are employed which are designed to reduce erosion. For the example scenarios
in Chapter 5, it will be assumed that no such practices are in place at the site of concern
or throughout the watershed to control erosion. Therefore, a value of 1.0 will be
assumed.
• Management practice factor, C: The final term in the USLE is the cover
and management practice factor, C, which primarily reflects how vegetative cover and
cropping practices, such as planting across slope rather than up and down slope,
influences erosion. C values can be no greater than 1.0, with this value appropriate for
bare soils. A C value of 1.0 is an appropriate choice for active landfills or sites of high soil
contamination (like Superfund sites) mostly devoid of vegetation. For an inactive landfill
with grass cover or any area with dense vegetative cover such as grass, a value of 0.1 or
less is appropriate. Values greater than 0.1 but less than 0.7 are appropriate for
agricultural row crops, which offer more protection than bare soil, but not as much
protection as dense vegetation. Three erosion estimates are required for scenarios
demonstrated in Chapter 5. One is for areas of high soil contamination, or the scenario
demonstrating the "off-site" source category. It will be assumed that the off-site
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contaminated site is largely devoid of vegetation in this case, and a value of 1.0 will be
assumed. A second erosion estimate is needed to characterize average unit soil loss
throughout a watershed draining into a surface water body. The example scenarios are
based on a rural setting which has agricultural and non-agricultural (i.e., rural residences)
areas. The C value in this circumstance will be assumed to be 0.3. Finally, an soil erosion
estimate is needed in the algorithm transporting contaminated soil from an area of high soil
contamination to a nearby site of contamination, as part of the algorithms developed for
the "off-site" source category. In this case, the land between a site of soil contamination
and the nearby site of exposure will be assumed to be covered with dense vegetation,
such as grass. In this case, the C value will be 0.1.
As just described, three unit soil loss estimates are required for this estimates and
the difference between the three will be expressed in the C term. Multiplication of the five
USLE terms gives unit soil loss estimates of 9.60 (with C = 1.0), 0.96 (with C = 0.1),
and 2.88 (with C = 0.3) t/ac-yr. The value of SLS and SLW for the demonstration of the
on-site scenario in Chapter 5 is 2.88 t/ac-yr. Since Equation (4-8) and other uses of unit
soil loss estimates are needed in kg/ha-yr, these unit losses are easily converted to 21515,
2152, and 6455 kg/ha-yr.
• A8 and Aw: These are the area terms, including the area of the
contaminated site, and the effective drainage area of the watershed, both in ha. The
scenarios demonstrated in Chapter 5 have assumed 0.4 ha (1 acre roughly) for exposure
sites described as rural residences, 4 ha (10 acres) for farms, and 10 ha (25 acres) for an
off-site area of soil contamination. If the area of contamination is at the site of exposure,
as in the "on-site" source category, then As should be assigned an area equalling the site
of exposure (and the concentration term, Cs, should equal the average soil concentration
over this site of exposure). If the area of contamination is away from the site of exposure,
as in the "off-site" source category, As should equal the total area of contamination (and
again Cs should equal the average soil concentration over this area).
The total area impacting a river system has been termed a watershed. For
purposes of this assessment, an "effective" drainage area will almost always be less than
the total area of a watershed. A "watershed" includes all the land area which contributes
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water to a river system. For large river systems, this area is in the order of thousands of
square miles and includes several tributaries and smaller streams feeding into the main
branch of the river. Each stream and tributary has its own sub-basin, whose sediment
originates from a land area much smaller than thousands of square miles. If the
contaminated site lies within that sub-basin, that it would be appropriate to include only
the area within that sub-basin as the effective drainage area. This is one circumstance
where an "effective drainage area" would be less than a total watershed area. Another
consideration for determining the effective drainage area is the positioning of the
contaminated site with respect to the point where water is extracted for drinking and fish
are caught for consumption. If these points are significantly upstream in the river system
in relation to the contaminated site, there is no reason to conclude that sediments or water
near where the water is extracted are impacted by the contaminated site. If these
withdrawal points are downgradient of the contaminated site, then there is reason to
believe that sediments and water are impacted. However, if they are downgradient from
the contaminated site but not at the bottom of the watershed, then sediment and water
quality further downgradient from the withdrawal points is not of concern and land
draining into these downgradient portions would not be part of the "effective drainage
area". One further possible consideration is how far upgradient in the watershed one
should go when determining the size of the effective drainage area. While sediments
introduced at the furthest points may eventually work their way down to the mouth of the
watershed, this may take geologic time and not recent historic time. Therefore, sediment
quality near a site of contamination need not consider these far reaches.
For a standing water body such as a lake or a pond substantially fed by ground
water recharge, an assumption that probably should be made using the simple framework
of this assessment is that all sediments within the lake/pond are completely mixed.
Therefore, the effective area should equals all area around the lake/pond contributing
sediment, and, as in the above discussion on river systems, a part of the land area
contributing sediments to streams or rivers which may feed the standing water body.
From this discussion, it is clear that determination of an effective drainage area
depends on site specific considerations, but it will likely be less than the total watershed
area. For purposes of demonstration, the effective drainage area, Aw, will be assumed to
be 4,000 hectares (10,000 acres, 15.6 mi2). Furthermore, it will be assumed that the
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water body in question is part of a river system, which mainly impacts the assignment of
the total suspended solids parameter, TSS (as discussed below). This assignment is not
based on any specific sites that have been studied. It is only justified as being a
reasonable size for dilution of contaminated soils which originate from contaminated site.
Given the other area terms discussed above, 0.4, 4, and 10 ha, then the assignment of
4000 ha would appear to add a substantial amount of clean soil for mixing considerations.
A useful data source for this term and the suspended sediment term below, for
specific sites in the United States, is Appendix F in Mills, et al. (1985). This appendix
includes a compilation of data from river and reservoir sediment deposition surveys,
including total drainage area, water body volumes, and rates of sediment deposition
(mass/area-time). A caution in using this and similar data bases when evaluating specific
sites is that, again, these total drainage areas are just that, total areas. Water bodies in
this data base are located in the 48 conterminous states. An estimate of suspended
sediment concentrations can be made using the water volume and the sediment deposition
rates from this data, and an assumption on sediment deposition velocity. The specific
weight of sediments in the water body, also supplied in this appendix, can be used to
estimate sediment deposition velocity.
• SD8 and SDW: These are the sediment delivery ratios applied to the
exposure site and the watershed as a whole. Such a ratio is required because not all the
soil which erodes from an area deposits into the receiving water body. The following
delivery ratio was proposed for construction sites (EPA, 1977):
SDS = (3.28 DL r°-22 <4-10>
where:
SDS = sediment delivery ratio from site of interest, unitless
DL = distance from site to receiving water body, m
3.28 = converts m to ft (empirical equation was developed for units of ft).
Note that the sediment delivery empirical equation simplifies all land features
pertinent to erosion to a function only of length. The equation was developed to estimate
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sediment loads from construction sites to nearby surface water bodies, and from distances
up to 250 m (800 ft, roughly). Without specific information on the sites from which it
was developed, it is assumed that the land area between the construction sites and the
receiving water body is "average" and this relationship can be used for applications other
than construction sites.
As noted in previous bullets, the example scenarios demonstrating the on-site
source category assumed Cs = Cw, and SLS = SLW. The impacted water body was
assumed to be 150 meters away from the site of contamination, also the site of exposure
for the on-site source category. This distance translates to a delivery ratio of 0.26.
Site-specific conditions could result in a larger (steeper slope, e.g.) or smaller proportion of
the eroded soil being delivered to the water body than would be estimated with this
equation.
Figure 4-5 shows a watershed delivery ratio as a function of watershed size (figure
from Vanoni, 1975). As seen, the ratio decreases as land area increases. The total
watershed size assumed for the example scenarios in Chapter 5 was 4,000 hectares, or
40 km2. From Figure 4-5, this translates to a watershed delivery ratio, SD of 0.1 5.
• fs: As soil erodes into the water body, it will settle onto the bottom to
become bottom sediment. Part of the settled material will become resuspended because
of turbulent flow. The finest materials in eroded soil may not settle for a long time, and
essentially always be in suspension. One way to arrive at the fraction of annually eroding
material which remains in suspension ("remains in suspension" for purposes of discussion -
in reality, little, if any, will remain in suspension, but will rather deposit and resuspend)
involves complex modeling. A wealth of such models exist, such as those described in
Wang (1989). The approach used here is more simple than those in Wang (1989).
If an average level of suspended material in the water were specified, in units of
mg/L, what would be known with otherwise required parameters is the total amount of
erosion reaching the water body (as discussed above) as well as the annual water volume
(discussed below). A required parameter for this assessment will therefore be the level of
suspended solids in the water body, TSS. With this parameter and the annual water flow
volume, Vwat, the total suspended load equals, TSS (mg/L) * Vwat (L/yr). The assignment
of these two terms are 10 mg/L and 1.524*1010 L/yr, leading to a total suspended load of
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100
OftAJNAGC APE* (km*)
1000
Source: Vanoni, 1975
Figure 4-5. Watershed delivery ratio, SDW, as a function of watershed
size.
1.524 * 1011 mg/yr, or 1.524M05 kg/yr. Total erosion into the water body, in similar
units, equals, As * SLS * SDS + (Aw - As) * SLW * SDW. With parameter assignments as
discussed above, the total annual erosion equals 3.87 x 106 kg/yr. Therefore, the fraction
of total load that is suspended is 0.04 (1.524*104/3.87*106).
Given this formulation, the fs term is not a model input value, but is solved on the
basis of the other parameters noted.
• TSS: This is the total suspended sediment in the water body. This value
will be lower for standing water bodies such as ponds or lakes as compared to streams or
rivers. The more turbulent flow in rivers will suspend sediments to a greater degree than a
relatively calm lake. A complex modeling exercise evaluating the impact of 2,3,7,8-TCDD
to Lake Ontario assumed a suspended sediment concentration of 1.2 mg/L (EPA, 1990b).
For use in pond or lake settings, an assumption of a suspended sediment concentration of
1-2 mg/L is reasonable. All example scenarios in Chapter 5 assume that the 4,000 ha
watershed drains into a river suitable for supporting fish for consumption and water for
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drinking purposes. General guidance offered for the potential for pollution problems in
rivers and streams as a function of average suspended sediment concentration are: 10
mg/L or less - no problem, 100 mg/L or less - potential problem, and greater than 100
mg/L - probable problem. A cutoff concentration for protection of aquatic life is 80 mg/L
(Mills, et al., 1985). The value assumed for TSS for all example scenarios in Chapter 9 is
10 mg/L, indicating no turbidity problems and a river supportive of fish for consumption.
* Vwat: The stream in the example scenarios will be assumed to derive its
annual flow only from the effective drainage area, Aw. This would imply that the
scenarios are best described as sub-basins ( see the discussion on effective drainage area,
Aw, above). Given the area of drainage, one way to estimate annual flow volume is to
multiply total drainage area (in length squared units) times a unit surface water
contribution (in length per time). The Water Atlas of the United States (Geraghty et al.,
1973) provides maps with isolines of annual average surface-water runoff, which they
define as all flow contributions to surface water bodies, including direct runoff, shallow
interflow, and ground-water recharge. The range of values shown include 5-15 in/yr
throughout the Midwest cornbelt, 15-30 in/yr in the South and Northeast, 1-5 in/yr in the
desert Southwest, and a wide range of 10-40 in/yr in the far West. For this assessment,
an assumed 1 5 in/yr is used to estimate the annual flow volume. Over a 4,000 hectare
drainage area, total flow volume equals 1.524 x 1010 L/yr (15 in/yr * 0.0254 m/in *
4,000 ha * 10,000 m2/ha * 1000 L/m3).
• Kdssed: This adsorption partition coefficient describes the partitioning
between suspended sediment and the water column. For numerous applications for
organic contaminants, particularly for estimating the partitioning between soil and soil
water, this partition coefficient has been estimated as a function of the organic carbon
partition coefficient and the fraction organic carbon in the partitioning media:
Kdssed = Koc OCssed (4-11)
where:
= partition coefficient between suspended sediment and water, L/kg
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Koc = organic carbon partition coefficient for contaminants, L/kg or cm3/gm
= fraction organic carbon content of suspended sediment, unitless.
The organic carbon partition coefficient, Koc, can be a measured value or it can be
estimated. Schroy, et al. (1985) listed an organic solids/water partition coefficient of
468,000 for 2,3,7,8-TCDD. Information in Jackson, et al. (1986), imply that this is a very
low partition coefficient for 2,3,7,8-TCDD. They obtained soil samples contaminated with
2,3,7,8-TCDD from 8 sites in the Times Beach area of Missouri, and 2 from industrial sites
in New Jersey. These contaminated soils had 2,3,7,8-TCDD concentrations ranging from
8 to 26,000 /t/g/kg (ppb), and organic carbon contents ranging from 0.01 5 to 0.08. They
determined soil water partition coefficients, Kds, for these soil samples, and using the
organic carbon fraction data, estimated Kocs for 2,3,7,8-TCDD. The mean Koc from these
ten samples was roughly 24,500,000. EPA (1990b) evaluated the Koc for sorption of
2,3,7,8-TCDD onto Lake Ontario sediments. They concluded that log Koc was greater
than 6.3 (Koc = 2,000,000), but less than 7.3 (Koc = 20,000,000).
In the absence of measured values, the Koc can be estimated from a chemical's
octanol water partition coefficient, Kow. Empirical equations relating Kow to Koc are
listed in Lyman, et al. (1982). Of six different equations listed in that reference, the
following derived by Karickhoff, et al. (1979) is used to estimate the Koc for the example
compounds in Chapter 5:
log Koc = log (Kow) -0.21 (4-12)
where:
Koc = organic carbon partition coefficient, L/kg
Kow = octanol water partition coefficient, unitless
This equation was empirically developed from a limited number of hydrophobic
contaminants (n = 1 0, R2 = 1 .00). It implies that Koc is very similar to Kow for strongly
sorbed compounds such as the dioxin-like compounds. Using the log Kow of 6.64 given
in this assessment for 2,3,7,8-TCDD in Karickhoff's relationship estimates a Koc of
roughly 2,700,000.
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* O^sed' OCS8ed: The organic carbon content of solids and sediments of
water bodies are generally higher than organic carbon contents of the surrounding lands.
Furthermore, organic carbon contents of suspended organic materials and solids are
typically greater than those of bottom sediments. A significant sink for strongly
hydrophobic contaminants such as the dioxin-like compounds is thought to be suspended,
or non-settling, organic material. In modeling 2,3,7,8-TCDD in Lake Ontario (EPA, 1990b)
using the WASP4 model, a compartment separate from suspended solids termed "non-
settling organic matter" served as a permanent sink. For purposes of this assessment, a
single reservoir of suspended materials onto which incoming dioxin-like compounds sorb is
principally characterized by OCssed, and the values selected for OCsed and OCssed should
reflect the relative partitioning behavior of suspended and bottom materials. As noted
above, these water body carbon contents are also related to the organic carbon contents
of surrounding soils. The model parameter, OCS|, is the soil organic carbon fraction and is
required for modeling of soil contamination by dioxin-like compounds. Foth (1978) lists
the organic nitrogen content of several soil types ranging from sand and sandy loam to
clay. The range from that list is 0.0002 - 0.0024 on a fractional basis. Assuming a
carbon to nitrogen ratio of 10 (Brady, 1984; who notes that C:N ratios vary from 8 to 15,
with the typical range of 10 to 12), organic carbon ranges from 0.002 to 0.024. A soil
organic carbon fraction, OCS|, is assumed to be 0.01 for all example settings in Chapter 9,
which is in the middle of this range. The organic carbon content of bottom sediments,
OCsed will be higher at 0.03. Bottom sediments originate as erosion from surrounding
land, but also include decay of organic materials within water bodies. The organic carbon
content of suspended materials can approach 0.20, but OCssed will be assumed to be 0.05
for the example settings in Chapter 5.
4.3.2. Vapor-Phase Air Concentrations
The algorithms for estimating vapor-phase concentrations of contaminants were
presented and derived in Hwang, et al. (1986). These procedures were developed for soil
surface and subsurface contamination with polychiorinated biphenyls, PCBs. The models
are based on the assumptions that: 1) PCBs move through the soil primarily by vapor
phase diffusion, i.e., leaching is not considered, 2) PCB vapor in the soil matrix reaches a
local equilibrium with pore air, 3) degradation processes for PCBs were not considered ,
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and 4) the PCB contamination occurs at the surface and extends down infinitely. These
assumptions are similar to the general types of assumptions that have been made for all
the algorithms estimating exposure media concentrations in this assessment. The
procedures in that PCB assessment were also used for this assessment. Details of the
derivation are presented in Hwang, et al. (1986).
The average flux rate over an exposure duration of ED can be estimated as:
= (2) (Eslp) (Dea) (Cs) (H) (41)
Kds I (n) (I) (ED) ]°-5
(4-13)
where:
FLUX =
slp
H
Kds
ED
I
average volatilization flux rate of contaminant from soil, g/cm2-s
soil pore porosity, unitless
effective diffusivity of contaminant in air, cm2/s
contaminant concentration in soil, ppm or mg/kg
Henry's Constant of contaminant, atm m3/mol
soil/water partition coefficient, cm3/g
exposure duration, s
interim undefined term for calculation, cm2/s
Pea ES!D
Psoil
Eslp + Psoil (1-Es,p)[Kds/(41 H)]
particle bulk density of soil, g/cm3.
The effective diffusivity, Dea/ is solved as a function of contaminant diffusivity in
air, and soil pore porosity:
where:
'ea
-sip
Ł , 0.33
'••sip
effective diffusivity of contaminant in air, cm2/s
molecular diffusivity of contaminant in air, cm2/s
soil pore porosity, unitless.
4-28
(4-14)
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The soil adsorption partition coefficient, Kds, is given as:
Kds = Koc OCS/ (4-15)
where:
Koc = contaminant organic partition coefficient, L/kg
OCsi = fraction organic carbon in soil, unitless.
It is noted in Hwang, et al. (1986) that this procedure would tend to overestimate
emissions and resulting exposures in situations involving small spills which would not
involve deep contamination. It is also noted that the average flux rate is inversely
proportional to the square root of the duration of exposure - the longer the duration of
exposure, the lower will be the average flux rate. Whereas this solution assumes an
unlimited reservoir of contaminant, it is an unsteady state solution (unlike other solution
strategies) and is essentially an average flux rate over an amount of time defined by the
exposure duration. Inherent in the solution was the consideration that residues dissipate
by volatilization at the surface layers, resulting in contaminants diffusing upwards from
deeper soil layers over time. With this longer path of diffusion, volatilized amounts
decrease, and hence the average flux over time also decreases.
Vapor-phase concentrations along the center (y = 0.0) of an area source can be
estimated from (Hwang, 1987):
r _ (2//7)0-5 FLUX a 1010 erf(e) ._ -0.5 (Z/sz)2
-
u
._ -0.5 (Z/sz) . /4,6)
It? ; l-r i w;
where:
Cva = vapor-phase concentration of contaminant in air, yi/g/m3
FLUX = Average volatilization flux rate of contaminant from soil, g/cm2-s
a = side length parallel to the wind direction, m
Um = mean annual wind speed, m/s
Sz = vertical dispersion coefficient in air, m
z = height of the exposed individual, m
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erf = error function
e = error function term, unitless
= b/(2*(2*Sy)'5)
b = side length perpendicular to the wind direction, m
Sy = horizontal dispersion coefficient in air, m
1010 = converts g/cm2-m to/yg/m3.
This was the model used to estimate on-site vapor-phase concentrations. The
dispersion terms, Sz and Sy can be estimated using site-specific wind rose data. In the
absence of data, these terms can be estimated assuming the most common stability class,
D, as:
Sy = 0.1414 X0-894 (4-17a)
Sz = 0.222 X0-725 (4-17b)
where:
S = horizontal and vertical dispersion coefficient, m
X = distance upwind of the contaminated site, m.
Background on Koc and OCs[ were given in Section 4.3.1. above. Guidance for
other terms in this algorithm now follow.
• Es( Porosity is defined as the pore space in soils occupied by air and
water, and for sandy surface soils show a range of 0.35-0.50. Medium to fine-textured
soils (loams, clays, etc.) show a higher range of 0.40-0.60 (Brady, 1984). Soil porosities
in the example settings were 0.50.
• H: Henry's Constants were discussed in Volume 2, Chapter 2. The
values of H used for the three example compounds were: for 2,3,7,8-TCDD - 1.65*10"5
atm m3/mole; for 2,3,4,7,8-TCDF - 4.99*10"6 atm m3/mole; and for 2,3,3',4,4',5,5;-
HPCB - 1.00MO3 atm m3/mole.
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• Pgoi(: Particle bulk density is defined as the mass of a volume of soil
solids. This contrasts the more common parameter, bulk density, which is the mass of a
unit of dry soil, which includes both pores and solids. Particle bulk density, PsoM, has a
narrow range of 2.60 to 2.75, and for general calculation purposes, Brady (1984)
recommends a value of 2.65 for average mineral surface soils, the value used for the
example settings.
• ED: The exposure duration is simply the amount of time individuals are
exposed. Two exposure durations were used in the demonstration scenarios, 9 years for
"central" and 20 years for "high end" exposures. Used in this algorithm, and as discussed
earlier, longer exposure durations translate to lower average volatilization fluxes. This
presumes a soil concentration assumed to be uniform over depth starting at time zero, and
to become depleted over time. The selected exposure durations of 9 (2.83M08 sec) and
20 years (6.31 *108 sec) was used.
• Dc: Molecular diffusivities in air of the example compounds could not be
found in the literature. However, diffusivities of one compound can be estimated from
«
another with the following (Thibodeaux, 1979):
(4-18)
D
b
where:
Da b = Molecular diffusivities of compounds a and b, cm2/s
MWa b = Molecular weights of compounds a and b, g/mole
Thibodeaux (1979) lists the molecular diffusivity of diphenyl at 25 C at 0.068. Given the
molecular weight of diphenyl of 154 g/mole, the diffusivities of the example compounds
are: 2,3,7,8-TCDD (MW = 322) = 0.047; 2,3,4,7,8-TCDF (MW = 340) = 0.046; and
2,3,3',4,4',5,5'-HPCB (MW = 396) = 0.043.
• Um: Mean annual windspeeds vary from between 2.8 and 6.3 m/s (EPA,
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1985b). An assumption of 4.0 m/s in the absence of site-specific average wind speeds
was made for the example scenarios of this assessment.
• a, b, z, and x: Simple assumptions can be made to assign values to the
length terms above: a, b, z, and x. Assuming a square-shaped contaminated site, a equals
b which equals the square root of the area of the site. A common assumption for z, the
height of the exposed individual, is 2 m. The x term can be assumed equal to a side
length (a or b), or can equal the side length plus the distance to the exposed individual if
the contamination is not on-site and dispersion is modeled as "near field." For the
residence and farm setting examples in Chapter 5, where the contamination was on-site,
the x term was equal to a side length.
4.3.3. Particulate-Phase Air Concentrations
The method for determining the flux of soil particles due to wind erosion for on-site
conditions was developed in EPA (1985b) based on Gillette's (1981) field measurements
of highly erodible soils. A key assumption for this solution is that the soil surface is
assumed to be exposed to the wind, uncrusted, and to consist of finely divided particles.
This creates a condition defined by EPA (1985b) as an "unlimited reservoir" and results in
maximum dust emissions due to wind only. This wind erosion flux is given as (EPA,
1985b):
Ee = 0.036 (1-V) (Um/Ut)3 F(x) <4-19)
where:
E_ = total dust flux of < 10 fjm particle due to wind erosion, g/m2-hr
-e
V = fraction of vegetation cover, unitless
Um = mean annual wind speed, m/s
Ut = threshold wind speed, m/s
F(x) = a function specific to this model.
EPA (1985b) provides details allowing for the application of this equation under a
variety of circumstances. The following is offered as guidance specific to on-site
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conditions:
• V: For a "residence" or "farm", grass or crops are likely to substantially
cover the soil, and the fraction of vegetative cover can range from 0.5 (minimal coverage)
to 0.9 (more lush coverage). For the residence example settings, V was set at 0.9 which
assumes a continual grass cover over the contaminated soil. The V for the farm settings
was instead 0.5. The area of contamination for the example farm settings was larger than
the residence setting, 10 acres to 1 acre. The land where crops were grown was also
contaminated; the 0.5 value for V assumes that the cropland is totally or partially bare at
some times - perhaps during spring land preparation and fall harvest.
• Um, Ut: As given above in Section 4.3.2. on vapor phase air
concentrations, the mean annual wind speed, Um, assumed in the example scenarios was
4.0 m/s. The threshold wind velocity, Ut, is the wind velocity at a height of 7 m above
the ground needed to initiate soil erosion. It depends on nature of surface crust, moisture
content, size distribution of particles, and presence of non-erodible elements. It can be
estimated on the basis of the following procedure (EPA, 1985b):
Step 1. Determine the Threshold Friction Velocity
This is the wind speed measured at the surface needed to initiate soil erosion. EPA
(1985b) shows how it can be determined as a function of soil aggregate size distribution.
However, for the "unlimited reservoir" approach for which Equation (4-19) was developed,
soil particles are assumed to be fine at 1.5 mm or less as an average. This translates to a
threshold friction velocity of 75 cm/s and less. A value of 50 cm/s might be reasonably
assumed to be representative of these types of surfaces, and was assumed for this
assessment.
Step 2. Estimate the "Roughness Height"
EPA (1985b) graphically shows the roughness height for a range of possible
conditions. Included in this range are a roughness height of 0.1 cm for natural snow, 1.0
cm for a plowed field, 2.0-4.0 cm for grassland, 4.0 cm for a wheat field or for suburban
residential dwellings, and up to 1000 cm for high rise buildings. The assumption made for
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the residence and farm example settings was 4.0 cm, following the information given for a
wheat field or a suburban residence.
Step 3. Estimate Ratio of Threshold Wind Speed at 7 m to Friction Velocity
A chart provided by EPA (1985b) shows this ratio as a function of roughness
height. For a roughness height of 4.0 cm, this ratio is seen to be 13.
Step 4. Estimate Threshold Wind Speed
This is simply the product of the ratio given in step 3 above and the friction
velocity. Using values given above, 50 cm/sec * 13 = 6.5 m/sec.
• F(x): The model-specific function, F(x), is determined by first calculating the
dimensionless ratio x, where x = 0.886 Ut/Um, and finding F(x) from a chart of F(x) versus
x, as provided in EPA (1985b). For Ut = 6.5 and Um = 4.0, x = 1.44 and F(x) = 1.05.
The unit dust flux is easily converted to a total contaminant flux by multiplying by
soil concentration and area:
WE = (2.8 x 10'13 ) Cs Ee As <4-20>
where:
WE = contaminant wind erosion emission rate, g/s
E = total dust flux of < 10 //m particle due to wind erosion, g/m2-hr
6
Cs = contaminant concentration in soil, ppb or ng/g
A = area of contaminated site, m2
SC
2.8*10"13 = converts ng/hr to g/sec.
The next step in estimate particulate-phase contaminant concentration is to
estimate the dispersion term. The model that is used is the same as the one used to
estimate on-site vapor phase dispersion given in Equation (4-16) above. The following two
changes obtain the correct "FLUX" term for use in Equation (4-16):
1) Instead of WE, a total flux term presented here in units (g/sec) consistent with
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other particulate flux terms discussed in this chapter, an appropriate "FLUX" for Equation
(4-16) is a unit flux term: Cs*Ee (ng/m2-hr). Since the algorithm for Ee was developed for
10 /;m size particles, the multiplication of Ee by Cs assumes that the concentration of
contaminant on 10 /ym size particulates is the same as that for the soil overall.
2) Cs*Ee is still not in the right units for Equation (4-16). The conversion term of
Equation (4-16), 1010, should instead be, .00028.
Substituting Cs*Ee for FLUX, and .00028 for 1010 in Equation (4-16) will allow for
the estimate of Cpa, the particulate phase concentration of contaminant in air, in units of
//g/m3.
4.3.4. Biota Concentrations
This section summarizes the algorithms to estimate contaminant concentrations in
fish, vegetation (including vegetables for human consumption and pasture grass or fodder
grown on contaminated soil for beef cattle consumption), beef, and milk. As will be
shown, all algorithms are simple empirical equations which relate an environmental media
concentration to a biota concentration, using a "biotransfer" or "bioaccumulation" factor.
4.3.4.1. Fish concentrations
The procedure and supportive data for the algorithm to estimate fish tissue
concentrations can be found in Cook, et al. (1991), and more recently in an assessment of
risk of 2,3,7,8-TCDD to aquatic life and associated wildlife (EPA, 1993) which EPA is
conducting as part of its reassessment of dioxin-like compounds. The information in those
reference focuses on 2,3,7,8-TCDD, although there is discussion on the related
compounds covered in this assessment including other PCDDs, PCDFs, and PCBs. Thase
compounds share a high degree of hydrophobicity that increases as the degree of
chlorination increases. Cook, et al. (1991) note that this corresponds in general to an
increase in lipophilicity and an increase in ability to bind to organic carbon in sediments and
to dissolved organic matter in water. However, these tendencies are not paralleled by an
increase in bioaccumulation. Only the 2,3,7,8-chlorine-substituted congeners are
substantially bioaccumulated by fish, although large quantities of other PCDD and PCDF
congeners are found in sediments. This pattern of bioaccumulation results because of
higher rates of metabolism of PCDDs and PCDFs in fish as compared to the
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2,3,7,8-chlorine-substituted congeners (EPA, 1992; Cook, et al., 1991, with references to
Muir et al., 1986; Gobas, 1990). While the highly chlorinated 2,3,7,8-substituted
congeners are very slowly accumulated, they have very slow elimination rates.
2,3,7,8-TCDD and other planar polyhalogenated aromatic hydrocarbons often have
not been detected in water from aquatic ecosystems even when biota are highly
contaminated. Because surface layers of bottom sediments are a good indicator of the
relative amount of chemical in the system over the time scale involved for bioaccumulation
of super-hydrophobic chemicals, a term known as the Biota to Sediment Accumulation
Factor, or BSAF, has been offered as a measure of site-specific bioaccumulation potential.
This term was recently proposed to replace equivalent terms which were known as the
Bioavailability Index, or Bl (Kuehl, et al., 1987; Cook, et al., 1991; EPA, 1990b), the
Accumulation Factor, AF (Lake, et al., 1990) and the Biota to Sediment Factor, or BSF
(Parkerton, et al., 1993; Parkerton, 1991; Thomann, et al., 1992). BSAF is defined as:
BSAF = C'ipid (4-21)
Loc
where:
BSAF = biota to sediment accumulation factor, unitless
^lipid = concentration of contaminant in lipid of fish, mg/kg,
Coc = concentration of contaminant in bottom sediment organic carbon,
mg/kg
The surface water algorithms estimate concentration of contaminant in bottom
sediments (see Section 4.3.1 above). This concentration, Csed, can be converted to an
organic carbon basis as a function of OCsed:
C = Csed (4-22)
ocsed
where:
Cnr> = concentration of contaminant in bottom sediment organic carbon,
OC
mg/kg;
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Csed = concentration of contaminant in bottom sediment, mg/kg;
d = fraction organic carbon in bottom sediment, unitless
The organic carbon content of bottom sediments was assumed to 0.03; see Section 4.3.1 .
for the derivation of Csed.
Since the accumulation of contaminant is assumed to occur only in fish lipid, a
correction term to estimate the whole fish tissue concentrations is needed since fish
consumption in g/day refers to whole fish consumption. The correction term is simply
f|ipid, and so whole fish concentrations are simply C,ipjd * f|ipid.
The BSAF was developed as a measure of bioaccumulation potential rather than as
a predictor, as it is being used here. It is uncertain as to whether measured BSAFs are
generally applicable to other water bodies. Efforts are underway to evaluate the general
applicability of BSAFs (P. Cook, Duluth Environmental Research Laboratory, US EPA, 6201
Congdon Boulevard, Duluth, MN 55804, personal communication). Using the BSAF
approach as a predictive tool greatly underplays the complexity of the processes
transferring contaminants from aquatic ecosystems to aquatic organisms. EPA (1993)
provides a comprehensive discussion on aquatic impacts and processes for 2,3,7,8-TCDD
and related compounds. Following are some of the key issues to consider:
1) Resident vs. Migratory Species: Parkerton (1991) applied a
bioenergetics-based bioaccumulation model in an attempt to duplicate BSAFs for
2,3,7,8-TCDD found for carp and blue crabs in the Passaic River, New Jersey. He showed
nearly a ten-fold difference in 2,3,7,8-TCDD BSAF calculated from data for resident
species as compared to migratory species in the Passaic River. This would be expected
for fish which also reside part of the time in relatively clean water bodies; migration would
enable depuration of residues from fish. The possibility that migration patterns might
explain some of the results for fish concentrations of 2,3,7,8-TCDD in the Lake Ontario
bioaccumulation study was also raised (EPA, 1990b). That assessment also discussed a
related issue of concern - to consider lakewide average sediment concentrations or
concentrations near where sampled fish were captured in calculating the BSAF. Even
within a large lake, more sedentary populations of fish may be impacted by localized
contamination.
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2) Past history of contamination: If contamination of surface water bodies
with hydrophobic compounds like the dioxin-like compounds has occurred principally in the
past, then it can be expected that most of the contamination occurs in or near the bottom
sediment layer and not within the water column. Furthermore, if inputs to water bodies
are declining or low in comparison to past loadings, then sediments would be undergoing
depuration - residue levels would be declining, and the system may not be equilibrium.
EPA (1990b) noted that very low BAF*s (defined as a fish to sediment ratio not including
the sediment organic carbon and fish lipid considerations of BSAFs) and BSAFs for
2,3,7,8-TCDD in Lake Ontario contrasts higher BAF*s for other hydrophobic compounds
such as DDE or PCBs. An explanation offered is that loadings to the Lake may be
declining, such that there is a substantial disequilibrium between sediments, water, fish,
and their prey. One parameter required in the bioenergetics model Parkerton (1991) used
(referred to in the above bullet) was a ratio of contaminant concentration in bottom
sediment to that in suspended sediment, rs/rw. In modeling exercises on the Passaic
River, he found closer agreement between measured and predicted BSAFs with this ratio
equal to 10 in contrast to 1, the only two values tested; a ratio of ten means that the
concentration of contaminant in bottom sediment is ten times higher than it is in the
suspended sediment. BSAFs predicted by the model were developed as the ratios in
modeled fish lipid concentrations divided by modeled bottom sediment organic carbon
normalized concentrations. Measured BSAFs used actual Passaic River fish lipid and
bottom sediment concentrations of 2,3,7,8-TCDD. BSAFs predicted with this ratio equal
to 1 were roughly 4 times as high as measured BSAFs, and BSAFs found with rs/rw equal
to 10 were twice as high as measured. A related result of his modeling exercise was that,
at best fit between modeled and measured BSAFs where the rs/rw was 10, dietary
exposures explained over 50% of the BSAFs for carp and 85% in blue crabs, in contrast to
water column exposures. He speculates that prey organisms consist of benthic animals
which ingest contaminated bottom sediment. If the food chain begins near bottom
sediments, and if food chain exposures are a principal explanation for fish tissue dioxin
concentrations, than it follows that a model would perform better when bottom sediment
concentrations drive fish tissue concentrations rather than water column concentrations,
or equivalently, when rs/rw = 10. Finally, he notes that 2,3,7,8-TCDD contamination in
Passaic river largely occurred as a result of historical loadings. The picture that emerges
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from Parkerton's modeling is as follows: sediments are serving as an internal source of
contaminants due to past historical loadings, and the water column is in disequilibrium
with bottom sediments and driven only by depuration of bottom sediment concentrations.
The bioaccumulation of these compounds in carp and blue crabs appears to be mediated
by trophic transfer via the benthic foodweb. In both the Lake Ontario and Passaic River
studies, concentrations of 2,3,7,8-TCDD were higher in deeper bottom sediments as
compared to surficial bottom sediments - this implies historical loadings and possibly
depuration of surficial residues.
This issue is non-trivial for the methodology of this assessment, since an
assumption for deriving suspended and bottom sediment concentrations is that the
contamination is ongoing, and that the hypothetical water body may be closer to a state of
equilibrium as compared to situations where contamination was principally in the past.
The BSAF assumed for 2,3,7,8-TCDD in the demonstration scenarios of 0.09 is more in
line with data from EPA (1990b) on Lake Ontario and from Parkerton (1991) from data in
Passaic River, then with other data (presented later) where historical loadings are not as
clear a principal source of bottom sediment contamination. The issue of ongoing versus
historical contamination should be considered when assigning site-specific BSAFs.
3) Variations among fish species: Feeding habits, age, migratory patterns, and
lipid contents (including lipid content of edible vs. inedible fish tissues) are just a few of
the interacting factors which determine a site-specific BSAF as a function of fish species.
The demonstration of this approach in Chapter 5 assigns a single BSAF to each of the
three example contaminants. Although not unlike other simplifications of this assessment,
such approaches are recognized as oversimplifications.
4) Study designs to obtain BSAFs: Although there is some evidence that
BSAFs specific to a contaminant may be applicable to other aquatic settings, data to
evaluate such a hypothesis is still sparse. Even data sets that do exist need to be carefully
evaluated before deriving BSAFs. Such an evaluation should consider sediment as well as
fish species data. Critical factors for sediment sampling include location, number, depth of
sampling, variability, availability of organic carbon fraction information, and so on. Similar
issues are pertinent for fish sampling and analysis.
Following now are guidance for the terms required for estimating fish tissue
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concentrations.
• BSAF: Table 4-1 summarizes literature from which biota sediment
accumulation factors for dioxin and furan congeners could be developed. Only five sets of
data were found in the literature. The data from the Wisconsin River (Kuehl, et al. 1987)
and that from 1 lake in Sweden (Kjeller, et al. 1990) both show decreasing BSAF with
increasing chlorination. The BSAF of 2.94 for 2,3,7,8-TCDD determined from a lake in
Sweden should be questioned since it is more than an order of magnitude different than
any of the other data. Causes for this discrepancy could be manifold. Some observations
from Kjeller, et al. (1990) might shed some light on this result. Although sediment data
was from three water bodies, 8 of the 9 Pike samples (pike samples were composites of
2-5 fish from one location in the water body) were from one of the water bodies. This is
why only data from the one water body was summarized in Table 4-1. This water body,
Lake Vanern, was clearly the most contaminated of the three water bodies studied. A
paper mill was located at the northern part of this lake and the authors concluded that
discharges from this mill impacted the lake. The average of 2,3,7,8-TCDD and 2,3,7,8-
TCDF organic carbon normalized concentrations for five sediment samples from this lake
was 297 pg/g; the analogous average concentration for 10 samples taken from another
lake, Lake Vattern (6 samples), and a river, Dala (4 samples), was 65 pg/g. A similar
disparity between Lake Vanern and the other water bodies is found with the
penta-CDD/CDF concentrations: 205 pg/g vs. 108 pg/g, with similar comparisons for the
hexa-, hepta, and octa-CDD/CDF. The sediment and corresponding pike sample nearest
this mill had the highest concentrations reported - pike samples were given as 3000 and
833 pg/g lipid normalized 2,3,7,8-TCDF and 2,3,7,8-TCDF (a composite from 5 pike taken
at this sampling station), respectively, and sediment was 1800 and 244 pg/g organic
carbon normalized for 2,3,7,8-TCDF and 2,3,7,8-TCDD. Note the BSAF for 2,3,7,8-TCDD
implied from this data point is 3.41. Another consideration for high BSAFs might be the
source of contamination. Speculation from the Lake Ontario and Passaic River field data
was that contamination principally occurred in the past, whereas in the Swedish data,
contamination appears to have been ongoing at the time of sampling. This might be one
indication that BSAFs for aquatic systems where contamination is ongoing might be
greater than from systems where the contamination is primarily historical.
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Table 4-1. Available Biota to Sediment Accumulation Factors, BSAF, for dioxin-like compounds.
Fish Water
Reference/Congener Species Body
Kuehl, et al., 1987 Carp Wisconsin
River
2,3,7,8-TCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDD
1,2,3,6,7,8- &
1,2,3,4,7,8-HxCdd
1,2,3, 6,7, 8-HxCDF
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8-HpCDF
US EPA, 1990b Lake
Ontario
2,3,7,8-TCDD Brown Trout
Lake Trout
Smallmouth Bass
White Perch
Yellow Perch
Kjeller, et al., 1990
Pike Lake
2,3,7,8-TCDD Vanern
1,2,3,7,8-PeCDD in Sweden
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
OCDD
2,3,7,8-TCDF
1 2348/1 2378-PeCDF
2,3,4,7,8-HxCDF
# Sed. samples
# Fish samples BSAF
1/1
0.27
0.06
0.06
0.035
0.037
0.0048
0.0033
55/81 0.03
55/81 0.07
55/14 0.05
55/38 0.20
55/77 0.03
4/6
2.94
1.03
0.17
0.086
0.018
0.002
1.40
0.25
0.71
Comments
Laboratory flow through experiment using Wisconsin
River sediment and Lake Superior water; BSAFs
determined from one "representative" sediment sample
and one "composited" fish sample; sediment organic
carbon and fish lipid contents given in article; no
other details provided.
Comprehensive field study on bioaccumulation of
2,3,7,8-TCDD in Lake Ontario; BSAFs are estimated
given 55 sediment samples and specific number of fish
samples as noted; report evaluates matching fish with
with sediment data from sites where fish were caught.
Results presented at right derived from data in
Kjeller, et. al (1990); data includes sediment
samples from four sites in Lake Vanern and 6
composited (2-5 fish in composite) pike associated
with the four sites; pike concentrations reported
in srticls on 3 Hold bssis* Ldks Vflnsrn is n@3r 9
paper mill.
(continued on next page)
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Table 4-1. (cont'd)
Fish Water
Reference/Congener Species Body
Kjeller, et al. (1990) (cont'd)
1 23479/1 23478-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8-HpCDF
OCDD
Parkerton, T.F. 1991 Passaic
River
2,3,7,8-TCDD Resident fish
Migratory fish
Blue Crab
n Sed. samples
# Fish samples BSAF
0.036
0.065
0.27
0.047
0.0009
0.023
0.006
0.0001
61/11 0.081
61/15 0.009
61/14 0.055
Comments
7 "resident" fish species were best represented by
carp; "migratory" species were eel and striped bass;
BSAFs at left are for 2,3,7,8-TCDD, given 61 bottom
sediment samples and specific number of fish samples
as noted; TCDD contamination attributed to historical
industrial input, particularly a 2,4,5-T plant operation
1940s to 60s.
Connecticut Department of 21 different
Environmental Protection (CDEP, 1992) water bodies
2,3,7,8-TCDD
2,3,7,8-TCDF
2,3,4,7,8-PCDF
Total TEQ
Carp
Channel catfish
White catfish
White sucker
Brown bullhead
Yellow perch
346/521
346/521
346/521
346/521
0.86
0.25
0.47
0.24
Data supplied by CDEP (1992); complete data
description, study design, and interpretation in
Chapter 7, Section 7.2.3.2. CDEP established a
monitoring program to evaluate the impact of newly
operating resource recovery facilities to soil,
sediment, and fish. BSAFs at left are for the four
congeners; they are derived from the average of 346
sediment samples and 521 total fish samples covering
the six species noted at left.
(continued on next page)
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Table 4-1. (cont'd)
Reference/Congener
Fish
Species
Water
Body
ft Sed. samples
# Fish samples
BSAF
Comments
US EPA, 1993
2,3,7,8-TCDD
different
water bodies
Smelt
Sculpin
Herring Gull
Bullhead
Sandworm
Clam
Shrimp
0.04
0.12
0.43
0.05
0.48
0.93
0.73
Results compiled by EPA (1993) for 2,3,7,8-TCDD;
details can be found in each study:
Batterman, et al. (1989)
same
EPA (1990b) and Braune and Norstrom (1989)
Cook (unpublished) as listed in EPA (1993)
Rubinstein, et al. (1983)
same
same
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The Swedish data also illustrates some of the complexities of interpreting literature
data. First, the sediment data was expressed concentrations normalized to "sediment
contents of organic material" (sic). This was interpreted as organic matter normalized, not
organic carbon normalized. Parkerton (1991) assumed that organic carbon was 45% of
organic matter to derive BSAFs when organic carbon data was unavailable; following this
lead, organic matter normalized concentrations in Kjeller, et al. (1990) were divided by
0.45 to arrive at organic carbon normalized concentrations. Also, there was not an exact
match in "sites" between sediment samples and fish samples; these sites were physical
locations within the large lake where samples were taken. There were five sites where
sediment samples were taken, and five sites where composited pike samples were taken in
Lake Vanern. However, one of the sediment and one of the pike samples were from
unique sites; only four sites had both sediment and pike samples. The results in Table 4-1
were derived using average sediment and pike concentrations from only these four sites.
Another way to have derived BSAFs would be to average all lake sediment and pike
concentrations; since there may be some relationship between sediment and pike
concentrations based on lake location, it was decided to include only the four sites with
both fish and sediment samples. Finally, there were two sets of results listed for
1,2,3,4,6,7,8-HpCDF as though there were two unique sets of analyses for the same
congener; this is why there are two entries for this congener in Table 4-1.
A complete discussion of the data generated by the Connecticut Department of
Environmental Protection (CDEP, 1992) is included in Chapter 7, Section 7.2.3.2.
Generally, water bodies tested were mostly in rural/suburban settings rather than urban
settings. Concentrations of 2,3,7,8-TCDD in surface soils and bottom sediments were in
the low ppt level, indicating background impacts. BSAFs generated with that data were
0.24 to 0.85 for TEQs, 2,3,7,8-TCDD, 2,3,7,8-TCDF, and 2,3,4,7,8-PCDF.
Excluding the Swedish data, there are 26 reported BSAFs for dioxin-like congeners
in Table 4-1. These range from 0.009 to 0.93, with lower BSAFs associated with higher
chlorinated congeners. A BSAF of 0.09 will be assumed for 2,3,7,8-TCDD in the
demonstration scenarios in Chapter 5. Although there is indications of declining BSAFs
with increasing chlorination, there is probably not sufficient grounds to assign a BSAF for
the second example compound, 2,3,4,7,8-PCDF, significantly different from that of
2,3,7,8-TCDD. The BSAF for this example furan will also be 0.09. In demonstrating the
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suite of dioxin-like congeners for the stack emission scenario, a profile of BSAF values is
crafted generally reflecting the trend of lower BSAF for higher chlorinated congeners, but
this profile cannot be rigorously defended, for obvious reasons.
It should be noted that all bioconcentration or biotransfer parameters, such as the
BSAF, are qualified as second order defaults for purposes of general use. Section 6.2. of
Chapter 6 discusses the use of parameter values selected for the demonstration scenarios,
including a categorization of parameters. Second order defaults are defined there as
parameters which are theoretical and not site specific, but whose values are uncertain in
the published literature. The parameter values in this category should be considered
carefully by users of the methodology.
EPA (1990b) estimates BSAFs for PCBs and other selected chemicals (DDE, HCB,
etc.) for Lake Ontario from several data sets. Parkerton, et al. (1993) summarizes BSAFs
for PCBs and other compounds from other water bodies using other data sets. A selected
summary by water body taken from these two sources for PCBs is given in Table 4-2.
Two trends are apparent. First, the BSAFs for PCBs appear to exceed those of the
dioxin and furan congeners by an order of magnitude and more. Second, and from limited
data, it would appear that BSAFs increase from dichloro- through hexa- or perhaps
hepta-chloro PCBs, and then decrease thereafter. An assignment of a BSAF for
2,3,3',4,4',5,5'-HPCB is not apparent from the data summary below. The data point from
Siskiwit for the single heptachloro-PCB, which was 2,2',3,4',5,5',6-HPCB, was estimated
by Parkerton (1991) as 12.5. The BSAF for flounder from New Bedford Harbor estimated
by Parkerton (1991) was 0.84, with BSAFs for lobster and crab as 1.29 and 2.74,
respectively. A value of 2.00 is assigned to 2,3,3',4,4',5,5'-HPCB for the example
scenarios in Chapter 5.
Finally, it should be noted that these assignments are based on data on vertebrate
rather than invertebrate aquatic species. It is generally recognized that invertebrates do
not possess the enzymatic capability to metabolize hydrophobic compounds as effectively
as higher chordates. As a result, invertebrate species such as mussels, clams, oysters,
shrimp, crabs and lobsters may have BSAF values much higher than those observed for
fish. Parkerton (1991) and Parkerton, et al. (1993) reviewed the literature to estimate
BSAFs of 1 to 5 for species including grass shrimp, sandworms, deposit feeding clams,
and blue mussel for PCDD/PCDFs and PCBs.
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Table 4-2. Available Biota to Sediment Accumulation Factors, BSAF, for PCBs.
Congener
Fish
Species
Water
Body
BSAF
Comments
PCB
trout, salmon, Lake Ontario 1.40, 0.77 Compiled in EPA (1990b) from several data sources, years of study, and fish species.
perch, bass 0.52, 0.86, Summary in this table includes all uniquely derived BSAFs for PCBs for species noted.
3.35, 1.42, PCBs not further identified except BSAF value of 0.58 specific to Aroclor 1254.
trichloro-PCB
tetrachloro-PCB
pentachloro-PCB
hexachloro-PCB
heptachloro-PCB
octachloro-PCB
lake trout,
whitefish
Siskiwit Lake
0.45-2.6
0.71-1.3
3.4-9.4
2.9-20.8
12.5
2.2-12.7
Compiled by Parkerton, et al. (1993) from data in Swackhammer, et al. (1988)
and Swackhammer and Hites (1988); Parkerton presents data for individual
congeners - summary at left aggregates by chlorination and includes both fish
species; only one data point presented for heptachloro-PCB.
Total PCB
Three species
of marine fish
Rio de La Plata
Argentina
4.40
Determined by Parkerton et al. (1993) from Columbo, et al. (1990) on total PCBs;
Columbo reference also has data on PCB IUPAC congeners 5-8, 14, 19, 28-31, 52,
101, 110, 138, 153, 180.
dichloro-PCB
trichloro-PCB
tetrachloro-PCB
pentachloro-PCB
hexachloro-PCB
heptachloro-PCB
octachloro-PCB
nonachloro-PCB
New Bedford
Harbor
flounder,
lobster, crab
0.11-0.59
0.26-0.65
0.65-1.02
1.05-2.08
1.29-4.00
0.84-2.74
0.23-1.17
0.02-0.38
Compiled by Parkerton, et al. (1993) from data in BOS (1990); summary at left
is the range of values specific to the PCB congener grouping, and averaged across
noted species.
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* *iipid: L'P'd contents of edible fish species have not been compiled, although
such a compilation would clearly be useful if applying a BSAF in an assessment mode such
as is done here. BSAFs are typically developed on the basis of whole fish lipid content, so
estimates of whole fish concentrations should be made with a whole fish lipid content.
Parkerton, et al. (1993) cautions, however, that lipid contents of edible portions of fish
may be lower than lipid contents of some of the fish portions that were sampled and used
to develop BSAFs. Non-edible high lipid content portions include, for example, liver and
hepatopancreas. Parkerton, et al. (1993) develops the parameter, /?, which is defined
as the ratio of the lipid content of the edible portion and the sampled tissue. To
demonstrate the impact of this ratio, Parkerton used data from Niimi and Oliver (1989)
which included PCB and other halocarbon compound concentration in whole fish and fillets
of fish taken from the Great Lakes. The /? (defined here as the ratio of lipid in fillet to lipid
of whole fish) for these fish, which included brown trout, lake trout, rainbow trout, and
coho salmon, ranged from 0.22 to 0.51. The ratio of fillet to contaminant concentrations
ranged from 0.20 to 0.54.
In the context of the current model, concentrations in fish for estimating exposure
are estimated as the product of: organic carbon normalized bottom sediment
concentrations * BSAF * f|ipid. BSAFs (in theory) are independent of fish tissue being
sampled - they are ratios of the organic carbon normalized concentration and fish lipid
concentration. Users should be aware, however, that the f|ipid value assigned should
correspond to the fish concentration of interest - that could be whole fish if the model is
used in validation exercises or edible fish if the model is used for exposure assessment.
Cook, et al. (1990) and EPA (1993) assumed a lipid content of 0.07 for fish in discussions
of BSAF and related methodologies for estimating bioaccumulation of 2,3,7,8-TCDD in
aquatic ecosystems. This assessment will also assume a f|ipid of 0.07, and since its use in
this context is in exposure assessment, this value could be thought of as a edible portion
lipid fraction.
Different lipid contents have been reported for the same fish, so generalizations are
difficult to make at this point. EPA (1990b) lists percent lipid contents for Lake Ontario
fish including brown trout: 14.3%, lake trout: 21.1%, coho salmon: 6.45%, yellow perch:
5.2%, and white perch: 17.1%. Kuehl, et al. (1987) lists a range of percent lipid for carp
taken at different days during a study of between 13.0 and 18.7%.
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4.3.4.2. Vegetation concentrations
Vegetation concentrations are required for the estimation of exposure to
homegrown fruits and vegetables, and also for the beef and dairy food chain algorithms.
Three principal assumptions are made to estimate vegetative concentrations:
• Outer surfaces of bulky below ground vegetation are impacted by soils
which contain dioxin-like compounds. Inner portions are largely unimpacted.
• Translocation of dioxin-like compounds from roots to above ground portions
of plants are negligible compared to other mechanisms which impact above ground
portions of plants. As such, translocation into above ground portions will be assumed to
be zero.
• Similar to the assumption concerning transport of contaminants from outer
to inner portions of below ground vegetation, it will be assumed that outer and not inner
portions of above ground bulky vegetation are impacted.
Concentration of contaminants in below ground vegetation is only required for
vegetables (carrots, potatoes, e.g.) grown underground. The basis for the below ground
algorithm is the experiments of Briggs, et al. (1982) on uptake of contaminants into barley
roots from growth solution, and their elaboration of a Root Concentration Factor. The
below ground concentration is given by:
C, RCF VGbg
(
where:
Cbgv = fresh weight concentration of below ground vegetables, mg/kg
Cs = contaminant concentration in soil, ppm or mg/kg
Kds = soil-water partition coefficient, L/kg
Koc*OCs|
Koc = contaminant organic partition coefficient, L/kg
OCS| = fraction organic carbon in soil, unitless.
RCF = root concentration factor equaling the ratio of the contaminant
concentration in roots (fresh weight basis) and the concentration in
soil water, unitless
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VGbg = empirical correction factor for below ground vegetation which
accounts for the differences in the barley roots for which the RCF
was derived and bulky below ground vegetables, unitless
Two processes, air-borne vapor phase absorption and air-borne particle deposition,
are assumed to contribute to above ground vegetation concentrations:
^abv = CVpa + Cppa (4-24)
where:
Cabv = concentration in above-ground vegetation, expressed on a dry weight
basis, mg/kg or ppm
Cvpa = contribution of concentration due to vapor-phase absorption or
airborne contaminants, mg/kg or ppm
Cppa = contribution of concentration due to wet plus dry deposition
of contaminated particulates onto plant matter, mg/kg or ppm.
The basis for a vapor-phase bioconcentration factor for various airborne
contaminants, including 1,2,3,4-
by Bacci, et al. (1990, 1992), w
FCDD, from the atmosphere to vegetation was developed
th amendments suggested by McCrady and Maggard
experiments on the vapor-phase
(1993), and McCrady (1994). Bacci and coworkers conducted laboratory growth chamber
transfer of 14 organic compounds from air to azalea
leaves, and developed a generalised model to predict the vapor-phase bioconcentration
factor based on a contaminant Henry's Constant, H, and octanol water partition
coefficient, Kow. A similar experiment by McCrady and Maggard (1993) conducted for
2,3,7,8-TCDD vapor transfer to grass leaves suggested that the Bacci empirical algorithm
to estimate the transfer factor would greatly overestimate it. Further details on these
experiments are in the section below on this critical bioconcentration parameter, termed
Bvpa in this assessment. The algorithm estimating plant concentrations as a function of
vapor-phase air concentrations is:
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c = Bvpa Cva VGag (4-25)
VP° —1000 da (4 25)
where:
CVpa = contribution concentration due to vapor-phase absorption or airborne
contaminants, mg/kg or ppm
Bvpa = mass-based air-to-leaf biotransfer factor, unitless [(//g contaminant/kg
plant dry)/(//g contaminant/kg air)]
Cva = vapor-phase concentration of contaminant in air, /yg/m3
VGag = empirical correction factor which reduces vegetative concentrations
considering that Bvpa was developed for transfer of air-borne
contaminants into leaves rather than into bulky above ground
vegetation
da = density of air, kg/m3, 1.19
1/1000 = converts resulting concentration from//g/kg to mg/kg.
Several exposure efforts for 2,3,7,8-TCDD (Fries and Paustenbach, 1990; Stevens
and Gerbec, 1988; Connett and Webster, 1987; Travis and Hattemer-Frey, 1991), have
modeled the accumulation of residues in vegetative matter (grass, feed, vegetables)
resulting from deposition of contaminated particulates. Key components of their approach,
as well as the one for this assessment, include:
• Vegetative concentrations result from particulate deposition onto plant
surfaces.
• Vegetative dry matter yield is the reservoir for depositing contaminants; this
reservoir varies according to crop.
• Not all particulate deposition reaches the plant, some goes directly to the
ground surface; the "interception fraction", less than 1.0, reduces the total deposition
rate. This fraction can be related to the percent ground that is covered by the vegetation.
• Weathering processes, such as wind or rainfall, remove residues that have
deposited onto plant surfaces via particle deposition, and this process is reasonably
modeled as a first-order exponential loss with an associated weathering dissipation rate.
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All the above references have justified a dissipation rate derived from a half-life of 14 days
(based principally on field measurements described in Baes, et al. (1984)); this is the value
used for all dioxin-like compounds in this assessment as well. As well, a portion of
particles depositing as wet deposition are not retained on the vegetation after the rainfall.
A retention factor reduces total wet deposition considering this.
• Vegetative concentrations may not reach steady state because of harvesting
or grazing, but a steady state algorithm is used.
The steady state solution for plant concentrations attributed to wet plus dry particle
deposition is:
where:
1000 k
(4"26)
= Vegetative concentration due to settling of contaminated particulates
onto plant matter, mg/kg or ppm
F = unit contaminant wet plus dry deposition rate onto plant surfaces,
//g/m2-yr
kw = first-order weathering dissipation constant, 1/yr
YJ = dry matter yield of crop j, kg/m2
1/1000 = converts ywg/kg to mg/kg
The unit contaminant wet plus dry deposition rate, F, is given as:
where:
pa
V
F = Cpa (Vd Ij + RNRwWp
(4-27)
unit contaminant wet plus dry deposition rate onto plant surfaces,
//g/m2-yr
air-borne particulate phase contaminant concentration,
deposition velocity, m/yr
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Ij = fraction of particulates intercepted by crop j during deposition,
unitless
RN = annual rainfall, m/yr
Rw = fraction of particles retained on vegetation after rainfall, unitless
Wp = volumetric washout factor for particulates, unitless
Following is brief guidance on assignment of values to the terms in Equations (4-
23) to (4-27).
• C8 and Kd8: This is the soil concentration and soil/water partition
coefficient, respectively. The soil concentration is specified for the on-site source
category. For the two source categories where soil contamination is distinct from the site
of exposure, the soil concentration at the site of exposure is estimated. As discussed in
Section 4.4.1 below, two soil concentrations including one for a no-till and one for a tilled
situation, are estimated. For estimating below ground vegetable concentration, the tilled
concentration is required. The soil partition coefficient is a function of the contaminant
organic carbon partition coefficient, Koc, and the soil organic carbon fraction, OCS|, as
discussed above in Section 4.3.1. Division of Cs by Kds results in the equilibrium soluble
phase concentration of the contaminant, in mg/L.
• RCF: Briggs, et al. (1982) conducted experiments measuring the uptake of
several compounds into barley roots from growth solution. He developed the following
relationship for lipophilic compounds tested (lipophilic compounds were identified as those
tested that had log Kow 2.0 and higher (n = 7, r = 0.981):
log RCF = 0.77 log (Kow) - 1.52 (4-28)
where:
RCF = root concentration factor equaling the ratio of the contaminant
concentration in roots (fresh weight basis) and the concentration in
soil water, unitless
Kow = contaminant octanol water partition coefficient, unitless
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Since his experiments were conducted in growth solution, the RCF is most appropriately
applied to soil water in field settings. This is why the Cs was divided by Kds in Equation
(4-23).
• VGbg: Tn's correction factor and the one used to correct for air-to-leaf
transfer of contaminants, VGag, are based on a similar hypothesis. That hypothesis for
VGbg is that the uptake of lipophilic compounds into the roots of this experiments is due to
sorption onto root solids. High root concentrations were not due to translocation to within
portions of the root hairs. Direct use of the RCF for estimating concentrations in bulky
below ground vegetation would greatly overestimate concentrations since an assumption
(stated above) is that there is insignificant translocation to inner parts of below ground
bulky vegetation for the dioxin-like compounds. Concentrations in outer portions of edible
below ground vegetation would mirror concentrations found in barley roots, by this
hypothesis.
VGbg can be estimated by assuming that the outer portion, or skin, of below ground
vegetables would contain concentrations that can be predicted directly using the RCF, but
that the inner portions would effectively be free of residue. The correction factor can be
estimated as the ratio of the mass of the outer portion to mass of the entire vegetable:
MASS ,w_
VGbp = slan (4-29)
b8 MASSvegetable
where:
VGbg = below ground vegetation correction factor, unitless
MASSskin = mass of a thin (skin) layer of below ground vegetables
MASSvegetab|e = mass of the entire vegetable
Simplifying assumptions are now made to demonstrate this ratio for a carrot and a potato.
First, it will be assumed that the density of the skin and of the vegetable as a whole are
the same, so the above can become a skin to whole vegetable volume ratio. The
thickness of the skin will be assumed to be same as the thickness of the barley root for
which the RCF was developed. Without the barley root thickness in Briggs, et al. (1982),
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what will instead be assumed is that the skin thickness is equal to 0.03 cm. This was the
thickness of a leaf from broad-leaved trees assumed by Riederer (1990) in modeling the
atmospheric transfer of contaminants to trees. The shape of a carrot can be assumed to
be a cone. The volume of a cone is given as (n-/3)r2l, where r is a radius of the base and I
is length. Assuming a carrot base radius of 1 cm and a length of 15 cm, the volume is 16
cm3. The curved surface area of a cone is given as: m(r2 + I2)1/2, which equals 47 cm2,
given the r and I assumptions. The volume of the cone surface area is 47 cm2 * 0.03 cm,
or 1.41 cm3. The skin to whole plant ratio for this carrot is 0.09 (1.41/1 6). A similar
exercise is done for a potato, assuming a spherical shape with a radius of 3 cm. The
volume is given as 4/3/rr3, or 113 cm3. The surface area of a sphere is 4m-2, or 113 cm2,
and the volume of this surface area is 3.39 cm3. The skin to whole plant ratio for the
potato is 0.03.
This exercise indicates upper bounds for such an empirical parameter. For exposure
assessments, other factors which reduce vegetative concentrations should also be
considered and will be considered in this empirical correction factor in this assessment.
Additional reductions in concentration result from peeling, cooking, or cleaning, for
example. Wipf, et al. (1982) found that 67% of unwashed carrot residues of 2,3,7,8-
TCDD came out in wash water, and 29% was in the peels. A peeled, washed carrot
correction factor might instead be, 0.09*0.04, or 0.004 (0.09 from above; 0.04 =
100% - 67% - 29%). A 96% reduction in the estimated VGbg for the potato (the potato is
cleaned and the skin is not eaten; additional reductions possibly when cooking the potato)
would equal 0.001. In a site-specific application, the type of vegetation, preparation, and
so on, should be considered. The VGbg for underground vegetables for this assessment is
assumed to be 0.01. This is less than the estimates of 0.09 and 0.03 for the carrot and
potato above, but greater than it might be if based on this discussion on cleaning,
washing, peeling, and so on.
• Cva, Cpa: The vapor-phase concentration of contaminant in air, Cva, used in
this algorithm is estimated using procedures described in Section 4.3.2 above. The
particle-phase concentration of contaminant in air, Cpa, is estimated using procedures
described in Section 4.3.3.
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• Bvpa: Bacci, et al. (1990, 1992) conducted laboratory experiments
on the air-to-leaf transfer of vapor-phase concentrations of 14 organic contaminants to
azalea leaves. With their results, they developed an empirical relationship for a vapor-
phase bioconcentration factor from air to azalea leaves, termed in this assessment the
Bvpa, but which was termed BCF by Bacci and coworkers. They related the Bvpa to the
chemical octanol-water and air-water partition coefficients, Kow and Kaw. The air-water
partition coefficient, Kaw, is a dimensionless form of Henry's Law constant, H, derived by
dividing H by the product of the ideal gas constant, R, and the temperature, T. The most
general form of the air-to-leaf transfer factor is on a unitless volumetric basis: [ng
contaminant/L or leaf]/[ng contaminant/L of air], and is given as:
log Bvol = 1.065 log Kow - log ( JL ) - 1.654 (4-30)
R T
where:
BVO| = Bacci volumetric air-to-leaf biotransfer factor, unitless [(/yg
contaminant/L of wet leaf)/(//g contaminant/L air)]
Kow = contaminant octanol water partition coefficient, unitless
H = contaminant Henry's Constant, atm-m3/mol.
R = ideal gas constant, 8.205 x 10"5 atm-m3/mol-deg K
T = temperature, 298.1 K
-1.654 = empirical constant
Bacci, et al. (1990) showed that the volumetric transfer factor can be transformed to a
mass-based transfer factor by assuming that 70% of the wet leaf is water, the leaf density
is 890 g/L, and the air density is 1.19 g/L:
_ 1.19 g/L Bvol
BV»* ~ 0.3 890 g/L
where:
( >
Bvpa = mass-based air-to-leaf biotransfer factor, unitless [(/yg contaminant/kg
plant dry)/(/yg contaminant/kg air)]
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Bvol = Bacci volumetric air-to-leaf biotransfer factor, unitless [{//g
contaminant/L of wet leaf)/(^g contaminant/L air)]
Bacci's experiments were conducted under conditions which would not account for
photodegradation of his test chemicals from the leaf surfaces. A recent study by McCrady
and Maggard (1993) which investigated the uptake and photodegradation of 2,3,7,8-
TCDD sorbed to grass foliage suggests a significant difference in experimental BVO| for
grass plants. The authors note that the log Bvo) for 2,3,7,8-TCDD and azalea plants, using
Bacci's empirical relationship, was estimated as 8.5. The experimental log Bvo, for
2,3,7,8-TCDD and grass plants reported by McCrady was 6.9 when photodegradation was
accounted for, and 7.5 in the absence of photodegradation. Since the photodegradation
experiments by McCrady best represent outdoor conditions, their work suggests that the
air-to-leaf transfer factor estimated by Bacci's algorithm may be 40 times too high for
vapor-phase transfer of 2,3,7,8-TCDD onto grass leaves.
While McCrady's experiments included consideration of photodegradation of
2,3,7,8-TCDD, it is uncertain as to how their results can be generalized to other dioxin-like
compounds and vegetations other than grass. There is very little information in the
literature on the photodegradation of dioxins and furans on plant surfaces. McCrady and
Maggard (1993) cite Crosby and Wong (1977) as the only other work measuring
photodegradation of 2,3,7,8-TCDD from leaf surfaces. In that work, 2,3,7,8-TCDD was
applied as a 15 ppm concentration in Agent Orange, and McCrady speculated that the
rapid photodegradation measured in those experiments occurred because the herbicide
formulation contained carriers and organic solvents that may have promoted
photodegradation. Some experiments conducted in organic solvents (Crosby, et al., 1971;
Buser, 1976) and in water (Friessen, et al., 1990) noted reductive dechlorination resulting
in dioxin compounds of lower chlorination. Other experiments did not find such reductive
dechlorination (Dulin, et al., 1986; Friessen, et al., 1990 who found reductive
dechlorination in one experiment, but not in another). An important issue to consider, at
least, for the process of photodegradation of dioxins and furans on leaf surfaces is the
possible formation of lower chlorinated congeners of non-zero toxic equivalency.
Another issue discussed by McCrady is that the theoretical time for the grass tissue
to reach a steady state in his experiments is much shorter than that indicated in the Bacci
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experiments. Using Bacci's results, McCrady noted that the azalea leaves theoretically
take greater than 400 days to reach equilibrium, in comparison to less than 20 days to
reach equilibrium for the grass plants in his experiments. This difference is not entirely
due to photodegradation. McCrady (personal communication, J. McCrady, Corvallis
Environmental Research Laboratory, EPA) suggests that the 50-day exposure time used in
Bacci's experiments may allow for considerable diffusion into the newly formed plant
surface wax. The sorbed TCDD residues may be trapped and unable to volatilize. Thus,
for estimating contaminant concentrations in animal feeds such as relatively short-lived
grass plants, the equilibrium Bvo] from the Bacci azalea model may overestimate the
contaminant concentration in grass. On the other hand, McCrady's experiments may have
been conducted in too short a time frame, with the sum of uptake and elimination phases
being less than 10 days in the various experimental designs. The volatilization and
photodegradation rates reported by McCrady may be higher than what might occur for the
longer exposure times expected in real world situations, where growth and residue
trapping may occur.
These arguments are being presented to demonstrate the uncertainty in choosing
either of the two reported Bvd values for estimating plant contaminant concentrations.
McCrady's results pertaining to 2,3,7,8-TCDD cannot be generalized to other dioxin-like
compounds or other contaminants in terms of commonly available contaminant parameters
such as H or Kow. Therefore, a McCrady framework similar to Bacci's for estimating
congener-specific Bvpa cannot be offered at this time. On the other hand, their work
strongly suggests that the Bacci model may be inappropriate for terrestrial vegetations of
this assessment, including vegetables/fruits and vegetations of the beef/milk food chain
model, and Bacci's experiments, because of their length of time, the use of an azalea leaf
of high wax content, and lack of an artificial light source simulating photodegradation, are
likely to have overestimated the air to leaf transfers.
What will be done for this assessment is to first estimate a congener-specific Bvo|
using the Bacci algorithm of Equation (4-30) above. Then, it will be transformed into a
mass-based Bvpa as in Equation (4-31), except that the assumptions McCrady and
Maggard used for fraction of grass plant that is wet weight, 85%, and the grass leaf
density, 770 g/L, will instead be used as more representative of vegetations of this
assessment. Most importantly, the Bvpa calculated this way will be empirically reduced by
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a factor of 40 for all dioxin-like congeners as suggested by the difference in McCrady's
experiments as compared to Bacci's.
It should be noted that all bioconcentration or biotransfer parameters, such as the
Bvpa, are qualified as second order defaults for purposes of general use. Section 6.2. of
Chapter 6 discusses the use of parameter values selected for the demonstration scenarios,
including a categorization of parameters. Second order defaults are defined there as
parameters which are theoretical and not site specific, but whose values are uncertain in
the published literature. The parameter values in this category should be considered
carefully by users of the methodology.
: The same discussion for this correction factor for below ground
vegetation applies here. Fruits such as apples, pears, plums, figs, peaches, and so on, can
be approximated by spheres, and upper bound estimates of correction factors would be
less than 0.05. Peeling, cooking, and cleaning further reduces residues. The VG for
unspecified above ground fruits and vegetables in this assessment is assumed to be 0.01.
Like VGbg, this value is assigned considering that it should be less than estimated just
based on surface volume to whole fruit volume ratios.
Two other VGag values are required for this assessment. One is for pasture grass
and the other for other vegetations consumed by cattle. Both are required to estimate
concentrations in these vegetations consumed by cattle in order to estimate beef and milk
concentrations. A VGag value of 1 .0 was used to estimate pasture grass concentrations
since there appears to be a direct analogy to exposed azalea and grass leaves. However,
VG should be less than the other general category of cattle vegetations defined in this
assessment, "hay/silage/grain". Recognizing that pasture grass is important in terms of
amount consumed in the lifetime of a beef cow, and the fact that it is a leafy vegetation, it
is considered seperately, whereas other cattle vegetations are lumped together in this
second category. As described below in Section 4.3.4.3, this second general category of
non-grass cattle vegetations include some thin leafy (hay) as well as bulky (corn silage and
other grains) vegetations to consider. A volume ratio of outer surface to whole surface
area to volume vegetation could be used to assign a value to VG, if specific assumptions
concerning proportions of each type of vegetative cattle intake were made. An
appropriate assumption for a fully protected vegetation such as grain would be zero.
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Silage can be considered part protected and part leafy. Since specific assumptions
concerning hay/silage/grain intake are not being made for this exercise, a simple
assumption that VG equals 0.50 for hay/silage/grain is instead made, without rigorous
justification.
The only experimental evidence that a VGag for vapor transfers of dioxin-like
compounds is justified came in a recent study by McCrady (1994). McCrady
experimentally determined uptake rate constants, termed kv for vapor phase 2,3,7,8-
TCDD uptake into several vegetations including kale, grass, pepper, spruce needles, apple,
tomato, and azalea leaves. Recall that the similar experimental design of both McCrady
and Maggard (1993), and Bacci, et al. (1990; 1992), included an initial phase where
vegetations in experimental chambers were exposed to the vapor-phase organic chemicals.
The uptake which occurs during this initial phase is described with the rate constant, kv
A second "elimination" phase then occurs where organic vapors are removed from the
chambers and the chemicals allowed to volatilize or otherwise dissipate from the
vegetation. The rate constant for this phase is termed k2. A steady state
bioconcentration factor, or Bvpa in this assessment, is then estimated as k.,/k2. The
uptake rate constants from air to the whole vegetations estimated in the recent
experiments by McCrady (1994) demonstrate the concept behind the VG parameter. The
uptake rate for an apple divided by the uptake rate for the grass leaf was 0.02 (where
uptake rates were from air to whole vegetation on a dry weight basis). For the tomato and
pepper, the same ratios were 0.03 and 0.08. The VGan was 0.01 for fruits and
ay
vegetables in this assessment, but note above that the simple exercise with a conical
carrot and spherical potato estimated a surface volume to whole fruit volume ratio of 0.09
(carrot) to 0.03 (potato); a value of 0.01 for fruits and vegetable empirically considers
factors such as washing or peeling which would reduce exposures. McCrady (1994) then
went on to normalize his uptake rates on a surface area basis instead of a mass basis; i.e.,
air to vegetative surface area instead of air to vegetative mass. Then, the uptake rates
were substantially more similar, with the ratio of the apple uptake rate to the grass being
1.6 instead of 0.02; i.e., the apple uptake rate was 1.6 times higher than that of grass,
instead of 1/50 as much when estimated on an air to dry weight mass basis. The ratios
for tomato and pepper were 1.2 and 2.2, respectively. In his article, McCrady (1994)
concludes, "The results of our experiments have demonstrated that the exposed surface
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area of plant tissue is an important consideration when estimating the uptake of 2,3,7,8-
TCDD from airborne sources of vapor-phase 2,3,7,8-TCDD. The surface area to volume
ratio (or surface area to fresh weight ratio) of different plant species can be used to
normalize uptake rate constants for different plant species." McCrady does caution,
however, that uptake rates are only part of the bioconcentration factor estimation, and is
unsure of the impact of surface area and volume differences on the elimination phase
constant, k2 (personnel communication, J. McCrady, US EPA, ERL-Corvallis, Corvallis, OR
97333). Still, his recent experiments do appear to justify the use of a VG parameter since
the B were developed on an air to whole plant mass basis, and his results are consistent
with the assignment of 0.01 for fruits and vegetables.
• kw: Fries and Paustenbach (1 990) note that this approach may
overestimate concentrations because crops can be harvested or pastures grazed before the
plant concentrations reach steady state, and that a kw based on a weathering half-life of
14 days may be too long given experimental results of Baes, et al. (1984) which showed a
range of 2-34 days, and a median value of 10 days. Stevens and Gerbec (1987)
considered harvest intervals by including the exponential term, (1-e"kt), and assigning
values of t based on harvest intervals of different crops. This assessment uses a kw of
18.02 yr'1, which is equivalent to a half-life of 14 days.
• I- and Y-: Interception values and crop yields were determined in the afore-
mentioned assessments based on geographic-specific crop yield data provided in Baes, et
al., (1984) and the following types of crop-specific relationships estimating interception
fraction based on yield (Y), also presented in Baes, et al., (1984):
corn silage: 1 = 1- e-°'768Y
hay/grasses: 1 = 1- e-2-88Y
lettuce: 1 = 1- e-°-068Y
Judgments by Fries and Paustenbach (1990) on high, medium, and low yields of silage,
hay, and pasture grass, and the use of the first two interception equations above (the first
for silage, and the second for hay and grass), can give some guidance on interception
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fractions and yields for these crops:
corn silage
hay
grass
Yield
(kg/m2)
0.30 (low)
0.90 (med)
1.35 (high)
0.25 (low)
0.45 (med)
1.30 (high)
0.05 (low)
0.15 (med)
0.35 (high)
Intercept
Fraction
0.20
0.50
0.64
0.51
0.73
0.98
0.13
0.35
0.64
This information can be used for cattle intake of vegetation, and the resulting beef and
milk concentrations. The medium values for grass, 0.1 5 kg/m2 yield and 0.35
interception, were used for the example setting in Chapter 5. An average of the medium
values for hay and silage, 0.63 kg/m3 yield and 0.62 interception, were used for the
second category of cattle vegetations for Chapter 5, the hay/silage/grain category.
Stevens and Gerbec (1988), using yields obtained from the Minnesota State
Agricultural Office, derived the following yield and interception estimates, respectively, for
vegetables for human consumption in their assessment: lettuce - 8.6,0.72; tomatoes -
12.0,0.55; and beans - 2.7,0.18. Average yields and interception fractions from their
exercise: 7.8 kg/m2 and 0.48, were used in the example setting in Chapter 5. These
vegetable yields are fresh weight, so they need to be converted to a dry weight basis in
order to estimate a Cppa appropriate for use in Equation (4-23). Since vegetables are
generally 80 ->90% water, a fresh to dry weight conversion factor of 0.15 was used,
resulting in an average vegetable dry matter yield of 1.17 (7.8 * 0.15). This was used in
the example settings in Chapter 5.
• Vd: Particles settle to the ground surface and plant surfaces due to the
forces of gravity. Gravitational settling velocity is a function of particle size, with more
rapid settling occurring with larger particles. The algorithm used to estimate the
concentration of contaminated particulates in air estimates the suspension of particles less
than and equal to 10//m, which is commonly referred to inhalable size particles. Seinfeld
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(1986) listed a gravitational deposition velocity of 1 cm/sec for 10//m size particles. This
deposition velocity will be used in this assessment, and in units of m/yr, this equals
315,360 m/y.
• RN: Geraghty, et al. (1973) provides a map showing isolines average annual
rainfall throughout the United States. This map shows low rates of 5 to 20 inches/year in
the desert Southwest, moderate rates of 25 to 40 in/yr in the Midwest cornbelt, 40 to 60
in/yr in the South, and so on. The example scenarios of Chapter 5 were described as
rural, with land in agricultural and non-agricultural settings. A rate of 1 m/yr (39 in/yr) will
be used in the example scenarios.
• Rw: It is assumed that dry depositions fully adhere to plant surfaces; the
weathering constant, kw, models the loss of the vegetative reservoir of particle bound
contaminants due to wind, rain, or other weathering process. However, it is not clear that
wet deposition should also be assumed to fully adhere during a wet deposition event.
Hence, the Rw parameter, or fraction of wet deposition adhering, was introduced. Prior
modeling efforts of the impact of depositions of dioxin-like compounds to vegetations are
unclear with regard to wet deposition. Stevens and Gerbec (1988), Fries and Paustenbach
(1990), Webster and Connett (1990), and Travis and Hattemer-Frey (1991) all model
particle deposition impacts of 2,3,7,8-TCDD to vegetations in air-to-beef/milk modeling.
None of them discuss the distinction in wet and dry deposition, and model "total
deposition" impacts, describing total as wet and dry deposition, total deposition, or simply
as deposition. On the other hand, McKone and Ryan (1989) reduce the wet deposition
portion of total deposition. They promote use of a "b", which they define as the fraction
of material retained on vegetation from wet deposition. They recommend a value between
0.1 and 0.3.
The clearest indication of the fate of wet deposition of particles can be found in
Hoffman, et al. (1992). In that field study, simulated rain containing soluble radionuclides
and insoluble particles labeled with radionuclides was applied to pasture-type vegetation
under conditions similar to those found during convective storms. The fraction of the
labeled particles found to remain on the vegetation after the rainfall varied from 0.24 to
0.37. Nine values comprised this range, including particle sizes of 3, 9, and 25 //m, and
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cover described as clover, fescue, and mixed (a site with old field vegetations including
fescue, grasses, weeds, and wild flowers). Based on this work, the Rw will be assumed to
be 0.30 for all vegetations and dioxin congeners of this assessment.
• Wp: Washout ratios are generally defined as the concentration of
contaminant in rain to the concentration of contaminant in air. Concentrations of
contaminants in air and rain water can be derived as a mass of contaminant divided by a
mass of air/water or a volume of air/water. Mackay, et al. (1986) shows that volume-
based washout ratios (mass of contaminant mixing in m3 air or water, e.g.) exceed mass-
based washout ratios (mass of contaminant mixing in kg of air or water) by a factor of
815, which is the ratio of water and air densities. The washout ratio used in this
assessment is a volumetric ratio based on methodologies described by Bidleman (1988).
Using a volumetric ratio then allows for direct use of contaminant concentrations
estimated in this methodology since they are already on a //g/m3 volume basis.
Bidleman (1988) defines the overall washout ratio as: (mass contaminant/volume
rain) •*• (mass contaminant)/(volume air). Bidleman (1988) also discusses that fact that
overall washout includes both wet deposition of particulates and scouring of contaminants
in the vapor phase. He includes methodologies for estimating the vapor/particulate ratios
for semi-volatile organic compounds (abbreviated SOCs) and also for estimating the
washout ratios for vapors. However, he claims that if H is sufficiently high, vapor
dissolution in droplets is negligible and only the particulate fraction is removed by wet
deposition. He claims this to be the situation for n-alkanes, PCBs, chlordane, DDT, and
2,3,7,8-TCDD. Developing overall washout ratios for these and several other SOCs, he
estimates that vapor scouring accounts for 1 % of the overall washout ratio for 2,3,7,8-
TCDD. For PCBs, he estimates similar percentages of 2, 4, and 28% for Aroclors 1 260,
1254, and 1248, respectively. Based on this work, it will be assumed that vapor scouring
of the dioxin-like compounds is small in comparison to wet deposition and the washout
ratio for this assessment will only be applied to the air-borne particulate concentration of
dioxin-like compounds.
Bidleman (1988) does not provide a chemical or site-specific equation which
estimates the particle-phase washout ratio (which he does for the vapor-phase washout
ratio). Rather, he summarizes available data and concludes that there is a wide range of
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the particle-phase washout ratio, Wp, for SOC: between 2x103 and 1x106. He claims that
a typical range is 105 to 106, and uses a Wp of 2 x 105 in his exercises to estimate the
overall washout ratio for several SOCs.
Koester and Hites (1992) list vapor and particle scavenging ratios for congener
groups of dioxin-like compounds. To derive these ratios, they used air concentrations for
congener groups that were taken at one time period in Bloomington and Indianapolis,
Indiana, and rainfall depositions of these compounds at these sites measured during a
second period of time. Using the Bidleman vapor/particle partitioning model used in this
assessment, they estimate the vapor/split for the air concentrations. With these
observations and models, they conclude that the overall washout ratio (sum of vapor and
particle ratios) ranges from 104 to 105, which contrasts the typical range of 105 to 106
noted above from Bidleman (1988). Also, their calculations indicate that vapor scavenging
of dioxin-like compounds is comparable to particle scavenging, also in contrast to the
Bidleman analysis summarized above. However, they did not state whether their washout
ratios were volume or mass-based. If they were mass-based, then a conversion to volume
based would put them in the 107 to 108 range, which seems improbable given the
Bidleman summary above. Therefore, it will be assumed that are volume-based, and they
are appropriate to use for this assessment. Since no clear trend for particle washout ratios
with regard to the degree of chlorination increased appears in Koester and Hites' data, the
midpoint of their calculated range, 5 * 104, will be used for all example compounds in this
assessment.
As a final note, the multiplication of the above terms, Wp * Cpa * RN * Rw, does
result in wet deposition in appropriate units of /yg/m2-yr, although that is not immediately
obvious. First, multiplication of Wp, in (fjg contaminant/m3 rain)-*-(/yg/m3 air), and Cpa, in
/yg contaminant/m3 air, leaves a partial term in units of //g contaminant/m3 rain. Then,
multiplication of this partial term times annual rainfall rate, thought of as m3/m2-yr instead
of m/yr, gives the final quantity in the appropriate units.
When calculating concentrations in below ground fruit and vegetables using
Equation (4-23), Cbgv is on a fresh weight basis since the RCF developed by Briggs, et al.
(1982) is on a fresh weight basis, and no correction for estimating exposures is necessary.
However, Cabv as estimated in Equation (4-24) is on a dry weight basis, and should be
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multiplied by a dry weight to fresh weight conversion factor when applied to above ground
fruits and vegetables. A reasonable estimate for this parameter for fruits and vegetables is
0.15 (which assumes 85% water), which was used in this assessment. When using
Equation (4-24) to estimate Cabv for the beef and milk food chain algorithm, a conversion
to fresh weight is not required, however, since the algorithms were developed assuming
dry weight concentrations.
4.3.4.3. Beef and milk concentrations
The algorithm to estimate the concentration of contaminant in beef and/or milk was
based on methods developed by Fries and Paustenbach (1990). They developed the beef
bioconcentration factor for 2,3,7,8-TCDD, which is defined as the ratio of the
concentration of the contaminant in beef fat to the concentration in the dry matter dietary
intake of the beef cattle. They discussed bioavailability, which, as they define it, is the
fraction of ingested contaminant which is absorbed into the body. It depends on the
vehicle of ingestion - dioxin in corn oil has a bioavailability in the range of 0.7 to 0.8, in
rodent feed it has an estimate of 0.5, while in soil it has a range of 0.3 to 0.4. They
emphasized the importance of the differences in diet between cattle raised for beef and
those which are lactating in explaining different food product concentrations. Although
there is likely to be some difference in the bioconcentration tendencies for lactating cattle
and beef cattle, Fries and Paustenbach in fact used the same bioconcentration for beef fat
and milk fat, and the same will be done here.
The concentration in the fat of cattle products is given as:
Cfat = (BCF DFS Bs ACS) + (BCF DFg ACg) + (BCF DFf ACf) (4-32)
where:
Cfat = concentration in beef fat or milk fat, mg/kg
BCF = bioconcentration ratio of contaminant as determined from cattle
vegetative intake (pasture grass or feed), unitless
DFS = fraction of cattle diet that is soil, unitless
Bs = bioavailability of contaminant on the soil vehicle relative to the
vegetative vehicle, unitless
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ACS = average contaminant soil concentration, mg/kg
DFg = fraction of cattle diet that is pasture grass, unitless
ACg = average concentration of contaminant on pasture grass, mg/kg
DFf = fraction of cattle diet that is feed, unitless
ACf = average concentration of contaminant in feed, mg/kg.
The following is offered as brief guidance to these terms and also the justification for the
values selected in the example Scenarios in Chapter 5.
• BCF: Fries and Paustenbach (1990) developed the concept of a beef/milk
bioconcentration ratio and applied it to 2,3,7,8-TCDD. BCF is defined as the concentration
of contaminant in fat of cattle products (i.e., dairy and beef) divided by the concentration
in dry matter intake. The key difference in the dietary intake of cattle raised for beef
versus cattle raised for dairy is that cattle raised for beef tend to be pastured more than
dairy cattle and be more exposed to contaminated soil, whereas lactating cattle are more
often fed high quality feed, including grains which are expected to be substantially residue
free since they are a protected vegetation. Based on literature studies of cattle consuming
feed contaminated with dioxin-like compounds, Fries and Paustenbach (1990) calculated a
BCF of between 4 and 6, and assumed a value of 5.0 for 2,3,7,8-TCDD. Being developed
directly from data of cattle ingesting contaminated feed, this BCF value of 5.0 already
considers the bioavailability of the experimental contaminated feed. It will be assumed
that the bioavailability of the cattle vegetations in this assessment equal that of the
experimental feed. Therefore, a value of 5.0 can go directly into Equation (4-32) when
applied to concentrations in grass and pasture. However, this value should not be applied
to soil, since it has been shown that TCDD on soil is less bioavailable than TCDD on other
vehicles. This is why a Bs appears in Equation (4-32) - it adjusts BCF when applied to a
soil concentration. The value of Bs is described below. The Fries and Paustenbach (1990)
literature review is reproduced in Table 4-3, which additionally shows results generated
based on information in McLachlan, et al. (1990).
Although the BCF of 5 determined by Fries and Paustenbach (1990) for 2,3,7,8-
TCDD appears high based on the literature for this compound. Fries and Paustenbach
(1990) discuss how short duration feeding trials (the 21 days of Jensen and Hummel
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Table 4-3. Ratios of dioxins and furans in milk fat (MF) and body fat (BF) to
concentrations in diets of farm animals.
Animal
Days Compound
BF:diet
MF:diet
Reference
Goats
Cows
Cows
56
21
70
2,3,7,8-TCDD
2378-TCDD
123678-HxCDD
1234678-HpCDD
OCDD
-
-
3.9
0.4
0.1
- 2.8
4.4
5.7
0.6
0.1 .
Arstilla et al. (1981)
Jensen &Hummel (1982)
Firestone, et al. (1979)
Steers 28 2378-TCDD 3.5
Heifers 160 123689-HxCDD 2.1
1234678-HpCDD 0.2
OCDD 0.05
1234678-HpCDD 0.3
OCDF 0.1
Jensen, et al. (1981)
Parker, et al. (1980)
Cow 921 2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234679-HpCDD
1234678-HpCDD
12346789-OCDD
234/78-TCDF
1234/78-PCDF
23478-PCDF
123478/9-HxCDF
123678-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
12346789-OCDF
TEQ
4.32 McLachlan, et al. (1990)
4.16
2.02
1.74
2.24
0.20
0.36
0.52
0.94
0.73
3.10
2.34
2.00
1.78
0.41
0.99
0.20
2.50
McLachlan, et al. (1990) was not a dosed feeding study; the single cow studied was
given normal rationing. The first sample was taken Feb 16, 1989, two months after the
last calving on Dec. 22, to maximize the possibility that steady state had been reached.
The 92 days listed was from Dec. 22 until the last sample on Mar. 24.
Source: Fries and Paustenbach (1990), and McLachlan, et al. (1990).
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(1982) and the 28 days of Jensen, et al. (1981)) do not result in steady state
bioconcentration ratios. Extrapolating the data to a point where steady state is speculated
to be reached, Fries and Paustenbach (1990) developed the arguments for the range of 4
to 6 for 2,3,7,8-TCDD. The second example compound in Chapter 5 was
2,3,4,7,8-PCDF. Fries and Paustenbach (1990) observed that bioconcentration ratios for
PCDDs and PCDFs decreased significantly as chlorination increased, although their
literature seems to imply that this effect is most pronounced for hepta- and octa- PCDDs
and PCDFs. They could not locate data in the literature for penta-PCDDs or PCDFs.
McLachlan, et al. (1990) was the only source found where BCFs for cow milk could
be generated for furan congeners. They conducted a mass balance of dioxin and furan
congeners in a lactating cow. They carefully accounted for 16 of the 17 dioxin and furan
congeners of toxicity equivalency to 2,3,7,8-TCDD in the intake of a lactating cow in food,
air, and water, and measured amounts in feces, urine, and milk, while attributing the rest
of the intake to a compartment they termed, storage/degradation/experimental error. They
obtained data well into steady state, and provided information necessary to estimate milk
BCFs including: average daily wet weight food ingestion intake by the cattle (dry weight
assumed to be 30% of wet weight for cattle feed); ng/day congeners in feed, water, and
air; L/day milk production (density assumed to be 0.9 g/cm3); and percent fat in milk. The
BCFs generated are shown in Table 4-3, and the one noted for 2,3,4,7,8-PCDF is 3.10. It
is clear that the data in Table 4-3 is not definitive in establishing BCFs for specific
congeners. Only the McLachlan data is complete, and it includes one cow and one
lactating period. The data of Firestone, et a!. (1979), as interpreted by Fries and
Paustenbach, shows a BCF for milk fat of 5.7 for 1,2,3,6,7,8-HxCDD, compared to the
milk fat BCF of 1.74 developed from McLachlan data.
In Chapter 5, the McLachlan data will be used for purposes of demonstration. The
BCF value for 2,3,7,8-TCDD value is 4.3 and the BCF for 2,3,4,7,8-PCDF is 3.1, in the
demonstration scenarios which include a dioxin, a furan, and a PCB. For the demonstation
of the incinerator, the suite of dioxin-like compounds are demonstrated, and the full BCF
set developed by McLachlan and coworkers will be used.
A review of the literature for PCBs is given in Table 4-4. Although PCBs, dioxins,
and furans are related compounds in terms of environmental fate characteristics, a
difference in bioaccumulation potential is noted with higher degrees of chlorination, based
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Table 4-4. Ratios of PCBs in milk fat (MF) and body fat (BF) to concentrations in diets of lactating cowsa
Animal
Lactating
Cows
Lactating
Cows
Lactating
Cows
Lactating
Cows
Lactating
Cows
Days
20
40
60
56
60
120
180
20
32
Concentration in
Compound Diet, ppm BFrdiet
Aroclor 1254
Aroclor 1 254
Aroclor 1254
dichloro-PCBs
tetrachloro-PCBs
pentachloro-PCBs
hexachloro-PCBs
heptachloro-PCBs
octachloro-PCBs
nanochloro-PCBs
Aroclor 1254
Aroclor 1254
Aroclor 1254
Aroclor 1254
Aroclor 1254
12.1
12.1
12.1 3.4
0.05
0.001
0.003
0.009
0.010
0.005
0.001
0.51 2.8
2.82 2.4
18.97 3.7
2.56
10.25
MFrdiet
3.1
4.4
4.8
0.4
5.9
1.2
2.2
2.3
3.8
4.0
3.7
3.9
4.8
1.2
4.2
Reference & Comments
Fries, et al. (1973)
Tuinstra, et al. (1981), data is:
average of 2 congeners
one congener
average of 2 congeners
average of 7 congeners
average of 8 congeners
average of 5 congeners
one congener
Willett, etal. (1987);
values at left reflect different
average intake over three periods noted.
Willett and Liu (1982)
Perry, et al. (1981)
a see text for full details of noted studies.
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on the study of Tuinstra, et al. (1981). Their work implies increasing bioaccumulation
potential as the degree of chlorination increases. They developed BCF values (defined in
the same manner as in this assessment) for a suite of congeners identified to occur in
Aroclor 1260 administered to lactating cows. Therefore, their data allowed for a partial
examination on congener bioaccumulation patterns. The results given in Table 4-4 are
interpreted from the data supplied in Tuinstra, et al. (1981). Tuinstra determined the
identity and percentage of specific congeners which comprise Aroclor 1260. He was able
to identify 36 congeners, but could only quantify 27 of them (because of the unavailability
of standards for 9 congeners). These 27 comprised 81%, by mass, of the Aroclor 1260.
Tuinstra was able to estimate BCF values for most, but not all, of the identified
congeners - for 23 of the 27 congeners they identified (which equaled 77% of the
congeners, by mass, of Aroclor 1 260). As seen in Table 4-4, the average congener-group
BCF value increases from about 2 to 4 going from hexa- to nanochloro-PCBs. However,
there was a wide range of measured BCF values for specific congeners. In the
heptagroup, for example, Tuinstra estimated BCF values between 0.4 and 5.2. Fries, et al.
(1973) showed increasing BCF values in milk fat at 20, 40, and 60 days for Aroclor 1254
up to a value of 4.8 at day 60. The body fat BCF value at 60 days, the only time such a
measurement was taken, was 3.4. The trend of having a higher BCF value for milk fat as
compared to body fat for lactating cows was also noted by Willett, et al. (1987). They
fed lactating cattle Aroclor 1 254 sorbed to ground corn. In three sequential periods of 60
days, they fed the cattle 10, 100, and then 1000 mg/day of Aroclor 1254. Given their
average daily dry matter intake of 19.5 kg during the experiment, the concentration during
each of those 3 periods was 0.51, 5.13, and 51.28 mg/kg (ppm). However, milk and
body fat concentrations of PCBs were given after 60, 120, and 180 days, so that for
estimation of the BCF value, what is needed is average concentration of Aroclor intake
after these periods. These averages are 0.51, 2.82, and 18.97 mg/kg. Given the reported
concentrations of PCBs in milk and body fat after these experimental periods, BCF values
were estimated and given in Table 4-4. Two studies, that of Willett and Liu (1982) and
Perry, et al. (1981), contained data from which estimates of BCF could be made, except
that these studies did not report daily dry matter intake. An estimate of 19.5 kg/day was
assumed for lactating cattle for these studies, which was the experimental dry matter
intake noted by Willett, et al. (1987). Willett and Liu (1982) dosed cattle for only 20
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days, and arrived at the lowest noted BCF value for Aroclor 1254, 1.2. The trend of
increasing BCF value over time of dosing was noted by Fries, et al. (1973). Willett, et al.
(1990) conclude that steady state is reached after about 60 days, so that estimates of
BCF made from experiments less than 60 days may not reflect steady state conditions.
Perry, et al. (1984) had a high BCF value, 4.2, despite the dosing period being only 32
days. This would appear to be the result of having a high concentration in the diet.
Similarly high BCF values with corresponding high concentration in dosed intake were
noted in Fries, et al. (1973) and Willett, et al. (1987). It should be noted that the
concentrations in body fat in the studies of Willett and Liu (1982) and Perry, et al. (1981)
were corrected as recommended by Willett, et al. (1990) in estimating BCF values.
Five trends for PCBs were discussed above: 1) that steady state is reached after
approximately 60 days, 2) that higher BCF values appear to result with higher
concentrations in feed, 3) that BCF values for milk fat may exceed those of body fat for
lactating cows (this also seems true for dioxins/furans; see Table 4-3), 4) that the BCF
values tend to increase with increasing chlorination of PCB congener groups, and 5) that
this fourth trend is based on a limited set of data and much variability exists within
specific congener groups.
Generally there is a sparsity and inconsistency in the data which would allow for
definitive estimation of BCF values for the example heptachloro-PCB example compound in
Chapter 5, 2,3,3',4,4',5,5'-PCB. Most of the data noted is for Aroclor 1254, and this
data implies BCF values between 1.2 and 4.8. Based on the results from Tuinstra, et al.
(1981) for the average of eight heptachloro-PCBs, a BCF value of 2.3 will be assigned to
2,3,3',4,4',5,5'-PCB.
It should be noted that all bioconcentration or biotransfer parameters, such as the
BCF, are qualified as second order defaults for purposes of general use. Section 6.2. of
Chapter 6 discusses the use of parameter values selected for the demonstration scenarios,
including a categorization of parameters. Second order defaults are defined there as
parameters which are theoretical and not site specific, but whose values are uncertain in
the published literature. The parameter values in this category should be considered
carefully by users of the methodology.
• Soil bioavailability, B8: This parameter reduces the bioconcentration ratio, F,
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considering that soil is a less efficient vehicle of transfer compared to feed. Remember
that the values of BCF appropriate for Equation (4-32) already consider bioavailability and
were developed from experimental data placing the BCF of 2,3,7,8-TCDD in the 4 to 6
range. Fries and Paustenbach (1990) reviewed several studies on the oral bioavailability of
TCDD in soil in the diet of rats, and concluded that soil is a less efficient vehicle of transfer
as compared to rat feed. If the same is true for cattle - that soil is less efficient than their
feed - than the BCF value must be reduced when applied to soil ingestion. Most studies
reviewed by Fries and Paustenbach used corn oil as the positive control, since there is a
high absorption of TCDD in rats when corn oil is used as the vehicle, with 70-83% of the
administered TCDD dose absorbed. Their literature review on rat data showed that the
bioavailability of TCDD in soil was between 0.4 and 0.5 that in corn oil, or 0.3 to 0.4
overall. The literature implied a range of 0.5 to 0.6 of TCDD in standard rat feed is
absorbed, and although few studies were available, a similar 50% absorption rate of TCDD
in cattle feed was noted. They concluded, therefore, that the rat data was a reasonable
surrogate for cattle. The Bs can be thought of as the ratio of BCF values between soil and
feed, or, (BCFsoi|)/(BCFfeed). If the difference in BCFsoi| and BCFfeed is explained solely by
bioavailability differences, than the ratio of overall bioavailability of soil to feed should
equal this ratio. As described above for rat data, the overall bioavailability of soil was 0.3-
0.4, and the overall bioavailability of feed was 0.5-0.6. The ratio of overall bioavailabilities
is, therefore, (0.3-0.4)7(0.5-0.6). If the argument that this ratio equals the ratio of BCFs is
valid, than this would lead to a Bs of 0.5 to 0.8. This implies that absorption of TCDD
when soil is the vehicle is 50 to 80% of what it would be if feed were the vehicle. These
assumptions and implications are made for this assessment, and the soil bioavailability
term, Bs, used for all example compounds in Chapter 5 is 0.65.
• Soil diet fraction, DF8: Fries and Paustenbach (1990) report that soil intake
by cattle feeding on pasture varies between 2 and 18% of total dry matter intake,
depending on whether the grazing area is lush or not. The soil diet fraction would be
lower for cattle which are barn-fed with minimal opportunity for contaminated soil intake.
Cattle raised for milk are rarely pastured, so one possible assumption for estimating milk
fat concentrations would be a DFS of 0.0. Fries and Paustenbach (1990) assumed
between 0 and 2% of the dry matter intake by lactating cattle was soil in various
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sensitivity tests. Since cattle raised for beef are commonly pastured, a conservative
assumption would be a high DFS of 0.15 (15%), although a more reasonable assumption
which would consider grazing in lush conditions and/or a portion of diet in feed or
supplemental feed leads to DFS less than 0.10. Fries and Paustenbach (1990) assumed
DFS of between 0 and 0.08 for beef cattle in various sensitivity tests. The example
settings in Chapter 5 assume 0.02 (2%) for lactating cattle, and 0.04 (4%) for beef cattle.
• Feed and grass diet fractions, DFf and DFg: The sum of the three diet
fractions, DFS 4- DFf + DFg must equal 1.0. Setting DFS equal to 0.02 (2%) for lactating
cattle assumed that they are pastured to some extent or could be taking in residues of soil
sticking to home grown feed. Assuming lactating cattle graze a small amount of time, the
DFg for lactating cattle will be 0.08 (8%). This assessment simplifies the definitions of
dairy and beef cattle diets by defining non-pasture grass vegetation simply as "feed".
Feeds include hay, silage, grain, or other supplements. While dairy cattle are lactating,
90% of their dietary intake is assumed to be in this general category. Beef cattle spend a
significant amount of time pasturing. However, their diet is supplemented with hays,
silages, and grains, and particularly so in colder climates where they need to be housed
during the winter. In this assessment, the simple assumption that they ingest equal
proportions of pasture grass and feeds is made. Therefore, with 4% soil ingestion, DFf
and DFa are both 48% for beef cattle.
U
Fries and Paustenbach (1990) summarize pertinent literature to conclude that cattle
raised for beef are not slaughtered without an intervening period of high-level grain
feeding. Agricultural statistics (USDA, 1992) show that 32.9 million cattle were
slaughtered in 1991. Of this number, 6.1 million were cows and bulls that likely did not
go through a feedlot prior to slaughter. Quarterly statistics from 1991 show that at any
time, cattle and calves on feed for slaughter range from 10 to 12 million. Fries uses these
statistics to conclude that 75 to 80% of the total beef supply is from animals that went
through a feedlot finishing process, and that the portion of beef that did not go through a
feedlot process are (generally speaking) those 6.1 million cows and bulls (personal
communication, G. Fries, USDA Agricultural Research Service, Beltsville, Maryland,
20705). He suggests that a representative feedlot finishing process would include a
length of 1 20 days and diet consisting of 20% corn silage and 80% grain. The grains can
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be assumed to be residue-free, since grains are protected and, as discussed above, little
within plant translocation of outer contamination can be assumed. Also, the ears of the
corn silage are in the same category, leaving only the stalks and leaves of the corn silage
impacted by atmospheric transfers of dioxin-like compounds.
A feedlot finishing process is important to consider if assessing beef impacts in a
site-specific assessment. However, data could not be found in the literature which
measured the impact of this process to beef concentrations. Such impacts could occur as
the result of increased weight gain from substantially residue-free feeds. Fries and
Paustenbach (1990) and Stevens and Gerbec (1988) modeled the impact of a residue-free
grain-only diet for four months prior to slaughter. Based on within-cow dilution and
depuration considerations, both efforts estimated that the feedlot process would reduce
beef concentrations by about one-half. This was the assumption used in the beef food
chain validation exercise described in Chapter 7, Section 7.2.3.9.
The demonstration scenarios of Chapter 5 assume that farming families slaughter a
portion of their cattle for home consumption. A dilution/depuration reduction is not
assumed for these demonstrations.
• Average contaminant soil concentration, AC8: The simplest assumption for
ACS would be that it equals the initial level of contamination, Cs. However, this would be
too high if the cattle also graze in uncontaminated areas. Where cattle have random
access to all portions of a grazing area with contaminated and uncontaminated portions, a
ratio of the spatial average of the contaminated area to the total area should be multiplied
by Cs to estimate ACS. If cattle spend more time in certain areas, these areas should be
weighted proportionally higher. Different assumptions for determining ACS might also be
in order when using Equation (4-32) to estimate milk fat as compared to beef fat
concentrations. Lactating cattle, if pastured, might graze on different areas than beef
cattle. After determining a spatial average based on current conditions, a second
consideration might be given to temporal changes. If soil levels are expected to change
over time (due to changes in source strength or other factors) then the concentrations
should be averaged over the exposure duration as well. The example scenarios in Chapter
5 where beef and milk exposures were estimated were termed "farms". The
methodologies in this chapter were used to estimate the average soil concentration over
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the entire farm property. Assuming the cattle are raised on the farm property, than 100%
of their intake of soil comes from the farm. This means that the average soil
concentration, ACS in Equation (4-32), is equal to the level of contamination given as the
initial level, or determined as average for the farm based on fate and transport algorithms.
• Average feed and pasture grass concentration, ACf and ACg: The
concentration of contaminant in pasture grass or feed is equal to Cabv as calculated in
Equation (4-24). As detailed in Section 4.3.4.2. above, pasture grass or feed grown
on-site can be impacted by air-to-plant vapor phase transfer and particulate deposition.
Refinements noted above include the empirical parameter VGag equals 1.00 when applying
the air-to-leaf transfer algorithm to pasture grass and 0.50 when applied to cattle feed. A
refinement noted here, and like ACS above, is that an assumption needs to be made about
the fraction of feed or fraction of pasture grass that is impacted by contamination. Part of
the feed diet could come from outside sources and not be contaminated, and part of the
grazing area could be far from a localized area of soil contamination, making it less
impacted by contaminated particulates or vapors. The simplest assumption is that the
entire vegetative diet of the cattle includes pasture grass and feed impacted by the
contaminated soil, in which case ACf and ACg would equal Cabv. For the sake of
simplicity and consistency, the assumption made for ACS was also made for ACf and AC
in the example Scenarios in Chapter 5. That is, the grass and feed intakes of beef and
dairy cattle originate within the farm property and concentrations in grass and feed are a
function of the soil concentrations within the farm property; ACf and ACg are equal to
Cabv as calculated in Equation (4-24). For site-specific situations, ACf and ACg should be
estimated as Cabv reduced according to assumptions on quality of cattle feed, and impacts
of air-borne contaminants on grazing land and cattle feed grown at the site where cattle
are raised.
There is one final but critical note on solving for beef and milk concentrations given
a solution for Cfat as in Equation (4-32). Human daily ingestion amounts are typically
expressed in whole product rather than the fat portion of product. Whole milk is 4% fat,
meaning that the Cfat needs to be multiplied by 0.04 to get whole milk concentration.
Similarly, beef is generally 18-22% fat, meaning that the Cfat needs to be multiplied by
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0.18-0.22 to get whole beef concentration. However, the ingestion rates in this
assessment for beef and milk were developed on a fat basis, so no adjustment is
necessary.
4.4. ALGORITHMS FOR THE "OFF-SITE" SOURCE CATEGORY
As noted in Section 4.1, the contaminated soil is remote from the site of exposure
for the "off-site" source category. A common example is an industrial site with soil
contamination or a landfill with contaminated soil. Section 4.4.3. below discusses some
considerations for specific types of off-site soil contamination, including the disposal of
ash from incinerators, the disposal of sludge from paper and pulp mills, and sites of
industrial contamination such as those on the NPL. The example setting in Chapter 5 is 10
hectares in size, has sparse vegetation, and has contamination levels of the example
compounds that are in the same range as those found on NPL sites. Since many of the
parameters in the algorithms discussed below are specific to particular off-site soil
contamination sites, guidance in this section will be specific to the example setting in
Chapter 5.
Several of the algorithms estimating exposure media concentrations for the off-site
source category are the same or very nearly the same as in the on-site source category.
Following now are bullet summaries for similarities and small refinements to these
algorithms. Sections below describe algorithms that are unique for the off-site source
category.
• Surface water impacts: Methodologies and assumptions for estimating
surface water impacts for the on-site source category (described in Section
4.3.1.) are also used for this source category. In applying this algorithm for
the example scenario demonstrating the off-site source category in Chapter
5, Example Scenario 3, the important assumption was made that the
average concentration of contaminants in the watershed was very low
compared to the concentration on the contaminated soil - hence, Cw
(average concentrations on watershed soils other than the contaminated
site) was set to 0.0. Setting Cw to 0.0 could also be justified by saying that
the demonstration scenario only demonstrated the incremental impact of the
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contaminated site. The unit erosion rate from the contaminated site, SLC, is
s
assumed to be 9.6 t/ac-yr (English units). The unit erosion rate from other
areas of the watershed, SLW, is assumed to be 2.9 t/ac-yr. Derivation of
these terms is given above in Section 4.3.1. The contaminated site is
assumed to be 150 meters away from the water body, and SDS is estimated
therefore as 0.26. The effective drainage area, Aw, is 4000 ha. From
Figure 4-5, it is seen the sediment delivery ratio associated with this
drainage area is approximately 0.15, which is assumed for the example
scenario in Chapter 5. The contaminated site for the example scenario is 10
ha.
Vapor-phase air concentrations: The volatilization of contaminants from soil
can be estimated similarly to the way described in Section 4.3.2 for the
on-site source category. Section 4.4.2 describes a dispersion model which
transports contaminants through the air to the exposure site. The far-field
dispersion model described in Section 4.4.2 differs from the near-field
dispersion model presented in Section 4.3.2.
Particulate-phase air concentrations: The same model for particulate flux
due to wind erosion is used for the off-site source category as described in
Section 4.3.3. However, two parameters might be different than described
above (Equation (4-18)} if the model is applied to off-site soil contamination
when the soil is bare. One is the vegetative cover, V, which might be more
appropriately assigned a 0.0 implying no ground cover for an active landfill
or an industrial site. The other is the threshold wind speed, Ut. The
different assumption would be in roughness height, assumed 4 cm for a
residence or farm setting, but perhaps more appropriately assumed to be 1.0
cm for bare soil. This value is appropriate for a tilled field (EPA, 1985b).
With this change, a Ut is calculated as 8.25 m/sec, and F(t) is calculated as
0.5. Note that V and Ut might be the same as a residential or farm setting if
the off-site soil contamination had a grass cover. Once a flux has been
calculated, the far-field dispersion model described in Section 4.4.2. below is
used for estimating air-borne particulate-phase contaminant concentrations
at the exposure site.
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• Biota concentrations: The basic strategy for estimating biota concentrations
- as a linear function of environmental media concentrations (bottom
sediment concentrations, soil, air) and based on bioaccumulation or
biotransfer factors (along with diet fractions, etc.) - remains the same.
Section 4.4.1. describes how exposure site soil concentrations are estimated
from concentrations at the off-site source. Exposure site soil then becomes
a "source" for plant, beef, and milk contaminant concentrations. Similarly,
air-borne particle and vapor-phase contaminants originating from the off-site
become sources for pasture grass and feed concentrations, which are above
ground vegetations. As described below, exposure site soil concentrations
are a function both of the amount of soil estimated to erode from the off-site
contamination, and of a mixing depth which is different for "tilled" vs.
"untilled" situations. The soil concentration used for cattle ingestion of soil
is assumed to be untilled. The soil concentration used to estimate
concentrations in underground vegetables is assumed to be tilled. The
algorithm to estimate fish tissue concentrations as a function of bottom
sediment concentrations remains the same for this source category.
The soil ingestion and dermal exposure pathways are still a function of exposure
site soil concentrations; i.e., no assumption of direct contact with the off-site
contamination is made. Also, both of these direct soil exposure pathways used the
untilled soil concentrations.
Section 4.4.1. discusses how exposure site soil concentrations are calculated from
off-site concentrations. Section 4.4.2. describes adjustments to the volatilization flux
algorithm and the far-field dispersion model which transports vapor and particulate-phase
residues to the nearby exposure site. Section 4.4.3. discusses considerations for specific
types of off-site soil contamination.
4.4.1. Exposure Site Soil Concentrations
The key assumptions for the solution strategy estimating exposure site soil
concentrations resulting from an off-site soil contamination source are: 1) the exposure
site soil becomes contaminated due to erosion of contaminated soil from the source to the
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exposure site, 2) the soil eroding off the site of contamination is "enriched" in comparison
to the soil at the site - eroded soil has higher contaminant concentrations than in-situ soil,
3) the amount of soil at the exposure site does not increase, which means that soil
delivered to the site via erosion is matched by an equal amount which leaves the site, and
4) not only does soil erode off the contaminated site en route to the exposure site, but soil
between the contaminated site and the exposure site also erodes to the exposure site.
The third and fourth assumption translate to:
Dl + D2 = R (4-33)
where:
D1 = mass of soil delivered from off-site contaminated source, kg
D2 = mass of soil delivered from land area between contaminated source
and exposure site, kg
R = mass of soil removed from exposure site, kg.
The mass balance equation for exposure site soil concentrations can now be
qualitatively stated as (with "AC" used as shorthand for change in exposure site soil
concentration over time):
(the incremental addition to C resulting from the change in erosion of
contaminated soil)
AC = (the incremental substraction of C resulting from removal of now
contaminated soil from the exposure site)
(the incremental substraction of C resulting from dissipation of
residues at the exposure site)
This can be expressed mathematically as:
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_ RC (4_34)
dt M M
where:
C = the exposure site soil concentration, mg/kg
D-| = mass of soil delivered from off-site contaminated source, kg/yr
C0 = concentration of contaminant at contaminated site, mg/kg
E = enrichment ratio, unitless
M = mass of soil at exposure site into which contaminant mixes, kg
R = mass of soil removed from exposure site, kg/yr
k = first order dissipation rate constant, 1/yr.
Assuming that the contaminant concentration at the exposure site, C, is initially 0,
Equation (4-34) can be solved to yield:
R + kM
\
L1
DlC0E ~ (4-35)
e
which computes C as a function of time, t (in years since k is in years). This can be
solved for various increments of time starting from a time when the exposure or
contaminated site initially became contaminated, or it can be simplistically assumed that
the contamination has existed at the contaminated site for a reasonably large amount of
time such that the exponential term approaches zero. This can be alternately stated that
the assumption is made that the system has reached steady state over a long period of
time. Either way, the exponential term drops out, and C is estimated as:
C - ° (4-36)
R + kM
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Equation (4-36) was used to estimate exposure site concentrations resulting from off-site
contamination for the example scenario in Chapter 5. Guidance for estimation of these
terms including justification for their values as selected in the example settings are:
• E: A discussion of the enrichment ratio was given in Section 4.3.1. Its use
in that application was to estimate the enrichment of soil delivered to a surface water
body and the resulting impacts to suspended and bottom sediments. It's value was placed
in the 1 to 5 range, and the value of 3 was chosen for all contaminants. The same value
will be used for estimating concentrations in soil that result from erosion of contaminated
soils to nearby sites of exposure.
• k: For the on-site source category, and for contaminated soil at the off-site
contaminated location, the assumption is made that residues do not degrade or dissipate
to the point of reducing the concentration of the "initial" soil levels. This was partly based
on information indicating generally low rates of biological or chemical degradation for the
dioxin-like compounds of this assessment, coupled with the assumption that on-site and
off-site contamination was sufficiently deep implying a reservoir of contaminant that would
remain available during a period of exposure. These assumptions are less likely to be valid
for residues which have migrated over the surface to deposit on the exposure site. The
deposition is likely to result in only a thin layer of contaminated soil. Though very small,
surface-related dissipation mechanisms such as photolysis, volatilization, or degradation,
might reduce surface soil contaminant concentrations. For these reasons, a "dissipation"
rate constant is assumed to apply to delivered contaminant, where the precise
mechanisms of dissipation are not specified, but could include transport (volatilization,
erosion) and degradation (photolysis, biodegradation) mechanisms. The studies on
2,3,7,8-TCDD described in Young (1983) imply a dissipation half-life of 10 years. Fries
and Paustenbach (1990) suggested the use of a half-life of at least 10 years, and used a
15 year half-life in their example scenarios on the impact of air-borne deposition of
2,3,7,8-TCDD originating from stack emissions. This assessment uses a dissipation
half-life of 10 years for all of the three example compounds in Chapter 5. This half-life
translates to a first-order dissipation rate constant of 0.0693 yr"1.
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• M: The delivered contaminant mixes to a shallow depth at the exposure
site. The mixing depth depends on activities which disturb the surface, such as
construction, plowing, vehicle traffic, movement of cattle or other animals, burrowing
action of animals, other biological activity, normal leaching, and raindrop splash. Mixing
depths for fallout plutonium have been found to be 20 cm on cultivated land and 5 cm on
uncultivated forest and rangeland (Foster and Hakonson, 1987). Fries and Paustenbach
(1990) suggested a depth of 15 cm for agricultural tillage, but assumed values of 1 and 2
cm for various sensitivity tests. However, they did not need to make a distinction
between tilled and untilled situation because vegetation (pasture grass and forage for
estimating beef and milk fat concentration; above ground fruits and vegetables for human
consumption) was assumed to be impacted only by paniculate deposition and not root
uptake. In another assessment on indirect impacts from incinerator emissions, EPA
(1990a) estimated vegetation concentrations as a function of particulate depositions, root
uptake, and air-to-leaf transfer from the vapor phase. Different mixing depths for untilled
and tilled concentration estimation was required. For root uptake estimation for vegetable
and other crops, the estimated soil concentrations assuming a tillage mixing depth of 20
cm. For soil concentrations in untilled situations, they assumed a mixing depth of 1 cm.
The methodology of this assessment uses 5 cm for the untilled and 20 cm for the tilled
conditions for the off-site soil source category. Soil concentrations for calculation of
concentrations of underground vegetables will be a function of a 20-cm depth. This
assumption is made because tilling gardens is assumed to distribute surface residues to the
20-cm depth. Soil concentrations for dermal contact, soil ingestion, and pasture grass and
soil intake for cattle grazing will assume a depth of 5 cm. These activities are assumed to
occur on soil which has not been tilled. As will be described in Section 4.5, tilled and
untilled depths of mixing are also required for the stack emission source category. For that
source category, the tilled mixing depth is also assumed to be 20 cm, but the untilled
mixing depth will instead be assumed to be 1 cm. The difference is made because of the
assumed differences in the processes of erosion and air deposition. Erosion is a turbulent
process, mixing soils from the contaminated site with soils between the contaminated and
exposure site, while air deposition of particles happens uniformly over a landscape in a less
turbulent manner.
Given the area of the exposure site, the mass of soil into which the eroded
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contaminant is mixed can be calculated as:
M = AesBsoil d (4-37)
where:
M = mass of soil for contaminant mixing per unit depth, kg/m
Aes = area of exposure site, m2
Bsoj| = soil bulk density, kg/m3
d = depth of mixing, m
• D1 and D2: The first step in deriving both these amounts of soil is to use
the Universal Soil Loss Equation (USLE). This approach was described above.
Justification was given for an assumption of unit soil loss from the contaminated site of
9.6 t/ac-yr (see Section 4.3.1). D1 equals this unit loss times the area of contamination
times a sediment delivery ratio. The example scenario in Chapter 5 assumed that the
exposure site was 150 meters from the contaminated site, and using Equation (4-10), the
sediment delivery ratio is 0.26. The unit loss assumed for the area between the
contaminated site and the exposure site is 0.96 t/ac-yr. Since this area is adjacent to the
exposure site, there is no sediment delivery, and D2 equals this unit loss times the area
between the contaminated and exposure sites.
D2 and D2 can now be expressed as:
D1 = 0.224 SLi SD^ ALS (4-38a)
D2 = 0.224 SL2 SD2 ABLE (4-38b)
where:
D-, 2 = mass of soil delivered from off-site contaminated source, D-|, and
from the land area between contaminated source and exposure site,
D2, kg/yr,
SL1 2 = average annual unit soil loss, Eng. tons/acre-year, equal to 9.6 t/ac-yr
for SL-| and 0.96 t/ac-yr for SL2
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2 = sediment delivery ratio, unitless, 0.26 for SD1 (with distance = 150
meters) and 1.00 for SD2,
ALS/ABLE = land area of contaminated site, ALS, and of area between
contaminated site and exposure site, ABLE, m2
.224 = converts t/ac-yr to kg/m2-yr.
An adjustment is made to the sediment delivery ratio, SD17 considering the size
discrepancies between the contaminated site and the exposure site. For example, if the
contaminated site is larger than the exposure site, then the amount of eroded soil delivered
150 meters downgradient would not all mix with soil at the exposure site. On the other
hand, if the contaminated site were smaller than the exposure site, than the full amount of
eroded soil delivered 150 meters downgradient would be contained within the exposure
site. A simple correction factor, equaling the ratio of a side length of the exposure site
(assumed square-shaped) and a side length of the contaminated site size (also assumed
square shaped), is used to adjust the sediment delivery ratio:
SDla = SDl CF (4-39)
where:
SD1a = adjusted sediment delivery ratio corresponding to SD-,, unitless
SD1 = sediment delivery ratio reducing the amount eroding from the
contaminated site to be delivered to the exposure site, unitless
CF = AES°-5/ALS°-5 if AES < ALS
1 if AES > ALS
AES = area of exposure site, m2
ALS = area of contaminated site, m2
Similar considerations are pertinent to the land area between the contaminated and
exposure site. Remember that the algorithm assumed that some "clean" (D2) and some
"contaminated" soil (D-,) erodes onto the exposure site, and that a similar amount of soil
entering the exposure site (R, which equals D1 + D2) leaves the exposure site so as to
maintain a mass balance. The amount of clean soil eroding from upgradient sources
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mixing with exposure site soil can be larger than the amount of contaminated soil if the
exposure site is larger than the contaminated site. If the exposure site is smaller than the
contaminated, and similar to the solution for SD1a above, then only the small corridor
defined by the size of the exposure site contributes clean soil. Either way (i.e., the
exposure site is larger or smaller than the contaminated site), the size of the land area
contributing clean soil is defined by the size of the exposure site. ABLE can be estimated
as the product of the distance between the exposure and contaminated site, and the side
length of the exposure site:
ABLE = DL SL (4-40)
where:
ABLE = 'an<^ area between contaminated and exposure site, m2
DL = distance from landfill to exposure site, m
SL = side length of exposure site, m
- fA }°-5
(AES)
ES
AES = area of exposure site, m2.
4.4.2. Off-Site Transport of Air-borne Contaminants
Estimating the dispersion and resulting exposure site concentrations of air-borne
contaminants, originating at the site of contamination in a vapor or a particle phase,
requires a different solution for the off-site as compared to the on-site situation. A
simplified solution, given as a virtual point source model, can be found in Turner (1970).
This model approximates the dispersion that occurs from an area source by using an
imaginary point source. This point is located upwind of the actual source at a distance
calculated to create a lateral dispersion at the site equal to its width:
2.03 FLUX A.c FREQ IO10 ,,
r> . - sc * (4-41)
Mr VD Sz Um ( )
where:
Cajr = concentration of contaminant in air, //g/m3
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FLUX = average contaminant flux rate, g/cm2-s
As = area of contaminated site, m2
FREQ = frequency wind blows from source to receptor, unitless
VD = virtual distance, source center to receptor, m
Sz = vertical dispersion coefficient, m
Um = average wind speed, m/s
1010 = converts g/cm2 to //g/m2.
The term, FREQ, has been added to this equation to appropriately account for
changing wind directions, and hence, obtain a more accurate annual average air
concentration. The vertical dispersion, Sz/ is estimated as an empirical function of the
distance from the source center to receptor:
Sz = 0.222 x°-725 - 1.7 , x < 1000 m (4-42a)
Sz = 1.260 x°-516 - 13.0 , x > 1000 m (4-42b)
where:
Sz = vertical dispersion coefficient, m
X = actual distance from source center to receptor, m.
The virtual distance, VD, is an empirical function of the width of the contaminated
area and the actual distance from source center to receptor:
VD = 2.514 a + X (4-43)
where:
VD = virtual distance, source center to receptor, m
a = width of contaminated area perpendicular to wind direction - defined
previously as side length for assumed square-shaped contaminated
area, m
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X = actual distance from source center to receptor, m.
Prior guidance on windspeed (Section 4.3.2) indicated windspeeds ranged from 2.8
to 6.3 m/sec, and suggested a mid-range of 4.0 m/sec in the absence of better
information. Where the wind blows from all directions equally, then it will blow from one
compass sector about 15% of the time. On these bases, a windspeed of 4.0 m/sec and a
FREQ of 0.15 were used in the example scenarios in Chapter 5. In most places, however,
wind direction is much less variable, and the appropriate value is best determined with site
specific information.
4.4.3. Specific Cases of Off-Site Soil Contamination
This section provides background information on specific sites of soil contamination
which have been studied for the presence and impact of dioxin-like compounds. These
include landfills used for disposal of ash from municipal waste combustion facilities, the
disposal of sludge from pulp and paper mills, and sites of soil contamination typified by the
sites monitored in the National Dioxin Study (in many cases, Superfund sites or sites that
were in some stage of being considered for inclusion on the NPL list at the time of the
study; EPA, 1987). Discussion of these particular sites does not imply that they represent
the bulk of such sites nationally, or that they are discussed here based on any critical
environmental or exposure rational. They are discussed because they present unique
issues for emissions and fate and transport of dioxin-like compounds from sites of soil
contamination, and because they have been studied. Issues discussed below are pertinent
for other types of off-site soil contamination sites.
4.4.3.1. Landfills receiving ash from municipal waste incinerators
Particular issues regarding landfills receiving ash include: the impact of soil cover on
releases, the ash concentrations, the size of such landfills, the quantity of ash generated
by incinerators, and the fugitive emissions that result from ash management. Key sources
providing information for this section include a methodology document describing
approaches to estimating environmental releases and exposures to ash (EPA, 1991), and a
contractor report applying these types of methodologies using site-specific data from
several ash landfills (MRI, 1990). Each of the identified topics will be discussed in turn.
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• Landfill Cover: Whether or not ash is covered once it is disposed of at the
landfill is critical in determining releases and subsequent exposures. Currently, practices at
operating landfills vary from no coverage after disposal on active portions of the landfill to
daily coverage of disposed ash. MRI (1990) visited six facilities disposing ash, including
ash monofills and municipal solid waste landfills. In one of the facilities, an ash monofill
located at the site of the combustor, the disposal area encompassed 1 5 acres and did not
use daily cover until final elevation was reached. At that time, a clean cover of 2 feet of
soil would be applied. At a second facility, located at the site of the combustor but
landfilling municipal solid waste as well as ash, ash was used for different purposes,
including a subbase roadbed material, as soil substitute for earth work, and as a daily
cover for MSW receipts. An assumption of bare surfaces (i.e., no vegetation) during the
period of landfilling activity, with concentrations of dioxin-like compounds equal to
concentrations in the ash would appear to be appropriate assumptions for practices at
these two landfills.
Where daily cover is employed, however, appropriate assumptions are not
straightforward. Of the remaining four sites studied by MRI, two employed daily covers
("clean cover material" in one case and a "HOPE liner" in the other, sic), and daily
coverage practices were not discussed for two sites. Approaches described for airborne
emissions and erosion losses would have to be modified when daily cover is applied. First,
losses of contaminants via overland soil or wind erosion could not be expected to occur
when cover (soil cover or otherwise) is in place, although the active part of the landfill
would be subject to erosion during an operating day. Even in that case, however, site-
specific practices might include little or no ash disposal during periods of soil-erosion-
producing storms. Depending on site-specific practices, one might estimate annual erosion
losses using methodologies described in this assessment, and then empirically reduce
erosions losses based on these practices and scientific judgement.
Air emissions from active portions of the landfill, as in wind erosion and
volatilization, also are obviously impacted by cover practices. These emissions would
occur during the actual disposal. Wind erosion and volatilization fluxes could be estimated
as given in earlier sections, and then reduced by two-thirds, which might correspond to an
assumption of disposal during 1/3 of a day or a year, etc. When covered by soil or a
synthetic cover, wind erosion losses would not occur. However, buried residues may
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diffuse through layers of clean soil and be released via volatilization.
Estimates of volatilization release via diffusion through clean cover have been made.
A rigorous approach for such estimates is detailed in Hwang, et al. (1986). Use of this
approach requires a computer to iteratively solve a partial differential equation, expressed
in terms of a Fourier series. It can be shown, with these equations, that the vapor
emission rate through such a cover will not reach steady state for hundreds of years.
Hwang's approach was applied to an earlier assessment for 2,3,7,8-TCDD (EPA, 1988b).
Calculations were performed for 2,3,7,8-TCDD contamination with a thickness of
contamination of 8 ft, and clean caps ranging from 10 to 25 cm. The results of this
exercise suggest that the average emission rate of a 70-year period are 1/4 to 1/5 of what
they would be without the cap. Based on this exercise, a simple assumption might be
made that a clean cap will reduce the average emission rate calculated without a clean cap
by 80%. However, these results are not consistent with those described in Jury, et al.
(1990). The analytical solution developed by Jury was demonstrated on 35 organic
compounds. One exercise conducted by Jury was to estimate the cover thickness
required to restrict volatilization to less than 0.7% of the mass incorporated in soil. For
2,3,7,8-TCDD, the thickness was estimated at 0.7 cm for a sandy soil and 0.2 cm for a
clayey soil. This appears to contradict the work of Hwang since it shows an essentially
insignificant loss for a cap much less thick than the 10-25 cm cap in the exercises using
Hwang's approach. However, Jury's approach allows for assumptions on degradation of
the buried compound. For that exercise. Jury assumed that the half-life for 2,3,7,8-TCDD
was 1 year. This is a very rapid degradation rate, given information that the dioxin-like
compounds resist degradation, particularly when not exposed to sunlight. On the other
hand, the Hwang model assumes no degradation loss, and as such, the generalization from
his exercise might be an overestimate. Hwang's exercise might also have overestimated
since it assumed a rather thick 8-ft layer of subsoil contamination. From these arguments,
it would appear that neither exercise appropriately evaluated the difference in volatilization
in a no cover versus a cover situation.
The above discussions concerned flux calculations when cover practices are used.
One set of adjustments discussed reduced a total potential flux of volatilized or wind
eroded losses based on a portion of the time that the ash would be uncovered. A second
discussion indicated that some loss via volatilization might be modeled with a clean cap.
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In any case, it is clear that cover practices will reduce losses. Cover practices must be
considered when evaluating the exposure to ash disposed of in landfills.
• Ash Concentrations: A key consideration, of course, in modeling transport of
dioxin-like compounds from an ash landfill is the concentration on the ash. Ash
concentrations of dioxin-like compounds have been found to vary widely, from non-detect
(generally less than 0.1 ppb) to the hundreds and thousands of part per billion. Table 4-5
appears in EPA (1991) and summarizes concentrations of dioxin-like compounds and PCBs
found in fly, bottom, and combined ash. These data are a summary of 19 references,
ranging in publication date from 1974 to 1990. It should be noted that, except for
2,3,7,8-TCDD and 2,3,7,8-TCDF, results listed are for congener groupings defined by
degree chlorination.
• Size of Landfill and Amount of Ash Landfilled: The size of the landfill and
the amount of ash applied daily or over time are both required for estimating exposures
nearby. These can both be obtained from site-specific observations. Amounts of daily
disposed ash are required to estimate fugitive paniculate emissions, as will be discussed
shortly. Amounts of daily or ultimate disposal are also tied to landfill size, or the portion of
a landfill that is active on a daily basis. One common practice is to fill cells of a landfill
one at a time, and once filled, to cover with a 2-ft {or so) layer of clean soil. The
appropriate size in this case is the average size of a landfill cell. If daily coverage is
applied, than the size for modeling purposes corresponds to the area over which daily
coverage occurs. This can also vary depending on the depth of disposal during a day. A
six-inch daily coverage, for example, would take twice as much space as a 1-ft depth of
daily disposal. If the intent of a day's disposal is to cover over the entire area of an active
cell, then depth of coverage need not be considered in determining landfill size.
Determination of landfill size (or the size of the active portion of the landfill) may be
required in the absence of site-specific information, such as in the planning stages for a
new incinerator. This is where details on landfill management need to be determined. One
important detail, as already noted, is the amount of ash generated for daily disposal. Cook
(1991) assumes that bottom and fly ash combined comprise about 11% of total receipts
on a volume basis. However, a relationship between ash generated and solid waste
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Table 4-5. Ranges of concentrations of PCDDs, PCDFs, and PCBs in municipal waste
combustor ash (results in ng/g or ppb).
Constituent
MCDD
DCDD
T3CDD
T4CDD
PCDD
H6CDD
H7CDD
OCDD
2,3,7,8-TCDD
Total PCDD
MCDF
DCDF
T3CDF
T4CDF
PCDF
H6CDF
H7CDF
OCDF
2,3,7,8-TCDF
Total PCDF
Mono CB
Di CB
Tri CB
Tetra CB
Penta CB
Hexa CB
Hepta CB
Octa CB
Nona CB
Deca CB
Total PCB
Fly Ash (ref)
2.0
0.4-200
1.1-82
ND-250
ND-722
ND-5,565
ND-3,030
ND-3,152
ND-330
5-10,883
41
ND-90
0.7-550
ND-410
ND-1800
Tr-2,353
Tr-887
ND-398
0.05-5.4
3.73-2,396
0.29-9.5
0.13-9.9
ND-110
0.5-140
0.87-225
0.45-65
ND-0.1
ND-1.2
ND
ND
ND-360
Combined Ash
ND
ND-120
ND-33
0.14-14
0.07-50
0.07-78
0.07-120
0.07-89
0.02-0.78
6.2-350
1.1
ND-42
ND-14
2.3-9
1.6-37
1.2-35
0.62-36
0.18-8.4
0.41-12
6.14-153.9
ND
0.126-1.35
0.35-14.3
16.5
ND
ND-39
ND
ND
ND
ND
ND-32.15
Bottom Ash
NR
NR
NR
< 0.04-410
ND-800
ND-1,000
ND-290
ND-55
< 0.04-6. 7
ND-2,800
NR
NR
NR
10.1-350
0.07-430
ND-920
ND-210
ND-11
ND-13
ND-1,600
ND-1.3
ND-5.5
ND-80
ND-47
ND-48
NR
NR
NR
NR
NR
ND-180
ND = not detected at the detection limit
Source: EPA (1991).
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received by an incinerator on a mass basis is more useful for estimating daily disposal
amounts. In a recent EPA (1990f) report on ash characterization, ash mass was estimated
as an average of 29.5% of municipal solid waste received in five facilities studies, with a
narrow range of 25-35%. This mass was estimated on a wet weight basis. Ash is wetted
when exiting the incinerator, and water comprises 20-30% of the total weight at that
point. If the ash is immediately trucked for landfill disposal, its total weight includes the
weight of this quench water. Often ash is stored at the incinerator site in piles prior to
disposal, that storage ranging from hours to days. In this circumstance, much of the
quench water would have drained off or evaporated, and then the total weight hauled
would be closer to a dry weight estimate. In summary, the amount of ash generated to be
disposed of a daily basis can be estimated as: the daily receipt of municipal solid waste
(tons) * a wet weight ash fraction (0.25-0.35) * a wet to dry weight conversion if
appropriate (wet weight * 0.80, e.g.).
• Fugitive Particulate Emissions: Fugitive emissions can occur from the
time ash exits the incinerator for temporary storage at the facility site (or immediate
loading onto trucks for disposal) until ultimate disposal. Approaches to estimate fugitive
releases from incinerator ash management are described in EPA (1991), and will be
summarized here.
As noted, ash can be wet when exiting the quench tank. If stored at the facility
site prior to disposal in a landfill, leaching from piles can occur. Because dioxin-like
compounds are strongly hydrophobic, however, the impact of leaching is unlikely to occur
much beyond the soil beneath and near the storage piles. If loaded onto trucks when very
wet, leaking onto roadways may also occur. If these storage piles are left uncovered, they
would of course be subject to erosion losses, which might move residues further from the
piles than just leaching of water from the piles.
Of more concern than water-borne losses due to ash management are fugitive
emissions of dry ash. Wind erosion, which can occur from open storage piles or
uncovered portions of the landfill, is a fugitive emission that has been discussed for soil
contamination. Specific practices in the management of ash can also result in fugitive
emissions. Such practices include: 1) loading onto and dumping out of trucks, 2) truck
transport from the incinerator facility to the landfill site, 3) truck or other traffic over paved
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or unpaved roadways at the incinerator site, at the landfill site, or other roadways
containing contaminated dust, and 4) spreading and compacting of ash at the landfill site.
A set of empirical emission factor equations for estimating fugitive particulate emissions,
called "AP-42" equations, have been developed by EPA's Office of Air Quality Planning
and Standards (EPA, 1985a; EPA, 1988a). Specifics on applying these equations for ash
management are described in EPA (1991). An example of their application using site-
specific information for ash management is detailed in MRI (1991). An abbreviated listing
of emission factor equations that have been used in these two publications are:
• Vehicular traffic over unpaved roadways. Dust on the surfaces of roads, both
unpaved and paved, can become suspended due to vehicular traffic. When these
roadways are near ash storage piles or within the landfill, that dust can become
contaminated. The emission factor equation for emissions from unpaved roadways is:
= 1 7 k S\ IVS\ i W \°J I™]™ /365-P\ (4-44)
1>7 k«" - ~ ~- ( }
\ 4 / \ 365
where:
Eup = emission flux for unpaved surfaces, kg/VKt (VKt equals vehicle
kilometer traveled)
^unp = particle size multiplier specific to the unpaved road emission flux
equation, unitless
s = silt content of unpaved roadway, %
Vs = vehicle speed, km/hr
W = vehicle weight, kg
nw = number of wheels per vehicle, unitless
P = number of days with at least 0.254 mm (0.01 inch) precipitation per
year, unitless.
• Emissions off trucks in transit. Although no emission factor equations have
specifically been developed for trucks while in transit from the incinerator facility to the
landfill, such emissions can occur if the ash is dry, and partially or completely uncovered.
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The following equation for estimating emissions from open storage piles has been
suggested for use in estimating fugitive emissions from trucks in transit (EPA, 1991; the
emission factor equation from EPA, 1985a). Note that use of this equation will require
specific management assumptions in order to estimate the number of uncovered hectares
per day: the number of trucks in use per day, the surface area of trucks, the percent of
uncovered area if a tarpaulin is used, the moisture content of ash, and so on.
(4-45>
where:
Et = particulates emitted from trucks in transit, kg/day/hectare
s = silt content material of ash, %
P = number of days with >0.25 mm precipitation per year
f = percentage of time that the unobstructed wind speed exceeds 5.4 m/s.
• Loading and unloading. The unloading operations at the disposal site may result
in the release of fugitive dust. The following emission factor equation provides emission
factors for kilograms of particulate emitted per megagram (metric ton, or 1000 kg) of soil
loaded and unloaded:
Elu = 0.0016 kunl ()L3 (f T1-4 (4-46)
where:
E|U = emission factor for loading and unloading, kg fugitive dust/MT ash
kun| = particle size multiplier, dimensionless
Um = wind speed, m/s
M = material moisture content, %.
• Spreading and compacting of ash at the landfill. An emission factor specifically
for ash spreading and compacting has not been developed. However, emission factor
equations for similar applications have been applied for estimating fugitive emissions due
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to spreading and compacting. MRI (1990) used an AP-42 emission factor developed for
dozer moving of overburden in western surface coal mines. Kellermeyer and Ziemer
(1989) assumed that the spreading and compaction of ash was analogous to vehicular
transport on unpaved surfaces, and used the emission factor for that process. A third
possible assumption is that the processes of spreading and compacting are analogous to
agricultural tillage. That emission factor equation for agricultural tillage is:
Eat = 5.38 kat S°-6 (4-47)
where:
Eat = emission factor for agricultural tillage, kg/ha
kat = particle size multiplier, dimensionless
s = silt content, %.
When applying such equations, there are further key issues to consider. These
include:
• Concentrations on fugitive ash emissions: When such an emission occurs from
ash surfaces, such as from storage piles, off trucks in transit, in spreading and
compacting, and so on, than there is a good argument to assume that such concentrations
on such emissions are "enriched" in comparison to an ash average. The argument here is
similar to the argument for enrichment assumed for eroded soils: processes resulting in
fugitive air emissions favor lighter particles with more surface area and hence more sites
for binding. No data could be found to assign a value to an ash enrichment ratio. MRI
(1990) did, however, take data on municipal waste combustor facility roadway dust, and
based on that data and statistical evaluations, speculated that fly ash constituted the
principal source of lead and cadmium found on paved surfaces. Since fly ash is finer than
bottom or combined ash, one hypothesis for this finding is that fugitive emissions from ash
management at the combustor site transported these finer particles to roadway surfaces.
This is not to imply, however, that concentrations in dust suspended from roadways due
to traffic should be higher in concentration than concentrations in ash - this enrichment
concept only applies to ash surfaces themselves. Rather, the concentration on roadway
suspended dust should be lower than on the ash. This is because contaminated dust on
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roadways mixes with clean dust from other sources. As noted, MRI (1990) did take
roadway dust samples, and their data appears to place such a dilution factor
(concentration on roadway dust divided by concentration on ash) in the range of 0.1 to
0.3. Specifically, they took paniculate samples from landfill haul routes while at the same
time taking samples of incinerator ash being delivered for disposal the same day. Each
paired sample (roadway particulate and ash), were measured for four metals: As, Cd, Cr,
and Pb. Several paired samples were taken on both paved and unpaved haul routes.
Ratios were then generated for roadway particulate metal concentrations over ash metal
concentrations. Results were: As - paved and unpaved ratios were similar and
consistently near 0.1 (roadside particulate concentrations of As were 10% of ash
concentrations of As), Cd - paved and unpaved ratios were similar and ranged between
0.0 and 0.4, Cr - paved ratios ranged from 0.3 to 0.6, while unpaved had a wide range of
0.3 to 2.0, Pb - paved and unpaved ratios were similar between 0.0 and 0.2. For
analogous situations - daily deliveries of contaminated ash - one might assume a dilution
factor in the 0.1-0.2 range.
• Selection of values for emission factor equations: As noted, all these equations
are empirical equations. They were developed from data on sites where such emissions
occur, such as strip mining sites. EPA (1988a) describes the range of conditions over
which such equations were developed. What is meant by "conditions" are such factors as
the range of vehicle weights in the data set, the range in number of wheels on such
vehicles, and so on. Application of these equations for situations not included within
these ranges should be done cautiously. Very critical also is the selection of the particle
size multiplier variable, k. These values range from about 0.10 to no higher than 1.0.
Lower k values are used to estimate emissions of the smallest sized particles; generally
particles less than 5 //m in diameter. Higher k values are used to estimate emissions of all
sized particles less than a higher diameter, usually either 1 5 or 30 //m. If these equations
are used to only estimate particulate inhalation exposures, than the k value corresponding
to 10 fjm sized particles, or inhalable sized particles, should be used. When used to
estimate total emissions, than the highest k value listed should be used. Such estimations
are appropriate when also evaluating impacts to off-site soils or vegetation.
• Controls for fugitive emissions: All these equations were developed when no
fugitive emission controls were in place. Common controls for roadway dust suppression
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include wetting or use of a chemical dust suppressant. Ash transported in trucks is
commonly wetted and/or a tarpaulin is used to control emissions off trucks. There is no
guidance or data on the effectiveness of such controls, but they must be considered. In
demonstrating these procedures, EPA (1991) assumed that controls on emissions resulted
in 90% reductions in potential emissions. If a control is known to be in place and used on
a regular basis, than this percent reduction is probably a reasonable starting assumption.
4.4.3.2. Land application of sludge from pulp and paper mills
This discussion focuses on an assessment on the land application of sludge from
bleached kraft and sulfite pulp and paper mills (EPA, 1990e). Focusing on this source of
sludge does not imply that pulp and paper mills produce more sludge than other industries,
or that sludge from pulp and paper mills contains more dioxin-like compounds than other
sludges. However, it is known that dioxin-like compounds are found in pulp and paper mill
sludges. Also, because of the 104-mill study in 1988, much information is available on
the content and disposal of this sludge (further information on the 104-mill study can be
found in EPA (1990c) and EPA (1990d)). Some of the issues briefly discussed below for
pulp and paper mill sludges would also pertain to sludges containing dioxin-like compounds
from other sources.
EPA (1990e) described frequency distributions of concentrations of 2,3,7,8-TCDD
and 2,3,7,8-TCDF for 79 mills reporting this information and also broke out the data based
on disposal option. Although EPA (1990e) used the disposal option breakout of
concentrations in their assessment of the impacts of the various options, it is not felt that
the disposal option of choice is based on concentration. Over all options, the median
(50% percentile as given in EPA (1990e)) and maximum 2,3,7,8-TCDD concentrations
found in sludges were 51 and 3800 ng/kg (ppt), respectively. The median and maximum
2,3,7,8-TCDF concentrations found were 158 and 17100 ppt.
Fate and transport for contaminants is sludge is dependent on disposal means. Of
the approximate 2.5 million metric tons of pulp and paper mill sludge generated annually
(as estimated in the 1988 104-mill study), five principal options for disposal were noted:
landfilling (44% of all sludge disposed), surface impoundments (24%), land application
(12%), incineration (12%), and distribution and marketing (8%). Impacts by incineration
were not discussed in EPA (1990e) and are not discussed in this section. Key issues
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pertaining to each disposal issue are now discussed.
• Landfilling: The issue of coverage as discussed above for ash landfills is
relevant for any landfill. However, fugitive particulate emissions during sludge handling
and disposal is not an issue as it was for disposal of ash from incinerators due to the
differences in moisture content. Sludge is much higher in moisture at the time it is
disposed of in comparison to ash - with moisture contents as high as 90%.
• Surface Impoundments: It was assumed in EPA (1990e) that sludge disposed of
in surface impoundments have a higher moisture content as compared to sludge disposed
of in landfills. Surface impoundments were located at the mill site, explaining the
assumption for a higher moisture content. A surface impoundment in the EPA (1990e)
assessment was defined as a facility in which the sludges are stored or disposed on land
without a cover layer of soil. For this type of management, soil cover would not be an
issue. Concentrations would be those measured in the sludge. Also, vegetative cover
would be expected to be minimal, which would influence parameters associated with soil
erosion.
• Land Application: Twelve percent of all sludge produced annually was land
applied. Four of the 104 mills applied the sludge to forest land, two mills land applied the
sludge to agricultural land, and two mills used the sludge for abandoned mine reclamation.
The high organic matter content (EPA (1990e) assumed a 25% organic carbon fraction in
sludge) and high fraction of clay-sized particles make sludge an attractive soil amender.
Sludge is either applied to the land surface with or without incorporation. When not
incorporated, sludge can be assumed to replace surface soils and concentrations would be
those in the sludge. When incorporated, soil concentrations can be estimated simply as (in
mg/kg): (mass of contaminant added, mg)/(mass of sludge added, kg + mass of soil in
mixing zone, kg). One key issue when incorporated is the number of years of such
treatments. Most of the land application uses of paper and pulp mill sludges reported in
EPA (1990e) made applications in only one year. As easily seen in the above suggested
equation, higher concentrations result with more years of incorporation. The other key
issue with incorporation, of course, is the depth of incorporation. For agricultural
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applications, the depth of incorporation assumed in EPA (1990e) was 15 cm, similar to the
20 cm incorporation assumed for home vegetable gardening in this assessment. For
silvicultural uses, the assumption in EPA (1990e) was 2.5 cm, which corresponds to some
but minimal mixing. For abandoned mine reclamation, the assumption was 0 cm
incorporation. Routes of exposure might also vary from focuses in this document
depending on land application choice. When applied to agricultural land, impacts to food
crops would demand particular attention (the procedures in this assessment were
demonstrated with home grown vegetables, although of course impacts to food crops are
critical when agricultural field soils are impacted by dioxin-like compounds). When applied
to forest land, ecological impacts might warrant particular attention, as was discussed and
demonstrated in EPA (1990e). A final issue to consider when land applying sludge to land
is a rate of dissipation/degradation of dioxin-like compounds. Landfills and surface
impoundments have ongoing surface applications and over time, the total depth of
applications in the range of meters, so an assumption of a constant source strength over a
period of exposure, as was assumed in this assessment for soil contamination sources, is
reasonable. However, if only a few centimeters of surface soil are impacted, which might
be the case for single applications to land and/or surface applications with no
incorporation, an assumption of dissipation may be warranted. EPA (1990e) assumed no
degradation of 2,3,7,8-TCDD in their assessment of impacts from land applications.
• Distribution and Marketing Uses: The volume of sludge distributed and
marketed was approximately 8% of the total amount of sludge generated for the 104-mill
study. For this use, sludge was composted and then sold as a soil amendment in
residential, agricultural, and commercial settings. More attention to the dermal contact
pathway appears appropriate for this usage. Site-specific factors, and the values for these
factors used in EPA (1990e), include: 1) depth of incorporation - 0, 1 5 and 25 cm in
assumptions characterized as high, best, and low estimates, 2) garden size - 0.016 and
0.022 hectares characterized as low/best estimate and high, and referencing a national
gardening survey, 3) rate of application - between 5 and 20 dry metric tons per hectare
references a USDA publication on use of sewage sludge compost for soil improvement and
plant growth, and 4) years of using such compost - 20 without specific reference. The
years of application is needed for estimating soil concentrations during and after the period
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of exposure, using a simple ratio as discussed above in land application.
4.4.3.3. Sites studied in the National Dioxin Study
The National Dioxin Study (EPA, 1987a) focused on sites of known or suspected
contamination of soil by 2,3,7,8-TCDD. There were 7 "Tiers" of investigation, with
roughly decreasing expectations of finding 2,3,7,8-TCDD. Tiers 1 and 2 included 2,4,5-
TCP production and associated disposal sites (Tier 1) and sites where 2,4,5-TCP was used
as a precursor in the manufacture of pesticidal products and associated disposal sites (Tier
2). These tiers had the highest expectation for finding 2,3,7,8-TCDD. There were
originally thought to be 450 sites that would fall in Tiers 1 and 2, but after investigation,
only 100 sites were included for study. Some were downgraded into Tier 3. Of the 100
sites studied, 20 were on or were proposed for inclusion in the Superfund National
Priorities List. Tiers 3 and 5 were associated with 2,4,5-TCP formulation (Tier 3) and use
(Tier 5). Tier 6 were organic chemical or pesticide manufacturing facilities were 2,3,7,8-
TCDD was suspected of being present. Tier 4 included combustion sources and are not
discussed further in this section. Tier 7, basically an examination of background areas, are
also not discussed here.
Issues that are identified as important in fate and transport modeling for this
subcategory of off-site sources include concentrations, the possibility of ground water
contamination, and site-specific characterization. These are discussed in turn.
• Concentrations: Only 11 of the 100 Tier 1 and Tier 2 sites were eventually
classified as requiring "no further action" because 2,3,7,8-TCDD soil concentrations were
very low, < 1 ppb, or not detected (with detection limits generally at 1.00 ppb). Where it
was detected, a general trend was to find very high concentrations where 2,4,5-TCP
production wastes were stored or disposed of, with much lower concentrations at soils
near these particular areas. At hot spots, concentrations were as high as 2,000 parts per
million, but generally soil concentrations were in the parts per billion. It was this parts per
billion generalization that led to the assignment of a 1 ppb soil concentration for the
demonstration of the off-site source category in Chapter 5. There were findings in the low
ppb range for Tiers 3, 5, and 6, but at much lower frequency and no findings higher than
the tens of ppb range. For exposure assessments, the characterization of soil
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concentrations in a site containing hot spots has to be carefully considered. For site
evaluations and proposed options for remediation, an areally weighted average might be
considered, although this could dilute loss estimates depending on the area chosen -
choosing a large area corresponding to property lines might, for example, lead to an
"average" concentration orders of magnitude lower than concentrations found in hot
spots. One approach which should be considered is a "hot spot" impact compared to an
areally averaged impact. It should also be remembered that removal of highly
contaminated soils is a common practice and another option for evaluation would be a
concentration assuming hot spots are removed.
• Potential for Ground Water Contamination: PCBs have been found in ground
water in sites associated with dielectric fluids of transformers. Oils can migrate through
soils as a separate immiscible phase and reach ground water, which has been the common
explanation for PCB impacts to ground water. Ground water contamination by 2,3,7,8-
TCDD has very rarely been found in ground water, although it has been released to the
environment in an oil matrix. The Times Beach area of Missouri is the principal example of
this release, where waste oils containing 2,3,7,8-TCDD were used for dust control.
Ground water sampling did occur in many of the Tier 1 and 2 National Dioxin Study sites,
but the results were mostly non-detects. One occurrence at 0.18 ppt was noted for the
Hyde Park site of Hooker Chemical in Niagara, NY, and a high of 1.8 ppb was found in an
on-site monitoring well at National Industrial Environmental Services in Furley, KS. There
were, however, numerous high occurrences in sub-soil samples in hot spot areas, in
bottom sediments of evaporation lagoons, and so on, in the hundreds of ppb range.
There have been some limited experimentation showing different patterns of
2,3,7,8-TCDD migration in soils in the presence of solvents or in an oily matrix. Palusky,
et al (1986) studied the mobility of 2,3,7,8-TCDD in soils associated with each of 6
solvents. Migration was found to be higher with aromatic solvents and chloroform in
comparison to saturated hydrocarbons and methanol. They speculated that the extent of
migration related to the solubility of 2,3,7,8-TCDD in the solvent. Puri, et al. (1989)
studied the migration potential of 2,3,7,8-TCDD in soil, water, and waste oil mixtures.
Over time, they observed a reversible sorption pattern of TCDD, and concluded that a
carrier medium with a significant amount of waste oil would play a dominant role in the
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movement of TCDD through soils.
• Site-specific Characterization: In the case of landfills or sludge land application
sites, the assignment of a soil concentration and an area can be made with some
reasonableness. Such is not the case with the industrial contamination sites such as those
studied in the National Dioxin Study, as briefly discussed above in the concentration bullet.
Most of the sites studied in the National Dioxin Study were in the order of tens of hectares
to below ten hectares. On the other hand, the Dow Chemical site in Midland, Michigan is
described as a site 607 ha in size (Nestrick, et al, 1986). That area corresponds to the
size of the property, and the many soil sampling sites within that area were described as
"background". Several of the pesticide formulator sites studied in Tier 3 were 2 hectares
or less in size. Many of the them were extensively or partially paved with buildings, which
complicate fate and transport modeling. Some of the Tier 5 sites of 2,4,5-TCP use were
agricultural fields, which are less complicated to describe. However, two sites were
described as 2500 acres in size, which again is very large and makes assignment of an
average soil concentration non-trivial. Other use sites were described as railyards and
railroad rights of way. While estimates of loss into air could be made in complicated sites
such as these, use of soil erosion modeling becomes very complicated if not undoable with
paved areas, buildings, drainage ditches, roads, and the like.
4.5. ALGORITHMS FOR THE STACK EMISSION SOURCE CATEGORY
Contaminants emitted from incinerator stacks are transported in air and deposit on
the exposure site, water bodies that may be used for drinking or fishing purposes, and on
surrounding land. Chapter 3 describes the application of the COMPDEP (reference) model
to obtain vapor-phase air concentrations and deposition rates of particles at a specified
distance from an example stack emission source. These quantities are assumed to be
given for purposes of discussion in this section; further discussion of the air transport
modeling is given in Chapter 3.
Estimating soil concentrations based on particulate depositions follows a similar
approach as estimating exposure site soil concentrations resulting from erosion of
contaminated soil from off-site areas of contamination. Section 4.5.1. describes how soil
concentrations are estimated given total (wet plus dry) deposition rates. Surface water
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impacts are assumed to result from direct deposition onto surface water bodies as well as
erosion from the impacted effective drainage area. This solution is an extension of the
solution given in Section 4.3.1. for the on-site source category, and is given in Section
4.5.2. Following now are bullet summaries for similarities and small refinements to
algorithms previously discussed:
• Air impacts: The atmospheric transport modeling described in Chapter 3
was comprised of two computer simulations: one which considered that
emissions were in a vapor form and were transported as such, and one
which considered that emissions were in particle form and likewise were
transported as such. The result of the vapor-phase runs was a unitized
ambient air concentration at various distances up to 50 km in all directions
from the stack. Only the results in the predominant wind direction were
used in this demonstration. The result of the particle-phase runs were an
ambient reservoir of air-borne contaminants sorbed to particulates (used only
for inhalation exposures), and wet and dry deposition unit rates also at
various distances up to 50 km. By "unitized", what is meant is that
emissions for the vapor or particle runs can be thought of as "1" mass/time
(g/sec) emissions. Results for all distances are linear with respect to this
emission rate; that is, if the rate of vapor contaminant determined to be
emitted is "5", than ambient air concentrations at any location are 5 times
what they are when "1" is assumed to be emitted. The same holds true for
emissions in the particle phase. Chapter 3 developed a framework for
assigning a vapor and a particle fraction for specific dioxin congeners. For
example, 2,3,7,8-TCDD was assumed to have a vapor fraction of 0.55
(55% was in vapor form) and a particle fraction of 0.45. The final model
results for air concentrations, and dry and wet deposition rates for all
congeners, starting from these unit model runs and then incorporating
congener-specific emission rates and vapor/particle splits, are given in Tables
3-12 to 3-17. The vapor-phase air concentrations were used to model vapor
phase transfers in the vegetative bioconcentration algorithms. They were
also used, summed with the simulated reservoir of particle-bound
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contaminants, to estimate the total reservoir of contaminant available for
inhalation exposures.
• Vegetative impacts: The rates of wet and dry deposition modeled by
COMPDEL were used to determine vegetative impacts. The model for
particle deposition impacts to vegetations is described in Section 4.3.4.2
above. Of course, this above section solves for dry deposition as a reservoir
times a dry deposition velocity (for dry deposition), and as a reservoir times
rainfall and a washout factor (for wet deposition); such a solution is not
required for the stack emission source category since the deposition totals
are estimated by the COMPDEP model. Other parameters for the vegetative
model - the Bvpa (air-to-leaf vapor transfer factor), the Rw (fraction of wet
deposition retained on vegetation surfaces), crop yields and interceptions,
and the vegetative washout factor, kw, are used for the stack emission
source category.
• Biota concentrations: The algorithm estimating concentration in fish tissue
based on bottom sediment concentrations is the same as in previous source
categories. Modeled rates of contaminant deposition on particles onto the
exposure site are used to estimate a "tilled" and an "untilled" soil
concentration, as described below in Section 4.5.1. Underground vegetable
concentrations are a function of tilled soil concentrations. The soil
concentration used for cattle soil ingestion is untilled. Beef and milk
concentrations are again a function of vegetative and soil concentrations,
diet fractions, and bioconcentration and bioavailability factors as described in
Section 4.3.4.3.
4.5.1. Steady-State Soil Concentrations
Chapter 3 describes the use of the COMPDEP Model to estimate the particulate
phase deposition rates at the exposure site. This total deposition rate, F, includes both dry
and wet deposition, and is used to estimate the steady state soil concentrations. The
deposition of contaminated particulates from the air is assumed to be somewhat analogous
to the process of eroding contaminated soil from an off-site source depositing on an
exposure site. Specifically, the following assumptions are also made: 1) only a thin layer
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of soil becomes contaminated, 2) this layer is either "untilled" or "tilled", depending on
surface activities, and 3) surface residues are assumed to dissipate with a half-life of 10
years corresponding to a first order decay rate of 0.0693 yr"1 . Considerations of
upgradient erosion and exposure site soil removal are not made. Depositions occur over
the exposure site and surrounding land area on an on-going basis. It might be said that
upgradient soil concentrations are similar to exposure site concentrations at all times. Like
the soil source categories, a tilled mixing depth of 20 cm is assumed. However, an
untilled mixing depth of 1 cm is assumed for this source category, in contrast to the 5 cm
assumed for the off-site soil source category. It is felt that the process of erosion
assumed to transport contaminated soil in the off-site soil source category to a site of
exposure is a more turbulent process. It assumes that contaminated soils mix with "clean"
soils that are between the site of contamination and the site of exposure. In contrast,
ongoing airborne deposition of particles is felt to be a less turbulent process impacting all
watershed soils simultaneously; hence the assumption of a 1-cm mixing depth. The
qualitative mass balance statement (similar to the one made above in Section 4.4.1, with
AC equalling change in exposure site soil concentrations over time) can now be made as:
(the incremental addition to C resulting from the change in
deposition of stack emitted particulates)
AC = (the incremental substraction of C resulting from
degradation of residues at the exposure site)
This is mathematically stated as:
(4-48)
where:
C = the exposure site soil concentration, mg/kg
F = deposition rate of contaminant on particles, mg/yr
M = mass of soil at exposure site into which contaminant mixes, kg
k = first order dissipation rate constant, 1/yr.
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The solution to this equation is:
C = -L ( 1 - e-fe ) (4-49)
which computes C as function of time, t. Similar to the assumption made above in
Section 4.4.1., the steady state solution for C is simply F/kM. The deposition rates
supplied by the COMPDEP model are in units of g/m2-yr, so a conversion to mg/yr requires
a multiplication by the land area of the exposure site and a multiplication of 1000 mg/g.
Procedures to estimate M are given above in Section 4.4.1.
4.5.2 Surface Water Impacts
The solution for stack emission impacts to surface water bodies is an extension of
the solution for soil contamination described in Section 4.3.1. Stack emissions deposit
onto soils within the effective drainage area to result in an average basin-wide soil
concentration. Soil erosion then delivers contaminants to surface waters as in Section
4.3.1. Stack emissions also directly deposit onto and impact the surface water body as
well. All the assumptions laid out at the beginning of Section 4.3.1 apply here as well.
New quantities needed for this solution include: a rate of contaminant deposition onto soils
of the effective drainage area used to estimate average soil concentrations (such
concentrations are estimated using the approach given in Section 4.5.1. above), a rate of
contaminant deposition onto the water body, and a rate of particulate matter deposition
onto the water body.
Equations (4-1) through (4-8) are now displayed again with these additions.
Cgwb ERV + DEPC = Cwat Vwat + Cssed Mssed + Csed Msed (4-50)
where:
CSwb = concentration on soil entering water body, mg/kg
ERW = total watershed annual soil erosion, kg/yr
DEPC = total annual direct deposition of contaminant, mg/yr
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= dissolved-phase concentration in water column, mg/L
Vwat = water body annual volume, L/yr
cssed = concentration on suspended sediment, mg/kg
MSSed = mass of suspended sediment introduced per year, kg/yr
Csed = concentration on sediment settling to bottom, mg/kg
Msed = mass of bottom sediment introduced per year, kg/yr
Mass balance and equilibrium equations continue:
ERW + DEPp = Mssed + Msed (4-51)
Mssed = f3ERw + fsdDEPp (4-52)
Msed = ( 1 - fa ) ERU + ( 1 - fad ) DEPp (4-53)
^ssed
Kissed
OC.
OC,
ssed
(4-
(4
Csed = Cgged—SSS. (4-55)
where:
DEPp = total annual direct deposition of particulate matter, kg/yr
fs = fraction of annual erosion remaining as suspended materials, unitless
fsd = fraction of annual deposition remaining as suspended material,
unitless
soil-water partition coefficient for contaminant in suspended
sediment, L/kg
= fraction organic carbon in suspended sediment, unitless
fraction organic carbon in bottom sediment, unitless
Substituting again as in Equation (4-7):
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Cswb ERW + DEPC =
ssed
f~DEP> + -oc.
ssed
- fs ) ERW
- fsd ) DEPp
(4-56)
As before, the bracketed quantity in the right hand side of Equation (4-56) can be termed
0, so that C88ed can be solved as (C8wb ERW + DEPc)/0. The numerator in this term can
be expanded to describe contaminant contributions by the effective drainage area which
has received depositions, the first quantity in the numerator, and to describe direct
depositions, the second quantity:
Cgwb ERW
DEPC = Cv SLW Aw E SDW
RDEPC Awat 1000
(4-57)
where:
DEP^ =
RDEPC =
A —
wat ~
1000 =
concentration on soil entering water body, mg/kg
total watershed erosion, kg/yr
annual deposition of contaminant on water body, mg/yr
enrichment ratio, unitless
average soil concentration of dioxin-like compound in effective area of
watershed, mg/kg
average unit soil loss for land area within watershed, kg/ha-yr
effective drainage area of watershed, ha
sediment delivery ratio for watershed, unitless
rate of contaminant deposition, g/m2-yr
area of water body, m2
converts g to mg
Again as before, the right hand side of Equation (4-57) can be termed, p, and the
concentration in suspended sediment, Cssed, is equal to p/. Other water body
concentration terms, Cwat and Csed, can now be solved using Equations (4-54) and (4-55).
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Guidance on these terms and assignment of values for the demonstration scenarios in
Chapter 5 is now given.
* C8wb ant' ^"w: Equation (4-57) shows all the terms necessary to arrive at an
estimate of the annual contaminant entry into the water body via erosion, the Cswb * ERW
term. Section 4.5.1 describes the algorithm to estimate soil concentrations given a
deposition rate of contaminant. One deposition rate will be chosen to represent average
deposition rates over the effective drainage area of the watershed (the effective drainage
area is termed Aw). This rate will be the rate given in COMPDEP modeling at 0.5
kilometers, which implies that the water body and the effective drainage area into the
water body are near the stack. Tables 3-15 and 3-16 (Chapter 3) display wet and dry
deposition rates for this distance. These rates are added to arrive at total deposition,
shown in Table 3-17. Second, a representative mixing depth to characterize average
watershed soil concentrations needs to be selected. Previous algorithms used a mixing
depth of 20 cm for tillage activities, specifically home gardening, and 1 and 5 cm for non-
tilled soil concentrations (1 cm for the stack emission and 5 cm for the off-site soil source
category). For the sake of demonstration, it will be assumed that a representative
watershed depth will equal 10 cm, which might be interpreted as an average of tilled and
untilled lands within the effective drainage area. The values for SLW (6455 kg/ha-yr), Aw
(4000 ha), ER (3), and SDW (0.15) were all given and discussed in Section 4.3.1. and will
not be repeated here.
• DEPC: The second quantity of Equation (4-57) describes the annual input to the
surface water body that comes from direct deposition. This term is RDEPC * Awat * 1000,
where RDEPC is the rate of contaminant deposition onto the water body, Awat is the area
of the water body, and 1000 converts g to mg. The rate of contaminant deposition at 0.5
km will also be used to describe direct deposition impact to the surface water body, since
for demonstration purposes, there is no justification for saying this distance is further or
nearer the point of stack emission. The area of the water body has not been required for
any other reason, and one will now be given. First, the effective drainage area of 4000 ha
is relatively small and will result in a relatively small stream, at 1.524 x 107 m3/yr flow
volume. This volume is also equal to the average cross sectional area of the stream (m2)
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times stream velocity (m/yr). Assuming a stream velocity of 4.73 * 106 m/yr (15 cm/sec;
1/2 ft/sec), which is reasonable for a small stream, the cross sectional area is solved as
3.22 m2. An average 1 meter depth and 3.22 meter width appear reasonable. This width
times the stream length would give stream surface area, Awat. Assuming a rectangular
shaped watershed, dimensions of 40 ha wide by 100 ha long (to arrive at the 4000 ha
effective drainage) seem reasonable. This length of 100 ha translates to 10000 meters,
and the full surface area of the stream is 32200 m2. This will be the value assumed for
A
"waf
• DEP : The rate of particulate deposition onto the lake is required to achieve a
mass balance of all annual soil erosion + particle deposition contributions to water body
solids. The rate of particulate matter emitting from the stack and arriving at downwind
locations was not supplied in Chapter 3. Instead, a literature value of 0.03 g/m2-yr
developed by Goeden and Smith (1989) will be used. This value was based on modeling
emissions from a resource recovery facility. Total particulate emissions from the stack
were projected to be 4.63 g/s, and the deposition rate onto a nearby lake was modeled to
be 0.03 g/m2-yr. No further information was supplied. Now, with the surface area as
solved for above at 32200 m2, the total particle deposition, DEPp in kg/yr, is 966 g/yr.
• f8 and fsd: These are the fractions of total erosion and depositing particles
remaining as suspended materials within a year. As discussed in the solution for the "on-
site source category" in Section 4.3.1, fs was solved for as: a value for total suspended
solid, TSS of 10 mg/L, multiplied by a total flow volume Vwat of 1.524 x 1010 L/yr,
12
divided by the total erosion into the water body, 3.87 x 10 mg/yr. This resulted in an f.
s
of 0.039. Note that this implies a total suspended load of 1 52,400 kg/yr. It could be
assumed that the minuscule 1 kg/yr of particles directly depositing onto the stream remain
in suspension during the year, on the basis of being smaller in size than eroded soil. This
assumption will, in fact, be made, but it will be supported as follows.
In a quiescent water body, settling occurs through gravity and can be expressed in
terms of Stokes Law:
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Vs = ( g I 18jl ) ( ps - p ) d2 (4-58)
where:
Vs = Stokes settling velocity, cm/sec
g = acceleration of gravity, 980 cm/sec2
/j = absolute viscosity of water, g/cm-sec (poise)
0.089 g/cm-sec @ 25 C
ps = particle density, g/cm3
p = density of water, 1 g/cm3
d = particle diameter, cm
For purposes of this discussion, a reasonable assignment of particle density of is
2.5 g/cm3 for depositing particles or eroding soil. Therefore, making substitutions, the
right hand side of Equation (4-58) reduces to 918 d2.
Now, assumptions for the particle sizes of eroding soil and depositing particles can
be made to arrive at a ratio of settling velocities, Vssoi|/Vspart. The basis for assigning an
enrichment ratio for delivery of contaminants via soil erosion was that fine-sized particles
were the ones eventually reaching the water body via erosion. Lick (1982) states that a
major fraction of the sediments (suspended and bottom) in the Great Lakes are fine
grained, silts and clays, and that data from Lake Erie indicates that 90% of the sediments
are of this category. Brady (1984) shows USDA's classification of soils according to
particle size, and gives a range of 0.0002 to 0.005 cm for silt sized particles and less than
0.0002 for clay size particles. The following assumptions are made to arrive at a
representative diameter for particles in eroded soil: eroded soil is comprised of a 50/50
split of these two sized particles, silt-sized particles are, on the average 0.0026 cm in
diameter, and clay size particles are 0.0001 cm in diameter. With these assumptions, the
average particle size for eroding soil is 0.0014 cm. The settling velocity for a 0.0014 cm
particle is 1.8 x 10~3 cm/sec. In Section 3.4.3, Chapter 3, the argument was developed
that 87.5% of the total emission rate of dioxin-like congeners would be associated with
particles less than 2 //m. The basis of this argument was a surface area to volume ratio,
with smaller particle sizes having significantly larger ratios. This does not mean that
87.5% of the 1 kg/yr depositing particles are of this size. However, for this discussion,
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the size of depositing particles will be assumed to be 2//m (2 x 10~4 cm), since these size
particles deliver most of the dioxin-like compounds to the water body (and the ultimate
purpose of this exercise is to determine a value for the fraction of depositing particles
which remain suspended and impact suspended sediment concentrations). The settling
velocity, Vspart, is estimated as 3.7 x 10~5 cm/sec.
The ratio Vssoi|/Vspart is about 50. Said another way and with all the assumptions
and simplifications made above, depositing particles will remain in suspension 50 times
longer than eroding soil in a quiescent water body.
Given this high a difference in settling velocities, it seems reasonable to assume fsb
equals 1.0. The fraction of soil erosion remaining in suspension, fs, will be estimated
given TSS, Vwat, etc., as before (see Section 4.3.1), only DEPp (the total amount of
depositing particles, in kg/yr) will comprise a given increment of suspended materials when
solving for fs.
* vwaf ocssed' OCsed' and Kd8sed: These have all been discussed in Section
4.3.1. The values for these parameters in the demonstration scenarios in Chapter 5 are:
Vwat = 1.524x 1010 L/yr, OCssed = 0.05, OCsed = 0.03, and Kdssed = OCsed * Koc,
where Koc is the organic partition coefficient of the contaminant.
4.6. ALGORITHMS FOR THE EFFLUENT DISCHARGE SOURCE CATEGORY
As discussed in Volume II, Chapter 3, dioxin-like compounds can be released to
waterways via various types of effluent discharges such as discharges from municipal
waste water treatment facilities and pulp and paper mills using chlorine bleaching. Also
discussed is the fact that these emissions have declined substantially in recent years,
especially from pulp and paper mills. Since the procedures for considering point source
discharges to waterways are somewhat different than those associated with the nonpoint
source procedures for soil contamination and stack emissions, they are covered separately
in this section. This source category is also different from others in that effluent
discharges into surface water bodies are assumed only to impact fish and water.
The approach used in this report is an extension of the "simple dilution" model
described in the Superfund Exposure Assessment Manual (EPA, 1988c). Other models are
available which offer more spatial and temporal resolution than the model described here.
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One such model is the Exposure Analysis Modeling System, or EXAMS (Burns, et al.,
1982, and Burns and Cline, 1985). The EXAMS and a simple dilution model were both
applied in an assessment of effluent discharges from pulp and paper mills (EPA, 1990d).
In this assessment, 98 of the 104 pulp and paper mills were modeled with both models
using site-specific information (water body flow rates from STORET for all but 6 of the
mills, effluent flow rates and contaminant discharges, etc.). Three key quantities - one
model result and two model parameters - led to a range of exposure conditions for humans
consuming fish impacted by discharges from these pulp and paper mills: a water column
concentration, a bioconcentration factor (BCF) applied to the water column concentration
to get fish tissue concentration, and a fish ingestion rate. The simple dilution model was
used to estimate total water concentrations - i.e., mg TCDD total/L water. The EXAMS
model was used to estimate dissolved phase water column concentration - i.e., mg TCDD
dissolved in water column/L water. Then, with each set of water concentrations, two sets
of exposure estimates (a low and a high estimate, in one sense) were generated - one with
a BCF of 5,000 and a fish ingestion rate of 6.5 g/day, and one with a BCF of 50,000 and
a fish ingestion rate of 30 g/day. Note that in deriving the range of results in that
exercise, the BCF was applied to both a total and a dissolved phase water concentrations.
EPA (1993) discusses several bioconcentration/bioaccumulation empirical parameters for
2,3,7,8-TCDD, and makes the clear distinction for those which are to be applied to a total
water concentration versus those applied to a concentration in the dissolved phase. The
dilution and EXAMS model study indicated that the simple dilution model generally
estimated higher water column contaminant concentrations compared to the EXAMS
model, although this trend was not consistent among all water bodies modeled. The
results from both models were comparable when the receiving water body had relatively
low suspended solids concentration.
One key limitation of the EXAMS and the simple dilution model for use with dioxin-
like compounds in aquatic systems is that they do not account for sediment transport
processes. The EXAMS model was designed to determine the fate of transport of
contaminants in the dissolved phase. Another spatially and temporally resolved model for
this source category is the Water Analysis Simulation Package, the most up-to-date
version termed WASP4 (Ambrose, et al., 1988). This model does include sediment
processes and has been applied in a comprehensive evaluation of 2,3,7,8-TCDD
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bioaccumulation in Lake Ontario (EPA, 1990b). It requires extensive site-specific
parameterization, but should be considered for more detailed site-specific evaluations of
strongly hydrophobic and bioaccumulating contaminants such as the dioxin-like
compounds.
The dilution model described below will be demonstrated in Chapter 5 with a set of
data developed using site-specific data from the 104 pulp and paper mills of the 104-mill
study. As will be discussed below, a hypothetical effluent discharge will have
characteristics developed as the average of key characteristics from the 104 mill study.
These key data include: flow rates of the receiving water bodies, suspended solids
concentration in these receiving water bodies, effluent discharge flow rates, suspended
solids in the effluent discharges, organic carbon content of solids in the effluent stream,
and discharges of 2,3,7,8-TCDD.
4.6.1. The Simple Dilution Model
The principal assumption for the simple dilution model is that contaminants released
into a water body uniformly mix and equilibrate with the surrounding water in an area near
the effluent discharge point. This area is commonly referred to as a "mixing zone". For
application of this model with dioxin-like compounds, what is desired is a concentration on
the suspended solids in this mixing zone. Multiplication of the organic carbon normalized
concentration on suspended solids and a Biota Suspended Solids Accumulation Factor, or
BSSAF, will result in a concentration of contaminant in fish lipids. This is defined similarly
to the BSAF used for other source categories of this assessment, except that the organic
carbon normalized concentration is that of suspended solids rather than of bottom
sediments.
The BSSAF is one of several empirical factors discussed for estimating the impact
to fish in water bodies impacted by 2,3,7,8-TCDD (EPA, 1993). Others include the BSAF,
total and dissolved phase bioconcentration factors (BCFs), and total and dissolved phase
bioaccumulation factors (BAFs). BAFs and similar to BSAFs and BSSAFs in that all three
reflect total exposure of fish to contaminant, including water column, sediment, and food
chain exposures. The BCFs reflect water column exposures only. EPA (1993) states that
there is currently no data available on organic carbon normalized concentrations of dioxin-
like compounds on suspended solids, hence no basis to compare BSAF and BSSAF. This
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assessment assumes a similar numerical assignment of BSSAFs and BSAFs.
The total water concentration in a simple dilution model is:
(4-59)
where:
Ctot = total water concentration, mg/L
MASSC = mass of contaminant in discharge, mg/hr
Qu = flow at a point just upstream of effluent discharge, L/hr
Qe = effluent flow, L/hr
Dissolved phase and suspended sediment concentrations are then estimated using an
approach developed by Mills, et al. (1985) and others:
Cwat = ™ —- (4-60)
;v 10 "6 )
Cwat (4-61)
where:
Cwat = dissolved-phase water concentration of contaminant, mg/L
Ctot = total water column concentration, sorbed + dissolved, mg/kg
(note: mg/kg is essentially equal to mg/L since 1 L = 1 kg)
Kdmix = suspended sediment-water partition coefficient for
contaminant in mixing zone, L/kg
TSSmjx = total suspended solids in water column in mixing zone, mg/L
Cssed - concentration of dioxin-like compounds on suspended
sediments, mg/kg
10"6 = converts mg/L to kg/L
The total suspended solids concentration in the mixing zone is a function of the suspended
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solids just upstream of the discharge point and the suspended solids introduced in the
effluent stream:
TSS,
'mix
TSSU Qu + TSSe Q6
(4-62)
where:
TSSmix .
TSS,, =
TSSe =
0-uA =
adjusted total suspended solids concentration, mg/L
total suspended solids concentration at a point just upstream of
effluent discharge, mg/L
total suspended solids concentration in effluent discharge, mg/L
upstream and effluent discharge flow rates, L/hr
The suspended solids partition coefficient in the mixing zone is a function of the
organic carbon partition coefficient of the contaminant and the organic carbon fraction of
suspended solids:
where:
Kdmix =
Koc
ocmix =
(4-63)
suspended sediment-water partition coefficient in the mixing zone,
L/kg
compound specific organic carbon partition coefficient, L/kg
organic carbon content of suspended sediments in the mixing zone,
unitless
This organic carbon content can be solved as the weighted average concentrations of the
organic carbon contents of the suspended solids in the effluent discharge and the
suspended solids of the receiving water body:
OC.
•mix
TSSU Qu OCU + TSSe Qe OCe
TSSU Qu + TSSe Qe
(4-64)
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where:
OCmix = organic carbon content of suspended solids in mixing zone, unitless
TSSU = total suspended solids concentration at a point just upstream of
effluent discharge, mg/L
TSSe = total suspended solids concentration in effluent discharge, mg/L
Qu,Qe = upstream and effluent discharge flow rates, L/hr
OCU, OCe = organic carbon contents of suspended solids upstream of the
discharge point and within effluent discharge stream
Fish lipid concentrations for this solution are then given as:
Cllpld = BSSAF (4-65)
where:
C|ipid = f'sn ''P'd concentration, mg/kg
BSSAF = biota suspended solids accumulation factor, unitless
^ssed =: concentration of dioxin-like compounds on suspended sediments,
mg/kg
OCmjx = organic carbon content of suspended sediments, unitless
Finally, whole fish concentrations are simply this lipid concentrations times a fraction of
fish lipid, or Clipid * flipid.
The key model parameter is the BSSAF. A value of 0.09 for 2,3,7,8-TCDD was
assumed for BSAF based on data from Lake Ontario. One important difference between
the Lake Ontario ecosystem and the effluent discharge source category is that the impact
to Lake Ontario is thought to be principally historical (EPA, 1990b), while for the effluent
source category, the impact is, by definition, ongoing. This difference may translate to
differences in assignment of BSSAF as compared to BSAF. Consider two aquatic settings
where bottom sediments are found to have equal concentrations of dioxin-like
compounds - one in which contamination is ongoing and one in which contamination is
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primarily in the past. For the aquatic setting where contamination occurred in the past,
water column and suspended sediment concentrations would be lower as compared to the
aquatic setting where contamination is ongoing, because water column impacts are only a
function of depuration of bottom sediments for the historically impacted water body. It is
certainly arguable that exposure of aquatic organisms is greater in the ecosystem where
impacts are ongoing, as compared to a system where impacts are historical, when bottom
sediment concentrations are equal in the two systems. Now recall the assumption made
for the soil contamination and stack emission source categories (in both cases the water
body impact is ongoing) concerning the relationship between suspended and bottom
sediments - that the organic carbon normalized concentrations are equal. If this is a valid
assumption for a system with ongoing impacts, and if in fact fish are relatively more
exposed when impacts are ongoing rather than historical, then this argues that a BSSAF
for an ongoing contamination setting should be greater in numerical value than a BSAF for
a setting where contamination was historical.
However, no data could be found to support such a hypothesis, and there would be
no numerical basis for an assumed difference between BSAF and BSSAF. For this reason,
the values assumed for BSSAF and BSAF are equal for this assessment. It should be
noted that all bioconcentration or biotransfer parameters, such as the BSSAF, are qualified
as second order defaults for purposes of general use. Section 6.2. of Chapter 6 discusses
the use of parameter values selected for the demonstration scenarios, including a
categorization of parameters. Second order defaults are defined there as parameters
which are theoretical and not site specific, but whose values are uncertain in the published
literature. The parameter values in this category should be considered carefully by users
of the methodology.
The effluent discharge solution algorithm was evaluated using data and information
from the 104 pulp and paper mill study (EPA, 1990c), which measured discharges of
2,3,7,8-TCDD from 104 mills in 1988, and from the National Study of Chemical Residues
in Fish (NSCRF; EPA, 1992a), which measured fish tissue concentrations of 2,3,7,8-TCDD
at points downstream from several of these mils. A third modeling study (EPA, 1990d)
collected critical data for this modeling evaluation, such as harmonic mean flows
downstream of the mills. Finally, the National Council for Air and Stream Improvement
(NCASI) provided details on their assessment of this data, which was used here.
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Importantly, this information included linking specific fish samples to specific mills. A full
description of this modeling evaluation is in Chapter 7, Section 7.2.3.6.
There was a dichotomy of model performance as a function of the size of the
receiving water body. For most of the mills, the receiving water bodies had harmonic
mean flows around 108 L/hr, with a range of 107 to 109 L/hr. A small number of mills,
however, discharged into more substantial receiving water bodies which had an average
flow of 5 x 1010 L/hr. Comparing model predictions of fish tissue concentrations for mills
discharging into the smaller water bodies, it was found that the model tended to
underpredict fish tissue concentrations - the average predicted whole fish concentration
was near 7 ppt, whereas the average observed whole fish concentration was near 15 ppt.
The same was not true for the large receiving water bodies. In that case, the average
whole fish tissue concentration observed was an order of magnitude or more higher than
predicted whole fish concentration. No precise explanation could be given for this result.
The most likely explanation is that, for these large water bodies, there were other sources
of dioxin releases. This comparative exercise did assume inherently that the effluent
discharge was the sole source of fish tissue concentrations of 2,3,7,8-TCDD.
It was noted that, for the smaller receiving water bodies, an increase in the
assumed BSSAF of 0.09 (which was the value of BSAF assumed otherwise in this
assessment) to 0.20 resulted in an average model prediction of fish tissue concentration of
near 15 ppt, essentially the same as the observed fish concentration. This could be some
empirical evidence for the argument developed above - that the BSSAF for a system with
ongoing impacts should be greater in numerical value than a BSAF developed from data on
an ecosystem where impacts were primarily historical.
In any case, parameters for the demonstration scenario in Chapter 5 for this source
category were derived from 104-mill data. Data from only 77 of the mills was used for
the following parameter developments. Mills not included are: 1) the ten mills discharging
into the largest water bodies, 2) 9 mills for which EPA (1990d) was unable to derive
harmonic mean flows from STORET data, and 3) 8 mills for which data on total suspended
solids content in the effluent stream was unavailable from EPA (1990c; actually 11 mills
did not suspended solids data, but three were in other categories deleted).
Values of model parameters for the demonstration are now summarized:
• TSSU, TSSe: The average upstream total suspended solids term from the 77
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mills, TSSU, was 9.5 mg/L. The average suspended solids concentration within the
effluent streams from the 77 mills was 70 mg/L.
• OCU, OCe: No information was available on the organic carbon content of the
suspended solids upstream of the effluent discharge point. A value of 0.05 was assigned,
which was the value assigned for other source categories. No data as well could be found
for the organic carbon content of the effluent solids. However, such solids are essentially
biosolids from biological treatments of mill sludges. The organic carbon content of such
solids is expected to be much higher than 0.05. The value recommended for OCe was
0.36 (Steven Hinton, PhD., P.E., National Council of the Paper Industry for Air and Stream
Improvement, Inc.; Department of Civil Engineering, Tufts University, Medford, MA
02155). This was based on an average proportion of carbon in algal biomass of 0.36
given in Morel (1983).
• Qu, Qe: Flow values for the receiving water and effluent stream were
summarized in EPA (1990d). The average effluent flow rate, Qe, for the 77 mills was
4.10 * 106 L/hr, and for the receiving water body, Qu, was 4.65 * 108 L/hr.
• Koc, BSSAF, f|jpid: Values of Koc and flipid are the same ones which have been
used for the other source categories. As discussed in the introduction to this section, the
Biota Suspended Solids Accumulation Factor, BSSAF, will be assumed to be the same as
the Biota Sediment Accumulation, BSAF. This value is 0.09 for 2,3,7,8-TCDD.
• MASSC: The mass of 2,3,7,8-TCDD exiting from the 77 mills averaged 0.197
mg/hr. However, this data was pertinent for 1988. Since then, pulp and paper mills have
reduced the discharge of dioxin-like compounds in their effluents by altering the pulp
bleaching processes. Gillespie (1992) reports that data on effluent quality from all 104
mills demonstrate reductions in discharges of 2,3,7,8-TCDD of 84%. On this basis, the
value of MASSC for all three example compounds will be 0.0315 mg/hr (16% of 0.197
mg/hr).
Using these parameters in the simple dilution model for 2,3,7,8-TCDD results in the
following:
1) If the mass loadings of 2,3,7,8-TCDD are assumed to be fully sorbed to solids in
the effluent discharge, and not to exist in the soluble phase in the discharge, than the
concentration of 2,3,7,8-TCDD on discharging effluent solids is 1.1 *10~4 mg/kg, or 110
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ppt.
2) The total suspended solids concentration in the mixing zone, TSSmix, equals
10.0 mg/L. The organic carbon content of suspended solids in the mixing zone, OCmix, is
estimated as 0.069. It is seen how the effluent discharge influences these two key
quantities: the unadjusted TSSU was given as 9.5 mg/L, and the unadjusted OCU was
0.05.
3) The overall suspended solids concentration of 2,3,7,8-TCDD in the mixing zone
after mixing and equilibrating with surrounding water, Cssed, was 4.5 ppt. This compares
to the concentration that might be on the effluent solids of 110 ppt, indicating more than
an order of magnitude reduction in concentration by mixing with solids of the receiving
water body, and partitioning into the water column.
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5. DEMONSTRATION OF METHODOLOGY
5.1. INTRODUCTION
This document has provided methodologies and background information for
conducting site-specific exposure assessments to the dioxin-like compounds. Volume II
contains key information pertinent to the methodologies described in this Volume. Chapter
2 of Volume II described physical and chemical properties of these compounds, Chapter 3
described sources of dioxin-like compound release, and Chapter 4 described their
occurrence in environmental and exposure media. This Volume lays out the methodologies
demonstrated in this chapter. Chapter 2 summarized an overall exposure assessment
framework. Chapter 3 described mechanisms of formation of dioxin-like compounds in
stack emissions and the fate and transport modeling of releases from the stack to a site of
exposure, and Chapter 4 provided methodologies to estimate exposure media
concentrations for four sources of contamination, which were termed source categories.
The purpose of this chapter is to put all this information together and demonstrate
the methodologies that have been developed. For this demonstration, exposure scenarios
are developed which are associated with the four source categories. These categories
were defined in Chapter 4, and are:
• On-site soil: The source of contamination is soil and both the source and
exposure site are on the same tract of land.
• Off-site soil: The source of contamination is soil and this source is located
distant and upgradient/upwind from the site of exposure.
• Stack emissions: Exposed individuals reside downwind of the site where
stack emissions occur and are exposed to resulting air-borne contaminants,
and soil and vegetation on their property is impacted by deposition of
contaminated particulates.
• Effluent discharge: A discharge of dioxin-like compounds in effluents
impacts surface water and fish. Exposure occurs through consumption of
the impacted fish and water.
The demonstration in this chapter is structured around what are termed exposure
scenarios. As defined in Chapter 2, an exposure scenario includes a description of the
physical setting of the source of contamination and the site of exposure, behavior of
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exposed individuals, and exposure pathways. Chapter 2 also described the objective of
exposure assessors to determine "central" and "high end" exposure scenarios. This
objective was an important one for this demonstration, and the strategy to design such
scenarios is detailed in Section 5.2 below. For the two soil source categories and the
effluent discharge source category, three dioxin-like compounds are demonstrated for each
of the exposure scenarios, including 2,3,7,8-TCDD, 2,3,4,7,8-PCDF, and 2,3,3',4,4',5,5'-
HPCB. For the stack emission source, a different approach is taken with regard to
compounds demonstrated. One compound is 2,3,7,8-TCDD, as in the other source
categories. An addition demonstration estimates TEQ exposures given emission rates of
dioxin-like compounds with non-zero Toxicity Equivalency Factors (TEFs) from a stack.
Individual congeners emitted by the stack are transported to a site of exposure using the
dispersion/deposition model, COMPDEP. Further modeling then takes the key quantities
for each congener, the air concentrations (vapor and particulate phases), in //g/m3, and the
total deposition (dry and wet deposition summed), in//g/m2-yr, and determines congener
specific exposure media concentrations. The toxic equivalent concentration for each
congener is estimated by multiplying the individual congener concentration estimated by
the individual congener's TEF. Finally, the individual TEQ concentrations are summed to
arrive at an exposure media concentration equalling total TEQ for that media.
Section 5.2 describes the strategy for development of the demonstration exposure
scenarios. Section 5.3 gives a complete summary of the demonstration scenarios.
Section 5.4 provides some detail on the example compounds demonstrated. Section 5.5
describes the source strength terms for the scenarios. Section 5.6 summarizes the results
for all scenarios, which are exposure media concentrations for all exposure pathways, and
exposure estimates which are Lifetime Average Daily Doses (LADDs) for all pathways and
for all example compounds.
5.2. STRATEGY FOR DEVISING EXPOSURE SCENARIOS FOR
DEMONSTRATION PURPOSES
Chapter 2 of this document contained Figure 2-1, a roadmap for assessing exposure
to dioxin-like compounds. These procedures assess individual exposures to known
sources of contamination. Central and high end exposure patterns, and exposure
parameters consistent with these definitions were proposed in that chapter. The
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demonstration in this chapter attempts to merge procedures for estimating individual
exposures to known sources of contamination and current thoughts on devising central
and high end exposure scenarios.
An exposure assessor's first task in determining patterns of exposure is to fully
characterize the exposed population in relation to the source of contamination. If the
extent of contamination can be characterized, then the exposed population would be
limited to those within the geographically bounded area. An example of this situation
might be an area impacted by stack emissions. Chapter 3 demonstrated the use of
COMPDEP atmospheric dispersion model to predict ambient air concentrations and
depositions rates for all points surrounding the stack. Results listed in Tables 3-12
through 3-17 were only for the prevailing wind direction. As can be seen on these tables,
the points of maximum impact were within 1 km of the stack. By overlaying the
concentration isopleths onto a population density map, the exposed population can be
identified. If the extent of contamination is not as clearly defined, such as extent of
impact of nonpoint source pollution (impacts from use of agricultural pesticides, e.g.) or
the compound is found ubiquitously without a clearly defined source, then the emphasis
shifts from geographical bounding to understanding ambient concentrations, exposure
pathways and patterns of behavior in general populations.
After identifying the exposed population, the next task is to develop an
understanding of the continuum of exposures. The exposures faced by the 10 percent of
the population most exposed has been defined as high end exposures. Those faced by the
middle of the continuum are called central exposures. Another important estimate of
exposure level is a bounding exposure, which is defined as a level above that of the most
exposed individual in a population. Arriving at such an understanding can be more of an
art than a science. One consideration is the proximity of individuals within an exposed
population to the source of contamination. For the incinerator example discussed above,
one might begin an analysis by assuming that bounding or high end exposures occur
within a kilometer from the stack, in the prevailing wind direction. Another important
consideration is the relative contribution of different exposure pathways to an individual's
total exposure. While individuals residing at this distance from the incinerator might
experience the highest inhalation exposures, they may not experience other exposure
pathways associated with contaminated soil on their property - such as consumption of
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home grown vegetables, dermal contact, or soil ingestion. Families with home gardens
and individuals who regularly work in those gardens may reside over a kilometer from the
incinerator and possibly be more exposed because of their behavior patterns. Screening
tools, such as the spreadsheets developed for this assessment, can be used in an iterative
mode to evaluate the interplay of such complex factors. When applied to a real world
situation, information should be sought as to the makeup and behavior patterns of an
exposed population.
The demonstration in this chapter attempts to be consistent with the goal of
quantifying central and high end exposures. However, it is not exhaustive in its analysis,
nor should it be construed as a case study with generalizable results. All the scenario
definitions, parameter values, and so on, were construed to be plausible and reasonable,
and to demonstrate the application of a site-specific methodology, not to set any
regulatory precedent.
Following are bullet summaries of key features of the structure and intent of the
demonstrations.
• Exposed populations: Exposed individuals are assumed to reside in a rural
setting. Exposures occur in the home environment, in contrast to the work
environment or other environments away from home (parks, etc.). The
presumption is made that the sources of contamination of this assessment
can occur in rural settings in the United States. Sources demonstrated
include basin-wide soils with concentrations characteristic of background
levels, much smaller areas of soils with concentrations that have been found
in industrial sites, stack emissions, and effluent discharges where
characteristics of the effluent stream including contaminant discharges were
developed from recent data from pulp and paper mills, (see Section 5.5.
below for more details on source strength terms). It is further assumed that
the behavior patterns associated with the exposure pathways can exist in
rural settings. Several of these behaviors characterized as high end relate to
individuals on farms as compared to behaviors characterized as central for
individuals not on farms. The exposed population for this demonstration,
therefore, consists of rural individuals in farming and non-farming residences.
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For each of the four source categories demonstrated, the exposed
populations can be further defined:
On-site soils: The on-site source category is demonstrated with soil
concentrations that have been found and characterized in the
literature as "background" and "rural", or not associated with an
identified source. For this source, the exposed population includes all
individuals within a rural area for which the background concentration
can be considered representative.
Off-site soil: Demonstration of this source category entails a finite
area of soil contamination, in contrast to the demonstration of the on-
site source category, where soils containing low levels of dioxin-like
compounds exist throughout a large region. The site of
contamination is a 10-acre site having elevated soil concentrations
that have been found in the United States in industrial sites. A
working hypothesis is made that the population most exposed are
those residing very near the site. Their soil is assumed to become
contaminated over time due to the process of erosion; these
processes normally do not carry contaminants long distances across
land, particularly land developed with residences or where erosion is
interrupted with ditches or surface water bodies. People from the
surrounding community can be impacted by visiting or trespassing on
the contaminated land, volatilized residues may reach their home
environments, they may obtain water and fish from impacted water
bodies, and so on. It seems reasonable to assume that those residing
near these sites comprise the principally exposed individuals, or
equivalently, the individuals experiencing the high end or bounding
exposures associated with these areas of soil contamination.
Stack emissions: As indicated earlier, the populations exposed to
stack emissions can be identified by overlaying results of an
atmospheric dispersion modeling exercise over a map containing
population density information. Such an exercise was not done for
this demonstration. Instead, simulated ambient air concentrations
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and deposition rates were taken from tables in Chapter 3 for two
locations, one for use in a central scenario and another for a high end
scenario.
Effluent discharges: This source category is unique from the others
in that soils or air are not impacted by the source. Only the surface
water body into which the effluent is discharged is impacted. The
only exposure pathways considered for this source category are
drinking water and fish ingestion. The exposure parameters used to
demonstrate this source category were those developed for the
central scenario. Those that could be recreationally fishing in the
impacted water body or using it as a source of drinking water could
be characterized as central, high end, or bounding. There is no
particular rationale for selecting central exposure behavior in
demonstrating this source category.
Proximity to sources of contamination: As noted above, the on-site soil
contamination source category was demonstrated using soil concentrations
typical of background levels that have been found in rural settings. In this
case, proximity to the source of contamination was not an issue. Proximity
to a stack emitting dioxin-like compounds was identified as an important
determinant for identifying the continuum of exposures. Assuming there is a
uniform distribution of exposure-related behaviors among exposed
populations, i.e., their behavior patterns are not a function of where they live
in relation to the stack, the most exposed individuals will be those exhibiting
high end exposure behavior nearest the stack. This was the assumption
made for purposes of this demonstration. A set of high end exposure
behaviors and pathways were demonstrated for individuals residing 500
meters east of the stack, and a set of central exposure pathways were
demonstrated for individuals residing 5000 meters east of the stack. The
highest ambient air concentrations, and dry and wet deposition rates were
simulated to occur at 200 to 1000 meters downwind, justifying 500 meters
as an appropriate point for assuming high end impacts. Tables 3-12 through
3-17 listing concentrations and depositions rates as a function show that air
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concentrations and dry depositions rates at 5000 meters are only about half
of what they are at 500 meters, although wet deposition rates are about 20
times higher at 500 meters as compared to 5000 meters. Without rigorous
justification, the model output (concentrations and deposition rates) at 500
and 5000 meters was felt to appropriately characterize high end and central
exposures. The above bullet justifies a definition of principally exposed
individuals as those nearest the site of high soil contamination in the
demonstration of the off-site source category, while recognizing that lesser
exposures can occur for other individuals in the community. These lesser
exposures will not be demonstrated. Instead, the off-site soil source
categories will only be demonstrated with a single, high end scenario.
Individuals exposed will be assumed to reside 1 50 meters downgradient
from the site of soil contamination. The above bullet also discussed how a
surface water body impacted by effluent discharges could be used (for
drinking and recreationally fishing) by individuals exhibiting central, high end,
or bounding exposure behavior patterns. Intuitively, proximity should be an
issue because impacts to fish and water are likely to be higher nearer to the
point of discharge. However, the simplistic model estimating impacts from
effluent discharges uses a simple dilution approach to obtain water and
suspended sediment concentrations. The suspended sediment
concentrations are used to estimate fish impacts. For this approach,
therefore, proximity cannot be rigorously evaluated. Exposure parameters
for water and fish ingestion corresponding to central behavior patterns were
used in the demonstration of the effluent discharge source category.
Central and high end exposure patterns: Chapter 2 described the exposure
pathways that are considered in this methodology, and justified assignment
of key exposure parameters (contact rates and contact fractions, exposure
durations, and so on) as central or high end estimates. That chapter notes
that the exposure pathways identified were those that were consistent with
the sources of contamination, and consistent with literature which identified
predominant media where these compounds were found. The bullet above
discussing exposed population indicated that several of the behavior
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assumptions were specific to individuals on farm, and that these behavior
patterns were evaluated as high end. High end behaviors assumed to be
different for individuals on farms versus central behaviors for individuals not
on farms include: residing on larger tracts of land (10 acres assumed for
farmers; 1 acre assumed for non-farmers), ingestion of home produced and
impacted beef and milk, tendencies to reside in a single location longer (20
years versus 9 years), tendencies to be present in the home environment
longer (90% of the time versus 75% of the time), and patterns of soil
dermal contact designed to be plausible for farmers working with soil versus
those incidentally contacting soil. Other patterns of behavior modeled as
central and high end are not specifically associated with farming and not
farming, but are assumed to be plausible for individuals in rural settings.
These include home gardening for fruit and vegetables, inhalation exposures,
children that ingest soil, and the use of impacted surface water bodies for
drinking and fish to be ingested.
Plausibility of source strength terms: The objective to determine plausible
levels of source strength contamination was an important one for this
demonstration. The source terms are soil concentrations, effluent discharge
rates, and stack emission rates. Section 5.5 describes the source terms in
detail.
Appropriate estimation of exposure media concentrations: The realism of
estimated exposure media concentrations is dependent on the
appropriateness of the models used for such estimations and the assignment
of parameter values for those models. One way to arrive at a judgement as
to the realism of estimated concentrations is to compare predictions with
observations. To the extent possible (i.e., given the availability of
appropriate data), model predictions of exposure media concentrations are
compared with occurrence data in Chapter 7 on Uncertainty. As is shown,
predictions fell within the realm of observed data. Chapter 4 describes the
justification of all model parameter values. Many of the parameters are
specific to the contaminants. Some contaminant properties were estimated
as empirical functions of contaminant-specific parameters, such as the
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octanol water partition coefficient, Kow, and others were measured values.
For non-contaminant parameters such as soil and sediment properties,
patterns of cattle ingestion of soil (and other bioaccumulation/biotransfer
parameters), and many others, selected values were carefully described and
crafted to be plausible.
5.3. EXAMPLE EXPOSURE SCENARIOS
As noted above, all exposures occur in a rural setting. Exposure pathways were
those which could be associated with places of residence in contrast to the work place or
other places of exposure. The example scenarios are structured so that all the behaviors
associated with high end exposures are included in the "high end" scenarios and all the
central behaviors are in the scenarios characterized as "central". To summarize, the
components which distinguished the high end exposure scenarios in contrast to the central
scenarios include:
• Individuals in the central scenarios lived in their homes and were exposed to
the source of contamination for only 9 years, in contrast to individuals in the
high end scenarios, who were exposed for 20 years (except for the exposure
pathway of soil ingestion, where the individuals are assumed to be children
ages 2-6, and in both the central and high end scenarios, the exposure
duration is 5 years).
• Individuals in the central scenarios lived on properties 1 acre in size, whereas
individuals in the high end scenarios lived on properties 10 acres in size.
• Individuals in the high end scenario associated with the stack emission
source category lived 500 meters from the incinerator, whereas individuals
in central scenario lived 5000 meters from the incinerator.
• High end individuals obtained a portion of their beef and milk using home
produced beef and milk - such individuals are obviously farmers. Beef and
milk ingestion pathways were not assessed for non-farming rural individuals,
representing the central scenarios.
• Ninety percent of the inhaled air and ingested water by the high end
individuals were assumed to be contaminated, whereas only 75% of these
exposures were with impacted media for the central individuals. This is
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based on time at home versus time away from home assumptions for central
versus high end individuals. Also, individuals in high end scenarios were
assumed to consume 2.0 L/day of water as compared to 1.4 L/day
consumed by individuals in central scenarios.
• Although their total intake of fruit and vegetables was assumed to be the
same, a larger proportion of the intake of those food products in the high
end household was home grown and impacted as compared to the central
household.
• The rates of ingestion of soil by children and of recreationally caught and
impacted fish were higher for the high end individuals than the central
individuals.
These are the distinguishing features for the central and high end exposure
scenarios. For the sake of convenience mainly, all the scenarios defined below as high end
are called "farms", and all central scenarios are called "residences". The assertion is not
being made that all behaviors are likely to occur simultaneously (or in some cases, simply
to occur) on a farm or a non-farm residence, although several of the high behavior patterns
are specific to farms. In an exhaustive site-specific analysis, one might begin by
evaluating all possible pathways, further evaluating pathways of most exposure, and then
determining what pathways occur simultaneously for identified individuals in the exposed
population. Only then can be the assessor begin to define a continuum of exposures.
The following bullets describe six exposure scenarios that are demonstrated. The
numbering scheme and titles will be referenced for the remainder of this chapter:
Exposure Scenarios 1 and 2: On-site Soil Contamination, Residence and Farm
Surface soils on a 4,000 m2 (1-acre roughly) rural residence (Scenario 1) and on a
40,000 m2 (10-acre roughly) small rural farm (Scenario 2) are contaminated with the three
example contaminants. The concentrations of the contaminants are uniformly set at 1
part per trillion, which was evaluated as reasonable background levels (see Section 5.5
below). Bottom sediment in a nearby stream becomes contaminated. Water and fish in
that stream are subsequently impacted. Fish are recreationally caught and eaten, and the
water is extracted for drinking purposes, perhaps at a downstream water system intake.
However, water concentration predictions are only those which are estimated to occur in
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the drainage area impacting the generally smaller size stream. For background soil
concentrations, river system impact should be similar to local stream impact justifying the
drinking water pathway for these scenarios.
Exposure Scenario 3: Off-site Soil Contamination, Farm
A 40,000 m2 rural farm is located 150 m (500 ft roughly) from a 40,000 m2 area
of bare soil contamination; an area that might be typical of contaminated industrial
property. The surface soil at this property is contaminated with the three example
compounds to the same concentration of 1 part per billion. This is evaluated as reasonable
for industrial sites of contamination of dioxin-like compounds, and three orders of
magnitude higher than concentrations for Scenarios 1 and 2. As in the above and all
scenarios, bottom sediment in a nearby stream is impacted, which impacts the drinking
water supply and fish which are recreationally caught and consumed by members of this
farming household. A similarly sized stream is impacted for this source category as in the
on-site source category. It is less likely that water concentrations for this stream would
be similar to concentrations at a point where water is withdrawn for drinking purposes.
Nonetheless, to be consistent with the demonstration of the on-site source category and
the stack emission source category, drainage area sizes and stream sizes were the same.
It can be said that the stream size is plausible for recreational fishing, so impacts to fish
are appropriately estimated and compared among the source categories.
Exposure Scenarios 4 and 5: Stack Emissions, Residence and Farm
A 4,000 m2 rural residence (Scenario 4} is located 5000 meters from an
incinerator, and a 40,000 m2 (Scenario 5) rural farm is located 500 meters downwind
from an incinerator. Emission data of the suite of dioxins and furans with non-zero TEFs is
available. This allows for estimation of impacts from 2,3,7,8-TCDD alone, and estimation
of TEQ impacts. The modeling of the transport of these contaminants from the stack to
the site of exposure and other points in the watershed used the COMPDEP model. Details
on the stack emission source and the COMPDEP model application are found in Chapter 3.
A nearby impacted stream feeds into a drinking water system and supports fish for
recreational fishing.
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Exposure Scenario 6: Effluent discharge into a river
As has been discussed, this source category is different from others in that the air,
soil, and vegetation at a site are not impacted. Rather, only surface water impacts are
considered. Therefore, central and high end behaviors associated with places of residence
are less pertinent for this source category. Exposure parameters associated with central
behaviors for the water and fish ingestion pathways were chosen to demonstrate this
source category. The source strength was developed from data on pulp and paper mill
discharges of 2,3,7,8-TCDD; more detail on this source strength term development is
provided in Section 5.5 below. The discharges of the other two example compounds are
assumed to be the same for purposes of demonstration. Obviously, however, there is less
of a tie to real data for the discharge rate for these other two example compounds. Also
noteworthy for this source category as compared to the others is the size of the surface
water body into which discharges occur. The other source categories all were
demonstrated on water bodies with annual flow rates of 1.5 * 1010 L/yr. The river size
into which the example effluent was discharged was developed from data from the 104
pulp and paper mill study (as discussed in Section 5.5 below). This river size was 4 *
1012 L/yr, two orders of magnitude larger than the other streams. In this demonstration,
therefore, use of impacted water in a drinking water system would appear to be more
plausible.
5.4. EXAMPLE COMPOUNDS
Three compounds were demonstrated for the two soil source categories, on- and
off-site soil contamination, and for the effluent discharge source category. For purposes
of illustration, one compound was arbitrarily selected from each of the major classes of
dioxin-like compounds. They are: 2,3,7,8-tetrachlorodibenzo-p-dioxin, 2,3,4,7,8-
pentachlorodibenzofuran, and 2,3,3',4,4',5,5'-heptachloro-PCB. For the remainder of this
chapter, these compounds will be abbreviated as 2,3,7,8-TCDD, 2,3,4,7,8-PCDF, and
2,3,3',4,4',5,5'-HPCB.
These compounds demonstrate a range of expected results because of the
variability of their key fate and transport parameters. The log octanol water partition
coefficients (log Kow) for 2,3,7,8-TCDD, 2,3,4,7,8-PCDF, and 2,3,3',4,4',5,5'-HPCB were
6.64, 6.92, and 7.71, respectively. Whereas the span of reported log Kow ranged from
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less than 6.00 to greater than 8.00, only a few reported values were at these extremes.
Increasing log Kow translates to the following trends: tighter sorption to soils and
sediments and less releases into air and water, less accumulation in plants and in cattle
products (beef, milk), and more accumulation in fish. The Henry's Constants for the three
compounds span the range of reported values, with the value of the PCB compound the
highest of all reported at 3.0 * 10"3. There were few values less than the 4.99 * 10~6
reported for 2,3,4,7,8-PCDF. Higher Henry's Constants translate to greater amounts of
volatilization flux. A summary of the chemical specific parameters for these three
compounds is given in Table 5-1.
For the stack emission demonstration. Scenarios 4 and 5, a different approach was
taken. Like the above source category demonstrations, exposures to 2,3,7,8-TCDD alone
are determined. Given that the stack emission data included emission rates for all dioxins
and furans with non-zero toxicity equivalency factors (abbreviated TEFs), and the
atmospheric transport modeling led to estimates of ambient air concentrations, and wet
and dry deposition rates at various distances for these compounds, an opportunity
presented itself for demonstrating an approach to estimating TEQ impacts. This approach
takes the individual deposition rates and concentrations for the dioxins and furans with
non-zero TEFs and models the exposure media concentrations individually with unique fate
and bioaccumulation parameters, and then determines a final TEQ exposure media
concentration using TEFs. Results for this approach are hereafter termed "TEQ" results.
The deposition rates, air concentrations, TEFs, and chemical specific parameters for
2,3,7,8-TCDD and the individual congeners are provided in Table 5-2.
5.5. SOURCE TERMS
This section describes the source terms for the example scenarios. Source terms
for the soil contamination sources, the on- and off-site soil sources, include the areas of
contamination and soil concentrations. This section also summarizes the exposure site soil
concentrations that result from erosion of contaminated soil from the nearby soil
contamination site in the example scenario demonstrating the off-site soil source category,
Scenario 3. The source terms for the stack emission scenarios, 4 and 5, are the emission
rates of contaminants from the stacks. Discussions of these rates are provided in Chapter
3. As noted in that Chapter, emission rates were determined from actual test data. This
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Table 5-1. Environmental fate parameters for the three example compounds demonstrated
for the soil contamination source categories and the effluent discharge source category.
Description
Kow: log octanol water part coef*
Koc: organic carbon part coef., L/kg
H: Henry's Constant, atm-m3/mole
Da: molecular diffusivity in air, cm2/s
k: dissipation rate for eroding
contaminants, yr"1
kw: first-order plant wash-off rate, yr"1
Bvpa: air-to-leaf transfer factor, unitless
BCF: beef/milk biotransfer factor, unitless
BSAF/BSSAF: bottom sediment (BSAF) or
suspended sediment (BSSAF) biota
sediment accumulation factor, unitless
RCF: root concentration factor, unitless
2,3,7,8-
TCDD
6.64
2.7M06
1.7*1Q-5
0.05
0.0693
18.02
1.0*105
4.3
0.09
3.9M03
2,3,4,7,8-
TCDF
6.92
5.1M06
5.0MO'6
0.05
0.0693
18.02
5.3*105
3.1
0.09
6.4M03
2,3,3',4,4,'
5,5- HPCB
7.71
3.2*107
1.0*10'3
0.05
0.0693
18.02
2.3*104
2.3
2.0
2.6*104
Kow is not strictly required for the fate and transport algorithms, but it was used in the
estimation of other parameters, and is otherwise a commonly known and important
environmental fate parameter
section does list TEQ emissions in grams per second, and the exposure site soil
concentrations that result from stack emission depositions. The source term for the
effluent discharge example scenario is the rate of discharge of dioxin-like compounds.
This is briefly discussed in this section, with reference to a more detailed discussion in
Chapter 7, Section 7.2.3.6.
Key source terms are summarized in Table 5-3. Following now are discussions on
these terms for all scenarios.
Scenarios 1 and 2
The residence in Scenario 1 is 4,000 m2 and in Scenario 2 is 40,000 m2 in size.
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Table 5-2. Key source terms and fate parameters for 2,3,7,8-TCDD and for individual dioxin and furan congeners with non-
zero TEFs for the demonstration of the stack emission source category1.
Compound
2378-TCDD
12378-PeCDD
1 23478-HxCDD
1 23789-HxCDD
1 23678-HxCDD
1234678-HpCDD
OctaCDD
2378-TCDF
23478-PeCDF
12378-PeCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OctaCDF
1 Column headings are
TEF: Toxic
H: Henry
TEF deposition air concen.
//g/m2-yr //g/m3
1.0 1.1*1CT6 1.4*10'11
0.5 3.9*10-6 2.9*10-11
0.1 6.3*10'6 3.7*10"11
0.1 9.6*10-6 5.4*1Q-11
0.1 8.6*1Q-6 4.9MO'11
0.01 8.6*10'5 4.9MO'10
0.001 1.8 *10'* 1.0*10'9
0.1 4.7*10'5 9.0*10'10
0.5 1.2*10'5 9.4M001
0.05 6.1*10'6 5.8M001
0.1 2.3*10'5 1.4*10'10
0.1 2.2*10'5 1.3«10'10
0.1 1.4*10'5 8.5*10'11
0.1 8.4MO'6 5.0* 10'11
0.01 3.0*10'5 1.7*10'10
0.01 1.3*10'5 7.5*10-11
0.001 6.0* 10"5 3.3*10-10
Equivalency Factor
's Constant, atm-m3-mole
log Kow: log octanol water partition coefficient
Bvpa: air-to-leaf transfer factor, unitless
BSAF: Biota
sediment accumulation factor, unitless
H
i.6*io-5
2.6 *10'6
1.2*10'5
1.2*1(r5
1.2*1Cr5
7.5*1Q-6
7.0* 10'9
8.6*10'6
6.2*1Q-6
6.2*10-6
1.4*10'5
6.1 *10'6
1.0*10'5
1.0*10'5
5.3*10'5
5.3*10-5
1.9*10'6
Deposition:
log
Kow
6.64
6.64
7.79
7.79
7.30
8.20
7.59
6.53
6.92
6.79
7.30
7.30
7.30
7.30
7.90
7.90
8.80
air concentration:
RCF:
Koc:
BCF:
RCF Bvpa Koc
3.9M03 1.0"105 2.7*106
3.9*103 6.3*105 2.7*106
3.0*104 2.3*106 3.8*107
1.3*10* 6.9*105 1.2*107
1.3*10* 6.9*105 1.2*107
6.2*10* 1.0*107 9.8*107
2.1*10* 2.4M09 2.4*107
3.2*103 1.5*105 2.1*106
6.4*103 5.3*105 5.1*106
5.1*103 3.8*105 3.8M06
1.3*10* 5.9*105 1.2*107
1.3*10* 1.4*106 1.2*107
1.3*10* 8.3*105 1.2*107
1.3*10* 8.3*105 1.2*107
3.7*10* 6.8*105 4.9*107
3.7*10* 6.8*105 4.9*107
1.8*105 1.7M08 3.9*108
wet + dry deposition at 500 m, /yg/m
BSAF
0.09
0.09
0.04
0.04
0.04
0.005
0.0001
0.09
0.09
0.09
0.04
0.04
0.04
0.04
0.005
0.005
0.0001
2-yr
BCF
4.32
4.16
2.02
2.24
1.74
0.36
0.52
0.94
3.10
0.73
2.34
2.00
2.00
1.78
0.41
0.99
0.20
vapor + particle phase air at 500 m, //g/m3
root concentration factor, unitless
organic carbon partition coeff., L/kg
beef/milk biotransfer factor, unitless
note: k, dissipation rate for eroding contaminants (.0693 yr"1), and kw, first-order plant washoff rate (18.01 yr"1) assumed to be equal for all congeners
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Table 5-3. Summary of key source terms for the six exposure scenarios and the three
example compounds.
1. Soil Concentrations, //g/kg (ppb)
Source Category 1 : On-Site Soil
Scenario 1 . Central
Scenario 2. High End
Source Category 2: Off-Site Soil
Off-site soil concentration
Scenario 3. High End No-till
Tilled
Source Category 3: Stack Emissions
Scenario 4. Central No-till
Tilled
Scenario 5. High End No-till
Tilled
Watershed soils
2,3,7,8-
TCDD
0.001
0.001
1.000
0.279
0.077
2,3,7,8-TCDD
IMG'7
5*10'9
1 « 1 O'6
5 * 1 0'8
1 * 1 0'7
2,3,4,7,8-
PCDF
0.001
0.001
1.000
0.279
0.077
TEQ
3MO'6
2*10'7
2*10'5
1 * 1 0'6
2»10-6
2,3,3',4,4,
5,5'-HPCB
0.001
0.001
1.000
0.279
0.077
I. Emission Rates for Stack
Emissions (g/sec) and Effluent
Discharges (mg/hr)
Source Category 3: Stack Emissions
Scenarios 4 & 5:
Source Category 4: Effluent Discharges
Scenario 6:
II. Land Areas, m2
Source Category 1: On-site Soil
Scenario 1. Central 4,000
Scenario 2. High End 40,000
Source Category 3: Stack Emissions
Scenario 4. Central 4,000
Scenario 5. High End 40,000
2,3,7,8-TCDD
9.2*10'11
0.0315
TEQ
1.5 MO'9
0.0315
0.0315
Source Category 2: Off-site Soils
Contamination Site: 40,000
Scenario 3. High End 40,000
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Chapter 4 in Volume II discussed soil concentrations of the dioxin-like compounds found in
the literature. As noted in that chapter, concentrations of the coplanar PCBs were not
found in the literature; soil concentrations assigned for 2,3,3',4,4',5,5'-HPCB will be the
same as the other two compounds. Scenarios 1 and 2 were designed to demonstrate
exposures to low concentrations which might be considered "background" soil
concentrations. Soil concentrations of 2,3,7,8-TCDD and 2,3,4,7,8-PCDF described as
"background" or "rural" by researchers were found in the non-detect to low ng/kg (ppt) in
Illinois, Ohio, and Minnesota in the United States (EPA, 1985; Reed, et al., 1990), and in
Sweden (Broman, et al. 1990) and England (Creaser, et al., 1989; Stenhouse and Badsha,
1990). Tier 7 of EPA's National Dioxin Study (EPA, 1987) consisted of "background"
sites, or sites that did not have previously known sources of 2,3,7,8-TCDD contamination.
The purpose of this tier was to provide a basis for comparison for the other 6 tiers of
study, which did include sites of known or suspected 2,3,7,8-TCDD contamination. The
results were that 17 of 221 urban sites and only 1 of 138 rural sites had detectable levels
of 2,3,7,8-TCDD, with a range of positives of 0.2 to 11.2 ng/kg (ppt). While the value of
1 ng/kg selected for these scenarios may not be a true "background" concentration, the
intent in designing Scenarios 1 and 2 was to select a concentration that might be typical
of areas where no known identifiable source impacts the soil.
Scenario 3
This scenario was designed to be plausible for properties located near inactive
industrial sites with contaminated soil. The selection of 1 jjg/kg (ppb) for the three
compounds was based on 2,3,7,8-TCDD findings associated with the Dow Chemical site
in Midland, Ml (EPA, 1985; Nestrick, et al. 1986) as well as the 100 industrial sites
evaluated in the National Dioxin Study (which included the Dow Chemical site; EPA,
1987). In that study, most of the sites studied had soil concentrations in the parts per
billion range. The farm size was 40,000 m2, as in all high end scenarios. Table 5-3
shows these concentrations for the example compounds at the site of contamination, and
also for the tilled and untilled condition at the sites of exposure. Exposure site soil is
assumed to become contaminated over time due to erosion of soil from the contaminated
site. The "tilled" condition distributes the eroded contaminants to a depth of 20 cm and
impacts the estimated concentrations on underground vegetables grown at home.
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The"untilled" condition distributes the eroded contaminants only to a depth of 5 cm, and
results in a soil concentration for which soil exposure pathways, ingestion and dermal
contact, are estimated. Note that there are no differences in concentrations at the
exposure site among the three example contaminants. Three key factors influence the
concentrations estimated to occur at sites near a site of soil contamination. First are the
source strength terms for the contaminated site - area of contamination and concentration.
These were the same for each of the example compounds. Second are the components of
the erosion algorithm - quantities of erosion, enrichment ratio, and distance from the site
of contamination. Again these were the same for all example compounds. Finally, there
are the key parameters determining exposure site concentration - the depth of mixing
layers and the contaminant dissipation rate. For all three compounds, a dissipation rate
corresponding to a 10-year half-life was assumed, and 5 and 20 cm mixing depths were
used in all cases.
Scenarios 4 and 5
Chapter 3 described the application of the COMPDEP model to estimate air-borne
concentrations and deposition rates of the contaminants in the vicinity of the hypothetical
incinerator, given contaminant emission rates in units of g/sec. Table 5-3 shows the
emission rates of 2,3,7,8-TCDD and TEQs. As discussed in Chapter 3, the emission
factors (mass compound emitted per mass feed material combusted) were typical of
incinerators with a high level of air pollution control, e.g., scrubbers with fabric filters.
The TEQ emission factor for the hypothetical incinerator, 4.5 ng TEQ/kg material
combusted, was within a range of 0.3 ng TEQ/kg municipal solid waste incinerated, to
200 ng TEQ/kg hospital waste incinerated. This range was developed from representative
test data for source-specific incinerators with a similar high level of pollution control
technology. Two hundred metric tons per day of material was assumed to be incinerated
at the hypothetical incinerator in order to arrive at emissions in appropriate units of g/sec.
Wet and dry particle deposition rates, in units of g/m2-yr, were determined for all dioxins
and furans, at various distances from the stack and in the prevailing wind direction. The
exposure sites of Scenarios 4 and 5 are located 500 and 5000 meters, respectively, from
the emission source. Although the deposition rate for a site whose midpoint is 500 meters
away can be precisely calculated as the average of several rates between, say 300 and
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700 meters, the deposition rates at 500 meters as listed in Tables 3-15 and 3-16 were
used. The same was done for the site at 5000 meters. Other deposition rates needed for
the stack emission source category were those used to estimate average watershed soil
concentrations and direct deposition onto the impacted water body. For both the central
and high end scenarios, rates of deposition at 500 meters were used for these purposes.
This might translate to an assumption that the stack was located near the impacted water
body. The soil concentrations at the sites of exposure and within the watershed resulting
from these depositions are listed in Table 5-3. It is noted that the soil mixing depth for the
untilled circumstance is not the same as in the off-site soil category, demonstrated in
Scenario #3. The mixing depth for untilled conditions is assumed to be 1 cm, instead of 5
cm. The reasoning is that particle deposition is a less turbulent process of transport as
compared to soil erosion - soil erosion was assumed to transport residues from a site of
off-site contamination to a site of exposure. The tilled mixing depth was 20 cm, as in the
off-site soil category. Finally, the mixing depth assumed to characterize watershed soils
on the average was 10 cm. This might assume, for example, that the watershed soils
include tilled (agricultural fields) and untilled (residential) soils.
Scenario 6
All key parameters used in Scenario 6 demonstrating the effluent discharge source
category were developed using data associated with the 104 pulp and paper mill study
(EPA, 1990). Derivation of the physical parameters including the flow rate of the receiving
water body, flow rate of the effluent stream, suspended solids concentrations of the
receiving water body and the effluent stream, and so on, are described in Section 4.6 of
Chapter 4. An exercise evaluating the simple dilution model for predicting impacts to
suspended solids in water body and subsequently to fish tissue concentrations resulting
from discharges from these mills is described in Section 7.2.3.6, Chapter 7. The bottom
line conclusion from that exercise was that the simple dilution model appears to work
satisfactorily for a screening model: predicted whole fish tissue concentrations for the
majority of mills were half as much as measured fish tissue concentrations. For the
minority of mills, those with the highest volumes of receiving water, the model did not
work as well. Predicted fish tissue concentrations were around an order of magnitude
lower than measured concentrations. The precise reason for this discrepancy is not
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known, but the most likely explanation that larger water bodies have more uses and more
sources of dioxin-like input - assuming that the fish tissue concentrations result singly from
the mill discharge and a few proximate mills may be inappropriate.
Parameters for Scenario 6 were derived from the mills for which the model best
performed. The average discharge rate from these mills was 0.197 mg 2,3,7,8-TCDD/hr.
However, this data was valid for the time of sampling, which was 1988. Since then, pulp
and paper mills have reduced the discharge of dioxin-like compounds in their effluents by
altering the pulp bleaching processes. Gillespie (1992) reports that data on effluent quality
from all 104 mills demonstrate reductions in discharges of 2,3,7,8-TCDD of 84% overall.
On this basis, the discharge rate assumed for 2,3,7,8-TCDD was 0.031 5 mg/hr (1 6% of
0.197 mg/hr). This same rate was assumed for the other two example compounds.
It is important to note that these discharge assignments are not intended to reflect
current discharges of dioxin-like compounds from pulp and paper mills, even for 2,3,7,8-
TCDD, but particularly for the other two example compounds. Data from the 104-mill
study did allow for development of a "composite" effluent discharger in certainly a
plausible setting (receiving water body and discharge flow rates, suspended solids, etc.)
for pulp and paper mills. Assigning what might be evaluated as a reasonable discharge
rate of 2,3,7,8-TCDD from pulp and paper mills for current conditions allows for the
example scenario to placed in some context, which was a primary objective of crafting all
example scenarios. Individual sources must be evaluated on an individual basis.
5.6. RESULTS
The results of this exercise include the exposure media concentrations for all
exposure pathways and scenarios, and the LADD exposure estimates. These two
categories of results are summarized in Tables 5-4 and 5-5. Following now are several
observations from this exercise. As a reminder for the TEQ demonstration for the stack
emission demonstration scenarios, #4 and #5, individual dioxin and furan congeners with
non-zero toxic equivalency factors (TEFs) were modeled with unique fate and transport
parameters until estimates of exposure media concentration were made. At that point, the
TEQ exposure media concentrations were estimated as: ICj*TEFj, where Cj are exposure
media concentrations for the individual congeners and TEFj are the TEF for the individual
congeners.
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Table 5-4. Exposure media concentrations estimated for all scenarios and pathways.
Exposure pathway/ Scenarios
scenario #1,2,3,6:
Scenarios #4,5:
1 . Concentration of contaminants in soil
for soil ingestion and dermal contact
pathways, //g/kg (ppb)
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
#4 Stack emissions, central
#5 Stack emissions, high end
2. Concentration of contaminants in air
in vapor phase for vapor inhalation
pathway, jjg/m3
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
#4 Stack emissions, central
#5 Stack emissions, high end
(note: for stack emission scenarios, #4 and
concentrations are total, including vapor +
2378-TCDD 23478-PCDF 233'44'55'-
HPCB
2378-TCDD TEQ
0.001 0.001 0.001
0.001 0.001 0.001
0.279 0.279 0.279
1MO'7 3*10'6
1MO-6 2*10'5
4*10~11 2*10"11 9*10"11
4*10"11 2*10"11 9*10~11
4*10~9 2*10"9 2*10"8
5*10'12 8*10'11
1*10~11 2*10"10
#5, air
particle phases)
3. Concentration of contaminants in air
in particulate phase for particulate
inhalation pathway, //g/m3
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
4. Concentration of contaminants in water
for water ingestion pathway, mg/L
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
#4 Stack emissions, central
#5 Stack emissions, high end
#6 Effluent discharge, central
6*1Q-13
5MO'12
2»10-io
3MO'11
3MO'11
2»10-10
4MO-15
2*10
-11
6*10-13
5*10-12
2»10-io
2MO'11
2*10-11
1MO-10
5MO'14
5MO-14
6MO'13
5MO-12
2,10-10
3MO-12
3MO-12
2*10-n
SMC
'12
(continued on the following page)
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Table 5-4. (continued)
5.
6.
7.
8.
Exposure pathway/ Scenarios
scenario #1,2,3,6:
Scenarios #4,5:
Concentration of contaminants in
fish for fish ingestion pathway,
mg/kg
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
#4 Stack emissions, central
#5 Stack emissions, high end
#6 Effluent discharge, central
Concentration of contaminants in below
ground vegetables (no below ground fruit
was assumed) for their respective
pathways, mg/kg fresh weight
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
#4 Stack emissions, central
#5 Stack emissions, high end
Concentration of contaminants in above
ground fruit and vegetables for their
respective pathways, mg/kg fresh weight
#1 On-site soil, central
#2 On-site soil, high end
#3 Off-site soil, high end
#4 Stack emissions, central
#5 Stack emissions, high end
Concentration of contaminants in
beef fat for beef ingestion
pathway, mg/kg dry weight
#2 On-site soil, high end
#3 Off-site soil, high end
#5 Stack emissions, high end
2378-TCDD
2378-TCDD
6MO-7
6*10'7
3*10-6
6MO-11
6*10-11
4MCT7
1MCT9
1*10-9
1MO'7
1MO-14
8*10-14
6*icr12
1MO'11
8MO-10
5*10-13
3*10-12
1MO'7
3MO'5
2*10-9
23478-PCDF
TEQ
6*1CT7
6MO'7
3MO'6
1 * 1 CT9
1 * 1 CT9
5MQ-7
1 * 1 0'9
1 * i cr9
1MO'7
2*10-13
1 *icr12
1MQ-11
2»10-11
1 * 1 cr9
3*icr11
1MO-io
IMG'7
2*icr5
4*10'8
233'44'55'-
HPCB
1 * 1 cr5
1 * 1 0'5
8 « 1 cr6
1 * 1 0'5
8*10-10
8*10-10
6*icr8
3*icr12
1MO-11
7MO-io
6MO'8
2*10-5
(continued on the following page)
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Table 5-4. (continued)
Exposure pathway/
scenario
Scenarios
#1,2,3,6:
Scenarios #4,5:
2378-TCDD 23478-PCDF 233'44'55'-
HPCB
2378-TCDD TEQ
9.
Concentration of contaminants in
milk fat for milk ingestion pathway,
mg/kg dry weight
#2 On-site soil, high end
#3 Off-site soil, high end
#5 Stack emissions, high end
6MO'8
2*10'5
2MO'9
5*10'8
1 » 1 0'5
3*10'8
3*10'8
9*1Q-6
It is important to understand that all observations made below are not generalizable
comments. Different results would arise from different source strength characteristics,
proximity considerations, model parameter values, different models altogether, and so on.
Chapters 6 and 7 on User Considerations and Uncertainty describes many areas of this
assessment which should be considered when evaluating the methodology or viewing the
results.
5.6.1. Observations Concerning Exposure Media Concentrations
• Soil Concentrations:
The lowest soil concentrations resulted from deposition of particles from the
example stack emission source. Concentrations for the stack emission central and high
end scenario were 4 and 3 orders of magnitude lower than the central and high end
scenarios demonstrating the on-site source category, respectively. This implies that the
example stack emission source would have little impact to nearby soils, since the on-site
source category was demonstrated with soil concentrations evaluated as typical of
background conditions. The order of magnitude difference in distance from the stack
between the central (5000 meters away) and high end (500 meters) scenarios is matched
by the same order of magnitude difference in soil concentrations. TEQ soil concentrations
were over an order of magnitude higher than 2,3,7,8-TCDD concentrations. The
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Table 5-5. Lifetime average daily dose, LADD, estimates for all scenarios and exposure
pathways (all results in mg/kg-day)
Exposure scenario/
pathway
Scenarios 2378-TCDD 23478-PCDF 233'44'55'
#1,2,3,6: HPCB
Scenarios #4,5: 2378-TCDD TEQ
#1 On-site soil contamination
Central exposure scenario
a. Soil ingestion
b. Soil dermal contact
c. Inhalation-vapor
d. Inhalation-particle
e. Water ingestion
f. Fish ingestion
g. Fruit ingestion
h. Vegetable ingestion
#2 On-site soil contamination
High end exposure scenario
a. Soil ingestion
b. Soil dermal contact
c. Inhalation-vapor
d. Inhalation-particle
e. Water ingestion
f. Fish ingestion
g. Fruit ingestion
h. Vegetable ingestion
i. Beef ingestion
j. Milk ingestion
#3 Off-site soil contamination
High end exposure scenario
a. Soil ingestion
b. Soil dermal contact
c. Inhalation-vapor
d. Inhalation-particle
e. Water ingestion
f. Fish ingestion
g. Fruit ingestion
h. Vegetable ingestion
SMC'13
4*10-14
1MO-15
2»10-17
7MO-14
1MCT12
2.10-16
2*10-14
3*icr12
9*10-13
3*10-15
3*icr16
3«10-13
i*icr11
1MO-15
7*Kr14
6*icr12
1*10-12
9*10-10
7*10~11
3MO'13
2*10-14
1 *10~12
6*icr11
9*10~14
5*10-12
8MO-13
4MO-14
5*1Q-16
2MO-17
4MO-14
1 *10"12
4MQ-16
2MQ-14
3MO'12
9*1Cr13
1 *10"15
3MO-16
1 *10"13
1*1Q-11
2*10-15
g,lo-14
4*10-12
g*10-13
9MO-io
7*10'11
1MO-13
2MQ-14
8*10-13
6MO'11
2*10-13
5MQ-12
8MO-13
4*10-14
3.10-15
2*10-17
6*10"15
3MO'11
1MO-16
1MO-14
3*1CT12
9*10"13
7*10-15
3*10"16
2MO'14
2*10~10
1MO-15
4»10-14
2*10"12
6MO-13
9*10-10
7MO-11
1MO-12
2MQ-14
1 *10~13
1 « 1 0'9
SMC'14
SMC'12
5-24
(continued on the following page)
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Table 5-5. (continued)
Exposure scenario/ Scenarios
pathway #1,2,3,6:
Scenarios #4,5:
#3 Off-site soil contamination
High end exposure scenario
(continued)
i. Beef ingestion
j. Milk ingestion
#4 Stack emissions
Central exposure scenario
a. Soil ingestion
b. Soil dermal contact
c. Inhalation-total
d. Water ingestion
e. Fish ingestion
f. Fruit ingestion
g. Vegetable ingestion
#5 Stack emissions
High end exposure scenario
a. Soil ingestion
b. Soil dermal contact
c. Inhalation-total
d. Water ingestion
e. Fish ingestion
f. Fruit ingestion
g. Vegetable ingestion
h. Beef ingestion
i. Milk ingestion
#6 Effluent discharge
Central exposure scenario
a. Water ingestion
b. Fish ingestion
2378-TCDD
2378-TCDD
1 * 1 (T9
3»10-10
1*1Cr16
3*10-19
i*icr16
7*icr18
1 *10~16
2*10-17
3*icr17
4MO-15
5*icr17
i*icr15
3*icr17
Tier15
3*10-16
4MO-ie
9*10-14
3*10-14
5*10-14
9*icr13
23478-PCDF 233'44'55'-
HPCB
TEQ
9»10-10 7MO'10
2*10~10 1*10~10
3MO'15
7*10-18
2MQ-15
1MO-16
2»10-15
1 *10~15
1MQ-15
8MO-14
1 «1Q-15
2*10"14
4*10-16
2MO-14
1*10-14
1 *10"14
2,1Q-12
6*10-13
3MO-14 6*10"16
1MO-12 3MO'11
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difference in 2,3,7,8-TCDD and TEQ impacts to all media mirrors the difference in stack
emissions of 2,3,7,8-TCDD and stack emissions of TEQ. As seen Table 5-3, 2,3,7,8-
TCDD emissions are 6% of TEQ emissions, and soil concentrations of 2,3,7,8-TCDD are
6% of TEQ soil concentrations. This trend in differences between 2,3,7,8-TCDD and TEQ
impacts occurs in all exposure media estimations. The highest soil concentrations at the
site of exposure resulted from erosion of contaminated soil originating at the 10-acre
contaminated site of Scenario 3. Concentrations at the sites of exposure were 0.279
A/g/kg (279 ppt) for the no-till algorithm which mixed delivered residues to a depth of 5 cm
and 0.070 (70 ppt) for the till algorithm which had a mixing depth of 20 cm. The soil at
the site of contamination 150 meters away was 1 //g/kg (1000 ppt or 1 ppb). Exposure
site soil concentrations resulting from erosion were the same for all three compounds.
This is because the same initial soil concentration was assumed at the site of
contamination, and the erosion algorithm contains only one chemical specific parameter.
This is the rate of dissipation for eroding contaminants. It was assigned a value of 0.0693
yr"1 (10-year half life) for all three example compounds.
• Vapor and Particle-Phase Air Concentrations:
One statement to make up front about vapor-phase air concentrations is that using
the descriptor "vapor-phase" does not necessarily mean that the contaminants are
expected to remain in a pure vapor state while air-borne. Residues which volatilize from
the soil are expected to initially be a vapor phase. However, it is possible that dioxin-like
compounds released into the air this way would not remain in vapor phase, but would
partly sorb to air-borne particulates. The assumption is made is this assessment that
contaminants released from the soil remain in the vapor phase for further modeling. This
assumption influences air-to-leaf transfers of vapors for estimating impacts to vegetations.
It also impacts the relative magnitudes of predicted concentrations in the vapor as
compared to the particulate phase for the soil source categories. As seen in Table 5-4, the
vapor phase concentrations of 2,3,7,8-TCDD are 1 to 2 orders of magnitude higher than
the particle phase concentrations for the soil contamination source demonstrations -
Scenarios 1, 2, and 3. In contrast, the reservoirs in the vapor and particle phases for
2,3,7,8-TCDD are comparable for the demonstration of the stack emission source
category. Scenarios 4 and 5. In that case, partitioning of 2,3,7,8-TCDD as released and
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transported is assumed to be 45% in the particle phase and 55% in the vapor phase. This
close partitioning results in comparable reservoirs at sites of exposure.
Concentrations of contaminants in the vapor phase range from 10"11 to 10~8//g/m3.
Similar and lower concentrations, in the 10~11 //g/m3 range, resulted from the volatilization
of background soil concentrations of 0.001 pg/kg of the three example compounds,
Scenarios 1 and 2. When the soil concentration of these compounds were three orders of
magnitude higher at a site 150 meters away, air concentrations at the exposure site were
about two orders of magnitude higher.
One interesting trend of note is that the vapor-phase concentrations for the central
and high end scenarios of Scenarios 1 and 2 are similar for each compound; i.e., the
2,3,7,8-TCDD concentration for Scenario 1 is the same as the 2,3,7,8-TCDD
concentration of Scenario 2 (although they are different within a Scenario for different
compounds; that will be discussed shortly). This is, in fact, the result of two inverse
trends of the solution algorithm. First, the average volatilization flux (mass/area-time) will
always be lower for the high end scenario as compared to the central scenario. This is due
to the solution algorithm assumption that residues available for volatilization originate from
deeper in the soil profile over time, so that the average flux is lower for longer periods of
volatilization. This is seen in the volatilization flux equation - Equation (4-13), Chapter 4 -
which has a time term (ED, or exposure duration) in the denominator. The high end
scenarios assume 20 years exposure duration compared to 9 years for the central
scenarios. This alone would have resulted in lower air concentrations in the high end as
compared to the central scenario. However, the dispersion of volatilized residues is a
direct function of the area over which volatilization occurs. This is expressed in terms of a
side length, parameter "a" in Equation (4-16), Chapter 4, as well as a dispersion term, Sz.
It is easy to show that increasing the area alone would have resulted in higher air
concentrations at the larger farm site of the high end scenario, 10 acres, as compared, to
the smaller residence of the central scenario, 1 acre. The two trends cancel each other
and vapor phase concentrations for a given compound are similar for both scenarios.
However, for different compounds within the same scenario, vapor phase concentrations
are different. This difference is due to chemical parameters, principally the Henry's
Constant, H. 2,3,3',4,4',5,5'-HPCB had the highest value for H, and it was 2 orders of
magnitude higher than the value for 2,3,7,8-TCDD and 3 orders of magnitude higher than
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the value for 2,3,4,7,8-PCDF. This drove the trend for air concentrations, as
2,3,3',4,4',5,5'-HPCB had the highest air concentrations, followed by 2,3,7,8-TCDD at a
concentration 1 order of magnitude lower and 2,3,4,7,8-PCDF at slightly lower than
2,3,7,8-TCDD.
Total air concentrations of 2,3,7,8-TCDD predicted to occur at exposure sites at
500 meters and 5,000 meters from a stack emission were in the 10"12 to 10~11 jug/m3
range. The air concentration estimated to result from a background soil concentration of 1
ppt (example Scenarios 1 and 2) was dominated by the vapor phase and equalled 4*10"11
//g/m3. The TEQ vapor and particle concentrations exceeded the analogous concentrations
of 2,3,7,8-TCDD by about a factor of 20. As in the soil concentration discussion above,
this difference is driven by the difference in emission rates of 2,3,7,8-TCDD and TEQs.
Even though air concentrations of 2,3,7,8-TCDD are the similar for Scenarios 4 and 5, the
stack emission source category, and Scenarios 1 and 2, the soil contamination source
category demonstrated at background soil concentrations, the soil concentrations are
much different, as noted above in the discussion on soil concentrations. Chapter 6,
Sections 6.3.3.9 and 6.3.3.11 discuss this dichotomy in performance between the soil
contamination source categories and the stack emission source categories - the dichotomy
being that while air concentrations from the stack emission demonstration and background
soils appear similar, the soil concentrations are much different.
Particulate-phase concentrations at the exposure sites of Scenarios 1 and 2 were
2 to 3 orders of magnitude lower than exposure site concentrations predicted to occur
from emissions at a contaminated site which is 150 meters away at the off-site location of
contaminated soil, Scenario 3. This was due principally to the 3 orders of magnitude
higher soil concentrations at these off-site soil contamination locations. Another trend is
that the particle-phase concentrations are the same for all three compounds within
Scenarios 1-3. This is because the algorithm to estimate particle-phase concentrations is
independent of chemical properties. The trend discussion above concerning vapor phase
concentrations resulting from volatilization and dispersion is not true for particulate phase
estimation. In this case, a steady flux is estimated which is not a function of time. The
same dispersion algorithm is used, however, so that the high end concentrations in
Scenario 2 are higher than the central concentrations in Scenario 1.
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• Drinking Water and Fish:
Concentrations of the example contaminants in water were 10~15 to 10~10 mg/L
(ppm; or equivalently 10~6 to 10~1 pg/L or ppq). Concentrations in fish ranged from 10~11
to 10~5 mg/kg, or in parts per trillion terms, which are common units used in expressing
fish concentrations in the literature, 10~5 to 1 ppt. The concentrations resulting from the
stack emissions were 4 to 5 orders of magnitude lower than the concentrations resulting
from the soil and effluent source discharge source categories. The concentrations
resulting from the effluent discharge were nearly identical to the concentrations resulting
from basinwide background soil concentrations of 1 ppt, which were used to demonstrate
the on-site soil source category. The fish concentrations resulting from the bounded area
of high soil contamination, where 10 hectares within the watershed had soil
concentrations of 1 ppb, were about an order of magnitude higher than the effluent
discharge or on-site soil sources.
The PCB concentrations were 1-2 orders of magnitude higher than the dioxin and
furan because the key bioaccumulation variables estimating fish tissue concentrations, the
Biota Sediment Accumulation Factor, BSAF, and the Biota Suspended Solids Accumulation
Factor, BSSAF (used only for the effluent discharge source category), is 2.0 for the
example PCB while it is 0.09 for the example dioxin and furan.
Concentrations of 2,3,7,8-TCDD are about an order of magnitude lower than
concentrations for TEQs. This mirrors the results for the air and soil, and reflects about an
order of magnitude higher stack emissions of TEQs than 2,3,7,8-TCDD. Also, there is no
difference between the central and high end scenarios for the stack emission source
category. The exposure sites are located at different points with respect to the stack - the
site for the central scenario is 5000 meters away from the stack, and the site for the high
end scenario is 500 meters from the stack. This impacts all exposure media estimations
except the fish and water estimates. Those two are a function of average watershed
impact to the stack emissions, not impact to the site of exposure.
• Fruit and Vegetable Concentrations:
Concentrations in these foods ranged from 10~14 to 10~7 mg/kg (ppm) expressed on
a fresh weight basis. Concentrations in below ground vegetables are found to exceed
those in above ground vegetables when the source of contamination is soil - the on-site
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and off-site examples scenarios, #1 - #3. When the source of contamination is stack
emissions, however, above ground concentrations exceed those of below ground. The
causes for this trend follow from the trend discussions on soil and air concentrations
above. First, the air concentrations for the stack emission demonstrations, 4 and 5, were
comparable to the air concentrations for the background soil scenarios, 1 and 2. This in
itself would lead to roughly similar above ground vegetation concentrations, and that in
fact is what happened. On the other hand, the soil concentrations were 3 to 4 orders of
magnitude lower for the stack emission demonstrations as compared to the background
soil demonstrations. This is the reason why above ground vegetation concentrations
exceeded below ground concentrations for the stack emission source category, while the
reverse was true for the soil contamination source category. Trends regarding vapor phase
transfers and particle depositions to vegetations are discussed more extensively in Chapter
6, Section 6.3.3.8.
As in the air and soil trends discussed above, off-site soil contamination in the
range of 1 /yg/kg (1 ppb; example Scenario #3) results in higher concentrations than on-
site background soil concentrations of 0.001 //g/kg (1 ppt; example Scenarios #1 and #2).
Another trend noted for Scenarios 1-3, where the initial soil concentrations were the same
among the three compounds, is that transfers from soil to plant are driven by chemical
parameters, particularly the octanol water partition coefficient, Kow. 2,3,3',4,4',5,5'-
HPCB had the highest Kow, with 2,3,4,7,8-PCDF and 2,3,7,8-TCDD at lower but similar
Kow. Higher Kow translates to tighter sorption to soil, and less transfer to plant, either
through root uptake or air-to-leaf transfer. This trend translated to the lowest
fruit/vegetable concentrations for 2,3,3',4,4',5,5'-HPCB. 2,3,7,8-TCDD and 2,3,4,7,8-
PCDF had similar fruit/vegetable concentrations for Scenarios 1-3.
The results for the stack emission source category indicate once again that TEQ
fruit and vegetable concentrations exceed those of 2,3,7,8-TCDD by about an order of
magnitude.
• Beef and Milk Fat Concentrations:
These concentrations ranged from 10~9 to 10~5 mg/kg, or equivalently, 0.001 to 10
ppt. These results were in terms of fat concentrations, which assumes that all the
compound bioconcentrates in the fat of beef and milk. To convert to a whole product
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basis, beef fat concentrations should be multiplied by approximately 0.18-0.22 (beef is
roughly 18-22% fat) and milk fat by approximately 0.02-0.04 (2-4% fat). Milk fat
concentrations were lower than beef fat concentrations in all cases, but within a factor of
two. This was due to assumptions concerning apportioning of total dry matter intake
between contaminated soil, contaminated pasture grass, and home-grown contaminated
feeds. Beef cattle were assumed to take in twice as much soil as lactating cattle, 4% of
their dry matter intake versus 2%, and much more leafy vegetations than lactating cattle,
48% pasture grass versus 8% pasture grass. Another observation that can be made is
similar to the observation concerning the key bioaccumulation parameters for fish, the
BSAF and the BSSAF. In those cases, it was noted that the factors for the PCB example
compound was much higher than those of either the dioxin or the furan - hence higher fish
concentrations result. In this case, however, the PCB example compound had the lowest
beef/milk biotransfer factor, BCF. The BCFs for the dioxin and furan example compound
were both 4.3 and 3.1 respectively, while the PCB BCF was 2.3. This resulted in
uniformly lower PCB beef and milk concentrations. Another noteworthy trend concerns
the comparison of off-site soil impacts (4 hectares at 1 ppb) to on-site soil impacts (a
basin-wide 1 ppt), with regard to fish and beef/milk. Specifically, there is only an order of
magnitude difference between fish concentrations in the on-site scenarios (1 & 2) versus
the off-site contamination scenario (3). Said another way, a basin-wide concentration of 1
ppt has nearly the same impact to fish as a small land area of 1 ppb. However, the same
is not true for beef/milk. The 1 ppt basin-wide soil concentration resulted in beef/milk
concentration 2 orders of magnitude lower than the 1 ppb small area of contamination
impact to beef/milk. An examination of model performance explains this pattern. The
pertinent trend is discussed further in Chapter 7, Sections 7.2.3.1, 7.2.3.2, and 7.2.4.1,
but briefly has to do with dilution of soil concentrations associated with transport
modeling. The ratio of surface water sediment concentration to on-site soil concentration
is termed the "sediment dilution ratio", and the ratio of exposure site soil concentration to
contaminated site soil concentration is called the "soil dilution ratio". The sediment
dilution ratios for example Scenarios 1 and 2 demonstrating basin-wide low concentrations
was 2.8, meaning that surface water sediments were in fact enriched compared to basin-
wide soil concentrations; the 1 ppt basin-wide soil concentrations translated to bottom
sediment concentrations of 2.8 ppt. For Scenario #3, demonstrating the impact of a
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smaller area of high soil concentration, the sediment dilution ratio was 0.016, meaning
that the 1 ppb soil concentration resulted in a bottom sediment concentration of 16 ppt.
Note the one order of magnitude difference in bottom sediments between Scenarios 1 & 2
(2.8 ppt) and 3 (16 ppt); this explains the one order of magnitude difference in fish
concentration results. However, the soil dilution ratio of Scenario 3 was 0.279, meaning
that the 1 ppb contaminated site concentration translated to 0.279 ppb concentrations to
which cattle are exposed. This compares to the 0.001 concentration to which cattle were
exposed in Scenarios 1 and 2. Note that the orders of magnitude difference here explains
the differences in estimated beef and milk concentrations between Scenarios 1 & 2 and 3.
For the stack emission high end scenario, TEQ concentrations continue to be higher
than 2,3,7,8-TCDD concentrations by about an order of magnitude.
5.6.2. Observations Concerning LADD Exposure Estimates
Much of the differences between exposure pathways and scenarios is due to
differences in exposure media estimation. Therefore, much of the above discussion is also
appropriate for trend analysis of Lifetime Average Daily Dose, LADD, estimates. What will
be noted below are unique observations. Unless otherwise noted, these observations
pertain to 2,3,7,8-TCDD:
• General:
LADDs over all example compounds ranged from 10~19 mg/kg-day to 10"8 mg/kg-
day. The highest exposures were associated with the off-site soil contamination scenario,
Scenarios #3. As discussed above, these scenarios had the highest exposure media
concentrations for all exposure media. LADDs for Scenario 3 ranged from 10~14 to 10~9
mg/kg-day. Fish and water ingestion exposures were very similar for Scenarios #1
(background soil concentrations, central exposures), #2 (background soil concentrations,
high end exposures), and #6 (effluent discharges, central exposures). Fish ingestion
LADDs for these three scenarios were in the 10"10 to 10~12 mg/kg-day range. Water
ingestion LADDs ranged from 10"15 to 10~13 mg/kg-day. However, fish and water
ingestion exposures were roughly 3 orders of magnitude lower for the stack emission
scenarios, #4 and #5, as compared to these three. Fish and water ingestion LADDs, on
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the other hand, were about 5 times higher for the off-site scenario, #3, as compared to
Scenarios 1, 2, and 6. Inhalation exposures were similar for the stack emission scenarios
(#4 and #5) and the on-site soil scenarios (#1 and #2), in the 10~15 mg/kg-day range.
LADDs for the soil related exposures - soil ingestion and soil dermal contact - were 3 to 4
orders of magnitude higher for the on-site soil scenarios as compared to the stack emission
scenarios. Soil ingestion for the on-site soil scenarios, #1 and #2, ranged from 10"13 to
10~12 mg/kg-day, and for soil dermal contact the impact was lower with LADDs ranging
from 10"14 to 10~13 mg/kg-day. Like other trends, the soil ingestion and soil dermal
contact impacts from a nearby site of high soil concentrations. Scenario 3, were the
highest at 9*10'10 mg/kg-day for soil ingestion and 7*10~11 mg/kg-day for soil dermal
contact. The stack emission scenario had lower exposures associated with beef and milk
ingestion as compared to the on-site scenarios, over an order of magnitude lower. The
range for Scenarios #2 and #5 was 10~14 to 10"10 mg/kg-day. The beef and milk ingestion
pathways for the off-site scenario, #3, were generally the highest of all example pathways
at 10~10 and 10~9 mg/kg-day. For the stack emission scenarios which evaluated 2,3,7,8-
TCDD and TEQ exposures, 2,3,7,8-TCDD exposures were about 5% of what TEQ
exposures were. This follows from the trends in exposure media concentration estimation
as discussed above.
• "Central" versus "High End":
Differences between analogous "central" and "high end" exposures for the on-site
soil source demonstration scenarios were near or less than an order of magnitude
(inhalation exposure for the central on-site scenario and the inhalation exposure for high
end on-site scenario are analogous exposures). This is because the exposure parameters
used to distinguish typical and high end exposures, the contact rates, contact fractions,
and exposure durations, themselves did not differ significantly, and these were the only
distinguishing features for the on-site soil source category. In the stack emission scenario,
placing exposed individuals either 500 or 5000 meters away from the incinerator did
significantly impact the results. In this case, the difference was closer to 2 orders of
magnitude for all exposures except water and fish exposures, which were not a function
of distance from the stack. The order of magnitude difference in distance added about an
order of magnitude difference in exposure media concentrations and hence LADD
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estimates. The high end scenarios were modeled after a rural farm and did have
exposures from home grown beef and milk food products. The central scenarios were
modeled after a non-farming rural residence, and did not have beef and milk exposure
pathways. Since beef and milk exposure pathways were noted as the highest exposures,
along with ingestion of fish (see next bullet), it would be appropriate to conclude that
farming families ingesting a portion of their home produced beef and milk are more
exposed than non-farming families without these exposures.
• Exposure pathway analyses:
It is inappropriate to compare and rank exposure pathways across all scenarios
because the source terms are different. However, relationships between different
pathways within each scenario can be discussed. Table 5-6 was constructed by summing
the LADDs for all pathways, and then determining the percent contribution by each
pathway. Before the summation, LADDs were corrected to account for absorption - all
ingestion LADDs assumed 50% absorption and inhalation LADDs assumed 75% (data on
bioavailabiiity from animal feeding studies, suggests that the absorption of 2,3,7,8-TCDD
is around 50%; 75% for inhalation reflects a general assumption of greater absorption for
this pathways; both simple assumptions made only for the purpose of this comparative
exercise). The dermal contact LADD was the only one where absorption was already
considered in its estimation: absorbed dose was estimated as 3% of dose contacting the
body. Also, this exercise assumes all pathways occur simultaneously, and so on. Table 5-
6 was generated only for the 2,3,7,8-TCDD example compound, and the rows are listed
generally from the highest to lowest percentage contribution. The following observations
are made from that table:
• In high end scenarios which assumed exposure to home grown beef, milk, and
fish. Scenarios 2, 3, and 5, exposures to these three foods dominated the results. In
Scenarios where beef and milk were not considered, but fish was considered, Scenarios 1,
4, and 6, fish exposures dominated. The general dominance of beef, fish, and milk
exposures underscores the importance of food chain exposures.
• Milk exposures were lower than beef exposures because of less milk fat
ingestion (10.5 g/day milk fat vs. 22 g/day beef fat) and lower concentrations in milk as
compared to beef (this was discussed above).
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Table 5-6. Percent contribution of the different exposure pathways within each exposure
scenario.*
Exposure Pathway
Meat Ingestion
Fish Ingestion
Soil Ingestion
Milk Ingestion
Soil Dermal
Vegetable Ingestion
Fruit Ingestion
Water Ingestion
Vapor Inhalation
Particle Inhalation
1
NA
56
36
NA
4
1
0
3
0
0
2
26
44
15
6
8
0
0
1
0
0
Scenario #
3 4
50
2
32
11
4
0
0
0
0
0
NA
27
23
NA
0
5
5
1
39
NA
5
72
1
3
23
0
0
0
0
1
NA
6
NA
95
NA
NA
NA
NA
NA
5
NA
NA
* Assumes exposed individual experiences all relevant pathways and exposures are
additive; see text for further explanation.
• Fish was the principal impacted media for the effluent discharge source
category, with fish ingestion 19 times higher than water ingestion, the only two pathways
considered for the effluent discharge category. Fish was an important route of exposure in
Scenarios #1 and #2 evaluating basin-wide low soil concentrations. It explains over half of
all exposures when beef and milk are not considered and still dominate when they are. It
is also important for the stack emission central scenario, #4. However, fish is much less
important than beef or milk for the high end stack emission scenario which had a beef and
a milk pathway, and when a small site of contamination is near a site of exposure (which
would be a farm raising a portion of the farming families beef and milk ingestions). The
dilution effect of a small site in a basin with regard to surface water impacts as compared
to a much less dilution effect of a small site near an exposure site was discussed in
Section 5.6.1 above, under the beef and milk bullet.
• Soil ingestion exposures were also noteworthy, particularly in scenarios that did
not consider beef and milk, the central on-site scenario, #1, and the central stack emission
scenario, #4. Soil ingestion was also the second highest pathway in the scenario
evaluating the impact of nearby soil contamination, #3, ranking higher than milk or fish
ingestion. Dermal exposures were non-trivial, but ranked behind the four ingestion
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pathways previously discussed, beef, milk, fish, and soil.
• Inhalation was the highest impact for the stack emission scenario when farm
animal products were not considered, in Scenario #4. Fruit and vegetable exposures were
noteworthy only in this same scenario. These trends imply that, where farm animal
products are not being produced near a stack emission source, fish and vegetative food
products still may dominate the overall exposure, but inhalation exposure can become
critical.
• Water ingestion exposures were very low in comparison to the other exposures
in these scenarios.
The LADD estimates of all example scenarios were derived assuming a limited
duration of exposure to the dioxin-like compounds, and also limited contact with exposure
media. A pattern of childhood soil ingestion was assumed to occur over a five-year period.
The central scenarios assumed a nine-year duration of exposure to the contamination, and
the high end scenarios assumed a twenty-year exposure period. The contact with
impacted media was only assumed to occur in the home environment - only a portion of an
individual's meat, milk, water, and fruit and vegetable ingestion was evaluated. This is
only one approach to scenario development; other approaches might consider the quality
of exposure media not associated with the home environment. For example, if the bulk of
an individual's ingestion of produce comes from local farms, and local farms may be
impacted by an incinerator, then perhaps 90-100% of an individual's fruit and vegetable
ingestion, rather than the 20-40% assumed in this assessment, should be considered
impacted.
Another issue to consider while interpreting these scenarios is their relation to
background exposures. Dioxin-like compounds are commonly found throughout the
environment, leading to "background" exposures even in situations where known sources
are not present. Since these scenarios are developed around defined sources, they should
be interpreted as incremental exposures beyond "background" levels. This interpretation is
clearly less satisfying for Scenarios 1 and 2 where soils within a watershed are
assumed to be contaminated at 1 ng/kg (ppt) level which, as discussed earlier in Section
5.5, may be close to a background level. In this sense. Scenarios 1 and 2 are more
representative of background exposures than incremental exposures. However, diet
fractions were applied indicating that only a portion of the food supply was contaminated.
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Also, the exposure duration was defined as less than a lifetime. For purposes of a true
background exposure analysis, it might be more appropriate to assume 100% for diet
fractions and lifetime exposures for example Scenarios 1 and 2.
An exercise was undertaken making the following changes in example Scenarios 1
and 2: 70 years exposure duration was assumed for all pathways except the childhood
pattern of soil ingestion (which remained at 5 years), and all contact fractions were set
equal to 1.00. All contact rates were unchanged for this exercise. The one contact rate
which might be changed is the rate of fish ingestion. For that pathway, the 1.2 (central)
and 4.1 (high end) g/day rates were estimated assuming a number of meals and meal sizes
that a rural individual would recreationally obtain from a nearby impacted water body. For
background impacts, it may be more appropriate to have an average ingestion rate, and
assume that the 0.6 pg/g fish concentration estimated for example Scenarios #1 and #2 is
typical of all fish ingested. That was not done for the results of this exercise that are
displayed in Table 5-7, but was done separately and will be discussed in the last bullet
below. Table 5-7 includes LADDs and the percent contribution from each pathway for the
original and the amended example Scenarios #1 and #2, and only for 2,3,7,8-TCDD.
Observations from this exercise are:
• All exposures increased except soil ingestion. Mostly the increase in LADD
were within an order of magnitude. The LADDs are still about an order of magnitude
lower than limited exposures estimated to occur from living near an area of high soil
contamination, as can be seen from comparing these lifetime results with those in Table
5-5 for example Scenario #3 (off-site source category).
• The increases to vapor phase inhalation exposures were small relative to other
increases. This occurs because average volatilization flux decreases as a function of time -
average fluxes over long periods of time are lower than average fluxes over short periods
of time (see Section 4.3.2, Chapter 4, for further explanation). This has a rippling effect
on fruit and vegetable concentrations as well as beef and milk concentrations. A trend of
decreasing volatilization flux is realistic when highly contaminated soils become depleted
over time. This was a key principal of the volatilization flux algorithm. However, this may
not be a realistic trend for ubiquitous concentrations which are likely to be replenished in
soil over time. Sensitivity analysis in Chapter 6, Section 6.3.3.1 shows that increasing
exposure duration from 20 to 70 years decreases average flux by a factor of 2. By
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Table 5-7. Exposures to low soil concentrations of 2,3,7,8-TCDD assuming lifetime
exposure durations and unlimited contact with impacted media, compared with exposures
assuming limited durations and limited contact.
Pathway
Limited Exposures
LADD, mg/kg-day Percent
Lifetime Exposures
LADD, mg/kg-day Percent
Example Scenario #1
On-site soil contamination
Concentration = 1 ng/kg (ppt)
Central exposure scenario
Soil ingestion
Soil dermal contact
Inhalation-vapor
Inhalation-particle
Water ingestion
Fish ingestion
Fruit ingestion
Vegetable ingestion
8MO-13
4*10-14
1*10-1B
2*10-17
7*10-14
1MO-12
2»10-16
2»10-14
36
4
0
0
3
56
0
1
8*1(r13
3*1Cr13
4*icr15
2»10-16
7*icr13
i*icr11
3*icr15
6*10'13
7
5
0
0
5
78
0
5
Example Scenario #2
On-site soil contamination
Concentration = 1 ng/kg
High end exposure scenario
Soil ingestion
Soil dermal contact
Inhalation-vapor
Inhalation-particle
Water ingestion
Fish ingestion
Fruit ingestion
Vegetable ingestion
Beef ingestion
Milk ingestion
3*10-12
9*10-13
3*1(r15
3*10-16
3MO'13
7*10-14
6*1(r11
1*10'12
15
8
0
0
1
44
0
0
26
6
3*icr12
3*icr12
6*10-15
1MO'15
1»10-12
SMO'11
1MO-14
6*icr13
3
6
0
0
1
35
0
0
44
11
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implication, vapor inhalation, fruit and vegetable ingestion, and beef and milk ingestion, are
all underestimated for a lifetime of exposure for example Scenario #2, by around the same
factor of 2. On the other hand, the discussion on sources in Volume II, Chapter 3, does
indicate that sources of release have been reduced over time. If so, an assumption of
ongoing replenishment to similar levels may not be warranted.
• The relative impact of soil ingestion dropped when assuming lifetime exposures.
It is interesting that direct soil impacts, a childhood pattern of soil ingestion and a lifetime
of soil dermal contact, still are noteworthy impacts, particularly when not considering beef
or milk exposures.
• Fish ingestion exposures were the predominant exposures when beef and milk
were not considered and became even more so for a lifetime of exposure in Scenario #1.
The same was not true for Scenario #2, where fish dropped in prominence when going
from a limited to a lifetime of exposures. The main reason for this trend was that only the
increase in exposure duration came into play for Scenario #2 and fish exposures - the rate
of fish ingestion was not changed for this exercise. However, both the exposure duration
and beef and milk ingestion rates increased for the lifetime exercise for Scenario #2.
• Different ingestion rates of fish were evaluated for example Scenario #2 and a
lifetime of exposure (2nd column on Table 5-7). The rate of 4.1 g/day used for the results
in Table 5-7 was increased to 6.5, 30, and 140 g/day. The 6.5 g/day was used in the
Ambient Water Quality Criteria document for 2,3,7,8-TCDD, and was described as an
average daily per capita consumption of freshwater and estuarine fish and shellfish (EPA,
1984). The 30 and 140 g/day were recommended as 50th and 90th ingestion rates in
EPA (1989) for recreational fisherman in an area where there is a large water body present
and widespread contamination is evident, and where site-specific information is
unavailable. Increasing to 6.5, 30, and 140 g/day increased the percent of impact for
Scenario #2, 70-year impacts, to 46, 79, and 95%, respectively. This does indicate that,
even when beef and milk are considered in a lifetime assessment in background settings,
ingestion of fish in such settings can be equally if not more critical.
An evaluation of total human exposure, and the relative impact of different
exposure pathways for 2,3,7,8-TCDD, was also undertaken by Travis and Hattemer-Frey
(1991). Their approach was based on modeling, using their Fugacity Food Chain model.
This model requires as input emission rates into air, soil, and water; these emission rates
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are then transformed into concentrations. Those concentrations are used to estimate food
crop concentrations, beef, milk, and fish concentrations. In the application of this model
by Travis and Hattemer-Frey (1991), emission rates were calibrated to arrive at air, soil,
and water concentrations that were supported by the literature. Resulting concentrations
in these three media were: 0.02 pg/m3 in air partitioned as 0.016 pg/m3 in particulate
form and 0.004 pg/m3 in vapor form, 0.96 pg/g in soil, and 0.003 pg/L in water. Using
the model to estimate exposure media concentrations, they then assumed contact rates as
given by Yang and Nelson (1986) to estimate human exposure. Assuming 100%
absorption, they concluded that typical human exposure is on the order of 35 pg/day
2,3,7,8-TCDD.
Their analysis will be compared to the analysis of this methodology. The example
scenario most like their's was example Scenario #2, which estimated exposures to soil
levels of 1.0 pg/g. The assumption used in the above sensitivity exercise of 100%
contact fractions rather than partial contact is also the appropriate assumption for
comparison. A key difference to keep in mind is that air and water impacts are estimated
given soil concentrations in this methodology, whereas air and water impacts are specified
with their model. This comparison is given in Table 5-8 below.
While the exposure of 6.3 pg/day estimated in Scenario 2 (assuming 100% contact
fractions) is within an order of magnitude of the 34.3 pg/day estimated by Travis and
Hattemer-Frey, there are a few critical differences in the two approaches. One was in the
contact rates. However, replacing the contact rates used by Travis and Hattemer-Frey
with the contact rates used in this methodology would not particularly change the total
exposure estimate - it would increase to 11.3 pg/day using the concentrations estimated in
this methodology. The second and more important difference is that air concentrations are
derived from soil concentrations in this methodology whereas they are input for the
Fugacity Food Chain model. Air concentrations are used for inhalation exposures, to
estimate fruit and vegetable concentrations, but most importantly, beef and milk
concentrations in a food chain model. The air concentrations used by Travis and
Hattemer-Frey, 0.02 pg/m3, is nearly three orders of magnitude higher than predicted by
the volatilization/dispersion modeling used in this methodology. Section 7.2.3.9 of
Chapter 7 examines air concentrations found in urban and rural environments. It is noted
that the compilation of air monitoring data conducted in Volume II of this assessment
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Table 5-8. Comparison of exposure pathway contributions to total daily exposure as
estimated in example Scenario #2 and in Travis and Hattemer-Frey (1991).
Quantity
Travis & Hattemer-Frey (1991)
Scenario #2
Inhalation
concentration, pg/m3
contact rate, m3/day
intake, pg/day
0.02
20
0.40
0.000027
20
0.0005
Water Ingestion
concentration, pg/L
contact rate, L/day
intake, pg/day
Soil Ingestion
concentration, pg/g
contact rate, g/day
intake, pg/day
Fruit and Vegetables
concentration, pg/g
contact rate, g/day
intake, pg/day
Milk
concentration, pg/g
contact rate, g/day
intake, pg/day
Beef
concentration, pg/g
contact rate, g/day
intake, pg/day
Fish
concentration, pg/g
contact rate, g/day
intake, pg/day
0.003
1.4
0.004
0.96
0.02
0.02
0.06
20
1.2
0.03
267
8.0
0.2
90
18.2
0.38
17.6
6.7
0.035
2.0
0.07
1.0
0.06
0.06
0.001 & 0.00001 2
192
0.032
0.0025
300
0.75
0.03
100
3.0
0.59
4.1
2.42
TOTAL INTAKE, pg/day
34.3
6.3
1 Quantities in this column were transformed from what is otherwise displayed in this document to be analogous to the
quantities given in Travis and Hattemer-Frey (1991). Specifically, the following was done: 1) all contact rates are the ones
that have been used in this document - however, all contact fractions are set at 1.00; 2) rate of child soil ingestion for this
scenario, 0.8 g/day, was normalized over a lifetime to obtain 0.06 g/day ingestion, and 3) beef and milk ingestion were
expressed in total ingestion rather than in fat ingestion; also beef and milk concentrations were expressed in whole product
rather than in terms of fat.
2 below and above ground vegetative concentrations, respectively, displayed; also, 192 g/day is the total above + below
ground consumption of fruits and vegetables; intake estimation breaks out above/below and fruit/vegetable. Finally, Travis
and Hattemer-Frey listed their concentration on a dry matter basis, while the above for Scenario #2 is fresh weight.
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indicated an average 2,3,7,8-TCDD concentration in urban air of 0.01 pg/m3, half as much
as the 0.02 pg/m3 used by Travis and Hattemer-Frey. These authors do note that their air
concentration originates from monitoring of urban air in Germany. Section 7.2.3.7,
Chapter 7, discusses the fact that had the models of this methodology predicted urban air
concentrations given low soil concentrations perhaps typical of rural environments, the
model should be questioned. On the other hand, the comparison of limited rural air
concentrations and the air concentrations predicted by a background soil concentration of
1 ppt is not favorable. In one literature article measuring concentrations in an area
described as a "remote countryside" in Sweden (Broman, et al. 1991), air concentrations
of 2,3,7,8-TCDD were measured at 0.0002 pg/m3. This remote countryside air
concentration is hypothesized to be lower than what is likely to occur in rural
environments where at least some sources of dioxin release relatively nearby are expected
to occur. The air concentration of 2,3,7,8-TCDD modeled in this assessment from
background soil concentrations of 1 ppt is nearly an order of magnitude lower than that at
0.000032 pg/m3. This suggests that the models of this assessment underestimate air
concentrations resulting from releases from soils - however, this is not a definitive
conclusion. As discussed in Volume II, the principal source of dioxin release into the
environment which affects the food chain and associated soils are emissions from
combustion sources. These sources provide a steady and ongoing input into the soil and
food chains. The true test of the soil release and dispersion algorithms of this assessment
would be to measure air concentrations of dioxin-like compounds released from soil into
residue-free air, and compare that with modeled concentrations. Such data could not be
found in the literature. Most importantly, the fact that air concentrations estimated to
result from background soil concentrations are much lower than air concentrations
surmised to occur in a rural environment does suggest that the on-site soil source category
is an inadequate framework to be estimating background exposures.
The air concentration of 2,3,7,8-TCDD of 0.02 pg/m3 was run through the food
chain algorithms of this assessment using the procedures outlined in Chapter 7, Section
7.2.3.9 of this volume, the air-to-beef food chain validation exercise. It was found that
the model predicted a whole beef concentration of 0.32 ppt (19% beef fat) and a whole
milk concentration of 0.10 ppt (4% milk fat), which is actually fairly similar to the 0.20
ppt whole beef and 0.03 ppt dairy concentrations as predicted by Travis and Hattemer-
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Frey. However, this is more by chance than deliberate, as there are substantial differences
in the specifics of the modeling approach and parameter values of this assessment and
that of Travis and Hattemer-Frey. Following is a summary of such differences in modeling
and 2,3,7,8-TCDD parameterization:
1) Travis and Hattemer-Frey assumed a vapor/particle split of 20%/80%, whereas
the models of this assessment assumed a 55%/45%. The air-to-leaf transfer factor,
defined identically for both modeling efforts, nonetheless came up with a different value.
The Bvpa of this assessment was 100,000, whereas for Travis and Hattemer-Frey, the B^
was 9883. The reason for this difference was that they assumed different values for
2,3,7,8-TCDD log octanol water partition coefficient, Kow, 6.85 versus the 6.64 of this
assessment, and Henry's Constant, H, 3.6*10"3 atm-m3/mole versus the 1.65MO'5 atm-
m3/mole of this assessment.
2) They used a "biotransfer" factor for estimating whole beef concentrations as a
function of mass ingestion of 2,3,7,8-TCDD, whereas this model used a
"bioconcentration" algorithm which only depends on the concentration in the dietary intake
and predicts the concentration in beef fat.
3) The key factors in the Travis and Hattemer-Frey approach are the mass ingestion
rates of dry matter in the diets of beef and dairy cattle, whereas the important factors for
this approach are the proportions in the dry matter categories. Travis and Hattemer-Frey
made different assumptions regarding proportions in vegetative and soil as compared to
this assessment. For the beef cattle, they assumed that 35% of their ingestion was in
unprotected forages, while 64% was in protected grains and 1% in soil. This assessment
assumed equal proportions in pasture grass - the analogy to unprotected forage - and 48%
in partially protected vegetations (hay, silage, grains), and 4% in soils. This assessment
also assumed a 50% reduction in beef concentrations due to feedlot fattening. While not
explicitly stating it, Travis and Hattemer-Frey also appeared to be modeling the feedlot
fattening diet, as the majority of beef cattle diet was in protected grains, and their article
did describe the diet as representing, "beef cattle destined for slaughter".
Although both modeling approaches appear to perform quite similarly, this simple
comparison shows how important the assignment of parameters can become in estimating
exposures. If any of the assumptions and/or parameters used by Travis and Hattemer-
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Frey used were instead used for this assessment (and vica versa), results would be
substantially different. Chapter 7 on Uncertainty critically evaluates the modeling
approaches and parameters used in this assessment. Chapter 6 contains additional
information pertaining to these models, including sensitivity analysis exercises, discussions
of model parameters, and discussions on other modeling approaches. Information in both
these Chapters should be reviewed when evaluating the validity of the approaches
demonstrated in this Chapter.
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REFERENCES FOR CHAPTER 5
Bacci, E.; Cerejeira, D.; Gaggi, C.; Chemello, G.; Calamari, D.; Vighi, M.; 1992.
Chlorinated dioxins: volatilization from soils and bioconcentration in plant leaves.
Bull. Environ. Contam. Toxic., 48:401-408.
Bacci, E.; Calamari, D.; Gaggi, C.; Vighi, M. (1990) Bioconcentration of organic chemical
vapors in plant leaves: experimental measurements and correlation. Env. Sci.
Technol. 24:885-889.
Broman, D., Naf, C.; Zebuhr, Y. (1991) Long-term high- and low-volume air sampling of
polychlorinated dibenzo-p-dioxins and dibenzofurans and polycyclic aromatic
hydrocarbons along a transect from urban to remote areas on the Swedish Baltic
Coast. Env. Sci. Technol 25:1841-1850.
Broman, D.; Naf, C.; Rolff, C.; Zebuhr, Y. (1990) Analysis of Polychlorinated Dibenzo-P-
Dioxins (PCDD) and Polychlorinated Dibenzofurans (PCDF) in Soil and Digested
Sewage Sludge from Stockholm, Sweden. Chemosphere (21): 1213-1220.
Greaser, C.S.; Fernandes, A.R.; AI-Haddad, A; Harrad, S.J.; Homer, R.B; Skett; P.W; Cox,
E.A. (1989) Survey of Background Levels of PCDDs and PCDFs in UK soils.
Chemosphere 18: 767-776.
Gillespie, W.J. (1992) Summary of data reflective of pulp and paper industry progress in
reducing the TCDD/TCDF content of effluents, pulps and waterwater treatment
sludges. Unpublished report available from the National Council of the Paper
Industry for Air and Waste Stream Improvement, Inc., 260 Madison Ave, New
York, NY 10016. August 17, 1992.
Nestrick, T.J.; Lamparski, L.L.; Frawley, N.N.; Hummel, R.A.; Kocher, C.W.; Mahle, N.H.;
McCoy, J.W.; Miller, D.L.; Peters, T.L.; Pillepic, J.L.; Smit, W.E.; Tobey, S.W.
(1986) Perspectives of a Large Scale Environmental Survey for Chlorinated Dioxins:
Overview and Soil Data. Chemosphere 15: 1453-1460.
Reed, L.W.; Hunt, G.T.; Maisel, B.E.; Hoyt, M.; Keefe, D.; Hacknew, P. (1990) Baseline
Assessment of PCDDs/PCDFs in the Vicinity of the Elk River, Minnesota Generating
Station. Chemosphere 21: 159-171.
Stenhouse, I.A.; Badsha, K.S. (1990) PCB, PCDD, and PCDF Concentrations in Soils from
the Kirk Sandall/Edenthorpe/Barnby Dun Area. Chemosphere 21: 563-573.
Travis, C.C., and H.A. Hattemer-Frey. (1991) Human exposure to dioxin. The Science of
the Total Environment 104: 97-127.
U.S. Environmental Protection Agency. (1984) Ambient water quality criteria document
for 2,3,7,8-tetrachlorodibenzo-p-dioxin. Office of Water Regulations and Standards,
Washington, D.C. EPA-440/5-84-007.
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U.S. Environmental Protection Agency. (1985) Soil Screening Survey at Four Midwestern
Sites. Region V. Environmental Services Division, Eastern District Office, Westlake,
Ohio, EPA-905/4-805-005, June 1985.
U.S. Environmental Protection Agency. (1987) National Dioxin Study. Office of Solid
Waste and Emergency Response. EPA/530-SW-87-025. August 1987.
U.S. Environmental Protection Agency. (1989) Exposure Factors Handbook. Exposure
Assessment Group. Office of Health and Environmental Assessment, Office of
Research and Development, Washington, D.C. EPA/600/8-89/043.
U.S. Environmental Protection Agency. (1990) USEPA/Paper Industry Cooperative Dioxin
Study "The 104 Mill Study" Summary Report and USEPA/Paper Industry
Cooperative Dioxin Study "The 104 Mill Study" Statistical Findings and Analyses.
Office of Water Regulations and Standards, July 13, 1990.
Yang, Y.Y., and C.R. Nelson. (1986) An estimation of daily food usage factors for
assessing radionuclide intake the U.S. population. Health Phys., 50: 245-257.
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6. USER CONSIDERATIONS
6.1. INTRODUCTION
The methodology in this document has been earlier described as screening level in
terms of theoretical sophistication, but site specific in its application. Chapter 2 described
concepts of exposure and assigned values to exposure parameters which define, for
purposes of demonstration, a central and a high end exposure pattern. Chapters 3 and 4
described algorithms for the fate, transport, and transfer of dioxin-like compounds, and
also assigned parameter values for purposes of demonstration. The methodology was
demonstrated in Chapter 5, using exposure and fate and transport parameters which had
been laid out in earlier chapters. Those who wish to use the methodology for further
analysis of incremental exposures to sources of dioxin-like compounds are now in a
position to use the same algorithms, perhaps many of the same parameter values. The
purpose of this chapter is to provide guidance on some key issues for potential users.
Section 6.2 discusses the use of the parameter values selected for the
demonstration scenarios in Chapter 5 for other applications. Section 6.3 is a sensitivity
analysis exercise on the parameters required for algorithms estimating exposure media
concentrations. Section 6.4 addresses the issue of mass balance with regard to the
source strength terms of the four source categories.
6.2. CATEGORIZATION OF METHODOLOGY PARAMETERS
Table 6-1 lists all the parameters, including names, definitions, and units, that are
required for the methodologies of this assessment except the exposure parameters.
Exposure parameters are given in Table 2-1 of Chapter 2. Table 6-1 also gives four
additional pieces of information for each parameter listed. Three are numerical values
which were used in the sensitivity analysis exercises that are described in Section 6.3.
below. The parameter values labeled "selected" were the ones used in the demonstration
of the methodologies in Chapter 5. Section 6.3. below justifies the high and low values of
parameters selected for sensitivity analysis. Other users of this methodology may wish to
view these high and low values as reasonable high and low possible values for their
applications; note however that the chemical specific parameters are those only for
2,3,7,8-TCDD. The fourth piece of information is a qualitative judgement on the part of
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Table 6-1. Parameters used to estimate exposure media concentrations for this assessment.
Parameter Name Definition
Low
Selected
High
Rating'
1 . Contaminated and Exposure Site Characteristics
A. Site of Exposure
AES
Eslp
Psoil
Bsoil
ocsl
dt
dnot
B. Site
ASC
Eslp
Psoil
ocsl
2. Soil
SLS
SLec
area of exposure site, m2
soil porosity, unitless
particle bulk density, g/cm3
soil bulk density, g/cm3
soil organic carbon fraction
tillage mixing depth, m
no-till mixing depth, soil source categories, m
of Contamination, Off-site Soil Source Category
area of off-site contamination, m2
soil porosity, unitless
particle bulk den, g/cm3
soil organic carbon fraction
and Sediment Delivery Parameters
contaminated site soil loss, kg/ha-yr
soil loss between exp. and cont. site, kg/ha-yr
4,000
0.35
2.55
1.20
0.005
0.10
0.01
4,000
0.35
2.55
0.005
2100
0
40,000
0.50
2.65
1.50
0.01
0.20
0.05
40,000
0.50
2.65
0.01
21520
2152
400,000
0.60
2.75
2.00
0.05
0.30
0.10
400,000
0.60
2.75
0.05
42000
21000
SS
SS
FOD
SS
SS
SOD
SOD
SS
SS
FOD
SS
SS
SS
(cont'd on next page)
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Table 6-1. (cont'd)
Parameter Name Definition
2. Soil and Sediment Delivery Parameters (cont'd)
SLW watershed soil loss, kg/ha-yr
ER enrichment ratio, unitless
Cw watershed contaminant cone, mg/kg
OCssed suspended sediment organic carbon fraction
OCsed bottom sediment organic carbon fraction
Aw watershed drainage area, ha
SDW watershed sediment delivery ratio, unitless
TSS total suspended sediment, mg/L
DLe distance to exposure site, m
DLW distance to water body, m
VWat: volume of water body, L/yr
3. Volatilization and Dust Suspension Parameters
ED exposure duration, yrs
V fraction of vegetative cover, onsite, unitless
V fraction of vegetative cover, offsite, unitless
Um average windspeed, m/sec
Ut threshold wind speed, onsite, m/sec
Low
2100
1
0
0.02
0.01
400
0.25
2
50
50
1.5x109
1
0.0
0.0
2.8
2.5
Selected1
6455
3
0
0.05
0.03
4,000
0.15
10
150
150
1.5x1010
20
0.5
0.0
4.0
6.5
High
21500
5
1x10'6
0.10
0.05
400,000
0.04
70
1000
1000
1.5x1012
70
0.9
0.9
6.3
11.3
Rating2
SS
SOD
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
(cont'd on next page)
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Table 6-1. (cont'd)
Parameter Name Definition
Low
Selected
1
High
Rating2
3. Volatilization and Dust Suspension Parameters (cont'd)
F(x)
ut
F(x)
FREQ
model-specific parameter, onsite
threshold wind speed, offsite, m/sec
model-specific parameter, offsite
frequency wind blows to site, unitless
0.87
2.5
0.87
0.05
1.05
8.25
0.50
0.15
0.05
11.3
0.05
0.50
SS
SS
SS
SS
4. Bioconcentration and Biotransfer Parameters
- FISH:
fish lipid fraction
- VEGETATION
FDW dry to fresh weight conversion
Vp particle deposition vel, m/yr
R annual rainfall, m/yr
Wp washout factor, unitless
R,,, retention of wet deposition, unitless
yield of grass, kg/m2 dry
grass intercept fraction, unitless
r feed cattle feed yield, kg/m2 dry
w
INT
gr
0.03
0.07
0.20
SS
0.05
1.5x105
0.3
2x103
0.00
0.05
0.13
0.25
0.15
3.2x105
1.0
5x1 04
0.30
0.15
0.35
0.63
0.30
7.0x1 05
2.0
1x106
1.00
0.35
0.64
1.30
SS
SOD
SS
SOD
SOD
SS
SS
SS
(cont'd on next page)
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Table 6-1. (cont'd)
Parameter Name Definition
Low
Selected1
High
Rating2
4. Bioconcentration and Biotransfer Parameters (cont'd)
"NTfeed
veg
"NTveg
VGbg
VGveg
VQflr
VGfeed
feed intercept fraction
vegetable yield, kg/m2 fresh
vegetable intercept fraction, unitless
below ground veg. correction factor, unitless
veg/fruit air-to-leaf correction factor, unitless
grass air-to-leaf correction factor, unitless
feed air-to-leaf correction factor, unitless
0.20
2.7
0.18
0.001
0.001
0.50
0.25
0.62
7.8
0.48
0.01
0.01
1.00
0.50
0.93
8.6
0.72
0.10
0.10
1.00
0.75
SS
SS
SS
SOD
SOD
SOD
SOD
- BEEF & MILK
BCSDF
DCSDF
BCFDF
DCFDF
BCGDF
DCGDF
BCGRA
DCGRA
BCFOD
DCFOD
Bs
beef cattle soil diet fraction, unitless
dairy cattle soil diet fraction, unitless
beef cattle feed diet fraction, unitless
dairy cattle feed diet fraction, unitless
beef cattle grass diet fraction, unitless
dairy cattle grass diet fraction, unitless
beef cattle fraction of cont. grazing land
dairy cattle fraction of cont. grazing land
beef cattle fraction of cont. feed
dairy cattle fraction of cont. feed
bioavailability of cont. on soil relative to vegetation
0.01
—
0.02
—
0.02
—
0.25
—
0.25
—
0.30
0.04
0.02
0.48
0.90
0.48
0.08
1.00
1.00
1.00
1.00
0.65
0.15
—
0.90
—
0.90
—
1.00
1.00
0.90
SOD,SS3
SOD,SS3
SS
SS
SS
SS
FOD,SS4
FOD,SS4
FOD,SS4
FOD,SS4
SOD
(cont'd on next page)
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Table 6-1. (cont'd)
Parameter Name Definition
Low
Selected
High
Rating"
5. Effluent Discharge Source Category
LD
0-e
0-u
oce
ocu
TSSe
TSSU
6. Stack
RDEPe
loading to surface water body, mg/hr
effluent flow rate, L/hr
upstream receiving water flow, L/hr
effluent organic carbon content, unitless
upstream organic carbon content, unitless
effluent total suspended solids, mg/L
upstream total suspended solids, mg/L
Emission Source Category
wet + dry deposition onto exposure site, //g/m2-yr
RDEPwat wet + dry deposition onto watershed, //g/m2-yr
RDEPSW
cva
RDEPp
dnot
wmx
fsd
wet + dry deposition onto water body, /yg/m2-yr
vapor phase air concentration at exp. site, //g/m3
deposition of particles onto water body, g/m2-yr
no-till mixing depth, stack emission source, m
average mixing depth of deposition over watershed, m
fraction of particles depositing onto water which
remain in suspension
0.00315
105
107
0.15
0.02
10
2
2.2MQ-6
2.2 MO'6
2.2MO'6
4.7MO'13
0.003
0.01
0.01
0.00
0.0315
4.1x106
4.7x1 0s
0.36
0.05
70
9.5
1.2*1Q-6
1.2*1(T6
1.2*1Q-6
7.6MQ-12
0.03
0.01
0.10
1.00
0.315
107
109
0.50
0.10
250
50
1.5 MO'7
1.5*10'7
1.5 MO'7
2.6MQ-12
3.00
0.05
0.20
1.00
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
SOD
SOD
FOD
(cont'd on following page)
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Table 6-1. (cont'd)
Parameter Name Definition
Low
Selected
1
High
Rating"
7. Contaminant Physical. Chemical, and Bioconcentration/Biotransfer Parameters
H
Da
Koc
0
Bvpa
BCF
BSAF
BSSAF
k
kw
RCF
Henry's Constant, atm-m3/mole"5
molecular diffusivity in air, cm2/s
organic carbon partition coefficient, L/kg
fraction of airborne reservoir sorbed, unitless
air-to-leaf transfer factor, unitless
beef/milk bioconcentration factor, unitless
biota sediment accumulation factor, unitless
biota suspended solids ace. factor, unitless
dissipation rate for eroding/depositing cont., yr~1
plant weathering rate constant, yr"1
root bioconcentration factor, unitless
1.65x1(T6
0.005
2.7x105
0.80
1.0x104
1.00
0.03
0.03
0.00693
51
1,600
1.65x10'5
0.047
2.69x1 06
0.45
1.00x105
4.32
0.09
0.09
0.0693
18
3,916
1.65x10-4
0.10
2.7x107
0.20
1.00x106
10.00
0.30
0.30
0.693
8.4
106,000
FOD
FOD
FOD
FOD
SOD
SOD
SOD
SOD
SOD
FOD
SOD
1 "Selected" values are those used in the demonstrations scenarios in Chapter 5, and high and low values are those used in sensitivity analysis exercises
in Section 6.3. below;
2 "Ratings" are qualitative judgements pertaining to the use of the selected values for use in other assessments - see text for more discussion
3 Note here that high and low values for dairy exposure, DCSDF for example, are not offered. This is because the bioconcentration tests were limited to
beef. This was done since general trends will be the same. The sum of all diet fractions must add to 1.00. Strictly speaking, the soil diet fractions vary
from site to site depending on the extent to which cattle are grazed, and the lushness of grazing land, etc.. However, there is not good data on soil
ingestion by cattle. It is recommended that users first evaluate cattle diet with regard to grass (i.e., pasturing) versus feed (wheat, hay, corn grain, etc.).
If they are pastured, then soil ingestion should be considered, with the fractions used here considered as reasonable defaults.
4 These fractions of grazing land and feed land as contaminated were assigned values of 1.00 which assumed that all grazing land was contaminated to
a concentration initially assumed, for the on-site soil source category, and the concentration solved for, for the off-site soil and stack emission source
categories. Similarly, all the grass and feed were impacted, meaning that all vegetative consumption by the cattle was from the farm. Users should
similarly consider all the grass and feed impacted unless some feed is imported, or grazing land is far from a point of soil contamination.
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the authors of this document as to the appropriateness of using the "selected" parameter
values for other assessments. This judgement is categorized in three ways:
1) First Order Defaults, or FOD: As defaults, these parameters are independent of site
specific characteristics and can be used for any assessment. Also, as first order defaults,
it is felt that the values selected for the demonstration scenarios carry a sufficient weight
of evidence from current literature such that these values are recommended for other
assessments. Several of the chemical specific parameters, such as the Henry's Constant,
H, and the organic carbon partition coefficient, Koc, fall into this category. The qualifier
above, "current literature", indicates that new information could lead to changes in these
values.
2) Second Order Defaults, or SOD: Like the above category, these parameters are judged
to be independent of site specific characteristics. However, unlike the above category, the
current scientific weight of evidence is judged insufficient to describe values selected for
demonstration purposes as first order defaults. SOD parameters of principal note are the
bioconcentration parameters specific to the chemicals, such the Biota Sediment
Accumulation Factor, or BSAF. This parameter translates a bottom sediment
concentration to a fish tissue concentration. The science is evolving for this parameter,
including thought on the extent to which BSAFs generated for one species at one site can
be generalized to other sites and/or species, the differences in BSAF between column and
bottom feeders, the differences between past and ongoing contamination, and so on.
Users should carefully review the justification for the SOD values selected for the
demonstration scenarios before using the same values.
3) Site Specific, or SS: These parameters should or can be assigned values based on site-
specific information. The information provided on their assignment for the demonstration
scenarios, and for selection of high and low values for sensitivity analysis testing, is useful
for determining alternate values for a specific site. A key class of SS parameters which
were not fully included in Table 6-1 above are the source strength terms - the soil
concentrations, effluent discharge rates, and stack emission rates. There are likely to be
site-specific applications of this methodology for which detailed information is unavailable.
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Often the midrange values selected for the demonstration scenarios are suitable for site
specific applications when data is unavailable.
The exposure parameters have not been categorized as have the contaminant fate
and transport/transfer parameters. Assignment of these values are critical as LADD
estimates are linearly related to parameter assignments - doubling exposure duration
assumptions double LADDs, and so on. All exposure parameters were developed based on
information and recommendations in EPA's Exposure Factors Handbook (EPA, 1989) and
Dermal Exposure Assessment: Principals and Applications (EPA, 1992). Some of the
exposure parameters of Table 2-1, Chapter 2, are appropriately described as FOD. These
include: lifetime, body weights, water ingestion rates, inhalation rates, and an exposure
duration for a childhood pattern of soil ingestion. All of the other exposure parameters are
better described as either SOD or SS. Attaining site-specific information is recommended
for them. However, this is often difficult for site specific assessments and impractical if
the procedures in this assessment are used in general assessments. In the absence of site
specific information, the following parameters can be considered SOD: adult exposure
durations of 9 years for central scenarios (whether they be modeled after "residential"
settings or not) and 20 years for high end scenarios (whether "farming" be the model for
high end exposures or not), childhood soil ingestion rates, the fruit/vegetable food
ingestion rates, the fraction of fruit/vegetable consumption that comes from a home
garden, and the fractions of time spent at home (which are applied to inhalation and water
ingestion pathways). The remaining exposure parameters pertain to the exposure
pathways evaluated as most critical to dioxin exposures. For this reason, users should
either pursue site specific information or carefully justify parameter selections in the
absence of site specific information. These include the rate of beef, milk, and fish
ingestion and the fraction of these food products which are impacted by the source. Fish
ingestion rates for the demonstration of methodologies in this assessment were 1.2 g/day
for central scenarios and 4.1 g/day for high end scenarios. These were developed using
an approach recommended in the Handbook when site specific data was unavailable.
Specifically, a meals per year of fish recreationally caught from the impacted water body
was assumed, and then this was translated to a grams per day consumed. These rates
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are both less than a national average estimate of fish consumption that was published in
an water quality criteria document for 2,3,7,8-TCDD, 6.5 g/day (EPA, 1984). The setting
for the demonstration scenarios was a rural setting which contained farm and non-farm
residences, but which did not contain a major water body for frequent recreational or
subsistence fishing purposes. Rather, a smaller size water body which allowed for more
occasional recreational fishing was assumed. In a setting where more substantial water
bodies exist which do supply fish for commercial and recreational use, fish ingestion rates
from these water bodies would be higher. The other parameters are the ingestion rates
and contact fractions for beef and milk ingestion. The ingestion rates for these food
products were 50% ingestion rates given in the Handbook. The contact fractions assigned
for the high end scenarios were developed from a USDA survey (USDA, 1966) of rural
farm households, some of which home produced. For home producers only, the percent of
their total ingestion of beef and milk which was homegrown was 44% and 40%,
respectively.
In addition to the above qualifications, the parameters of this methodology
have been categorized in terms of their role in the methodology. The following is a brief
description of three principal categories.
Category 1. Human behavior exposure parameters
These are the contact rates, contact fractions, exposure durations, lifetime and
body weights used in the following equation for lifetime average daily dose:
Lifetime Average Daily Dose (LADD) = (exposure media concentration x
contact rate x contact fraction x exposure duration ) /
(body weight x lifetime) (6-1)
Category 2. Fate, transport, and transfer parameters
These parameters are all the parameters required to estimate exposure media
concentrations, except those specifically associated with a contaminant - chemical-specific
parameters are included in Category 3 below. All fate, transport, and transfer parameters
are listed, defined, and further subcategorized in Table 6-1. Not included in the
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discussions in Section 6.3 are perhaps the most important terms in this category, and
these are critical source strength terms: the concentrations of dioxin-like compounds for
the soil source categories {onsite and offsite source categories), and the release quantities
of dioxin-like compounds into the air for the stack emission source category and into the
surface water for the effluent discharge source category. A general comment that can be
made for fate and transport parameters is that values for the demonstration scenarios
were selected to be midrange and plausible, and that this document provides information
on selecting alternate values for site-specific applications. Most of the parameters in this
category fall under the SS qualification. Subcategories within the fate and transport
category include:
- Contaminated and exposure site characteristics: These are areas, soil properties,
and depths of tillage (which are depths to which residues transported by erosion or
deposition are mixed in conditions of tillage such as agriculture or gardening, and no
tillage). Like the soil concentration term, the area of contamination is a site-specific and
critical parameter. Soil properties were assigned to be midrange and typical of agricultural
soils. Depths of mixing for tilled and untilled circumstances are not known with certainty,
and these two parameters were characterized as SOD.
- Soil and sediment delivery parameters: These include parameters associated the
erosion of contaminated soil from a site of contamination to a nearby site of exposure
and/or to a nearby surface water body. All but one of the parameters in this subcategory
are physical, site-specific parameters which should be evaluated for site specific
applications. The one parameter not of this description is the enrichment ratio, which
describes the enrichment of eroded soil with dioxin-like compounds, and was assigned a
rating of SOD. Geometric parameters include watershed drainage area, water body
volumes, and distances. Physical parameters include soil loss estimates, organic carbon
contents, water body suspended solids, and background watershed contaminant
concentrations.
- Volatilization and dust suspension parameters: These parameters are associated
with suspension, dispersion, and transport of contaminants from contaminated soils. One
parameter included in this category is the exposure duration, which appears to be
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misplaced. In fact, the exposure duration is used to determine the average vapor phase air
concentration - this is further discussed in Section 6.3 below. Parameters in this category
are site-specific and should be evaluated for specific methodology applications.
- Bioconcentration and biotransfer parameters: These include parameters
describing the biota and the media surrounding the biota which influence the transfer of
dioxin-like compounds from the media to the biota. Some of these parameters are site-
specific, although obtaining values may be difficult. Included here are annual rainfall, fish
lipid contents, a fresh to dry vegetable weight conversion factor, and yields and intercept
fractions for vegetation categories. Others are theoretical; values for these were
determined from the literature and can be used for other assessments if better information
is unavailable. Included here are atmospheric deposition velocities of particles, washout of
wind-suspended particles from the atmospheric, the retention of wet particle depositions
on vegetations, empirical correction factors for atmospheric to plant and soil to plant
transfers, and the bioavailability of soil as compared to vegetation as a vehicle of transfer
of dioxin-like compounds to cattle. These were given a rating of SOD. A third group
describes exposure of cattle to dioxin-like compounds through their diet. These include
fractions of cattle diet which are soil, pasture grass, and cattle feed, and the extent to
which these three are impacted by the source of contaminant. Sensitivity analysis below
shows how beef concentrations are impacted by changes in assumptions of how cattle are
exposed to dioxin-like compounds through their diet. Since beef and milk dietary
exposures are most critical for human exposure, the cattle exposure assumptions made for
demonstrating the methodologies of this assessment should be carefully considered before
using them for other assessments.
- Effluent discharge source category: These are three physical parameters that can
be determined on a site-specific basis, and include flow rates of the effluent and receiving
water body, organic carbon contents of suspended solids in the effluent and the receiving
water body, and suspended solids content of the effluent and the receiving water body.
- Stack emission source category: In fact, most of the parameters required to
evaluate the impact of stack emissions to a nearby site of exposure have been included in
other categories. Sensitivity analysis only focuses on parameters and issues unique to this
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category. One set of input values are contaminant wet and dry deposition rates. Three
depositions are required: one for the site of exposure, one to represent depositions on
watershed soils which drain into the water body, and one to represent direct deposition
onto the water body. These were all generated using the COMPDEP model, as described
in Chapter 3. Two other key inputs generated by the COMPDEP model are the ambient air
vapor phase and particle phase concentrations of contaminant at the site of exposure. All
such quantities are a function of that model's algorithms and parameter input
requirements, particularly the release rate from the stack. Information on the COMPDEP
model and its application is given in Chapter 3 and not discussed further in this chapter.
Users can determine air concentrations and contaminant deposition rates in other ways,
and use those in the methodologies to determine impacts and exposures. The no-till depth
of mixing at the site of exposure, dnot, is required for the off-site soil source algorithm as
well. It's selected value for the stack emission source category was 1 cm in contrast to 5
cm assumed for the off-site soil source category; hence its impact is examined twice in
Section 6.3. below. The only other unique parameters not included in other subcategories
are the average watershed mixing depth (used for determining watershed soil
concentrations, which are then used to determine impacts to water bodies) and the
fraction of particles depositing on water bodies which remain in suspension. These are
both theoretical values and can be used in other assessments lacking better information.
Category 3. Chemical properties of dioxin-like compounds
The ten chemical-specific parameters required for the algorithms of this assessment
fall under two categories, FOD and SOD. As such, they are all independent of the
specifics of the site. The parameters deemed FOD are chemical fate and transport
parameters, some of which are common and often determined in laboratory conditions.
These include the Henry's Constant, the organic carbon partition coefficient, molecular
diffusivity in air, a plant weathering rate constant for contaminated particles, and the soil
dissipation rate for eroding or depositing contaminants. The selected values for these
parameters are, in the authors' opinion, the best values derivable from current data. A
second set of chemical specific parameters are associated with
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bioconcentration/biotransfer algorithms. Some of them are determined from field data
(data on dioxin-like compounds or other compounds), and others are determined by
experimentation and with that experimentation, development of empirical relationships
between a critical transfer factor and the chemical's octanol water partition coefficient.
The authors cannot be definitive in a judgement that values given to these parameters be
considered default, hence the SOD rating. For these compounds, field/experimental data is
conflicting or there simply is a lack of appropriate data. Parameters included in this
category are a soil to below ground vegetation transfer factor, two air to plant factors: the
air-to-leaf vapor phase transfer coefficient and the plant washoff rate constant, two water
body to fish parameters: the biota to sediment accumulation factor and the related biota to
suspended solids accumulation factor, and a beef/milk bioconcentration factor.
6.3. SENSITIVITY ANALYSIS
Sensitivity analysis was undertaken in order to evaluate the impact of model results
with changes in model parameters. The following sections describe the limitations,
methodology and parameter selections, and results.
6.3.1. Limitations of the Sensitivity Analysis Exercises
The exercises were not comprehensive and/or definitive. Following are some key
limiters:
• The COMPDEP model was not evaluated in this section. Chapter 3 describes the
COMPDEP model. No sensitivity analysis runs were performed on COMPDEP model output
for this chapter. This section does evaluate the impact of different deposition rates and
modeled ambient air concentrations on exposure sites soils, surface water, and biota.
• Sensitivity to changes in exposure parameters was not evaluated. The basic
equation for evaluating lifetime average daily dose was given above as Equation (6-1).
Chapter 2 described all terms in this equation except the exposure media concentration,
which was the focus of Chapter 4. Because LADD estimates are a linear function of all
exposure parameters, sensitivity analysis was not performed on LADD exposure estimates.
The focus of this section instead is on the fate, transport, and bioconcentration/biotransfer
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algorithms used to estimate the exposure media concentration term in Equation (6-1).
• The analysis was not exhaustive in its coverage. Principal algorithms in the
fate, transport, and transfer of dioxin-like compounds were evaluated, and all parameters
required for algorithms were tested at least once. However, not all possible tests were
conducted. Before noting those, following is a list of algorithms which were tested:
- Volatilization/suspension and transport of vapor/particle phase airborne residues
from a site of soil contamination to a nearby site of exposure (using algorithms developed
for the off-site soil source category);
- Volatilization/suspension and dispersion of vapor/particle phase contaminants for
the circumstance where soil contamination is at the site of exposure (on-site soil source
category);
- Transport via erosion of contaminants at a site of soil contamination to a nearby
site of exposure to impact exposure site soils (off-site soil source category);
- Transport via erosion of contaminants at a site of soil contamination to a nearby
surface water body, to impact bottom sediments, water, and fish (off-site soil source
category);
- Transfers of contaminants from soils to below ground vegetables and from air to
above ground vegetations (on-site soil source category);
- Transfers of contaminants from soils and vegetation to beef (on-site soil source
category);
- Direct discharges of dioxin-like compounds into surface water bodies, and the
effect of surface water and effluent parameters on fish and water concentration estimation
(effluent discharge source category); and
- Particle depositions and ambient air concentrations, which result from stack
emissions, onto exposure site soils, watershed soils, surface water bodies, and biota
(stack emission source category);
The exercise was purposefully limited since several possible exercises would have
been duplicative. For example, impacts to beef and milk in the off-site soil source
category are, of course, modeled within this assessment, but specific sensitivities to beef
and milk concentration predictions with parameter changes within the off-site soil source
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category are not explicitly evaluated below. Parameters required for the beef
bioconcentration algorithm are evaluated in the context of the on-site source category, and
these are the same ones required for all three source categories which include beef
impacts (the effluent discharge source category does not include beef and milk impacts).
Further, only impacts to the beef algorithms were tested. The milk bioconcentration
algorithm was not tested because principal conclusions from the beef exercise are
generally true for the milk algorithm. Any generalizations from the on-site source category
exercises are transferable to the other two source categories.
A related limitation has to do with the cascading effect of certain parameters. For
example, a key contaminant parameter is the organic partition coefficient, Koc, which
impacts (among other concentrations) vapor phase air concentrations. Air concentrations
are used to estimate above ground vegetation concentrations, including those of grass and
cattle feed. Beef concentrations are a function of concentrations in grass and cattle feed.
What is not done for this example (and many others like it) is to evaluate the impact of
changes in Koc to beef concentrations. What is done, however, is as follows. The
sensitivity of air concentration predictions to changes in the partition coefficient are
evaluated. Then, the sensitivity to grass and cattle feed concentrations to plus and minus
one order of magnitude differences in estimated vapor phase air concentrations are
evaluated. In this way, any possible parameter change(s) which influences air
concentrations within a plus/minus order of magnitude range is evaluated for grass and
feed concentrations. Finally, beef concentration estimations are evaluated within a similar
plus/minus order of magnitude change for grass and feed concentrations. With some
examination, therefore, the effect of cascading impacts can be determined.
The impact of changing soil concentrations to estimates of exposure media
concentrations (air, water, biota) is linear and direct in all cases - i.e, increasing soil
concentrations by a factor of five increases all impacted exposure media by the same
factor of five. For this reason, soil concentrations are not displayed in the sensitivity
graphs displayed in the next section, with one exception. This was in the estimation of
beef concentrations from soil contamination. Beef concentrations are a function of
concentrations in the dry matter diet of the cattle, including soil, grass, and cattle feed.
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Therefore, if soil concentrations were to change and concentrations on the other intakes
were to not change, than beef concentrations would not be a linear and direct function of
soil concentrations. However, and in the context of this sensitivity analysis, when
changing only soil concentrations, vegetative concentrations are linearly and directly
impacted by the same order of magnitude change. Therefore, beef and milk
concentrations turn out to be linearly related to soil concentrations.
A final limitation to note is that this exercise does not evaluate the multiple effects
of changing more than one independent parameter simultaneously. Other numerical
methods, particularly Monte Carlo, can be used to evaluate the impact of simultaneous
changes to model parameters. Applications of this technique to dioxin exposure
assessments are discussed in Chapter 7 of this volume.
There are instances where parameters were evaluated as dependent and changes
were made simultaneously. One example is in three parameters which are related to the
size of a watershed (also termed the "effective drainage area" since such an area might be
smaller than a surrounding river system watershed), and which are important in
determining the impact of a bounded area of soil contamination to a nearby surface water
body. These three include the watershed size, the watershed sediment delivery ratio
(which decreases as watershed size increases), and the surface water body volume (which
increases as watershed size increases, assuming sources of water - surface runoff,
interflow, and groundwater recharge - remain the same on a per unit area basis). To test
the impact of watershed size to surface water and sediment concentrations, all three
parameters were changed simultaneously in modeling a small and a large watershed. One
set of parameters which might not be independent, but which were treated as such in the
sensitivity testing, are the chemical specific parameters. For example, a higher organic
carbon partition coefficient might be associated with a lower Henry's Constant - tighter
binding to soils means less of a tendency to volatilize. Empirical relationships between
such chemical specific parameters have not been established, and since there is
uncertainty in precise values selected for the dioxin-like compounds, chemical specific
parameters were treated as independent parameters.
• Only a high and a low value for model parameters were tested; no discussions of
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likelihood for parameter values or distributions of parameter values are included. Certainly
the identification of all model parameters and the justification for assignment of high and
low values will be helpful to others using the methodology. Assignment of parameter
values for purposes of demonstrating the methodologies in Chapter 5 should be carefully
considered when users apply this methodology for specific purposes or specific sites.
6.3.2. Methodology Description and Parameter Assignments
Four of the six example scenarios of Chapter 5 served as "baselines" in the
sensitivity analysis exercises. The single scenario for the off-site soil source category,
example scenario #3 in Chapter 5, served as the basis for testing on these algorithms: 1)
transport of vapor and particulate phase airborne contaminants from a site of
contamination to a nearby site of exposure, and 2) transport of soils via erosion to nearby
sites of exposure and to surface water bodies to impact bottom sediments, fish, and
water. The source strength for this scenario, in summary, was a 40,000 m2 (4 ha, 10 ac)
area of soil concentrations of 1 //g/kg (ppb) within a watershed of size 4,000 ha
(40,000,000 m2; 10,000 ac; 15.5 mi2) with soils otherwise at 0.0 ppb. The high end
scenario for the on-site soil source category, example scenario #2 in Chapter 5, served as
the baseline for testing on these algorithms: 1) suspension and dispersion of vapor and
particle-phase contaminants at a contaminated site, which was also the exposure site for
the on-site source category, 2) impacts of soil concentrations and other parameters to
below ground vegetations, and air concentrations and other parameters to above ground
vegetations, and 3) impacts of soil, grass, and feed concentrations, and other parameters,
to beef concentrations. The source strength for this scenario, in summary, were soil
concentrations within a 4,000 ha small farm of 1 ng/kg (ppt). The high end example
scenario for the stack emission source category, example scenario #5, served as the basis
for the testing the impact of particle depositions and ambient air concentrations on soils
and biota. The ambient air concentrations and deposition rates at the site of exposure 500
meters from the stack served as the baseline source strength terms. The single scenario
for the effluent discharge source category, example scenario #6, was used to evaluate the
impact of parameters required for that source category on fish and water concentrations.
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The source strength in that case was a discharge of 0.0315 mg/hr into a surface water
body with a harmonic mean flow rate of 4.7x108 L/hr. Assignment of that baseline
discharge was based on data from the 104 pulp and paper mill study, and then considering
reductions is discharges which have occurred in these pulp and paper mills since the 104
mill study in 1988.
The baseline chemical for all these sensitivity runs was 2,3,7,8-TCDD; i.e., all the
chemical specific parameters were those assigned to this example compound. The high
and low values for parameter testings were determined starting with the 2,3,7,8-TCDD
assignments. Care was not taken to encompass a range of possible values for all dioxin-
like compounds. However, the ranges that were tested are mostly inclusive of the dioxin-
like compounds. What will be noted and discussed below is that mostly the model
response to chemical-specific parameters is linear or nearly linear, so that model responses
to values outside the ranges tested can be evaluated easily.
All the initial parameter values required for all four source categories, and the values
selected for high and low sensitivity analysis were listed above in Table 6-1. Following are
brief discussions on the selection of these high and low values. Longer discussions on all
parameter values can be found in Chapter 4, which included justifications for all parameter
values selected for the demonstration of the methodologies in Chapter 5. Often, ranges of
possible values were discussed in Chapter 4; those ranges were the basis of high and low
parameter values selected below. Discussions in Chapter 4 are not repeated here, but are
referenced below. The summaries below are organized in the same order as the parameter
listings in Table 6-1.
• Contaminated and exposure site characteristics: These are the area and
distance parameters, and the soil characteristic parameters of the site of contamination
and the site of exposure. The "site of contamination" refers to the bounded area of high
soil concentration for the off-site source category. The "site of exposure" for these
sensitivity runs is the small farm which was the basis for the definition of the "high end"
example scenarios demonstrated in Chapter 5. The area of the site of exposure, AES, and
site of contamination, ASC, are both 40,000 m2 in the demonstration scenarios, which is
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equal to 4 ha or 10 ac. Low and high values tested were 4,000 m2 (0.4 ha, 1 ac) and
400,000 m2 (40 ha, 100 ac). The soil description parameters include soil porosity, ESLP,
particle bulk density, Psoil, soil bulk density, Bsoil, and the organic carbon fraction, OCsl.
The assignment of high/low values to these parameters were developed from Brady (1984)
and cover a reasonable range of agricultural field soils. The no-till and tillage depths, dnot
and dt, refer to the depth to which eroded soil or depositing particulates mix at the site of
exposure. The no-till depth was set at 5 cm and was varied between 1 and 20 cm, and
the tilled depth was varied between 10 and 30 cm. The no-till concentrations were used
to estimate soil concentrations for soil related exposures: soil ingestion and soil dermal
contact, and also for the beef and milk bioconcentration algorithm. The tilled
concentrations were used only to estimate the concentration in below ground vegetations.
• Soil and Sediment Delivery Parameters: Contaminated soil erodes from a site
of contamination, a 4 ha site in the demonstration scenarios, to a nearby site of exposure
and also to a nearby stream. The distance to the site of exposure from a site of
contamination, DLe, was set at 150 meters for the example scenarios, and varied between
50 and 1000 meters in this exercise. The same initial distance of 150 meters was the
distance to the nearby stream, DLW, and it was also varied between 50 and 1000 meters.
The unit amount of soil eroding off the site of contamination, SL9, was initialized at 21520
kg/ha-yr, equal to 9.6 Eng. ton/ac-yr (abbreviated t/ac-yr hereafter). Assumptions inherent
in this estimate include: midcontinent range of annual rainfall erosivity (which is also the
middle of the range of rainfall intensities of the US), midrange agricultural soil erosivity, a
gentle 2% slope, no man-made erosion protection (ditches, etc.), and bare soil conditions.
A doubling of this amount to 42,000 kg/ha-yr (19 t/ac-yr) was used as a high erosion
estimate off the site of contamination. This could reflect any number of different
assumptions, such as more erosive soil, more erosive rainfall, steeper slopes, and so on.
A low estimate of one-tenth the default value, at 2100 kg/ha-yr (1 t/ac-yr), could reflect all
the same assumptions except a dense cover of grass or weeds, which changes the bare
soil assumption leading to a "C" (cropping management factor) of 1.0 to a C of 0.1. The
erosion amount of 2152 kg/ha-yr was the initial amount assumed for a second unit erosion
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term needed in this assessment, a unit erosion typical of land area between the
contaminated and the exposure site, SLec. The critical assumption in this initialization was
that all conditions for this land area were similar to the contaminated site, except that the
ground was densely covered with grass or weeds. The value of SLec was reduced to 0
kg/ha-yr for the low, which is unrealistically low but might give a sense of how the
algorithm would perform if mixing with soil between the contaminated and exposure site
were not considered. The high value was 21,000 kg/ha-yr, which is similar to the initial
assumption for the contaminated site, could reflect similar erosion conditions between the
contaminated site and the exposure site. The third unit soil loss parameter required is one
which reflects average erosion conditions within the watershed draining into the water
body, SLW. This was initialized at 6455 kg/ha-yr (2.88 t/ac-yr) which reflects similar
erosion conditions as the contaminated site (soil erosivity, rainfall intensity, average
slopes, lack of support practices) except some erosion protection due to vegetation - C
equal to 0.3 instead of 1.0. It was reduced to 2100 kg/ha-yr, which might translate to C
equal to 0.1, and increased to 21,000, which was equal to the initial higher erosion from
the contaminated site. The range of the enrichment ratio, ER, was noted at between 1
and 5 for its application in agricultural runoff field data and model simulations (Chapter 4,
Section 4.3.1), and was given an initial value of 3 in this application. High and low values
tested were 5 and 1. An average watershed concentration of contaminant was set at 0
for the off-site demonstration scenarios, where the soil concentration of 2,3,7,8-TCDD
(and the other example compounds) was set at 1 ppb. This was selected so that the off-
site impact to surface water bodies could be demonstrated as an incremental impact. A
concentration of 2,3,7,8-TCDD of 1 ppt was, however, justified as a "background"
concentration for demonstrating the on-site source category. The value was used to
evaluate the impact of a bounded site at 1 ppb when a background concentration of 1 ppt
is also assumed to exist. Three parameters reflect watershed size. These include the
effective drainage area, Aw, the watershed sediment delivery ratio, SDW, and the volume
of the receiving water body, VOLW. These are related and should therefore be changed in
tandem. The initial watershed size of 4000 ha (15.4 mi2) was reduced to 400 ha (1.5 m2)
and increased to 400,000 ha (1540 mi2). Since the water body volume was estimated
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using a in/yr runoff times an area, it was concurrently reduced 1 order of magnitude for
the small watershed test and increased two orders of magnitude for the large watershed.
The values of SDW were estimated using Figure 4-5 (Chapter 4), which shows watershed
delivery ratios as a function of watershed area. The remaining three parameters further
described the water body, and were the total suspended solids, TSS, and the organic
carbon contents of suspended and bottom sediments, OC88ed and OC8ed. The initial value
of TSS of 10 mg/L is typical of a moving water body (stream, river) supportive of fish and
other aquatic life. It was reduced to 2 mg/L, which is typical of a stationary water body
(pond, lake, reservoir) and increased to 50 mg/L, which begins to be high for a water body
expected to be supportive of fish. The organic carbon contents were initialized at 0.05 for
OCssed and 0.03 for OCsed. The premise was that they were related - that sediments in
suspension were lighter and likely to be higher in organic carbon content than bottom
sediments. They were also changed in tandem to 0.02 (OCssed) and 0.01 (OCsed) for a
low organic carbon sensitivity test and 0.10 and 0.05 for a high organic carbon test.
• Volatilization and Dust Suspension Parameters: Distances and areas are
pertinent to estimating vapor-phase and particulate-phase air concentrations, and these
have been discussed above in the first two categories. One parameter included for
sensitivity testing in this category is the exposure duration, ED. It is included in these
exercises because the estimation of average volatilization flux over a period of time is a
function of that period of time. The derivation of the flux model assumed contamination
originates at the soil surface at time zero, and over time, originates from deeper within the
soil profile. Therefore, the flux decreases over time (because residues have to migrate
from deeper in the profile), and the average flux over a period of time will decrease as that
period of time increases. This is further discussed in Chapter 4, Section 4.3.2., and in the
original citation for the volatilization flux algorithm, Hwang, et al. (1986). The exposure
duration assumed in the high end scenarios was 20 years, this was changed to 1 and 70
years in sensitivity tests. A range of average windspeeds, Um, around the U.S. was noted
at 2.8 and 6.3 m/sec, and these two values were used around the selected value of 4.0
m/sec. The frequency with which wind blows from a site of contamination to a site of
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exposure, FREQ, was set at 0.15, which is appropriate if one assumes that wind blows in
all directions roughly equally. It was changed to 0.05 and 0.50, which might translate to
an assumption of a prevailing wind direction, either away from or towards a site of
exposure. The remaining parameters, fraction of vegetative cover, V, threshold wind
speed, Ut, and model specific function, F(x), all refer to the wind erosion algorithm which
suspends contaminated particulates into the air. Sensitivity tests were applied to this trio
for the on-site and the off-site source categories. V for the off-site scenario was initialized
at zero, implying bare ground cover; it was increased to 0.9 reflecting dense ground cover
in the single sensitivity test here. It was set at 0.5 for the on-site small farm
demonstration scenario, reflecting some bare ground conditions (in the agricultural fields,
e.g.) as well as some dense vegetation (in other grassed areas of the farm property). It
was decreased to 0 and increased to 0.9. The parameters Ut and F(x) reflect intrinsic
erodibility of the soil and were varied together. Values were selected to reflect a high and
low wind erodibility soil, following guidance in EPA (1985), the primary reference for the
wind erosion algorithm.
• Bioconcentration and Biotransfer Parameters: The only such factor for fish
concentration estimation was the fraction of fish lipid, f|jpj,j. The brief discussion on this
parameter in Chapter 4 (Section 4.3.4.1) indicated a range of around 5 to over 20%.
Considering that the lipid content of edible portions of fish are less than whole fish lipid
contents, a value less than 5%, 3% (0.03), was chosen as the low value, and also
considering edible lipid content considerations, an upper value of 20% (0.20) was
selected.
Several parameters are required for the vegetation concentration algorithm, most of
which were associated with the algorithm for dry plus wet deposition of particulates. One
parameter not associated with fate and transport was the dry to fresh weight conversion
factor, FDW. The algorithm calculates vegetative matter concentrations on a dry weight
basis, which is appropriate for the role of vegetation in the beef/milk bioconcentration
algorithm. However, ingestion rates of fruits and vegetables are on a fresh weight basis,
so dry weight concentrations have to be converted to a fresh weight basis. The initial
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value of 0.15 assumes that fruits and vegetables are 85% liquid. The high and low values
tested for this parameter were 0.30 (70% liquid) and 0.05 (95% liquid). Four parameters
are described as empirical correction factors for the air-to-leaf algorithm adopted for vapor
phase transfers to vegetation (three of the parameters), and for the soil-water-to-root
algorithm adopted for below ground vegetation. There is one each for the four principal
vegetations considered: below ground vegetables/fruits - VGbg, above ground
vegetables/fruits - VGveg, grass - VGgr, and feed - VGfeed. The concept for assignment of
values to these parameters was the same, and briefly is as follows. The principal
biotransfer factors (air-to-leaf and soil-water-to-root) were developed in laboratory
experiments where relatively thin vegetations (azalea leaves for air-to-leaf transfers and
barley roots for soil-water-to-root transfers) were used. Concurrently, there is evidence
that the strongly hydrophobic/lipophilic dioxin-like compounds are found only in outer
portions of vegetations and not inner portions of bulky vegetation; there is very little
translocation of dioxin-like compounds into and within vegetation. Therefore, the full
vegetation concentrations of thin vegetations measured in the laboratory experiments (and
the laboratory experiments did use dioxin-like compounds among the several used) would
most likely mirror only the outer surface concentrations found for dioxin-like compounds in
bulky vegetations, and not full vegetation concentrations of bulky vegetations. As such,
an empirical correction factor, based on a surface area to volume calculation, was
introduced to arrive at full vegetation concentrations for bulky vegetations. These were
principally the fruits and vegetables and the surface area to volume calculations led to
assignments of VGbg and VGveg of 0.01. These were reduced to 0.001 and increased to
0.10 in sensitivity testing. The VGgr was set at 1.00 since grass was thought to be
analogous to the azalea leaves. Although there is insufficient justification to change VGgr,
a lower value of 0.50 was chosen. The VGfeed was set at 0.5, recognizing that some
cattle feed is unprotected and thin vegetation such as hay, while others are protected
grains such as corn grain. That value was changed to 0.25 and 0.75 in sensitivity testing.
There is one required parameter for the dry deposition algorithm, and this is the particle
deposition velocity by gravity settling, Vp/ in m/yr. The initial value of 3.2x105 m/yr, from
a velocity assumption of 1 cm/sec, was given by Seinfeld (1986) as the gravitational
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settling velocity for 10//m particles. This is the appropriate size to consider since the
wind erosion algorithm was developed only for inhalable size particulates, those less than
10//m (EPA, 1985). This was reduced to 0.5 cm/sec and 2 cm/sec (transformed to m/yr)
for sensitivity testing. Three of the vegetation bioconcentration parameters are associated
with the particulate wet deposition algorithm. These are the atmospheric washout ratio,
Wp, the retention of particles on vegetation, Rw, and the annual rainfall amount, R. The
definition, derivation, and ranges for these values are described in Chapter 4, Section
4.3.4.2, and are not repeated here (the ranges are given in Table 6.1). The remaining
bioconcentration parameters are the yield and crop intercept values for the three above
ground vegetations: vegetables/fruits (Yveg, INTveg), grass (Ygr, INTgr), and cattle feed
^feed' 'NTfeed). Again, discussions of chosen, and high and low, values for these
quantities are given in Chapter 4, Section 4.3.4.2 (and displayed in Table 6.1). It is noted
that these two terms are correlated - high yields are correlated with high interception
amounts. In sensitivity testing, therefore, these parameters were changed in tandem.
The remaining bioconcentration/biotransfer parameters are for the beef/milk
bioconcentration algorithm. One of the parameters relates the bioavailability of soil relative
to the bioavailability of vegetation, where bioavailability refers to the efficiency of transfer
of a contaminant attached to a vehicle. Fries and Paustenbach (1990) developed the
bioconcentration factor, BCF, from studies where cattle were given contaminated feed.
The studies of McLachlan, et al. (1990), from which BCFs for dioxin congeners were
derived and used for this assessment, also used standard cattle feeds. This feed is
assumed to be analogous to the vegetation in cattle diet; therefore, the experimental BCFs
can be directly applied to vegetation in cattle diets. However, Fries and Paustenbach also
hypothesized that soil is less bioavailable than feed, based on some rat feeding studies,
and therefore the BCF developed from feed cannot directly be used on a soil
concentration - it should be reduced. Information in Fries and Paustenbach led to an
assignment of 0.65 for the soil bioavailability factor, B8. This was reduced to 0.30 and
increased to 0.90 in sensitivity testing. Three parameters describe the proportion of the
dry matter in the diet of beef cattle that is soil, BCSDF, grass, BCGDF, and feed, BCFDF.
The sum of these three terms, by definition, equals 1.00. Beef cattle are principally
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pastured (where incidental soil ingestion occurs), with supplemental feeds including hay,
silages, and grain, particularly in cooler climates where they are housed during the winter.
Values of 0.04 for BCSDF, 0.48 for BCGDF, and 0.48 for BCFDF were used in the
demonstration scenarios. The same three parameters are required for cattle raised for
dairy products: DCSDF for soil, DCGDF for grass, and DCFDF for feed. The dairy cattle
model was one of very little pasturing, principally being fed high-quality grain indoors while
they were in lactation: DCSDF of 0.02, BCGDF of 0.08, and DCFDF of 0.90. A final set
of four parameters describes the proportion of these dietary intakes that are contaminated.
Two are defined as the fraction of grazing land that is contaminated - BCGRA for beef
cattle and DCGRA for dairy cattle. The initial assumption of 1.00 for both these
parameters meant that all the vegetations as well as all the soil in the cattle diets was
contaminated (since soil was assumed to be ingested during grazing). The last two
similarly are defined as the proportion of feed that is contaminated - BCFOD for beef cattle
and DCFOD for dairy cattle. They were also set at 1.00, perhaps indicating that feed was
grown on-site. Rather than change these diet fraction assumptions and extent of
contamination assumptions individually or in tandem (if necessary), what is done instead is
to model four different scenarios relating to cattle exposures. Also, what is done here is
to model only the beef cattle exposure. Generally, the trends that result from changes in
the diet pattern will be analogous between the beef and dairy cattle. These four scenarios
and the parameter changes made are:
1) High and low soil ingestion Low: BCSDF = 0.01
BCGDF = 0.50
No changes to BCGRA or BCFOD; BCFDF = 0.49
diet assumptions changed to
reflect high and low soil High: BCSDF = 0.1 5
ingestion patterns BCGDF = 0.43
BCFDF = 0.42
2) Low exposure conditions BCSDF = 0.01
Grazing is under lush conditions, so BCGDF = 0.50
soil ingestion and diet pattern is BCFDF = 0.49
modeled as "low" soil ingestion above; BCFOD = 0.25
also, most feed is purchased externally
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and uncontaminated; BCFOD reduced
from 1.00 to 0.25
3) Low extent of contamination BCGRA = 0.25
Diet assumptions are unchanged from BCFOD = 0.25
initial assumptions; only it is assumed
that 25% instead of 100% of dry matter in
cattle diet is contaminated
4) High/low lifetime pasturing Low: BCSDF = 0.02
Tests for beef cattle only assuming BCGDF = 0.08
heavy lifetime pasturing, 90% grass, and BCFDF = 0.90
light lifetime pasturing, 08% grass High: BCSDF = 0.08
BCGDF = 0.90
BCFDF = 0.02
• Effluent Discharge Source Category: Section 4.6, Chapter 4, discusses briefly
how data from the 104-mill pulp and paper mill study (EPA, 1990b) were used to develop
initial parameters required for this source category in its demonstration in Chapter 5. The
use of the 104-mill data in a model evaluation exercise is expanded upon in Chapter 7,
Section 7.2.3.6. The data is also used here to assign high and low values for four of the
seven required parameters for this source category. Two have to do with flow rates: 0^
which is the effluent flow rate, and Qu which is the receiving water flow rate. The range
of Qe is from 106 to 107 L/hr, which are the low and high surrounding the 4.1x106 rate
used in the demonstration scenario in Chapter 5. The range of Qu is 107 to 109 L/hr
(excluding the top ten receiving water bodies, which were in the 1010 L/hr range and for
which model did not appear to perform adequately), and these were the low and high
around the 4.7x109 L/hr rate used in Chapter 5. Two parameters describe the suspended
solids content of the effluent, TSSe, and the suspended solids content of the receiving
water body, TSSU. TSSe ranged from 10 to 250 mg/L in the 104-mill study, so this was
the range around the 70 mg/L used as the initial value. Data from STORET used to
develop TSSU led to an average of 9.5 mg/L and a range of less than 1 to 50 mg/L; a
range of 2 (a reasonable value for a stationary water body such as a pond or lake) to 50
mg/L was tested. One required parameter was, of course, the rate of contaminant
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discharge, LD, in units of mg/hr. The assumed value was 0.0315 mg/hr, and this
decreased and increased an order of magnitude for low and high testing. The remaining
two parameters are the organic carbon contents of effluent solids, OCe, and upstream river
suspended solids, OCU. A range based on data was not available for these parameters.
OCe was assigned a value of 0.36 based on the fact that solids in effluent discharges are
primarily biosolids, and this value was one cited for surface water algae; values of 0.15
and 0.50 were tested. The value of 0.05 for OCU was the value assumed for
demonstration of other source categories, where the parameter was called OCssed. The
same range of 0.02 to 0.10 for OCssed was used for OCU.
• Stack Emission Source Category: The parameters in this category listed in
Table 6-1 are the only ones which are unique to this source category (one parameter, the
no-till mixing depth at the exposure site, dnot, is also used for the off-site soil source
category, but its assigned value was 5.0 cm for that source category, and 1.0 cm for the
stack emission source category; that is why it is listed for both source categories). As
seen, there are only a very few unique parameters. Most of these are associated with
surface water impact, and one series of tests evaluated the impact of parameter changes
to surface water concentrations and fish concentrations. These include the contaminant
deposition rates, RDEPwat and RDEP8W, which are depositions onto the watershed draining
into the surface water body and the surface water body itself (units are //g/m2-yr). The
initial values for these were those modeled to occur 500 meters from the stack. This
assignment for the stack emission demonstration scenarios, #4 and #5 in Chapter 5,
assumes that the stack is located essentially next to the water body. These depositions
rates are specific to 2,3,7,8-TCDD. Rates of 2,3,7,8-TCDD deposition at 200 meters and
at 5000 meters were used as high and low values, respectively. It should be noted that
depositions are higher at 200 meters and lower at 5000 meters as compared to 500
meters, but air concentrations are lower at 200 meters as compared to 500 meters. This
trend occurs because wet deposition is highest nearest the stack. Total depositions are
driven by these high wet deposition totals; hence total depositions at 200 meters exceed
those at 500 meters. However, dispersion modeling shows that ambient air
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concentrations of contaminants in the vapor phase (given the wind data and all other
parameters and assumptions in using the COMPDEP model for the demonstration
scenarios) are highest 500-1000 meters from the stack. For sensitivity testing,
differences in model performance as a function of distance from the stack will be
evaluated. RDEP is the deposition of particles themselves and was supplied in order to
maintain a mass balance of solid materials entering the water body. The default value of
0.03 g/m2-yr was taken from Goeden and Smith (1989) for a study on the impacts of a
resource recovery facility on a lake. They estimated a total deposition of particles to the
lake from all sources was 74.4 g/m2-yr. Assuming the stack is unlikely to contribute all
A
sources of particles to a water body, a high value was chosen as 3 g/m -yr, and a low
value was given as 0.003. The fraction of depositing particles remaining in suspension,
fsd, was initialized as 1.00 (meaning that all directly depositing particles remain in
suspension) based on an argument that the small particles emitted from the stack and
transported directly to the surface water body would settle to surface water bottoms
much more slowly than other solids entering water bodies. A low value of 0.00 was
tested (meaning that all solids directly depositing within a year settle quickly to become
bottom sediments). The average watershed mixing zone depth, dwmx, was initialized at
0.10 m (10 cm) which is midway between the 1 cm assumed for non-tilled conditions and
20 cm assumed for tilled conditions. This assumption might translate to a rural watershed
comprised equally of farmed and unfarmed land. It was reduced to 1 cm and increased to
20 cm in sensitivity testing. A second series of tests evaluated biota impacts at the site
of exposure, vegetables/fruits and beef/milk. Parameter inputs for these tests include the
ambient air concentration and depositions at the site of exposure, Cva and RDEPe, and the
no-till depth of mixing, dnot. The no-till depth of mixing was increased from 1 to 5 cm.
Concentrations and depositions of 2,3,7,8-TCDD at 200 and 5000 meters were tested.
The baseline quantities at 500 meters were varied to reflect different vapor/particle
partitioning assumptions. Currently, the assumption is that 2,3,7,8-TCDD emissions are
55% in the vapor phase and 45% in the particle phase. Linear adjustments to the
emissions in vapor and in particle form can be made to stack emissions. Concentrations
and depositions at specific locations are then adjusted in the same linear manner to reflect
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different vapor/particle partitioning assumptions. Two assumptions tested include 10%
vapor/90% particle and 90% vapor/10% particle.
• Contaminant Physical and Chemical Properties: The initial values for testing of
this category of parameters were the ones used for 2,3,7,8-TCDD. Generally, the high
and low values tested are those which may represent a range for this contaminant only,
not all dioxin-like compounds. However, several of the ranges also encompass values that
could be pertinent to other compounds. It should be remembered that this is simply a
model performance exercise and nothing else. Also, it could be argued that some of the
parameters should be changed in tandem - that there may be a relationship between
soil/water adsorption, as modeled by Koc, and bioconcentration. Such relationships were
not explored in these exercises. Notes on the parameters are as follows:
1. Henry's Constant, H - The value of 1.65x10"5 atm-m3/mole was used for
2,3,7,8-TCDD. Except for a heptachloro-PCB, Henry's Constants for the dioxin-like
compounds ranged from 10~6 to 10~4. Because of this, the initial value was reduced and
then increased an order of magnitude for this test.
2. Molecular Diffusivity in Air, Da - This parameter is needed for the volatilization
flux algorithm. Because no values were available for the dioxin-like compounds, values
were estimated based on the ratios of molecular between a dioxin-like compound of
interest and a compound for which a Da was available - in this case, diphenyl. The range
of values tested are 0.005 cm2/s as a low and 0.10 cm2/s around the initial value of
0.047 cm2/sec.
3. Organic Carbon Partition Coefficient, Koc: The Koc is perhaps the single most
influential parameter in this assessment, impacting surface water concentrations, vapor
phase air concentrations, and directly or indirectly, all biomass concentrations (fish,
vegetations, beef/milk). The literature for 2,3,7,8-TCDD shows a range of Koc under 106
(from Schroy, et al., 1985) to over 2x107 L/kg (Jackson, et al., 1986). The value selected
for 2,3,7,8-TCDD was 2.69x106 based on an empirical relationship between Koc and Kow
developed by Karickhoff, et al. (1979) (see Section 4.3.1., Chapter 4). The values tested
were one order of magnitude less (2.7x105) and one order of magnitude more (2.7x107)
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than the value initially assumed for 2,3,7,8-TCDD.
4. Air-to-Leaf Vapor Phase Transfer Factor, Bvpa: The initial value for 2,3,7,8-
TCDD was estimated as a function of a contaminant's octanol water partition coefficient,
Kow, and Henry's Constant, H (Equation 4-30, Chapter 4). This arrives at the volume-
based transfer factor, BVO|, which is then transformed to a mass based Bvpa (Equation 4-
31). The empirical formulation for Bvpa was developed in a series of experiments by Bacci,
et al. (1990, 1992). A second experiment by McCrady and Maggard (1993) demonstrated
that the Bacci experiments would overestimate the transfer of 2,3,7,8-TCDD to grass
leaves by approximately a factor of 40. An air-to-beef food chain validation exercise
described in Chapter 7, Section 7.2.3.9, describes the rationale and model results which
led to the approach taken in this assessment for assignment of Bvpa for all congeners:
determine a value of Bvo| based on Bacci's empirical algorithm, reduce it by a factor of 40
based on the McCrady and Maggard experiments, and transform it to a Bvpa considering
the plant density and liquid content of vegetation as given in McCrady and Maggard. For
2,3,7,8-TCDD, the resulting Bvpa is 1.0x105. Plus or minus an order of magnitude will be
tested as a high and low value for Bvpa.
5. Particle-Phase Fraction, 0: This fraction was used in the stack emission source
category for determining the portion of emitted contaminant that was and remained in the
particle phase from stack to exposure site. Details on the measured and theoretical
partitioning is given in Chapter 3 of this Volume. As discussed there, measured
partitioning of 2,3,7,8-TCDD in ambient showed a very small amount in the particle phase,
13%. However, speculation was that the monitoring method itself could lead to an
underestimate in the particle phase, and for that reason, a theoretical approach was used
to partition the dioxin. This led to a 0 of 0.45 for 2,3,7,8-TCDD. The stack emission
demonstration will be used to evaluate the impact of instead assuming 0.20 or 0.80 for
2,3,7,8-TCDD 0.
6. Root Bioconcentration Factor, RCF: The initial value for 2,3,7,8-TCDD was
estimated as a function of octanol water partition coefficient, Kow. Assuming a log Kow
of 6.64, RCF was solved as 3916. Different assumptions for log Kow were used to
estimate high and low values of RCF for this exercise. Examining literature Kow for the
dioxin-like compounds, no log Kow are less than 6.0 (the lowest at 6.2) and only one
value estimated to exceed log Kow equal 8.5. A high and low RCF were estimated,
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therefore, using log Kow of 6 and 8.5. This led to tested values of RCF of 1260 and
106,000.
7. Beef/milk Bioconcentration Factor, BCF: Unlike the RCF, Bvpa, and Koc (but like
the BSAF and BSSAF as noted blow), there are no empirical formulas developed for BCF as
a function of more common parameters such as Kow. The literature summary and
interpretation of 2,3,7,8-TCDD cattle feeding studies by Fries and Paustenbach (1990) led
them to assign a value of 5.0 for 2,3,7,8-TCDD. The study by McLachlin, et al. (1990)
allowed for generation of BCF values for 16 of the 17 congeners of dioxin toxicitiy
equivalency, and the results from that study are used for this assessment. The 2,3,7,8-
TCDD BCF was 4.3, which is close to the value of 5.0 promoted by Fries and Paustenbach
(1990). Their summary, duplicated as Table 4-3 in Chapter 4, showed BCF less than 1.0
for higher chlorinated dioxin-like compounds. For sensitivity testing, values of 1.0 and
10.0 were used as low and high values for BCF.
8. Biota Sediment and Biota Suspended Solids Accumulation Factors, BSAF and
BSSAF: EPA (1993) summarizes several water column based and sediment (both
suspended and bottom) based empirical parameters used to estimate fish concentrations
given a water or sediment concentration. Two of these are the BSAF and BSSAF, which
are used in this assessment. Although no data exists to determine values of the
suspended solids factor, BSSAF, EPA (1993) suggests that BSAF values could be used.
The range of BSAF values for 2,3,7,8-TCDD discussed in EPA (1993) is 0.03 to 0.30, and
this was the low and high values selected for both BSAF and BSSAF. The literature
summary on BSAF included in Chapter 4 of this assessment does include studies which
imply higher BSAF for 2,3,7,8-TCDD. One study, which focused on bottom feeders (carp,
catfish, etc.), found a BSAF for 2,3,7,8-TCDD (CDEP, 1992) of 0.86, whereas the range
of 0.03 to 0.30 focused on column feeders. A high value of 2.94 (Kjeller, et al., 1990)
was found in a lake in Sweden speculated to be impacted by an active pulp and paper mill.
This high value appears to be an outlier not found in other field data sets.
9. First-order Plant Weathering Factor, kw: This is used to simulate the
weathering of contaminated particulates which have settled on plant matter via dry and
wet deposition. Several modeling efforts have used the same kw as used in this effort;
that kw is 18.01 yr"1, which corresponds to a half-life of 14 days (see Section 4.3.4.2,
Chapter 4). Values of 51 (half-life of 5 days) and 8.4 (half-life of 60 days) yr"1 were used
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to test the impact of this parameter.
10. Dissipation Rate Constant for Eroding or Depositing Contaminants, k: Evidence
for soil degradation of the dioxin-like compounds indicates that residues even millimeters
below the soil surface degrade at a very slow rate, if at all (see Chapter 2, Volume 2 of
this assessment). This was the basis for not considering degradation of soil sources of
dioxin-like compounds in this assessment. However, when residues migrate to impact
only a thin layer of soil at a distant site, the processes of volatilization or photolysis (the
one degradation process which appears to transform dioxin-like compounds in the
environment) are likely to impact delivered residues. A rate constant of 0.0693, which
corresponds to a 10-year half-life, was used in two instances for this methodology - for
erosion of off-site soils onto exposure site soils, and for deposition of stack emissions onto
exposure site soils (see Section 4.4.1 for a further discussion on this parameter). This
value was changed to 0.693 (half-life of 1 year) and 0.00693 (half-life of 100 years) yr"1
in sensitivity testing.
6.3.3. Results
The results of the sensitivity analysis are principally described in a series of 14 bar
graphs. The Y-axis is on a log scale and shows changes in media concentration estimation
when the high and low parameter substitutions are made. The Y = 1 line is the value of
the media concentration with all baseline parameter selections; the precise value of that
media concentration is noted on each graph. Other y-axis values are arrived at as the ratio
of the pertinent media concentration estimated with the altered parameter over the
baseline concentration; a y-axis value of 0.1, for example, means that the concentration
with the parameter substitution was one-tenth the concentration under baseline
conditions. Also noted on each graph is the pertinent source strength term - for air
concentration sensitivities, soil concentrations are noted, and so on. The parameters
tested are named on the x-axis, and these names correspond to the names in Table 6.1.
The definition and baseline value of these key parameters are noted below each graph.
The high and low values tested are appropriately placed either above (when the
concentration increases with the parameter change) or below the bar graphs. These
parameters are the only ones which impact the tested media concentration. Of course,
the soil concentration also impacts the media concentration, but as noted in the previous
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section, soil concentrations have a direct and linear impact in all cases, and so are not
displayed on the figures. Observations from each figure now follow.
6.3.3.1. Estimation of off-site air concentrations in the vapor phase Results for
this test are shown in Figure 6-1. No single change resulted in estimations over an order
of magnitude different from that made with baseline parameters. The model is insensitive
to porosity and particle bulk density parameters, ES|p and Psoi). The results are also
reasonably insensitive to ranges for organic carbon content of soil, OCS|, and windspeed,
Um. For all other parameters, there appears to be roughly an order of magnitude spread
over the range of parameters tested. Increasing the exposure duration to 70 years would
decrease prediction by roughly one-half and decreasing the duration to 1 year would
roughly double concentrations. As discussed earlier in Section 6.2., the volatilization
algorithm assumes that contamination begins at the soil surface at time zero, and residues
available for volatilization originate from deeper in the profile over time. The result of this
assumption is that the flux decreases as time increases. This is the only algorithm of this
assessment where an assumption of a decreasing source strength over time is made. The
impact does not appear too critical, however, as estimated air concentrations only increase
by a factor of 3 when assuming a 1 year duration or decrease by 45% when assuming 70
years duration.
6.3.3.2. Estimation of off-site air concentrations in the particulate phase Results
from this test are shown in Figure 6-2. The y-axis in this test spans two orders of
magnitude since changes in the parameters describing the inherent wind erodibility of the
soil, Ut and F(x), results in over an order of magnitude higher and lower than concentration
estimations as compared to estimations using the selected values of Ut and F(x). The
assumption of bare soil conditions at the site of contamination led to a value of 0.0 for V,
the vegetative cover parameter. If the contaminated site had a reasonably dense
vegetative cover leading to a V of 0.9, air concentrations at the nearby site of exposure
would be about an order of magnitude less. The impact of area (ASC), distance (DLe), and
frequency (FREQ) on exposure site concentrations mirror those for vapor-phase air
concentrations. That is because these three are used in the same far-field dispersion
algorithm. Another parameter used for the far-field dispersion algorithm is windspeed, Um.
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1 n
I U
1
•§
(0
<-
1
i
I 1;
j?
_
if
t"
_
01 -
1 note: 1.7E-4
Csoil-1ppb 0.50 27E+5
0.005 2.8
I 0.60 2.70 I
1 0.35 2.55 \
™ 1 6-3
70
0.05
400,000
50
0.05
0.10
t
1.7E-6 0.005
4,000 2JE+7
WOO
Cair-
4.3E-9
ug/m3
^
.1 ! I 1 I "~"1 1 ! 1 1 1
ED QCsl Eslp Psoil Urn FREQ DLeASC H Koc Da
Parameter Name
Figure 6-1. Results of sensitivity analysis of algorithms estimating exposure
site vapor phase air concentrations resulting from off-site soil contamination.
Parameter Name
80il
Cair
ED
ocsl
PS'P
rsoil
um
FREQ
DLe
ASC
H
Koc
D,
Definition Selected
soil concentration at off-site area, ng/kg (ppb) 1.00
air concentration at exposure site, jjQ/m3 4.3x10"9
exposure duration, yrs 20
soil organic carbon fraction 0.01
soil porosity, unitless 0.50
particle bulk den, g/cm3 2.65
average windspeed, m/sec 4.0
frequency wind blows to site, unitless 0.15
distance to exposure site, m 150
area of off-site contamination, m2 40,000
Henry's Constant, atm-m3/mole 1.65x10"5
organic carbon partition coefficient, L/kg 2.69x106
molecular diffusivity in air, cm2/s 0.047
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100:
>act with high/low parameter substf
o _,
— _ o
0.01 -
note:
Csoll - 1 ppb
6.3
1 2.8
0.9
high emdibilHy
400,000 ^ 0-5°
4,000 1 °'°5
1,000
low erodibility
V Urn Ut,F(x) ASC DLe FREQ
Parameter Name
Cair-
2.2E-10
ug/m3
^
Figure 6-2. Results of sensitivity analysis of algorithms estimating exposure
site particle phase air concentration resulting from off-site soil contamination.
Parameter Name
Csoil
Cair
V
F(x)
ASC
DLe
FREQ
Definition Selected
contaminated site soil concentration, //g/kg (ppb) 1.00
exposure site air concentration, //g/m3 2.2x10~10
fraction of vegetative cover, offsite, unitless 0.0
average windspeed, m/sec 4.0
threshold wind speed, offsite, m/sec 8.25
model-specific parameter, offsite 0.50
area of off-site contamination, m2 40,000
distance to exposure site, m 150
frequency wind blows to site, unitless 0.15
However, interestingly, the impact of that parameter is reversed between the vapor and
particulate phase algorithms. For the particulate phase, the windspeed has more of an
impact in increasing wind erosion and hence the reservoir of airborne contaminant -
increasing windspeed increases air concentrations. For the vapor phase, windspeed does
not playa role in estimating volatilization flux, but only a role in the far-field dispersion
model. In that role, increasing wind speed increases dispersion and decreases
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concentrations. Noteworthy for the particle phase algorithm is that estimated
concentrations are independent of any chemical-specific parameters; wind erosion
suspending the particles is only a function of climate, ground cover, and soil erodibility.
Also noteworthy is that the baseline air concentration of contaminants on particles is over
an order of magnitude lower than the baseline air concentration of contaminants in the
vapor phase. Besides having implications for particle phase and vapor phase inhalation
exposures, this difference also has implications for impacts to vegetation concentrations
and subsequently to beef and milk concentrations.
6.3.3.3. Estimation of soil erosion impacts to nearby sites of exposure Results
from this test are shown in Figure 6-3. This model shows little sensitivity to two
parameters, the bulk density of soil at the site of exposure, Bsoi|, and the amount of
"clean" soil (that which is between the contaminated and exposure site) which erodes
onto the exposure site, SLec, along with the contaminated soil. These will not be
discussed further. In contrast to SLec, the model has a direct linear impact with the
amount of soil eroding from the contaminated site, SLS. Decreasing that amount by a
factor of 10 decreases exposure site soil concentrations by the same amount, and
doubling contaminated site erosion also doubles exposure site soil concentrations.
The model appears to show insensitivity to the distance between the exposure and
contaminated site, DLe. However, this result should be viewed cautiously. The sediment
delivery ratio equation, described in Chapter 4, Section 4.3.1., was developed to estimate
sediment loads from construction sites to nearby surface water bodies, and from distances
up to 250 m. Its application to distances beyond that are questionable, and applications
from one land area to another land area rather than from one land area to surface water,
should also be questioned. At the model baseline distance of 1 50 m, the SDS (sediment
delivery ratio) is 0.26. At 1000 m, it is 0.17, which is a marginal dropoff for what
appears to be a significant increase in distance. The distance becomes increasingly
important when there are obstructions between the contaminated and the exposure site
such as ditches, roads, and so on. When using this methodology, one should consider not
relying on the sediment delivery ratio equation for: 1) transport of soils beyond 250
meters, 2) when the exposure site is upgradient from the site of contamination (in its
development for construction sites, the assumption that a water body is downgradient
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I ":
1
s
g
t
1 :
Ł
-
E
j-i 4
Cso//-r.O7pp6
4,000 °°1 O'00693
400,000 42,000 5
1.20 50
1 I
2.00 1
aro ?«^
400,000 4,000
o
21,000
1
2,100 0.693
f
Cos-
0.279 ppb
,
^^^
>4ŁS Sso// dnof Di6 >4SC Sis SZ.ec ER k
Parameter Name
Figure 6-3. Results of sensitivity analysis of algorithms estimating exposure
site soil concentrations resulting from erosion from off-site soil
contamination.
Parameter Name
AES
Bsoil
dnot
DLe
ASC
SLe
ER
k
Definition Selected
contaminated site soil concentration, ng/kg (ppb) 1.00
exposure site soil concentration, ng/kg (ppb) 0.279
area of exposure site, m2 40,000
soil bulk density, g/cm3 1.50
no-till mixing depth, m 0.05
distance to exposure site, m 150
area of off-site contamination, m2 40,000
contaminated site soil loss, kg/ha-yr 21520
soil loss between exp. and cont. site, kg/ha-yr 2152
enrichment ratio, unitless 3
dissipation rate for eroding/depositing cont., yr'1 0.0693
from the site seems reasonable), and 3) when there are obvious land features which would
retard erosion. At the model's baseline value of 150 meters to a nearby site of exposure
and to a nearby water body, use of the sediment delivery equation is expected to yield
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reasonable results.
One noteworthy trend from Figure 6-3 is that exposure site soil concentrations are
not a direct linear function of areas of the exposure and contaminated sites, which they
shouldn't be. It is assumed in the erosion algorithm that contaminated soil delivered to the
exposure site mixes evenly throughout the exposure site to a depth defined by the tillage
depth. The algorithm corrected the amount of contaminated soil delivered to an exposure
site when the exposure site was smaller than the contaminated site; it reduced the amount
of contaminated soil delivered according to an appropriate size ratio. The baseline scenario
had the contaminated and exposure site the same size at 40,000 m2. For this situation, all
contaminated eroded soil from the site reaches and mixes with soil at the exposure site.
When the contaminated site increased an order of magnitude to 400,000 m2, the soil
concentration at the exposure site only doubled; it did not increase by an order of
magnitude. It is unreasonable to assume that all the eroded soil would crowd into the
smaller exposure site. When the contaminated site decreased an order of magnitude to
4,000 m2, the exposure site soil concentration likewise decreased by an order of
magnitude. In this case, like the case when the contaminated and exposure site were of
the same size, all the contaminated soil eroding in the direction of the exposure site mixes
into exposure site soil, so the resulting average soil concentration at the exposure site is
linearly related to the concentration at the contaminated site. A similar trend is noted with
changes in the exposure site area term.
The impact to changes in depth of tillage is nearly, but not quite, linear. Decreasing
the no till depth of mixing, dnot, from 0.05 m to 0.01 m increased soil concentrations by a
factor of 3.5 roughly, while increasing dnot to 0.10 decreased concentrations by 45%. A
similar, nearly linear, impact is noted with the changes tested for tillage depth, dt. For
figure clarity, these results were left off Figure 6-3, but decreasing the depth from an
initial 0.20 m to 0.10 m increased concentrations by just under a factor 2, and decreasing
it to 0.30 m decreased concentrations by just under 33%.
The model has a linear impact with the enrichment ratio, ER. Its baseline value is
3.0; decreasing it to 1.0 decreases exposure site soil concentrations by 67%, and so on.
The most interesting result from this sensitivity analysis exercise, however, was the test
with the dissipation rate constant for eroding contaminants, k. This parameter was
introduced in the equation since it was hypothesized that dioxin-like contaminants at and
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near the surface might be subject to dissipation processes such as photolysis or
volatilization. Dioxin-like contaminants would reside near the soil surface for eroding or
depositing (the stack emission source category) contaminants. This contrasts the
assumption that soil concentrations at a site of soil contamination are steady over time -
no dissipation or degradation constant is applied. The baseline value for k is 0.0693 yr"1,
which is equivalent to a ten-year half-life. With that and all other baseline parameters,
exposure site non-tilled soil concentrations are 0.279 ppb, or 28% of the contaminated
site concentration of 1.00 ppb. When the dissipation half-life is increased to 100 years,
essentially no dissipation, the exposure site soil concentration increases to over 1.00 ppb,
or over that of the contaminated site. This is an improbable, if not impossible, result. Of
all the parameters which have an incorrect value to have led to this result, the most likely
one is the enrichment ratio. This multiplier increases the concentration on eroded soil from
that which was on the site. It has a direct linear impact on exposure site soils - increasing
it from its baseline of 3.0 to 5.0 increased exposure site soils by 67%. It is reasonable to
assume that soil which erodes from a site is finer and more rich in organic matter in
comparison to in-situ soils on the average, and that the eroded soils are "enriched" in
comparison to in-situ soils for organic contaminants whose binding to soils is a function of
organic carbon content of soils. This concept of enrichment has been used and
demonstrated with field data, although not with field data of dioxin-like compounds. This
exercise may have demonstrated that the assumed enrichment ratio of 3.00 is too high.
Further, it may be reasonable to conclude that since dioxin-like compounds are so tightly
sorbed to soils in general that the enrichment effect is not as pronounced as a 3.00
enrichment ratio implies.
A final issue evaluated for the soil erosion algorithm is the assumption of a steady
state. As discussed in Section 5.4.1, Chapter 5, the solution did have a time term, which
at time t = 0, erosion from a contaminated to an exposure site begins. Assuming long
periods of time results in this exponential term approaching 1.0. The term was, therefore,
dropped, to arrive at the steady state algorithm for soil erosion impacts. To test the
impact of this assumption, tests were run including the exponential term with t equal to 1,
2, 3, 4, 5, 10, and 15 yrs. Expressing results in terms of the percent of steady state
concentration reached at the end of each year, the results for these time intervals are 28,
49, 63, 74, 81, 96, and 99%, respectively. As seen, 81% of steady state is reached
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after 5 years, and 96% is reached after 10 years. Therefore, if off-site contamination has
existed for 5-10 years or more, estimates of exposure site concentration (and related
exposures) with a steady state assumption should be reasonable. However, if an
assessment is to be done for a site to be newly impacted, such as a planned landfill, than
the steady state approach would lead to some overprediction of concentrations and
exposures. For the first five years, concentrations would average about 60% of steady
state, and for the first 10 years, concentrations would average about 75% of steady state.
This would be of most concern for a childhood pattern of soil ingestion, which would be
40% lower for the first five years of a landfill operation as compared to a steady state
assumption. Otherwise, it is seen that the steady state assumption does not greatly
impact exposure site concentrations.
6.3.3.4. Estimation of soil erosion impacts to nearby surface water bodies
Results from this test are shown in Figure 6-4. One immediate point to make about this
bar graph is that the same magnitude and direction of change is noted for both water
concentrations and bottom sediment concentrations. This is actually true for all but two
of the parameters in Figure 6-4. These two are the organic carbon content parameters,
OCssed and OCsed, and the organic carbon partition coefficient. First, the direction of the
change is not the same. Increasing the sorption of dioxin-like compounds onto sediments
increases the concentration on sediments (of course), but decreases the concentration in
water. For the "low organics" test, water concentration increases by a factor of 2.7
rather than slightly decreases as in Figure 6-4, which for this case, displays only the
impact to bottom sediments. For the "high organics" test, water concentrations decrease
to 0.60 of what they were in baseline conditions. The high Koc decreases water
concentrations to 0.1, and the low Koc increases water concentrations 7 times. Both
these trends are distinctly different than the sediment trends; they were left out of the
graph in order not to crowd the graph (or require another one be drafted), and also
because water concentrations in the sub-ppq range are of minimal concern for exposure.
The comments in the above section concerning the enrichment ratio are pertinent
for this algorithm. The comments in the above section concerning the sediment delivery
ratio equation, which for this algorithm is used to determine a value for SDW, also pertains
to this algorithm. However, if a site is near a surface water body, it seems that the origins
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1 nn
I UU -
I .»:
M :
* 1 E
5 :
o>
2
? 0.1 =
t3 :
Q.
E
On 1
note: Csoil- 1 ppb
400,000 smaii
watershed
50
1000
4,000
42.
21,500 Csed
000 ^ 5 ^
II • 1 • • '
m I | •
0 low 1 so
organlcst 2.7E(5)
2100 1
2100
large
watershed
•0.016 ppb
•0.2ppq
. \J | 1 1 1 1 1 | T 1
DLw ASC AW SLs SLw Cw OCsed ER Koc TSS
SDw
Vwat
OCssed
Parameter Name
Figure 6-4. Results of sensitivity analysis of algorithms estimating surface
water and bottom sediment concentrations resulting from a site of soil
contamination.
Parameter Name
Definition
Selected
ASC
"w
SDW
Vwat
SLS
SLW
cw
ocsed
ocssed
ER
Koc
TSS
concentration in off-site area, //g/kg (ppb) 1.00
concentration in bottom sediment, ^9/kg (ppb) 0.016
concentration in water, pg/L (ppq) 0.2
distance to water body, m 150
area of off-site contamination, m2 40,000
watershed drainage area, ha 4,000
watershed sediment delivery ratio, unitless 0.15
volume of water body, L/yr 1.5x1010
contaminated site soil loss, kg/ha-yr 21,520
watershed soil loss, kg/ha-yr 6,455
watershed contaminant cone, mg/kg 0
bottom sediment organic carbon fraction 0.03
suspended sediment organic carbon fraction 0.05
enrichment ratio, unitless 3
organic carbon partition coefficient, L/kg 2.69x106
total suspended sediment, mg/L 10
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of the sediment delivery ratio equation - developed from data on construction sites near
surface water bodies - are more appropriate. The key source strength terms tested, the
area of contamination, ASC, and the soil loss rate from the site of contamination, SLg,
both have a direct linear impact on the both sediment and surface water concentrations.
The other soil loss term, the erosion rate for the watershed, SLW, also has a direct linear
impact. What also appears to be critical in this algorithm is the size of the watershed.
The reduction of impacts seen with the "large" watershed necessitated a y-axis spanning
two orders of magnitude.
The algorithm seemed fairly insensitive to the remaining four parameters tested.
The average watershed concentration, initialized at 0.0 in order to just show the
incremental impact from the contaminated site, was increased to 1 ppt. This was also the
concentration used to demonstrate the on-site source category, and was identified as a
possible background concentration of dioxin-like compounds. If this is a reasonable
selection for a background soil concentration, it is seen in Figure 6-4 that background soils
have a marginal impact on a water body which is impacted from a site of elevated soil
concentrations. The impact of the organic carbon partition coefficient, Koc, on bottom
sediments appears small despite the fact that the Koc range spans two orders of
magnitude. This is an indication that it is so high for the dioxin-like compounds, that (at
least in the algorithm of this assessment), its assignment is not critical for sediment
concentration estimations. The same lack of impact appears to be the case for the organic
carbon content of water body sediments, and the level of suspended solids in the water
column.
6.3.3.5. Estimation of fish tissue concentrations Fish tissue concentrations for
three of the four source categories of this assessment are a direct function of bottom
sediment concentrations; the one source category where this is not true is the effluent
discharge source category, where fish tissue concentrations are a function of suspended
sediment concentrations. As laid out in Section 4.3.4.1, Chapter 4, whole fish tissue
concentrations are estimated as: (Csed/OCsed) * BSAF * f(lipid). Therefore, any
parameters which impact bottom sediments for the three pertinent source categories
impact fish tissue concentrations. All the sensitivities to bottom sediment concentrations
displayed in Figure 6-4 and discussed in the above section follow through with fish tissue
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concentrations. In Figure 6-5 and from the formulation above, it is seen that fish tissue
concentrations are a direct linear function of bottom sediment concentrations. There is the
same linear relationship between whole fish tissue concentrations and the other three
parameters displayed on Figure 6-5. The linear relationship is direct for Coc, BSAF, and
f(lipid), and inverse for OCsed. It is noted that the concentration on bottom sediments,
Csed, is impacted by the value assigned to OCsed. However, as described in the previous
section, the impact to Csed with changes to OCsed is marginal and in the same direction.
For example, reducing OCsed from its baseline of 0.03 to 0.01, reduces Csed by a small
amount. The impact to fish tissue from changes in OCsed is more pronounced and
essentially in an inverse linear manner, as shown by the formulation above and in
Figure 6-5.
6.3.3.6. Estimation of on-slte air concentrations in the vapor phase The impacts
of parameter changes for this algorithm are shown in Figure 6-6. These impacts are very
similar to those for the algorithm estimating exposure site concentrations from an off-site
area of soil contamination, which is shown in Figure 6-1. The principal difference in the
algorithms is that the on-site algorithm has a near-field dispersion algorithm, whereas the
off-site algorithm has a far-field dispersion algorithm. For all those parameters which only
impact the volatilization flux only, the impacts are the same in the on-site and off-site
categories. These include the exposure duration, ED, the organic carbon content, OCS|,
soil porosity, ES|p, particle bulk density, PsoM, and the three chemical-specific parameters,
Henry's Constant, H, organic carbon partition coefficient, Koc, and molecular diffusivity,
Da. The impact of area is interestingly different in the onsite as compared to the off-site
algorithm. For the off-site algorithms, the area term, ASC, impacts the source strength,
with an order of magnitude increase in ASC increasing exposure site air concentrations by
a little over 2 times (>200%). For the on-site algorithms, the area term, AES, impacts the
dispersion algorithm, and the same order of magnitude increase in area only increases
concentrations by around 30%.
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10;
3
1
(5
o.
1
1
0.1 -
10X note: Csoll - / ppb
Csed - 0.01$ ppb
0.
30 0.20 0.01
0.
Cfish
/
05
0.03
0.03
0.1 X
-0.003 ppb
Csed BSAF ffiipid) OCsed
Parameter Name
Figure 6-5. Results of sensitivity analysis of algorithms estimating fish tissue
concentrations given bottom sediment concentrations.
Parameter Name
BSAF
ocsed
fiipid
Definition Selected
soil concentration at off-site area, ng/kg (ppb) 1.00
bottom sediment concentration, jug/kg (ppt) 0.016
whole fish concentration, ng/kg (ppb) 0.003
biota sediment accumulation factor, unitless 0.09
bottom sediment organic carbon fraction 0.03
fish lipid fraction 0.07
6.3.3.7. Estimation of on-site air concentrations in the paniculate phase The
impacts of parameter changes to this algorithm are shown in Figure 6-7. Like the
similarity between on-site and off-site impacts for concentrations in the vapor phase, these
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1 0 -
1
i :
1 ':
a
Ł
3
E
0.1 -
1 note: Csoil -1 ppt
1.7E-4 2.7E+5
400,000 0.005 2.8
II 0.60 2.75 |
I • • 1
i «
4.000 1 63
70 0.05
0.10
1.
1.7E-6 2.7E+7 0.005
ED AES OCsl Eslp Psoil Urn H Koc Da
Cair-
4.2E-11
ug/m3
/
Parameter Name
Figure 6-6. Results of sensitivity analysis of algorithms estimating on-site
vapor phase air concentrations from on-site soil contamination.
Parameter Name
C,ir
ED
AES
OC8,
E.!P
P«oil
um
H
Koc
D.
Definition Selected
exposure site soil concentration, ng/kg (ppt) 1.00
exposure site air concentration, //g/m3 4.2x10"11
exposure duration, yrs 20
area of exposure site, m2 40,000
soil organic carbon fraction 0.01
soil porosity, unitless 0.50
particle bulk den, g/cm3 2.65
average windspeed, m/sec 4.0
Henry's Constant, atm-m3/mole 1.65x10~5
organic carbon partition coefficient, L/kg 2.69x106
molecular diffusivity in air, cm2/s 0.047
impacts are also similar to those for the algorithm estimating exposure site particle phase
concentrations from an off-site area of soil contamination, which is shown in Figure 6-2.
And also similar is the difference in the role of the area terms for the off-site versus the
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1 nn
I UU -
1 '"
1
3 E
1 1 =
5
s
| 0.1 =
I :
a.
On 1 -
note:
Csoil - 1 ppt high erodlbillty
6.3
1 1
1 I
1 2.8
1
0.9
low erodib/llty
V Urn
400,000
IX
4,000
Ut, F(x) AES
Cair-
4.7E-12
ug/m3
/
. U I i i ~ i
Parameter Name
Figure 6-7. Results of sensitivity analysis of algorithms estimating on-site
particle phase air concentrations from on-site soil contamination.
Parameter Name
CSOH
v"'r
utm
F(x)
AES
Definition Selected
exposure site soil concentration, ng/kg (ppt) 1.00
exposure site air concentration, //g/m3 4.7x10~12
fraction of vegetative cover, onsite, unitless 0.5
average windspeed, m/sec 4.0
threshold wind speed, onsite, m/sec 6.5
model-specific parameter, offsite 1.05
area of exposure site, m2 40,000
on-site particulate algorithms; the area term in the off-site algorithms impacts the source
strength and in the on-site algorithm, it impacts the dispersion.
6.3.3.8. Vapor-phase transfers and particle phase depositions to above ground
vegetations Concentrations in above ground vegetations are a function of vapor-phase
transfers and particle phase depositions. Vapor and particle reservoirs originate from
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contaminated soils as volatilization and wind erosion, respectively. Atmospheric dispersion
and deposition modeling delivers concentrations and depositions, respectively, from a
stack to a site of exposure. This section focuses on the sensitivities of the transfer
algorithms for the contaminated soils source categories. The same general trends would
occur for the off-site soil and the stack emission source categories. The principal
difference is in the relative proportions of the contaminant which are in the vapor and
particle phases. As discussed below, more contaminant is delivered via particle
depositions for the stack emission source category as compared to the soil contamination
source categories.
Vapor transfers and particle depositions are evaluated in Figures 6-8 and 6-9.
Three vegetations are modeled for this assessment, including vegetables/fruit, grass, and
cattle fodder. The latter two are for the beef/milk bioconcentration algorithm, the first for
human exposure via consumption of unprotected fruits or vegetables.
For vapor-phase impacts shown in Figure 6-8, it would appear that changes to total
vegetation concentrations are critically a function of parameters specific to the vapor
transfer algorithm. There is between one and two orders of magnitude range of plant
concentrations predicted over the range of the vapor phase transfer coefficient, B ,
tested. This parameter is uncertain as well as very influential in this methodology. Also
influential and uncertain is the empirical parameter introduced to model the difference
between the leaves of the experiment for which Bvpa was developed and the bulky
vegetation to which the Bvpa is applied, the VG parameters (VGveg, VGgr, and VGfod). The
need for such a correction factor is justified given the evidence that dioxin-like compounds
to do not translocate into vegetations. The leaf concentrations in the experiments for
which Bvpa was derived are likely to be analogous only to the outer layer concentrations in
bulky vegetations, not the whole plant (or whole fruit/vegetable) concentrations. This
empirical parameter was set to 0.01 for bulky fruits/vegetables, but was set at 1.00 for
grass, under the assumption that grass is similar to leaves, and 0.50 for cattle fodder,
which is assumed to contain some bulky (grains) and leafy (hay) vegetations. In any case,
the impact to vegetation concentrations with changes to the VG parameters is significant
and displayed on Figure 6-8.
A dry weight to fresh weight conversion factor, FDW, is required for estimating
above ground concentrations of vegetable/fruits. This is because the algorithms estimate
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10;
1
i
| ';
i ;
0.1 -
WX 1E6 WX 1E6
10X 1E6
1U* 0.10
0.
1E
1X
4 0.0
0.30
01
0.05
Vegetables/Fruit
1
1.0
\
0.50
E4
°-1X Grass
1
Cveg~
Cgr-<
Cfeed
0.75
\\
0.25
-.4
0.1X c w
Feed
Cair Bvpa VG FDW Cair Bvpa VG Cair Bvpa VG
1E(-5)ppt
'E(-3)ppt
•2E(-3)ppt
/
/
Parameter Name
Figure 6-8. Results of sensitivity analysis of algorithms estimating above
ground vegetation concentrations due to vapor phase transfers.
Parameter Name
8r
vc
vu
veg
V
-------
DRAFT-DO NOT QUOTE OR CITE
1
1
i
w
1
9 1
§ -
5
J> -
^
i
0.
01 _
vegetables/fruits
1E6 a A
- 7E5 8-4
1 1.0 de^ I I
• i 1 1 " 1 1
0.3 o.1 sparse | 1
1-5ES «
55
10X
Cveg-1
Cgr-4E
Cfeed-.
10X
'
0.1X
grass/
0.1 X b*l
veg/
fruits
E(-5)ppt
'-3)ppt
>E(-3)ppt
/
/
. 1 T i r [ i i ~ — i 1
R Wp Rw Y.INT Vp kw Cpart Cpart
Parameter Name
Figure 6-9. Results of sensitivity analysis of algorithms estimating above
ground vegetation concentrations resulting from particle phase depositions.
Parameter Name
'part
^veg
Cgr
Cfod
R
W
veg
'NTve
kw
Definition
soil concentration, ng/kg (ppt)
particle-phase air concentration, //g/m3
vegetable/fruit concentration, ng/kg fresh wt
grass concentration, ng/kg dry wt
fodder concentration, ng/kg dry wt
annual rainfall, m/yr
washout factor, unitless
rainfall retention factor, unitless
vegetable yield, kg/m2 fresh
vegetable intercept fraction, unitless
particle deposition velocity, m/yr
plant wash-off rate constant, yr"1
Selected
1.00
5x10'12
0.00001
0.004
0.002
1.0
5x104
0.3
7.8
0.48
3.2x105
18.01
the particle deposition algorithm, but is left out of Figure 6-9 for clarity. In fact, FDW is
applied once vapor phase and particulate phase contributions to vegetable/fruit
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concentrations are already summed; in other words, it is not tied to either the vapor or
particle phase algorithms.
The impact of all the particle phase parameters to overall plant concentrations is
less than that of vapor transfers, as seen in a comparison between Figures 6-8 and 6-9.
For the parameters including rainfall amount (R), washout factor (Wp), denseness of
vegetation (as modeled by yield, Y, and intercept fraction, INT), velocity of particle
deposition (Vp), and plant weather dissipation rate, kw, results in Figure 6-9 are for
vegetable/fruits and not grass or fodder. Vegetables/fruits are more impacted by particle
depositions than grass/fodder, and as seen, there is less than half an order of magnitude
impact from the range of values for these parameters tested. The impact of depositions
on vegetable/fruit concentrations occurs because the correction factor for vegetables,
VGveg, is equal to 0.01, which minimizes the vapor-phase contributions to vegetable
concentrations in comparison to the contributions of the vapor phase concentrations for
grass and fodder concentrations, which have correction factors of 1.00 (for grass) and
0.50 (for fodder).
Model results on the proportion of above ground plant concentrations that are due
to air-to-leaf transfer and particulate deposition were examined for the soil contamination
and stack emission source categories for 2,3,7,8-TCDD, and results are summarized in
Table 6-2. Results show that vapor phase transfers tend to dominate vegetative
concentrations, although particle phase concentrations are important for bulky fruits and
vegetables. Again, the critical difference in the two plant types is the use of an empirical
VG which reduces the magnitude of vapor phase impact for bulky vegetations. Results
also show that the relative impact of vapors and particles is a function of distance for the
stack emission source category. For the central stack emission Scenario, #4, where the
site of exposure is 5000 meters from the stack, vapor transfers generally have more of an
impact to vegetation as compared to the high end Scenario, #5, where the site of
exposure is 500 meters away.
It is possible that the impact of particle depositions is being underestimated, for at
least four reasons:
• The wind erosion algorithm estimating air-borne contaminant concentrations for
soil contamination source category only estimates concentrations of PM-10, or
inhalable size particulates, those 10//m size diameter and less, while the COMPDEP
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Table 6-2. Contribution of above ground vegetation concentrations of 2,3,7,8-TCDD from
air-to-leaf transfers and particulate depositions.1
Air-to-leaf Particulate
Description2 Transfers Deposition
Scenarios 2: On-site
Soil Source Category
vegetables/fruit 49 51
pasture grass 94 6
fodder 95 5
Scenarios 3: Off-site
Soil Source Category
vegetables/fruit 68 32
pasture grass 97 3
fodder 98 2
Scenarios 4: Stack Emission
Source Category
vegetables/fruit 55 45
pasture grass 94 6
fodder 95 5
Scenarios 5: Stack Emission
Source Category
vegetables/fruit 32 68
pasture grass 89 11
fodder 88 12
1 Results are in percent of total contribution.
2 Scenario 2 demonstrated the "on-site" source category, where soil at the exposed individuals home was contaminated at a
level of 1 ng/kg (ppt) 2,2,7,8-TCDD; Scenario 3 demonstrated the "off-site" source category, where soil at a contaminated
site 150 meters away was initialized at 1 fjglkg (ppb) 2,3,7,8-TCDD; Scenarios 4 and 5 demonstrated the stack emission
source category - in Scenario 4, the exposure site was 5000 meters from the emitting stack, and in Scenario 5, the
exposure site was 500 meters from the stack.
model considers all size particulates emitted from stacks. Larger size air-borne
particulates, while not inhalable, would deposit onto vegetation.
• For the off-site soil source category which involves soil contamination distant
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from the site of exposure, only the off-site locations provide the source of air-borne
particulates. Meanwhile, algorithms are in place estimating exposure site
contamination, albeit to thin surface levels. Certainly, the reservoir of air-borne
particulates depositing onto vegetation would also include contributions from where
the vegetation is located and the surrounding land, not only from the area of soil
contamination.
• For the stack emission source category, resuspension of deposited particles and
deposition onto plants is not considered. This omission is similar to the omission
noted in the bullet above.
• The modeling does not consider the splash effect of rainfall, which would
deposit soil onto the lower parts of plants. This would make the most impact for
grass and for vegetables near the ground surface such as lettuce.
The precise impact of these factors might be investigated more fully in a later
assessment with additional models. Tests were run for this sensitivity analysis by
increasing the amount of particulate phase contaminants depositing onto vegetation by an
order of magnitude to the on-site demonstration scenarios, without changing the vapor
phase contributions. The vapor phase/particulate phase contributions to above ground
fruits and vegetables, originally 49%/51% (from Table 6-2 above), changed to 9%/91%
with an order of magnitude increase in particulate phase contributions. Vegetable
concentrations increased by a factor of 6. The impact was less for grass and fodder, with
concentrations increasing by a factor of 1.7. The impact was comparable for the off-site
soil contamination source category.
One possible theoretical shortcoming of the plant concentration algorithm for the
soil source categories is that contaminants which volatilize from soil are assumed to
remain in the vapor phase. Given the affinity of the dioxin-like compounds to soil, it seems
possible, if not likely, that a portion of the volatilized flux will sorb to airborne particles and
become part of the particulate reservoir. As discussed in Chapter 3, Section 3.4.5.5,
Bidleman (1988) provides an algorithm to estimate what fraction of a total "available"
reservoir of airborne contaminant would sorb to airborne particles. Assuming that
volatilized residues would comprise an available reservoir, this sorbed fraction ranges from
10 to 70% (roughly), depending on the density of particles in the air. A test was
conducted to see what impact partitioning of the vapor phase reservoir partly into the
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particulate reservoir would have on vegetative concentrations. First, 25% of the
volatilized vapor phase reservoir was transferred to the particulate reservoir, and then
50%was transferred. The results of this test are shown in Table 6-3.
Vapor phase transfers still explain more of the grass and fodder concentration than
particle depositions, with 60-65% of the plant concentration explained by vapor transfers
when 50% of the reservoir is transferred to the particle phase reservoir. The overall
impact to grass and fodder concentrations, however, is small, with only an 21-26%
reduction when 50% of the vapors are transferred. As has been discussed, particle
depositions are important for vegetable/fruit concentrations. Therefore, increasing the
particle phase reservoir while decreasing the vapor phase reservoir greatly increases the
dominance of particle impacts to vegetations - 92% of vegetable/fruit concentrations are
due to particle impacts when 50% of the vapor phase reservoir is transferred, and the
vegetable/fruit concentrations increase by about a factor of 3 with this transfer.
It might be concluded from this test that modeling the sorption of volatilized vapor-
phase dioxin-like contaminants would: 1) mostly impact the inhalation exposures, with
vapor phase exposures being reduced equal to the amount modeled to move into the
particulate reservoir and particulate inhalations increase by the additional amount added to
the particulate reservoir - total vapor plus particle phase inhalation exposure would not
change, 2) tend to increase the concentrations in vegetables/fruits with the subsequent
impact on exposures from consumption of fruit and vegetables, and 3) apparently have
little overall effect for grass and fodder concentrations, and hence little effect on beef and
milk concentrations.
6.3.3.9. Estimation of below ground vegetation concentrations
One important factor to note up front about below ground vegetable concentrations
as compared to above ground vegetable concentrations (no underground fruits are
assumed in this assessment) is that below ground vegetable concentrations are about two
orders of magnitude higher than above ground vegetable concentrations for the soil
contamination demonstration scenarios. Since no fruit is assumed to be grown
underground, this difference does not affect fruit ingestion exposures. However, 28 g/day
of a total of 106 g/day vegetable ingestion is assumed to be underground vegetables.
Given the difference in concentration estimations, below ground vegetables explain 97%
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Table 6-3. Results of the sensitivity test of modeling vapor/particle partitioning for
volatilized residues (note: soil concentration equals 1 ppt in tests below).
Vegetation
I. Baseline
Veg/fruit
Grass
Fodder
Percent contribution
concentration due
Vapor transfers
49
94
95
of vegetative
to:
Particle depositions
51
6
5
Percent
Vegetative Change from
Cone., ppt Baseline
0.0000109
0.0038
0.0019
II. 25% transfer of vapor phase to particulate reservoir
Veg/fruit
Grass
Fodder
18
79
83
82
21
17
0.0000221 +103
0.0033 -13
0.0016 -16
III. 50% transfer of vapor phase to particulate reservoir
Veg/fruit
Grass
Fodder
8
60
65
92
40
35
0.0000333 +206
0.0030 -21
0.0014 -26
of the total exposure via ingestion of impacted vegetables. Sensitivity of underground
vegetable concentrations to parameter changes for the soil contamination source category
becomes important from this perspective.
On the other hand, the trend for the stack emission source category is exactly the
opposite - above ground vegetable concentrations exceed below ground vegetable
concentrations by two orders of magnitude. If air and soil concentrations were equal (or
proportional) for the demonstration of both source categories, than the impact to
vegetations would be equal (or proportional). Obviously, that was not the case. The soil
concentration of 2,3,7,8-TCDD of 1 ppt (ng/kg) for the on-site soil source demonstration
scenarios translated to a total airborne reservoir of 5E(-11) //g/m3 (vapor plus particle
phase reservoirs). For the stack emission demonstration scenario 5, where the exposure
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site was 500 meters from the stack emission source, the vapor plus particle phase
concentration of 2,3,7,8-TCDD was 1.4E(-12) /yg/m3, which is reasonably similar to the air
concentration of 5E(-11) //g/m3 modeled from a soil concentration of 1 ppt. However, the
soil concentration for the stack emission demonstration scenario for the 20-cm mixing
zone depth used for underground vegetation concentration was 0.00005 ppt, more than 4
orders of magnitude lower than the 1 ppt level for the soil contamination source
categories. Therefore, below ground vegetables for the stack emission source category
will be more than 4 orders of magnitude lower for the stack emission source category as
compared to the soil source category.
This question now is whether this is a reasonable outcome. Should deposition from
an airborne reservoir in the range of 10~11 //g/m3 2,3,7,8-TCDD result in a soil
concentration closer to 1 ppt than 0.00005 ppt? Is the algorithm estimating soil impacts
from depositions inherently underestimating soil concentrations? Likewise, should
emissions from soils at 1 ppt result in air concentrations higher than 10~11 //g/m3? Is the
soil emission/dispersion algorithms inherently underestimating air concentrations? If air to
soil impacts were being underestimated, and/or if soil to air impacts were being
underestimated, than more correct model performance would lead to more equivalent
outcomes with regard to vegetation impacts. As stated above, if soil and air
concentrations for each demonstration scenario were equal (or proportional), then impacts
to vegetation would be equal (or proportional). Air-to-soil and soil-to-air model
performances are now examined briefly.
In Section 7.2.3.1 in Chapter 7, the capability of the deposition algorithm to
estimate soil concentrations is examined. The hypothesis examined is that air
concentrations of 2,3,7,8-TCDD in a rural setting should correlate to untilled soil
concentrations in a rural setting, and that hypothesis can be evaluated using the deposition
algorithms of the stack emission source category. The conclusion from that section was
that it would appear that the deposition algorithm may, in fact, be underestimating soil
concentrations. The amount of underestimation was speculated to be about one order of
magnitude. Uncertain model parameters identified in that section were the untilled mixing
zone depth, the velocity of deposition, and the dissipation half-life for depositing residues.
It is also noted that the algorithms of this assessment do not consider detritus production
as an input to the soil reservoir. If the deposition algorithm is indeed underestimating soil
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concentrations, than in fact an airborne reservoir on the order of 10"11 //g/m3 might
translate to a concentration higher than 0.00005 ppt, although a one order of magnitude
increase to 0.0005 ppt is still much less than 1 ppt.
One could also even question the use of a contaminant dissipation rate which is
applied to depositing residues which are assumed to be tilled in a home garden or an
agricultural field. Routes of dissipation for dioxin-like compounds are physical transport,
such as wind or soil erosion, or volatilization, or chemical, such as photolysis, which is the
only degradation route shown to be relevant to these compounds. These are phenomena
relevant to surface residues, not buried residues. The use of a 10-year dissipation rate for
both tilled (a mixing zone depth of 20 cm) and untilled (a mixing zone depth of 1 cm)
settings could be a fundamental flaw in the approach - perhaps a dissipation rate should
only be applied to untilled soil impacts. In addition to the argument presented above, this
is another reason suggesting tilled soil concentrations resulting from stack emissions
should be higher than are estimated in this assessment, and hence underground vegetable
concentrations should be higher than are estimated in this assessment.
The other hypothesis is that the soil to air algorithms of the soil source categories
are underestimating air concentrations. In fact, evidence developed in other parts of this
document suggest that air concentrations resulting from soil concentrations may be
underestimated. One piece of evidence discussed in different sections of this Volume is
that air concentrations modeled to result from background soil concentrations are lower
than air concentrations measured in pristine settings. In one literature article measuring
concentrations in an area described as a "remote countryside" in Sweden (Broman, et al.
1991), air concentrations of 2,3,7,8-TCDD were measured at 2*10'10//g/m3. The air
concentration of 2,3,7,8-TCDD modeled in this assessment from a 1 ppt background soil
concentration of 2,3,7,8-TCDD is nearly an order of magnitude lower than that at 4*10~11
//g/m3. This suggests that the models of this assessment underestimate air concentrations
resulting from releases from soils. Another piece of evidence is developed in Section
7.2.3.8, which compares plant:soil ratios as determined by the model with those
developed in experimental and field conditions. The soil contamination models of this
assessment appear to be leading to above ground plant:soil ratios that are 1-2 orders of
magnitude lower (i.e., plant concentrations may be 1-2 orders of magnitude
underestimated) than analogous ratios for experiments where soil can be surmised to be
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the only source of dioxin. One possible explanation offered is that the air concentrations
are being underestimated.
If air concentrations resulting from soil concentration would be modeled to be
higher than they are currently, then the dichotomy identified above would be narrowed. If
soil concentrations resulting from air depositions are modeled to be higher than they are
currently, then the dichotomy identified would be even further narrowed. Evidence
summarized above suggests that both are plausible. Clearly, more evaluation of the soil to
air algorithms of the soil contamination source categories and the air to soil algorithms of
the stack emission source category, is warranted.
Given a soil concentration, in any case, the impacts of parameter changes for the
algorithm predicting concentrations in underground vegetables are shown in Figure 6-10.
The two orders of magnitude range for the root concentration factor, RCF, translates to a
two order of magnitude range of concentration estimation. The same is true for the
empirical correction factor applied to below ground vegetables, VGbg, and the organic
carbon partition coefficient, Koc. A smaller impact is noted for the organic carbon fraction
of soil, OCS|. Koc and OCs( are required for this algorithm because vegetable
concentrations are a function of soluble phase concentrations, not soil concentrations.
Increasing Koc and/or increasing OCS, results in decreasing the water concentrations,
explaining why the high values for these parameters reduce vegetable concentrations.
One final note is that the dry to fresh weight ratio, FDW, is not on this figure, while
it does appear on Figure 6-8. This is because the RCF was developed on fresh weight
basis already, so no conversion to a fresh weight is required.
6.3.3.10. Beef fat concentration estimation The impacts of parameter changes
to beef fat concentration estimation for the soil source category is shown in Figures 6-11.
First, it is noted that changes in soil concentration result in linear changes in beef
concentrations - a ten-fold increase in soil concentration results in the same ten-fold
increase in beef concentration. This is because changes to soil concentration alone result
in the same proportional change in the concentrations estimated to be in grass and cattle
feed. Changes to grass and feed concentrations with no change in soil concentrations
(which could result from different parameter selections in estimating grass and feed
concentrations) do not result in as substantial a change - a tenfold increase in vegetation
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100 =
1 •
1
•i 10:
5 -
«
1 :
j ;
s :
^ 0.1 =
1 ^
- :
0.01 -
106,000
0.10 270,000
1260
Cv»,
0.005 PP
wt
1 ^
1 fa
• 1
0.03 w
0.001 27,000,000
RCF VGbg Koc OCsl
' '
]-1.5E(-3)
t fresh
Jght
^x
te:Cso//-
pptdry
eight)
Parameter Name
Figure 6-10. Results of sensitivity analysis of algorithms estimating below
ground vegetation concentrations resulting from soil to root transfers.
Parameter Name
^V/Afl
ocsl
RCF
Koc
Definition Selected
vegetable concentration, ng/kg (ppt) fresh wt. 0.0015
soil concentration, ng/kg (ppt) 1.00
below ground veg. correction factor, unitless 0.01
soil organic carbon fraction 0.01
root bioconcentration factor, unitless 3,916
organic carbon partition coefficient, L/kg 2.69x10s
concentrations only increases beef concentration by a factor of two; a tenfold decrease in
vegetation concentration only reduces beef fat concentrations by about 20%.
This is a key and insightful result. If the models and their parameterization are
valid, it indicates that the bulk of impact to beef and milk fat is from ingestion of soil for
the soil source categories. With a closer look at the results for the demonstration of the
on-site soil category, demonstration scenario #2, it is found that soil ingestion explains
90% of the beef fat concentration, despite being only 4% of their diet. Grass (48% of
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10-
g :
| ;
V)
\ •
1 :
1
o.i -
10X
high
IK '"
|
•
0.1 X
0.1X 1
0.9
0.3
Exposure
Conditions
I
low tow
to
Soil Ext
Ingestton Contai
Cs Cgr BCF Bs
Cfeed
Extent of
Pasturing
high
C
*
low
w
entof
nlnatlon
bf at -0.1 2
^ PPt
Parameter Name Exposure Scenario
Figure 6-11. Results of sensitivity analysis of algorithms estimating beef fat
concentrations resulting from soil contamination.
Parameter Name
Definition
Selected
Cs soil concentration, ng/kg 1.00
Cgr grass concentration, ng/kg dry wt. 0.04
Cfeed feed concentration, ng/kg dry wt. 0.02
Cbfat beef fat concentration, ng/kg 0.12
BCF beef/milk bioconcentration factor, unitless 4.32
Bs bioavail. of cont. on soil relative to vegetation 0.65
BCSDF beef cattle soil diet fraction, unitless 0.04
BCFDF beef cattle feed diet fraction, unitless 0.48
BCGDF beef cattle grass diet fraction, unitless 0.48
BCGRA beef cattle fraction of cont. grazing land 1.00
BCFOD beef cattle fraction of cont. feed 1.00
their diet) and feed (48%) of their diet explain 7 and 3% of beef fat concentrations. The
story is the same for milk fat concentrations. Soil explains 87% of the milk fat
concentrations, despite being only 2% of the dairy cattle's diet. Feed (90% of their diet)
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and grass (8%) explain 11 and 2% of milk fat concentrations.
Changes to other parameters, or groups of parameters, result in expected changes
to beef fat concentrations. A doubling (roughly) of the assigned beef bioconcentration
factor, BCF, from 4.3 to 10 doubles beef concentrations; reducing it to 1 reduces
concentrations by a factor of 5. The soil bioavailability factor, Bs, has a rather small
impact around its 0.3 to 0.9 range. The other tests run on the beef and milk algorithm
looked at cattle dietary exposures as modeled by the diet fraction parameters, and the
parameters describing the fractions of intake that are contaminated. In one test, patterns
of soil ingestion were examined. As noted above, soil ingestion is critical for both beef
and milk concentration estimation. In the high and low soil ingestion tests, diet fractions
were altered to reflect high (15% of the beef cattle diet) and low (1 %) soil ingestion
patterns. The impact is nearly linear, with an increase from 4 to 15% nearly quadrupling
beef fat concentrations, and a reduction from 4 to 1 % reducing concentrations by almost
75%. The same trend is seen with changes in dietary exposures to reflect a lifetime of
pasturing versus minimal lifetime pasturing. A lifetime of pasturing translated to an
assumption of 90% pasture grass intake, and a greater proportion of incidental soil
ingestion, from 4 to 8%. With this rise in soil ingestion mainly, the beef fat concentration
doubled. Similarly, with minimal pasturing and half as much soil ingestion at 2%, beef fat
concentrations halved. A different test described a condition where exposure to
contaminated dry matter was minimized. This was modeled with two modifications: 1)
the soil ingestion patterns were low as modeled in the previous test and 2) only 25% of
the feed was assumed to be impacted by the soil contamination - i.e., 75% of it was
residue free perhaps from being externally purchased and not impacted by soil
contamination. The impact of the feed assumption was insignificant, and the fact that
beef concentration were reduced by about 75% was due mainly to the reduced soil
ingestion assumed - 1 %, down from the initially assumed 4%. The final test was termed,
low extent of contamination. In this test, 75% of the grazing land was residue free, which
reduced both soil and pasture grass concentrations by 75%, and 75% of the feed was
also assumed to be residue free. With 75% reductions in concentrations in dry matter
intake, beef concentrations both were reduced by the same 75%. These tests
demonstrate the importance of assumptions on cattle dietary exposures on estimated beef
concentrations, and by analogy, milk concentrations.
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6.3.3.11. Vegetable and beef fat concentrations resulting from stack emissions
Results of sensitivity analysis of algorithms estimating above and below ground vegetables
and beef fat concentrations resulting from stack emissions are shown in Figure 6-15. This
examination only looks at impacts that are specific to the stack emission source category;
earlier sections described impacts with the several other parameters required for these
biota impact algorithms.
First, it should be noted that the trends for soil impacts are the same as those for
below ground vegetables. This is because underground vegetable concentrations are a
direct linear function of soil concentrations. Second, trends for milk fat concentration are
not exactly the same, but very similar to those of beef fat. The only difference in the
algorithms estimating beef and milk fat concentrations are the assumptions concerning
cattle exposure. The general trends discussed below regarding the impact of soil, the
distance from the stack, and the vapor/particle partitioning also occur with milk fat
concentration estimation.
One trend to easily spot and explain is the impact of the assumption on the no-till
mixing depth. This impacts beef concentrations because soil ingestion by cattle is
assumed to occur on untilled soil. This test indicates that the increment of beef
concentration due to soil ingestion by cattle is small; that diluting depositing residues into
either 1 or 5 cm mixing depth does not impact results. This trend is different then that of
the soil source categories, where soil ingestion is critical to beef/milk concentrations. The
following shows the percent of beef and milk concentrations that are due to soil, pasture
grass, and feed ingestion in example scenarios for on-site soil contamination (Scenario #2)
and the stack emissions (Scenario #5):
Percent impact due to ingestion of:
Description Soil Grass Feed
Soil contamination, beef 90 7 3
Soil contamination, milk 87 2 11
Stack emission, beef 5 59 32
Stack emission, milk 3 15 82
As seen here, soil only accounts for 5 and 3% of beef and milk concentration impacts
from stack emissions in the example scenario. The principal cause for this difference in
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10:
1
s ;
t>
o.i -
below ground above ground
vegetables vegetables beef fat
20
50C
Vm
10/90
90/10
10 m
200m 10/90
1
I
90/10
5000m
Cbgv -
Cagv -
Cbf - i
90/10
0.01 1 y
• 1
0.05 nnn \
200m 1
1 10/90
5000m
8E-8ppt
3E-6ppt
E-3ppt
/
/
/
TDEPe vapor/ TDEPe vapor/ dnot TDEPe vapor/
Cair particle Cair particle Cair particle
Parameter Name
Figure 6-12. Results of sensitivity analysis of algorithms estimating above
and below ground vegetation, and beef fat concentrations resulting from
stack emissions.
Parameter Name
bgv
cbf
TDEPe
vapor/particle
Definition
below grd. veg. cone., fresh wt, ng/kg (ppt)
above grd. veg. cone., fresh wt, ng/kg (ppt)
beef fat concentration, ng/kg (ppt)
total dep, dry + wet, on exp. site, /yg/m2-yr
vapor phase concentration at exp. site, //g/m3
(note: 200 and 5000 m refers to use of TDEPe
and Cair at these distances in sensitivity testing)
percent of contaminant arriving at exposure
site assumed to be in vapor and particle phases
no-till depth at exposure site, m
Selected
0.002
1.2*10-6
7.6*1012
0.01
trends between the soil source and stack emission source is the differences in the soil to
air trends for the soil source category versus the air to soil trends for the stack emission
source category. The dichotomy in these model performances was discussed in Section
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6.3.3.9 above. Briefly, emissions from the soil source starting with a 1 ng/kg (ppt) soil
concentrations resulted in air concentrations in the order of 10"11 fjg/m3. The
demonstration of the stack emission source also led to total air concentrations on the order
of 10"11 //g/m3 at the farm site of exposure. However, depositions onto soils for the stack
emission demonstration scenario led to no-till (1 cm mixing depth) soil concentrations on
the order of 10~3 ng/kg. Since the soil source demonstration had much higher soil
concentrations, its contribution to beef and milk concentration was higher as compared to
the stack emission source category.
The other two model trends evaluated included impacts of different distances away
from the stack emission, and the vapor/particle partitioning at the stack. The farm was
assumed to be 500 meters from the stack for the high end demonstration of the stack
emission source category. Nearer to the stack at 200 meters, ambient air concentrations
and dry deposition amounts were lower, but wet deposition was at its maximum. One
effect of this was that vegetable concentrations increased. Below ground vegetables
increased by about a factor of 4, due to the same increase in soil concentration as a result
of much higher wet deposition. Above ground vegetation increased by about 50%. As
seen on Table 6-2, particle depositions dominated above ground vegetable/fruit
concentrations. Therefore, an increase in overall particle depositions due to an increase in
wet depositions led to increased above ground vegetable/fruit concentrations. However,
the trend was not the same for beef and milk fat. As seen on Table 6-2, vapor
contributions dominated grass and feed concentrations. Therefore, a drop in ambient air
vapor phase concentrations at 200 meters as compared to 500 meters dominated the
result, and the net impact was to reduce beef fat concentrations. From Figure 6-12, it is
seen that beef fat concentrations were reduced by about one-half when using COMPDEP
output from 200 m instead of 500 m. Further from the stack at 5000 meters, all biota
concentrations were lower. Vapor phase air concentrations were roughly halved, and dry
and wet deposition were lower by 60 and 80% respectively. This led to substantial
reductions in vegetable concentrations. Interestingly, beef fat concentrations were lower
at 5000 meters than at 200 meters, but not by much. This is because vapor phase
concentrations at 5000 meters were in fact greater than they were at 200 m. The net
results, according to the modeled depositions and air concentrations, is that beef and milk
fat impacts are ironically fairly similar at 200 and 5000 meters.
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The baseline vapor/particle partitioning for 2,3,7,8-TCDD was 55% vapor/45%
particle. When decreasing the vapor to 10% and increasing the particle to 90%, both
vegetations increased. Below ground vegetables increased because below ground
vegetables were not a function of vapor phase concentrations, only of soil concentrations,
which were a function of particle depositions. Above ground vegetable concentrations
increased as well. As seen on Table 6-2, above ground vegetables were more impacted by
particle depositions than vapor transfers for the stack emission high end scenario, #5.
Increasing the particle depositions would increase their overall dominance and lead to
increases in vegetable concentrations.
Interestingly, the trend was different for beef fat impacts. In this case, beef fat
concentrations increased when the vapor reservoir was increased, although the increase
was small. The reason beef fat concentrations increased can be seen in Table 6-2. Grass
and feed were more impacted by vapor phase transfers than particle depositions, the
opposite trend noted for vegetables. Therefore, a rise in vapor phase concentrations will
even further dominate grass and feed concentrations, the major components of beef cattle
diet. With an increase in grass and feed concentrations, beef fat concentrations increase.
6.3.3.12. Water and fish concentrations resulting from effluent discharges The
impacts of parameter changes for algorithms estimating water and fish concentrations are
shown in Figure 6-13. First, it should be noted that fish and water impacts are included in
the same graph because the impacts to both concentrations are exactly the same with the
noted changes in parameters, with one exception. This exception is the partition
coefficient, Koc. Increases in Koc result in higher suspended sediment concentrations,
which lead to higher fish tissue concentration estimations, but lower water concentration
estimations. Increasing Koc by an order of magnitude actually decreases water
concentrations to 14% of its baseline value, or 0.14 on the y-axis of Figure 6-13.
Decreasing Koc by an order of magnitude increases water concentrations by a factor of
2.4. Roughly, the location of the high and low Koc points on Figure 6-13 should be
reversed for water concentration impacts. Also, the biota to suspended solids
accumulation factor, BSSAF, and the fish lipid content, f|ipid, are specific to fish tissue
estimations.
6-65 4/94
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