SEPA
EPA 100/R-08/004 I June 2008
www.epa.gov/osa
United States
Environmental Protection
Agency
Framework for Application of
the Toxicity Equivalence
Methodology for
Polychlorinated Dioxins,
Furans, and Biphenyls in
Ecological Risk Assessment
Office of the Science Advisor
Risk Assessment Forum
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Cover photo of the rainbow trout is courtesy of Ken Hammond, USDA. Cover photos of
the mink and cattle egret are courtesy of the United States Environmental Protection
Agency Great Lakes National Program office.
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EPA/1 OO/R-08/004
June 2008
Framework for Application
of the Toxicity Equivalence Methodology for
Polychlorinated Dioxins, Furans, and Biphenyls
in Ecological Risk Assessment
Office of the Science Advisor
Risk Assessment Forum
U.S. Environmental Protection Agency
Washington, D.C. 20460
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy. Mention of trade names or commercial products does not constitute endorsement
or recommendation for use.
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TABLE OF CONTENTS
LIST OF FIGURES v
LIST OF TABLES v
LIST OF TEXT BOXES v
PREFACE vi
AUTHORS, CONTRIBUTORS, AND REVIEWERS viii
LIST OF ABBREVIATIONS AND ACRONYMS x
1. INTRODUCTION 1
1.1. DEFINITIONS 3
1.2. EVOLUTION OF THE TOXICITY EQUIVALENCE METHODOLOGY 4
2. TOXICITY EQUIVALENCE METHODOLOGY 9
2.1. AHR-MEDIATED MECHANISM AND ASSIGNMENT OF RELATIVE POTENCY. 9
2.2. SELECTION OF THE APPROPRIATE RELATIVE POTENCY FACTORS 11
2.3. TOXICITY EQUIVALENCE CONCENTRATION 12
3. APPLICATION OF THE TOXICITY EQUIVALENCE METHODOLOGY IN
ECOLOGICAL RISK ASSESSMENT 14
3.1. CONSIDERATIONS IN PLANNING 14
3.1.1. Benefits of the Toxicity Equivalence Methodology 16
3.1.2. Methodological Considerations 17
3.2. CONSIDERATIONS IN PROBLEM FORMULATION 18
3.2.1. Assessment Endpoints 18
3.2.1.1. Susceptibility: Sensitivity 19
3.2.1.2. Susceptibility: Exposure 22
3.2.1.3. Susceptibility: Integration of Sensitivity and Exposure Considerations 23
3.2.1.4. Ecological Relevance 24
3.2.2. Conceptual Model 25
3.2.3. Analysis Plan 27
3.3. CONSIDERATIONS IN ANALYSIS 27
3.3.1. Characterization of Exposure 28
3.3.1.1. Congener-Specific Analyses 28
3.3.l.2.ChemicalFateofPCDDs,PCDFs,andPCBs 28
3.3.1.3. Choices for the Exposure Dose Metric 29
3.3.1.4. Bioaccumulation ofPCDDs, PCDFs, andPCBs 31
3.3.1.5. Examples of TEC Calculations for Fish, Birds, and Mammals 35
3.3.2. Three Dimensional Relative Potency Matrix - A Tool for Visualization and
Selection of RePs or Derivation of RPFs 43
3.3.2.1. EndpointRelevance 44
3.3.2.2. Species Similarity 45
3.3.2.3. Dose Relevance for Effect and Consistency with Dose-Response Relationship
45
3.3.2.4. Application of Three Dimensional Relative Potency Matrix - Examples of
ReP Data Prioritization Choices for Deriving RPFs 46
3.3.2.4.1. Example 1: Incomplete ReP data sets 46
3.3.2.4.2. Example 2: Species similarity versus endpoint similarity 47
3.3.2.4.3. Example 3: Dose-response and exposure relationships 48
3.3.2.5. Summary of Selection ofTEFs, RPFs, or RePs 51
in
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3.3.3. Characterization of Ecological Effects 52
3.4. CONSIDERATIONS IN RISK CHARACTERIZATION 53
3.4.1. Risk Estimation 53
3.4.2. Lines of Evidence 53
3.4.3. Summary of Uncertainties 55
3.4.3.1. Uncertainty Associated With the Toxicity Equivalence Me thodology 55
3 A3.\.\.AHRligands 55
3.4.3.1.2. Additivity assumption 55
3.4.3.1.3. Relative potency data 56
3 A.3.1 A. Point estimates 56
3.4.3.2. Uncertainty Associated With Application of the Toxicity Equivalence
Methodology in Ecological Risk Assessment 57
3.4.3.2.1. Other methods 58
3.4.3.2.2. Uncertainties in characterization of exposure 58
3.4.3.2.3. Uncertainties in characterization of ecological effects 59
3.4.3.2.4. Uncertainties in risk estimation 59
4. CONCLUSIONS 60
REFERENCES 62
GLOSSARY OF TERMS 77
IV
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LIST OF FIGURES
Figure 1. Chemical structure of PCDDs, PCDFs, and PCBs 2
Figure 2. Structure of PCB molecule and positions for chlorine substitution 6
Figure 3. The framework for ecological risk assessment 15
Figure 4. An aquatic food web depicting hypothesized bioavailability and trophic transfer of
2,3,7,8-TCDD through sediment and the water column 25
Figure 5. Application of the toxicity equivalence methodology in ecological risk assessment for
exposure to PCDDs, PCDFs, and PCBs 26
Figure 6. Estimating chemical concentrations in eggs and diet by applying BAFs and BSAFs for
PCDDs, PCDFs, and PCBs 30
Figure 7. PCDDs, PCDFs, and PCBs: effects on vertebrates 35
Figure 8. Fish TECs calculated with TEFs-WHO98 appropriately from concentrations in eggs
versus inappropriately from concentrations in sediment 40
Figure 9. Bird TECs calculated with TEFs-WHOgg appropriately from concentrations in eggs
versus inappropriately from concentrations in sediment 40
Figure 10. Mammal TECs calculated with TEFs-WHO05 appropriately from concentrations in
diet versus inappropriately from concentrations in sediment 41
Figure 11. Three dimensional relative potency matrix for selection of RePs and derivation of
RPFs for risk assessment 44
LIST OF TABLES
Table 1. Number of poly chlorinated dioxin, furan, and biphenyl congeners 5
Table 2. World Health Organization TEFs for mammals, birds, and fish 8
Table 3. Effects of 2,3,7,8-TCDD and related chemicals in different animal species 20
Table 4. An example of estimating TECs in fish eggs from average concentrations of PCDD,
PCDF, and PCB congeners measured in surface sediment samples of a reservoir 37
Table 5. An example of estimating TECs in bird eggs from average concentrations of PCDD,
PCDF, and PCB congeners measured in surface sediment samples of a reservoir 38
Table 6. An example of estimating TECs in the diet of otter from average concentrations of
PCDD, PCDF, and PCB congeners measured in surface sediment samples of a reservoir.
39
Table 7. ReP selection matrix for Caspian terns (example 2) 48
Table 8. ReP selection matrix for mink (example 3) 50
LIST OF TEXT BOXES
Text Box 1. Clarification of terminology 3
Text Box 2. Questions for planning 14
Text Box 3. Questions for problem formulation 18
Text Box 4. Questions for analysis 27
Text Box 5. Key to symbols and notations used in equations 3-1 to 3-9 32
Text Box 6. Questions when calculating TECs 42
Text Box 7. Questions for risk characterization 53
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PREFACE
Polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls
(PCBs) are commonly found as contaminants in complex mixtures in the environment, including
in animal tissues. For more than a decade, the U.S. Environmental Protection Agency (EPA) and
other organizations have estimated the combined risks that such mixtures pose to human health
using a method known as the toxicity equivalence methodology. Application of this
methodology in ecological risk assessments has proceeded more slowly, in part because of the
variety of species from different taxonomic classes (e.g., fish, birds, and mammals) that need to
be considered.
As both data and experience with the methodology have accumulated experts have come
to the consensus that the toxicity equivalence methodology can strengthen assessments of
ecological risks (Van den Berg etal., 1998, 2006; U.S. EPA, 2001a; NRC, 2006). Consultations
between EPA and the Department of Interior (DOT) on water quality criteria, based on 2,3,7,8-
TCDD alone, for protecting endangered species in the Great Lakes led these agencies to more
intensively explore the application of the toxicity equivalence methodology in ecological risk
assessment. In 1998, EPA and DOT sponsored a workshop that recommended the development of
further guidance on application of the toxicity equivalence methodology (U.S. EPA, 200 la). This
framework has been developed in direct response to that workshop recommendation. In July
2003, EPA released a draft framework for a 90-day public comment period. In addition, an
external peer review was conducted over several months from October 2003 to February 2004,
culminating in a final report on February 9, 2004. Links to these documents can be found at
http://www.epa.gov/osa/raf/tefframework/.
Organized in accordance with EPA's Guidelines for Ecological Risk Assessment (U.S.
EPA, 1998), this framework is intended to assist EPA scientists in using the toxicity equivalence
methodology in ecological risk assessments that involve dioxins and dioxin-like chemicals, as
well as to inform EPA decision makers, other agencies, and the public about this methodology.
While this framework touches on many aspects of ecological risk assessment, it is not intended
to be a comprehensive guide to risk assessment involving dioxin-like chemicals. Rather, the
framework provides an introduction to the toxicity equivalence methodology, offers
considerations for how and when to apply it, and presents practical examples of its use. Readers
are referred elsewhere for details on topics such as chemical analysis, environmental fate and
transport modeling, and development of stressor-response profiles for dioxin-like chemicals.
The Ecological Toxicity Equivalence Factor (Eco-TEF) framework is intended for
guidance only. It does not establish any substantive "rules" under the Administrative Procedure
Act or any other law and will have no binding effect on EPA or any regulated entity. Rather, it
represents a nonbinding statement of policy. EPA believes that this framework provides a sound,
up-to-date presentation of a method for use in conducting risk assessments involving dioxins and
dioxin-like chemicals, and serves to enhance the application of the best available science.
However, EPA and others may conduct risk assessments for dioxins and dioxin-like chemicals
using approaches and methods that differ from those described in this document for many
reasons, including, but not limited to, new information, new scientific understandings, and new
science policy judgments. The science surrounding hazard and risk analysis for dioxins and
dioxin-like chemicals continues to be intensively studied and thus is rapidly evolving. Specific
guidance presented in the framework may become outdated or may otherwise require
modification to reflect the best available science. Application of this framework in future risk
assessments will depend on EPA decisions that its approaches are suitable and appropriate.
VI
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These judgments will be tested and examined through peer review, and any risk analysis will be
modified as deemed appropriate.
This framework was prepared by a Technical Panel under the auspices of EPA's Risk
Assessment Forum. The Risk Assessment Forum was established to promote scientific consensus
on risk assessment issues and to ensure that this consensus is incorporated into appropriate risk
assessment guidance. To accomplish this, the Risk Assessment Forum assembles experts from
throughout EPA in a formal process to study and report on these issues from an Agency-wide
perspective.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
AUTHORS
This framework was prepared by a technical panel under the auspices of EPA's Risk
Assessment Forum and reflects the contributions of participants at a 1998 workshop on the
application of 2,3,7,8-TCDD TEFs in fish and wildlife.
TECHNICAL PANEL
Tala Henry (Co-Chair), Office of Prevention, Pesticides and Toxic Substances, U.S. EPA,
Washington, DC 20460
Patricia Cirone (Co-Chair), Office of Environmental Assessment, Region 10, U.S. EPA, Seattle,
WA 98101
Philip Cook, Mid-Continent Ecology Division, National Health and Environmental Effects
Research Laboratory, Office of Research and Development, U.S. EPA, Duluth, MN 55804
Michael DeVito, Experimental Toxicology Division, National Health and Environmental Effects
Research Laboratory, Office of Research and Development, U.S. EPA, Research Triangle
Park, NC 27711
Bruce Duncan, Office of Environmental Assessment, Region 10, U.S. EPA, Seattle, WA 98101
Robert Pepin, Water Division, Region 5, U.S. EPA, Chicago, IL 60604
Steven Wharton, Office of Partnerships and Regulatory Assistance, Region 8, U.S. EPA, Denver,
CO 80202
RISK ASSESSMENT FORUM
Elizabeth Lee Hofmann, Office of the Science Advisor, U.S. EPA, Washington, DC 20460
Melissa Kramer, Office of the Science Advisor, U.S. EPA, Washington, DC 20460
Seema Schappelle, Office of the Science Advisor, U.S. EPA, Washington, DC 20460
Pamela Noyes, Office of the Science Advisor, U.S. EPA, Washington, DC 20460
Scott Schwenk, Office of the Science Advisor, U.S. EPA, Washington, DC 20460
INTERNAL PEER REVIEWERS
Linda Birnbaum, National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. EPA, Research Triangle Park, NC 27711
Chris Cubbison, National Center for Environmental Assessment, Office of Research and
Development, U.S. EPA, Cincinnati, OH 45268
Dale Hoff, Office of Ecosystems Protection and Remediation, Region 8, U.S. EPA, Denver, CO
80202
Matt Lorber, National Center for Environmental Assessment, Office of Research and
Development, U.S. EPA, Washington, DC 20460
Suzanne K. M. Marcy, National Center for Environmental Assessment, Office of Research and
Development, U.S. EPA, Anchorage, AK 99513
Diane Nacci, National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. EPA, Narragansett, RI 02882
John Nichols, National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. EPA, Duluth, MN 55804
Ruth Prince, Waste and Chemicals Management Division, Region 3, U.S. EPA, Philadelphia, PA
19103
Vlll
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Donald Rodier, Office of Prevention, Pesticides, and Toxic Substances, Office of Pollution
Prevention and Toxics, U.S. EPA, Washington, DC 20460
Glenn W. Suter II, National Center for Environmental Assessment, Office of Research and
Development, U.S. EPA, Cincinnati, OH 45268
EXTERNAL PEER REVIEWERS
William J. Adams, Rio Tinto, Magna, UT 84044
Scott B. Brown [Deceased], Environment Canada, National Water Research Institute,
Burlington, Ontario, Canada L7R 4A6
Peter L. deFur, Environmental Stewardship Concepts, Richmond, VA 23233
John P. Giesy, Jr., Department of Zoology, Michigan State University, East Lansing, MI 48824
Mark E. Hahn, Woods Hole Oceanographic Institution, Woods Hole, MA 02543
Barbara L. Harper, AESE, Inc., West Richland, WA 99353
Bruce K. Hope, Oregon Department of Environmental Quality, Portland, OR 97204
Sean W. Kennedy, Environment Canada, Canadian Wildlife Service, Ottawa, Ontario, Canada
Kl A OH3
Charles A. Menzie - Panel Chair, Menzie-Cura & Associates, Inc., Winchester, MA 01890
Christopher D. Metcalfe, Trent University, Peterborough, Ontario, Canada K9J 7B8
Richard E. Peterson, Endocrinology-Reproductive Physiology Program, University of
Wisconsin-Madison, Madison, WI 53706
Martin van den Berg, University of Utrecht, Institute for Risk Assessment Sciences (IRAS), NL-
3508 TD Utrecht, The Netherlands
IX
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LIST OF ABBREVIATIONS AND ACRONYMS
2,3,7,8-TCDD
AHR
BAF
BCF
BSAF
DOT
EC
ECO-TEF
ED
EPA
EROD
HpCB
HpCDD
HpCDF
HxCB
HxCDD
HxCDF
IPCS
LD
LOAEL
NATO/CCMS
NOAEL
NRC
OCB
OCDD
OCDF
PCBs
PCDDs
PCDFs
PeCB
PeCDD
PeCDF
QSAR
ReP
RPF
TCB
TCDD
TCDF
TEC
TEF
TEFs-NATC-89
TEFs-WHO
94
2,3,7,8-tetrachlorodibenzo-p-dioxin
aryl hydrocarbon receptor
bioaccumulation factor
bioconcentration factor
biota-sediment accumulation factor
U.S. Department of Interior
effective concentration
ecological toxicity equivalence factor
effective dose
U.S. Environmental Protection Agency
ethoxyresorufin-O-deethylase
heptachlorinated biphenyl
heptachlorinated dibenzo-p-dioxin
heptachlorinated dibenzofuran
hexachlorinated biphenyl
hexachlorinated dibenzo-^-dioxin
hexachlorinated dibenzofuran
International Programme on Chemical Safety
lethal dose
lowest observed adverse effect level
North Atlantic Treaty Organization/Committee on the Challenges of Modern
Society
no-observed adverse effect level
National Research Council of the National Academies
octachlorinated biphenyl
octachlorinated dibenzo-p-dioxin
octachlorinated dibenzofuran
polychlorinated biphenyls
poly chlorinated dibenzo-p-dioxins
polychlorinated dibenzofurans
pentachlorinated biphenyl
pentachlorinated dibenzo-^-dioxin
pentachlorinated dibenzofuran
quantitative structure-activity relationship
relative potency
relative potency factor
tetrachlorinated biphenyl
tetrachlorinated dibenzo-/?-dioxin
tetrachlorinated dibenzofuran
toxicity equivalence concentration
toxicity equivalence factor
TEFs (sometimes also referred to as I-TEFs) adopted by the NATO/CCMS in
1989
TEFs adopted by the WHO in 1994
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TEFs-WHO
98/05
TMDL
WHO
WHO-ECEH
WHO-IPCS
TEFs adopted by the WHO in 1998 and 2006; developed at a WHO-ECEH
and WHO-IPCS expert meetings in 1997 (mammalian, avian and fish
TEFs) and 2005 (mammalian TEFs only).
total maximum daily load
World Health Organization
WHO European Centre for Environmental Health
WHO International Programme on Chemical Safety
PCB abbreviations:
TCB tetrachlorinated biphenyl
PeCB pentachlorinated biphenyl
HxCB hexachlorinated biphenyl
HpCB heptachlorinated biphenyl
OCB octachlorinated biphenyl
PCBs polychlorinated biphenyls
PCDD abbreviations:
TCDD tetrachlorinated dibenzo-^-dioxin
PeCDD pentachlorinated dibenzo-p-dioxin
HxCDD hexachlorinated dibenzo-p-dioxin
HpCDD heptachlorinated dibenzo-p-dioxin
OCDD octachlorinated dibenzo-^-dioxin
PCDDs polychlorinated dibenzo-^-dioxins
PCDF abbreviations:
TCDF tetrachlorinated dibenzofuran
PeCDF pentachlorinated dibenzofuran
HxCDF hexachlorinated dibenzofuran
HpCDF heptachlorinated dibenzofuran
OCDF octachlorinated dibenzofuran
PCDFs polychlorinated dibenzofurans
XI
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Xll
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1. INTRODUCTION
Polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls
(PCBs) (Figure 1) are persistent bioaccumulative contaminants that are found ubiquitously in
environmental matrices, including tissues offish, birds, and mammals. The most well-studied
chemical in this group is 2,3,7,8-tetrachlorodibenzo-/>-dioxin (2,3,7,8-TCDD). Demonstrated
toxic effects of 2,3,7,8-TCDD in fish, birds, and mammals include adverse effects on
reproduction, development, and endocrine functions; wasting syndrome; immunotoxicity; and
mortality. Several PCDDs, PCDFs, and PCBs have been shown to cause toxic responses similar
to 2,3,7,8-TCDD, in both laboratory and field situations. In this document, the term "dioxin-like
effects" is used to refer to those effects that are similar to those caused by 2,3,7,8-TCDD, and the
term "dioxin-like chemicals" is used to refer to chemicals that exert such effects through binding
to the aryl hydrocarbon receptor (AHR). For further information regarding dioxin-like effects
observed specifically in wildlife species, refer to U.S. EPA (1993, 2001b) and references therein.
It should be noted that a number of chemicals other than PCDDs, PCDFs, and certain PCBs may
also exert dioxin-like effects through binding to the AHR (see Section 2.1). Although these
chemicals are not specifically addressed in this framework, if they meet the criteria discussed in
Section 2 they may be included in assessments that apply the toxicity equivalence methodology.
Presently, evidence is sufficient to conclude that a common mechanism of action,
involving binding of the chemicals to the AHR as the initial step, underlies 2,3,7,8-TCDD-like
toxicity elicited by these PCDDs, PCDFs, and PCBs (Van den Berg et al., 1998, 2006; Hahn,
2002a). PCDDs, PCDFs, and PCBs present in the environment are generally found as complex
mixtures such that assessment of ecological risk requires a means of quantifying their combined
effects.
The purpose of this framework is to describe a methodology for assessing risks
associated with exposure to complex mixtures of PCDDs, PCDFs, and dioxin-like PCBs. It is not
a comprehensive guide for conducting a risk assessment for PCDDs, PCDFs, and dioxin-like
PCBs, rather it describes how to apply a specific tool, the toxicity equivalence methodology,
within the broader context of an ecological risk assessment. Accordingly, the intended audience
for this framework is risk assessors who have a working knowledge of EPA's Guidelines for
Ecological Risk Assessment (U.S. EPA, 1998) and are familiar with issues related to conducting
risk assessments for dioxin-like chemicals (U.S. EPA, 1993, 2001a). This framework provides
informed risk assessors with a summary of technical insights and recommendations from a
variety of documents and expert workshops on the topic of toxicity equivalence methodology
and its application in ecological risk assessment (U.S. EPA, 1987, 1989, 1991, 2000a, 200la).
The framework also provides ecological risk assessors with an understanding of the uncertainties
associated with the application of the methodology in general and with situation-specific
decisions made in applying the methodology within their risk assessments.
In this framework, definitions and a description of how the methodology has evolved are
described in Chapter 1. Chapter 2 summarizes the toxicity equivalence methodology. Chapter 3
provides ecological risk assessors with an understanding of issues to consider when applying the
toxicity equivalence methodology in ecological risk assessments. Chapter 3 is organized
according to the phases of ecological risk assessment (planning, problem formulation, analysis,
and risk characterization). Chapter 4 concludes by summarizing important benefits, implications,
and uncertainties of the toxicity equivalence methodology as one of several methods within the
broader context of ecological risk assessment.
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General Structure
Representative Examples
6 ^ 4
Polychlorinated dibenzo-p-dioxins (PCDDs)
Polychlorinated dibenzofurans (PCDFs)
Polychlorinated biphenyls (PCBs)
2,3,7,8-Tetrachlorodibenzo-/>-dioxin(TCDD)
Cl
2,3,7,8-Tetrachlorodibenzofuran(TCDF)
3,3',4,4',5-Pentachlorobiphenyl (PeCB)
(PCB 126)
Figure 1. Chemical structure of PCDDs, PCDFs, and PCBs.
Numbers by aromatic ring carbons in general structures represent potential chlorine substitutions.
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Text Box 1. Clarification of terminology.
Acronym used Analogous acronyms
in this framework found in the literature
ReP
RPF
TEF
TEC
Term used
in this framework
Toxicity equivalence
REP, ReP, RP, RPF, TEF
REP, ReP, RP, RPF, TEF
IEF, I-TEF, TEF-WHO,
REP, RPF, RP
TEqC, TEQ, TEq
Analogous terms
found in the literature
Toxicity Equivalency,
Toxicity Equivalent,
Toxic Equivalency,
Toxic Equivalent
1.1. DEFINITIONS
To date, many different terms and acronyms have been used to describe the potency, or
the strength to cause toxic effects, of individual PCDDs, PCDFs, and PCBs relative to 2,3,7,8-
TCDD (see Text Box 1). For example, a
Toxicity Equivalence Factor (or TEF) has
been used to describe the relative potency
of dioxin-like chemicals to affect a single
endpoint in a single study as well as to
describe a relative potency value based on
the results of several studies. Inconsistency
in the use of various terms and
abbreviations associated with the toxicity
equivalence methodology can contribute to
confusion and misunderstanding, and has
led to recommendations to further clarify
terminology and acronyms (U.S. EPA,
200la). In response, this framework
establishes a clear, systematic, and unified
terminology scheme for the toxicity
equivalence methodology, building on the
terminology adopted at the World Health Organization European Centre for Environmental
Health (WHO-ECEH) international consultation (Van den Berg et al., 1998).
This framework employs the following definitions:
Relative Potency (ReP) - Estimate of the potency, relative to 2,3,7,8-TCDD, of an
individual chemical to cause a particular AHR-mediated toxic or biological effect
in an individual organism, cellular, or biochemical assay. The relative potency
estimate for a given chemical must be derived from a single in vitro or in vivo
study, that is, a study in which the potencies of a PCDD, PCDF, or PCB congener
and a reference chemical (2,3,7,8-TCDD or PCB 126) to cause a particular effect
are measured in a single experiment or by the same authors using the same study
design in both experiments. Such an ReP may be suitable for use in risk
assessment, becoming an RPF. Furthermore, some TEFs are currently based on
RePs.
Relative Potency Factor (RPF) - Estimate based on one or more studies of the
potency, relative to 2,3,7,8-TCDD, of an individual chemical to cause AHR-
mediated toxicity or biological effects, determined using careful scientific
judgment after considering all available relative potency data. The ReP database
used to derive an RPF for a chemical may include multiple endpoints, species,
and/or in vitro or in vivo studies. RPFs may be used as alternatives to TEFs when
more specific data for the species, endpoint, and/or site conditions are judged to
improve the accuracy of the risk assessment. If the RPF is based on a single ReP,
the RPF is equal to the ReP.
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Toxicity Equivalence Factor (TEF) - Estimate of the potency, relative to 2,3,7,8-
TCDD, of an individual poly chlorinated dibenzo-p-dioxin, dibenzofuran, or
biphenyl congener, determined using careful scientific judgment after considering
all available relative potency data. EPA presently applies this term only to TEFs
derived through an international scientific consensus-building process supported
by the World Health Organization (Van den Berg et al, 1998, 2006).
Toxicity Equivalence Concentration (TEC) - The TEC is the product of the TEF
or RPF multiplied by the concentration for an individual dioxin-like chemical.
The total TEC for a mixture is calculated as the sum of 2,3,7,8-TCDD
equivalence concentrations of all dioxin-like chemcials present in the mixture.
The WHO-ECEH consultation report (Van den Berg et al., 1998) clarified the
terminology used in the toxicity equivalence methodology to distinguish between REPs and
TEFs. The term relative potency was introduced to refer to estimates of the potencies of
individual PCDDs, PCDFs, and PCBs congeners, relative to 2,3,7,8-TCDD, to cause a particular
toxic or biological effect as determined in a single study. This framework adopts the WHO-
ECEH terminology and definition, except that the acronym "ReP" is used rather than "REP" to
be consistent with use of lower case letters when two or more letters in an acronym represent a
single word. This framework also adopts the WHO-ECEH definition of TEFs as estimates of the
relative potencies of individual dioxins, furans, and PCBs, relative to 2,3,7,8-TCDD, derived
using careful scientific judgment after considering all available data. TEFs are used to convert
concentrations of individual dioxin-like chemicals in tissues or diet to 2,3,7,8-TCDD toxicity
equivalent concentrations.
Additionally, this framework extends the WHO-ECEH terminology by introducing the
term relative potency factor., abbreviated RPF, as an intermediate between ReP and TEF. An
RPF refers to an estimate based on one or more studies of the potency, relative to 2,3,7,8-TCDD,
of an individual chemical to cause AHR-mediated toxicity or biological effects. Hence, the term
RPF is directly analogous to TEF, but an RPF is derived in the context of a specific risk
assessment rather than by international expert consensus. It is hoped that adoption of these more
logically consistent and grammatically correct terms will ultimately aid in understanding and use
of the methodology.
1.2. EVOLUTION OF THE TOXICITY EQUIVALENCE METHODOLOGY
In the 1970s and 1980s, human health risk assessments of complex mixtures of PCDDs
and PCDFs were generally performed including only 2,3,7,8-TCDD or assuming that all dioxin-
like chemicals were equally potent to 2,3,7,8-TCDD (U.S. EPA, 1987, 1989). A review of the
scientific information currently available clearly demonstrates that both of these assumptions
were inaccurate. While many PCDD and PCDF congeners act through a common mechanism of
action (binding and activation of the AHR) and induce similar biochemical and toxicological
effects, the relative potency of individual dioxin-like chemicals to induce such effects has been
shown to vary.
The first use of a toxicity equivalence-like method for risk assessment purposes was
described by Eadon et al. (1986) as a means to estimate potential human health risks associated
with a PCB transformer fire in Binghamton, New York. In an examination of the initial human
health risk assessment methodologies designed to address the emission of dioxins and furans
from waste incinerators, EPA also concluded that TEFs were the best available interim scientific
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policy for dealing with complex mixtures of these contaminants. Hence, in 1987, EPA adopted
an interim procedure, based on TEFs, for estimating the hazard and dose-response of complex
mixtures containing PCDDs and PCDFs in addition to 2,3,7,8-TCDD (U.S. EPA, 1987).
Following adoption of the toxicity equivalence methodology in the United States and
Canada, the North Atlantic Treaty Organization/Committee on the Challenges of Modern Society
(NATO/CCMS) examined the methodology and concluded that it was the best available interim
method for PCDD/PCDF human health risk assessment (NATO, 1988a, b). The TEFs proposed
for the different dioxin-like chemicals were refined by the NATO/CCMS based on inclusion of
more recent data sets, resulting in a greater number of the TEFs being based on toxicity observed
in vivo. The NATO/CCMS panel assigned TEFs to octachlorinated dibenzo-p-dioxin (OCDD)
and octachlorinated dibenzofuran (OCDF), and removed TEFs for all congeners lacking chlorine
in the 2,3,7,8-positions. Although it was indicated that, theoretically, it may be possible to detect
nearly all of the 210 PCDD/PCDF isomers in the environment, only the seventeen 2,3,7,8-
substituted PCDD and PCDF congeners were known to significantly bioaccumulate (Table 1).
EPA officially adopted the revised TEFs in 1989 (TEFs-NATO89), with the caveat that the
methodology remain interim and continued revisions be made (U.S. EPA, 1989; Kutz etal.,
1990). The use of the toxicity equivalence methodology for human health risk assessment and
risk management purposes has since been formally adopted by a number of other countries (e.g.,
Canada, Germany, Italy, the Netherlands, Sweden, and the United Kingdom) (Yrjanheiki, 1992).
Table 1. Number of polychlorinated dioxin, furan, and biphenyl congeners
Chemical Class
Dioxins (PCDDs)
Furans (PCDFs)
Biphenyl s (PCBs)
Number of Congeners
75
135
209
Dioxin-like Chemicals
7
10
12
During the initial development of the toxicity equivalence methodology for
PCDDs/PCDFs, a number of researchers were also examining the structure-activity relationships
for PCBs (see reviews by Safe and co-workers, Leece etal., 1985; Safe 1990; 1994). These
studies revealed that only PCB congeners substituted in the meta and para positions (Figure 2)
were approximate stereo isomers of 2,3,7,8-TCDD and induced dioxin-like biochemical and
toxicological effects. PCBs with a single chlorine substitution in an ortho position on the
biphenyl (mono-ortho) have diminished dioxin-like activity. In some organism classes, (e.g.,
fish), the reduction in dioxin-like activity is substantial.
In 1991, EPA convened a workshop to consider TEFs for PCBs (Barnes et al., 1991; U.S.
EPA, 1991). From the workshop it was concluded that a small subset of the PCBs displayed
dioxin-like activity and met the criteria for inclusion in the methodology. It was also noted that
the PCBs not included in the toxicity equivalence methodology (i.e., the non-dioxin-like PCBs)
are not a single class of chemicals and have multiple toxicities with separate structure-activity
relationships (Barnes et al., 1991).
-------
meta ortho ortho
3' 2' 2
para 4
.// \
6" 6
ortho ortho
Figure 2. Structure of PCB molecule and positions for chlorine substitution.
In the years since initial adoption of the toxicity equivalence methodology, additional data have
accumulated on the toxicological potency of individual PCDDs, PCDFs, and PCBs relative to
2,3,7,8-TCDD. A joint project to harmonize toxicity equivalence methodologies for dioxin-like
chemicals, conducted by the WHO-ECEH and the International Programme on Chemical Safety
(IPCS), resulted in the development of a database consisting of all relevant toxicological data for
dioxin-like chemicals available through 1993. Following a review of almost 1,200 peer-reviewed
publications, 146 were selected and analyzed to derive TEFs for PCBs (TEFs-WHOg/t). Based on
the reported results for 14 different biological and toxicological parameters from a total of 60
articles, a panel of experts from eight countries recommended interim TEFs for 13 dioxin-like
PCBs (Ahlborg et a/., 1994). Application of this methodology in human health risk assessment
was reaffirmed in EPA's Supplementary Guidance for Conducting Health Risk Assessment of
Chemical Mixtures (U.S. EPA, 2000a).
At a second WHO-ECEH consultation in 1997, the TEFs for PCDDs, PCDFs, and PCBs
were reviewed and the toxicity equivalence methodology expanded, based on availability of
additional data, to include class-specific TEFs for mammals, birds, and fish. TEFs for seven
PCDD, 10 PCDF and 12 PCB congeners for mammals, birds, and fish (TEFs-WHO98; Table 2)
were included in the resulting report (Van den Berg et a/., 1998). At the WHO-ECEH
consultation, the TEFs previously assigned to PCB 170 and PCB 180 were withdrawn and a TEF
for PCB 81 was established, such that the number of PCB congeners with TEFs assigned was
reduced from 13 to 12 (Van den Berg et a/., 1998). It should be noted that the species and
endpoints examined for assignment of TEFs varied among individual dioxin-like chemicals. Van
den Berg et al. (1998) also provide greater documentation on how the expert panel at the WHO-
ECEH consultation selected studies for consideration, derived relative potency factors from
individual studies, and developed TEFs from the existing database. Although a number of
uncertainties associated with the toxicity equivalence methodology have been identified (Van
den Berg et al., 1998), it was the conclusion of the WHO-ECEH consultation that an additive
toxicity equivalence methodology remained the most appropriate risk assessment method for
assessing complex mixtures of dioxin-like PCDDs, PCDFs, and PCBs.
In 1998, EPA and DOI sponsored a meeting entitled: "Workshop on the Application of
2,3,7,8-TCDD Toxicity Equivalency Factors to Fish and Wildlife. " The maj or obj ective of the
workshop was to address uncertainties associated with the use of the toxicity equivalence
methodology in ecological risk assessment. Thirty-one experts from academia, government,
industry, and environmental groups participated in the workshop. General conclusions regarding
application of the toxicity equivalence methodology in ecological risk assessment included:
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• The toxicity equivalence methodology is technically appropriate for evaluating risks
to fish, birds, and mammals associated with AHR agonists and it can support risk
analyses beyond screening-level assessments.
• The methodology entails less uncertainty and is less likely to underestimate risks than
are methods based on single chemicals. Specifically, because the methodology takes
into account the possible effects of the suite of dioxin-like chemicals found in
complex environmental mixtures, it is less likely to underestimate risk than methods
based on only one of these chemicals (i.e., 2,3,7,8-TCDD). Further, because total
PCBs in the environment can be comprised of many chemicals that vary in
concentration and relative potency as AHR agonists, the toxicity equivalence
methodology provides a means for accounting for their variable potency.
• The uncertainties associated with using the methodology are not thought to be larger
than other sources of uncertainty within the ecological risk assessment process (e.g.,
dose-response assessment, exposure assessment, and risk characterization).
For a thorough understanding of the technical issues discussed and conclusions drawn
from the EPA/DOI workshop, refer to U.S. EPA (200la).
In 2005, the WHO International Programme on Chemical Safety held another expert
meeting during which the 1998 mammalian TEFs-WHOgg were re-evaluated. Preceding this
meeting, a one-day public hearing was convened at which members of the expert panel discussed
various aspects of the TEF concept with stakeholders and interested parties. The 2005 WHO re-
evaluation relied extensively on the mammalian TEF database recently published by Haws et al.
(2006); however, the expert panel used all available RePs, including those from studies
published since 1997, in making their assessments. Changes made to the mammalian TEFs-
WHOgg are reflected in Table 2 and designated as TEFs-WHOos. This expert panel also
concluded that additivity, an important pre-requisite of the TEF concept, was further confirmed
by recent in vivo mixture studies by Walker et al. (2005) and Van den Berg et al. (2006).
In 2006, the National Research Council of the National Academies (NRC) re-affirmed
the scientific basis and credibility of the use of the toxicity equivalence methodology in risk
assessment. As part of their evaluation of the draft Exposure and Health Reassessment of 2,3,7,8-
TCDD and Related Compounds (U.S. EPA 2003a), the NRC reviewed EPA's use of the Toxicity
Equivalence Methodology in assessing risks from dioxin-like compounds. The NRC concluded
that, "...the toxic equivalency factor methodology provides a reasonable, scientifically justifiable,
and widely accepted method to estimate the relative potency of DLCs" (DLC = dioxin-like
compounds; NRC, 2006). In addition, the NRC Committee examined a number general of issues
that have been raised regarding the assumptions underlying the use of the toxicity equivalence
methodology. One important conclusion of the Committee that is particularly relevant to this
document is that addressing the additivity assumption. The Committee concluded that "from an
overall perspective, this assumption appears valid, at least in the context of risk assessment"
(NRC, 2006).
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Table 2. World Health Organization TEFs for mammals, birds, and fish
Congener
TEF
Mammals1
Birds2
Fish2
Dioxins
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
1
1
0.1
0.1
0.1
0.01
0.0003
1
1
0.05
0.01
0.1
0.001
0.0001
1
1
0.5
0.01
0.01
0.001
0.0001
Furans
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-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
OCDF
0.1
0.03
0.3
0.1
0.1
0.1
0.1
0.01
0.01
0.0003
1
0.1
1
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
0.05
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Non-ortho PCBs
3,3',4,4'-TCB (77)
3,4,4',5-TCB (81)
3,3',4,4',5-PeCB (126)
3,3',4,4',5,5'-HxCB (169)
0.0001
0.0003
0.1
0.03
0.05
0.1
0.1
0.001
0.0001
0.0005
0.005
0.00005
Mono-ortho PCBs
2,3,3',4,4'-PeCB (105)
2,3,4,4',5-PeCB(114)
2,3',4,4',5-PeCB(118)
2',3,4,4',5-PeCB (123)
2,3,3',4,4',5-HxCB (156)
2,3,3',4,4',5'-HxCB (157)
2,3',4,4',5,5'-HxCB (167)
2,3,3',4,4',5,5'-HeCB (189)
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.0001
0.0001
0.00001
0.00001
0.0001
0.0001
0.00001
0.00001
O.000005
O.000005
O.000005
O.000005
O.000005
O.000005
O.000005
O.000005
Source: Van den Berg et al., 2006; 2Van den Berg et al., 1998.
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2. TOXICITY EQUIVALENCE METHODOLOGY
The toxicity equivalence methodology is a tool for assessing the cumulative toxicity of a
complex mixture of dioxin-like PCDDs, PCDFs, and PCBs. To apply the methodology to such a
mixture, the following activities need to be performed for each chemical present in the mixture:
• Verify that the chemical is known to act through the AHR-mediated mechanism of
action.
• Review potency estimates of the chemical relative to 2,3,7,8-TCDD based on in vivo
or in vitro studies.
• Select or derive an appropriate relative potency estimate (ReP, RPF, TEF) for the
chemical.
• Measure or predict the concentration of the chemical in the appropriate tissues or diet
of each species being assessed.
• Apply the relative potency estimate for the chemical to calculate its TEC.
Extensive research efforts and numerous expert workshops have resulted in both
verification that certain PCDDs, PCDFs, and PCBs act by the AHR-mediated mechanism of
action and derivation of relative potency estimates for these dioxin-like chemicals. These efforts
are summarized and references are provided in Sections 1.2 and 2.1 of this document. The
selection or derivation of the appropriate relative potency estimates and the calculation of a TEC
are required for each ecological risk assessment that uses the toxicity equivalence methodology.
These activities are summarized in Sections 2.2 and 2.3 and discussed further in Chapter 3.
2.1. AHR-MEDIATED MECHANISM AND ASSIGNMENT OF RELATIVE POTENCY
Inherent in the toxicity equivalence methodology are the assumptions that the effect of
individual AHR agonists act via the same AHR-mediated mechanism and that their combined
effects are additive. The general basis for the methodology is the observation that the AHR
mediates most if not all biological and toxic effects induced by AHR agonists (Safe, 1990; Okey
etal., 1994; Birnbaum, 1994; Hankinson, 1995; NRC, 2006). Recent advances in molecular
biology have provided techniques to verify that a functional AHR is required for dioxin-like
toxicity to be elicited. In organisms and cells that have been engineered such that the expression
of a functional AHR is reduced or eliminated, toxicity following exposure to 2,3,7,8-TCDD and
other AHR agonists has been reduced or eliminated. Hence, dioxin-like chemicals exert their
activity by binding with the AHR (Sewall and Lucier, 1995; DeVito and Birnbaum, 1995).
However, just because a congener can bind to the AHR, does not necessarily mean that it is able
to "activate" all of the processes which underlie the development of toxic effects in an organism.
Hence, none of the current WHO-TEFs are based on AHR binding alone.
The scientific defensibility of the second assumption - that the combined effects of AHR
agonists are additive - has been raised since the onset of the use of TEFs. Arguments challenging
this assumption include the presence of competing agonists or antagonists in various complex
mixtures from environmental sources, interactions based on non-dioxin-like activities
(antagonism or synergism), and the fact that dose-response curves for various effects may not be
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parallel for all AHR agonists assigned TEFs. Despite these concerns, empirical data support the
use of the additivity concept.
A substantial effort has been made to test the assumptions of additivity and the ability of
the toxicity equivalence methodology to predict the effects of mixtures of AHR agonists. These
efforts have included environmental, commercial, and laboratory derived mixtures. Studies in
fish and wildlife species of mixtures of PCDDs, PCDFs, and PCBs support the additivity
assumption (Zabel et al, 1995; Walker et al, 1996; Tillitt and Wright, 1997). Further, numerous
studies that have examined the effects of environmental mixtures in marine mammals and avian
species show a correlation between toxic effects and dietary concentrations (Ross et al., 1996;
Summer etal., 1996a, b; Giesy andKannan, 1998; Restum etal, 1998; Shipp etal, 1998a, b;
Ross, 2000). More recently, the 2005 WHO-IPCS expert panel (Van den Berg etal. 2006) re-
visited the additivity assumption issue and found that additivity, an important pre-requisite of the
TEF concept, was further confirmed by recent in vivo mixture studies by Walker et al. (2005).
Likewise, the NRC review of EPA's Exposure and Health Reassessment of 2,3,7,8-TCDD and
Related Compounds included an evaluation of the additivity assumption. The NRC Committee
concluded that "from an overall perspective, this assumption appears valid, at least in the context
of risk assessment" (NRC, 2006).
Several criteria have been developed that are deemed requisite for including a chemical
in the toxicity equivalence methodology. These criteria were first employed in assigning TEFs
for PCBs (Ahlborg, 1994) and were affirmed in the process of assigning taxonomic class-specific
TEFs (Van den Berg etal, 1998; 2006). The criteria are:
• Structural similarity to 2,3,7,8-TCDD;
• Demonstrated binding to the AHR;
• Demonstrated ability to elicit an AHR-mediated toxic or biochemical effect; and
• Persistence and bioaccumulation in the food chain.
Using these inclusion criteria, the expert panel at the WHO-ECEH consultation
developed a TEF scheme (TEFs-WHO98) that includes 7 PCDD, 10 PCDF, and 12 PCB
congeners (Table 2).
In June 2005, a WHO-IPCS expert meeting was convened at which the mammalian
TEFs-WHOgg were re-evaluated. For the re-evaluation, the refined mammalian TEF database
published by Haws et al. (2006) was used as a starting point. The expert panel used all available
RePs, whether or not they were included in this database, and made decisions based on a
combination of ReP distributions from the database, expert judgment, and point estimates (Van
den Berg et al, 2006). Changes in TEF values were determined by the expert panel for one
dioxin (OCDD), three furans (2,3,4,7,8-PeCDF, 1,2,3,7,8-PeCDF, and OCDF), two non-ortho
PCBs, and all relevant mono-ort/zo-substituted PCBs (Van den Berg etal, 2006). These recent
changes in the mammalian TEFs (TEFs-WHOgg/os) are represented in Table 2 and in the relevant
examples provided in this document.
For PCBs, the toxicity equivalence methodology applies only to dioxin-like PCBs. Other
PCBs, sometimes referred to as "non-dioxin-like PCBs," are not a single class of chemicals and
may have an additional spectrum of toxicological properties that are not accounted for in the
toxicity equivalence methodology. Although current evidence indicates that the greatest potential
for effects on ecological endpoints of most concern (e.g., growth, survival, reproduction) from
exposure to PCB mixtures is from the AHR agonists (Giesy and Kannan, 1998; Rice et al,
10
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2002), risk estimates based solely on the 12 dioxin-like PCBs may underestimate the total PCB
risk. Hence, because PCB mixtures contain both dioxin-like and non-dioxin-like congeners,
assessing ecological risks posed by both types of congeners may be warranted.
A dual analysis of risks based on total PCBs and on toxicity equivalence for dioxin-like
PCBs is an approach that may be taken to assess PCB mixtures (Beltman et a/., 1997; Brunstrom
and Halldin, 2000; Finley etal., 1997; Giesy and Kannan, 1998; U.S. EPA 2005; note, however,
that only Giesy and Kannan incorporated the 1998 taxa-specific TEFs-WHOgg in their analysis).
EPA currently recommends this combined approach for assessing PCB cancer risks to humans
(U.S. EPA, 1996). As more information becomes available about the toxicity mechanisms and
relative potency of specific non-dioxin-like PCB congeners, alternative methods for assessing
their risk will likely emerge.
In addition to the PCDDs, PCDFs, and PCBs that are the subject of this framework, a
wide variety of structurally diverse anthropogenic chemicals are capable of interacting with the
AHR (Denison and Nagy, 2003). These chemicals also have a broad range of potencies at
inducing dioxin-like effects in experimental systems. Other chemicals that bind and activate the
AHR include industrial chemicals (e.g., polyhalogenated biphenyls, halogenated naphthalenes,
chlorinated paraffins), pesticides (e.g., hexachlorobenzene), combustion products (e.g.,
unsubstituted poly cyclic aromatic hydrocarbons (PAHs)), and flame retardants and their
byproducts (e.g., brominated dioxins, dibenzofurans, biphenyls, diphenyl ethers, and
naphthalenes). The expert panel at the 1997 WHO consultation concluded that "at present,
insufficient environmental and toxicological data are available to establish a TEF value" for
these other chemicals (Van den Berg et a/., 1998), and the 2005 WHO-ECEH meeting came to
similar conclusions (Van den Berg etal., 2006). Likewise, the NRC Committee concluded that
currently, insufficient toxicological and environmental distribution studies and lack of consensus
TEFs may hinder consideration of these chemicals in risk assessments but that EPA should
include these chemicals in TEC calculations when TEFs are developed (NRC, 2006).
Conceptually, a methodology based on toxicity equivalence can be applied to other
chemicals that share a common mechanism of toxicity and to which aggregate exposure may
occur. For example, EPA has recently issued guidance based on the toxicity equivalence concept
for assessing cumulative health risks of pesticides that have a common mechanism of action
(U.S. EPA, 2002). In ecological risk assessment, application of toxicity equivalence to chemicals
other than those that interact with the AHR has been more limited. The government of Canada
has recently used a toxicity equivalence approach in assessing certain nonylphenol ethoxylates
(Environment Canada and Health Canada, 2001). Toxicity equivalence and common mechanism
of action also provide the foundation for recent efforts to develop water quality values for
mixtures of type I narcotic chemicals in general and PAHs in particular (DiToro et a/., 2000;
DiToro and McGrath, 2000). Many of the principles described in this framework may be
applicable to other chemical mixtures, but risk assessors should take care in deciding whether a
toxicity equivalence approach is appropriate for their mixture of concern (U.S. EPA, 2000a).
2.2. SELECTION OF THE APPROPRIATE RELATIVE POTENCY FACTORS
One of the most important considerations to be made when applying the toxicity
equivalence methodology to ecological risk assessment is what relative potency value to use for
each dioxin-like chemical. One approach is to use the TEFs-WHOgg/os. Alternatively, ReP data
from a single study or from multiple relevant studies may be selected as the basis for deriving an
RPF to be used in lieu of a TEF. A clear understanding of the difference between RePs, RPFs,
11
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and TEFs is critical for making this decision and is thus described here. The issues to consider
when selecting an estimate are described in Section 3.3.2 of this framework.
The relative potency of a dioxin-like chemical may be determined from a variety of effect
concentrations; for example, effective concentration (ECX), effective dose (EDX), lethal dose
(LDX), no-observed adverse Effect Level (NOAEL), lowest observed adverse effect level
(LOAEL), benchmark dose, or entire dose-response curves have all been used. To date, RePs
have most commonly been determined as the ECso, EDso, or LDso of 2,3,7,8-TCDD divided by
the ECso, ED50, or LD50 of the individual dioxin-like chemical. RePs have been derived from in
vitro and in vivo studies and include endpoints ranging from biochemical changes (e.g.,
cytochrome P4501A induction) to mortality. An RPF may be derived from a database of ReP
values that includes multiple endpoints, species, and in vitro or in vivo studies. Both RePs and
RPFs may be derived and used as alternatives to TEFs when more specific data for the species,
endpoint, and/or site conditions are judged to improve the accuracy of a risk assessment. An ReP
or RPF may also be derived and used for dioxin-like chemicals not currently assigned a WHO-
TEF, but for which data are judged sufficient to include in an assessment of AHR-mediated
risks. Risk assessors can learn more about other halogenated chemicals that meet the criteria for
inclusion in the TEF methodology in Van den Berg et al. (1998; 2006) and the references
therein.
Values of the TEFs-WHOgg/os reproduced in Table 2 were determined based on the
consensus judgment of the experts present at the WHO consultations (Van den Berg et a/., 1998;
2006). The TEFs-WHOgg/os were derived from considering all available RePs and then rounded
up or down to the nearest half-order of magnitude for fish and bird TEFS (Van den Berg et a/.,
1998) and one order of magnitude for mammal TEFs (Van den Berg etal., 2006). A summary,
through 1996, of available RePs can be found in the Karolinska Institute database. A link to this
database is available at http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=55669 (Haws et a/.,
2006). Additional data from which to determine RePs and/or derive RPFs have been reported in
the literature since 1996, and it is expected that more will be available in the future.
2.3. TOXICITY EQUIVALENCE CONCENTRATION
The 2,3,7,8-TCDD TEC is the primary expression of exposure to an organism in an
ecological risk assessment involving complex mixtures of PCDDs, PCDFs, PCBs, and/or any
other AHR agonists which may contribute to the toxicity. While the TEC is best based on dioxin-
like chemical concentrations in tissues of organisms at risk, in ecological risk assessments it has
often been based on concentrations in the diet.
k
TEC = X Cn * TEFn
n=\
(2-1)
Where: Cn = concentration of dioxin-like chemical n in an organism or its diet
TEFn = toxicity equivalence factor for dioxin-like chemical n
(Note: An RPF can replace the TEF term)
k = number of toxic dioxin-like chemicals in mixture
When TECs in organisms of concern or their diet are unknown, they may be calculated
from dioxin-like chemical concentrations in water, sediment, or soil only if bioaccumulation
12
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(e.g., BAFs, BSAFs, or bioaccumulation modeling) is appropriately incorporated to relate the
concentrations of each dioxin-like chemical in the media to concentrations in the organism or its
diet (see Sections 3.3.1.3 and 3.3.1.4 for further discussion).
13
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3. APPLICATION OF THE TOXICITY EQUIVALENCE METHODOLOGY IN
ECOLOGICAL RISK ASSESSMENT
In this framework, application of the toxicity equivalence methodology is presented in
the context of each phase of the ecological risk assessment paradigm: planning, problem
formulation, analysis, and risk characterization (See Figure 3). Note that this framework focuses
on providing specific information necessary for applying the toxicity equivalence methodology
within an ecological risk assessment involving PCDDs, PCDFs, and PCBs, but does not discuss
the many other aspects necessary for conducting such a risk assessment. Risk assessors may refer
to the Guidelines for Ecological Risk Assessment for additional information on components of
ecological risk assessment (U.S. EPA, 1998). Issues beyond the toxicity equivalence
methodology that are pertinent to problem formulation, analysis (i.e., characterization of
exposure and effects), and risk characterization for dioxin-like chemicals have been described in
depth previously (U.S. EPA, 1993; 1995b, c; 2001b; 2003a, 2005). Risk assessors are referred to
these publications to address broader issues associated with conducting a risk assessment
involving PCDDs, PCDFs, and PCBs.
3.1. CONSIDERATIONS IN PLANNING
Under the Guidelines for Ecological Risk Assessment (U.S. EPA, 1998), the problem
formulation phase of a risk assessment is preceded by a dialogue among risk managers, risk
assessors, and other interested parties. During this planning phase, risk managers and risk
assessors develop management goals and determine the size and scope of the ecological risk
assessment that is needed to support
the risk management decision.
Planning involves a
determination of the likely chemicals
of concern and the method(s) for
estimating risks from exposure to these
chemicals. Multiple factors (cost, time,
data adequacy, scientific uncertainty,
political or social conditions) may be
considered in selecting methods and
measurements. The cost of analytical
methods or measurements will vary
depending on the complexity of the
matrix and the data quality objectives
for each project. Each method has its
source of uncertainty due to lack of
Text Box 2. Questions for planning
Are there chemicals of concern other than those with
dioxin-like activity?
Is evaluation of "dioxin-like" toxicity risks necessary to
meet risk management objectives?
Is the accuracy provided by a congener-specific
chemical analysis and toxicity equivalence
methodology necessary for making risk management
decisions at the site?
Will TEFs-WHO98/o5 or more specific RPFs be needed
to make risk management decisions?
Will multiple lines of evidence (bioanalytical results,
field studies) be used to inform the risk management
decision?
knowledge and variability. The risk
assessor needs to define these for each
method to provide the proper
foundation for selection of the method appropriate for the particular decision. The risk assessor
and others interested in evaluating the risks should review all methods carefully to ascertain the
most appropriate method for their specific situation. Text Box 2 provides questions that should
be considered during planning.
14
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Ecological Risk Assessment
PROBLEM FORMULATION
A
N
A
L
Y
S
I
S
Characterization
of Exposure
Characterization
of
Ecological
Effects
RISK CHARACTERIZATION
Communicating Results
to the Risk Manager
Risk Management
Figure 3. The framework for ecological risk assessment
Source: U.S. EPA, 1998
For example, it is possible that even though dioxin-like chemicals are present, risks posed
by another chemical(s) may be expected to exceed risks posed by the dioxin-like chemicals (e.g.,
trace amounts of low potency dioxin-like chemical are present vs. large amounts of another toxic
chemical). If achieving the management goal for the primary chemical(s) of concern also results
in the removal of risks posed by the dioxin-like chemical(s), it may be prudent to use methods
that are less resource intensive than congener-specific chemical analysis and the toxicity
equivalence methodology to estimate risks from and/or monitor concentrations of the dioxin-like
chemicals.
15
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Risk assessors should consider the following when selecting the most appropriate
analytical measurement for estimating risks from dioxin-like chemicals:
1. Environmental PCB mixtures often cannot be adequately described by referencing
Aroclor standards due to the subjective assignment of congeners (i.e., Aroclors are
identified on the basis of congener profiles present in the original formulation, but
environmental weathering may significantly change these congener profiles such that
subjective judgment is used to determine which Aroclor the environmental mixture
resembles after weathering).
2. Homolog (level-of-chlorination) analysis can overestimate the total PCDD, PCDF, and
PCB concentrations because the congeners measured in a specific homolog group
analysis may overlap (i.e., also be measured in another homolog group analysis).
3. Measurements of mixture concentrations (e.g., Aroclors, homologs, and total PCBs)
are not amenable to fate and transport or bioaccumulation modeling.
4. Large uncertainty may be introduced in assessing exposure and effects by assuming
that congener profiles present in commercial mixtures used in toxicity tests (e.g.,
Aroclors) are representative of PCB profiles in weathered environmental samples
(either exposure media or biota).
5. The toxicity equivalence methodology cannot be directly applied to homolog groups,
Aroclors, or total PCBs.
6. Uncertainty associated with application of TEFs to PCB congener concentrations
estimated from Aroclor or homolog analyses is probably large due to differential
weathering and fate and transport processes.
7. A dual analysis of risks based on total PCBs and on toxicity equivalence for dioxin-
like PCBs is an approach that may be taken to assess dioxin and non-dioxin-like
effects of PCB mixtures.
8. Regardless of the measurements or models used in the risk assessment, the chemical
measurements should be reported in a manner that is transparent to the risk managers,
including a characterization of the uncertainties associated with undetected chemicals.
9. In any risk assessment the dose metric (i.e., the measurement or prediction of chemical
concentrations) should be consistent between the exposure assessment and the effects
assessment. For example, if the dose-response relationship used in the effects
assessment is based on tissue concentrations, exposure estimates would also need to be
based on concentrations in tissue of the species of concern.
3.1.1. Benefits of the Toxicity Equivalence Methodology
This framework is designed to address those risk assessments where PCDDs, PCDFs, and
PCBs are present and the toxicity equivalence methodology is the appropriate method for
estimating the AHR-mediated risks. In these cases, use of the toxicity equivalence methodology
results in more accurate exposure and effects analysis for dioxin-like chemicals. Consequently,
16
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risk managers may better formulate risk management strategies and evaluate risk management
alternatives to mediate the effects of such chemical stressors.
The toxicity equivalence methodology is appropriate and applicable in ecological risk
assessments involving both aquatic and terrestrial systems (U.S. EPA, 200la). The toxicity
equivalence methodology is well accepted in the scientific community, the international risk
assessment community, and EPA for human health risk assessment (Ahlborg et a/., 1994; Barnes
etal, 1991;Eadonefor/., 1986; U.S. EPA, 1987; 1989; 1991; 200la; NATO, 1988a, b; NRC,
2006; Van den Berg etal, 1998; 2006; Yrjanheiki, 1992). Certain aspects related to application
of the methodology (e.g., bioaccumulation) have been better described and studied in aquatic
systems, but the same principles apply to terrestrial systems.
In addition to being applicable to risk assessments of different levels of complexity, the
toxicity equivalence methodology can be applied to assessments that evaluate the likelihood that
effects were caused by past exposure to chemical stressors (retrospective assessments) and
assessments that predict the likelihood of future adverse effects (prospective assessments). An
example of the former is an aquatic system where adverse effects have been observed in fish and
fish-eating birds and mammals, and the ecological risk assessor wishes to determine the degree
to which existing sediment contamination from dioxin-like chemicals may be responsible. An
example of the latter is the evaluation of the potential impacts of an industrial facility anticipated
to discharge dioxin-like chemicals into an aquatic system. In both examples, when coupled with
techniques to estimate dioxin-like PCDD, PCDF, and PCB fate, transport, and accumulation in
living organisms, the toxicity equivalence methodology could be used to estimate the cumulative
toxicity of dioxin-like chemicals to species of concern.
3.1.2. Methodological Considerations
As with any method, the ecological risk assessor should understand and verify that
assumptions inherent in applying the toxicity equivalence methodology are valid for the specific
situation to which the methodology is being applied (e.g., the chemicals of concern are AHR
agonists; organisms are sensitive to an AHR-mediated mechanism of toxicity; congener-specific
exposure data are available). The toxicity equivalence methodology described in this framework
only accounts for ecological effects associated with dioxin-like chemicals. Additional methods
must be employed to account for other effects associated with dioxins, furans, and PCBs as well
as other chemicals that may be present.
If the toxicity equivalence methodology is selected, the managers and risk assessors must
decide whether to use the default TEFs-WHOgg/os or more specific RPFs or RePs. Use of the
TEFs-WHOgg/os has several advantages, including: 1) minimal effort required on the part of the
risk assessor in selecting and/or reviewing relative potency studies; and 2) consistency in
approach used across sites. However, it may be decided to increase accuracy by selecting RePs
and deriving RPFs that are more specific for species and/or endpoints of concern. The decision to
use TEFs-WHOgg/os or more specific RePs and RPFs will depend on the risk management goal,
the resources available to complete the risk assessment, site specific conditions, and availability
of ReP data for the species and/or endpoint of concern. The benefits of deriving assessment-
specific RPFs are described in Section 3.3.2.
These are some, but not all, of the variables that should be considered when selecting the
appropriate method for chemical analyses and estimating risks from exposure to dioxin-like
chemicals.
There are several other methods (bioanalytical tools, field surveys) that may provide
additional lines of evidence to support the risk estimate derived from using the toxicity
17
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equivalence methodology. Bioanalytical tools have the advantage of measuring the integrated
effects of complex mixtures of AHR agonists. Such bioanalytical tools have the potential of
accounting for, in biological response, chemicals that act via the AHR which would not be
identified by a chemical analysis that measures only PCDDs, PCDFs, and PCBs. TECs
determined by bioanalytical means can typically be obtained more quickly and at a lower cost
than TECs determined by chemical analysis. However, due to current technical limitations, lack
of standard testing procedures, and lack of established quality criteria associated with existing
bioanalytical tools, the experts at the EPA/DOI workshop concluded that such bioanalytical tools
should not be used as an alternative to congener-specific analysis and the toxicity equivalence
methodology (U.S. EPA 200la). Rather, these bioanalytical analyses are complementary tools,
particularly useful for defining the spatial extent of contamination, for prioritizing remedial
actions, and for providing a relative measure of TEC between different environmental media
(U.S. EPA, 2001a; Van den Berg etal., 1998). The uncertainties associated with bioanalytical
methods are discussed in Section 3.4.2.
3.2. CONSIDERATIONS IN PROBLEM FORMULATION
Problem formulation, which follows planning, provides the foundation for the entire risk
assessment (U.S. EPA, 1998).
During problem formulation,
preliminary hypotheses about
why ecological effects have
occurred, or may occur, as a
f , and PCBs?
consequence or exposure to
Text Box 3. Questions for problem formulation.
Are the chemicals of concern dioxin-like PCDDs, PCDFs,
Assessment Endpoint - Has the initial evaluation of
ecological setting identified species that are both exposed
to and sensitive to "dioxin-like" toxicity?
Conceptual Model - Does the conceptual model describe
the relationship and linkages between sources, fate &
transport, bioaccumulation of dioxin-like chemicals, and
exposures to identified assessment endpoint entities?
Are congener-specific exposure data available or
obtainable?
dioxin-like PCDDs, PCDFs, and
PCBs are generated and
evaluated. Problem formulation
also involves selecting
assessment endpoints that are
relevant to risk management
decisions (Section 3.2.1),
developing conceptual models
that describe the key relationships
between dioxin-like PCDDs,
PCDFs, and PCBs and
assessment endpoints (Section 3.2.2), and preparing an analysis plan (Section 3.2.3). Text Box 3
shows questions that should be considered during problem formulation.
3.2.1. Assessment Endpoints
Assessment endpoints are "explicit expressions of the environmental values that are to be
protected, operationally defined as an ecological entity and its attributes" (U.S. EPA, 1998).
Three principal criteria are used to select assessment endpoints: susceptibility to known or
potential chemical stressors, ecological relevance, and relevance to management goals.
Susceptibility involves two major factors: sensitivity (how readily an organism is affected by
these chemicals) and exposure (the frequency, duration, and intensity of contact between an
organism and these chemicals). This section considers the unique characteristics and effects of
dioxin-like PCDDs, PCDFs, and PCBs in identifying the organisms and attributes that may be
candidates for assessment endpoints under the first two criteria, susceptibility and ecological
18
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relevance. The third criterion, relevance to management goals, is discussed only briefly, since it
relates to the values placed on different assessment endpoints rather than particular
characteristics of dioxin-like chemicals.
3.2.1.1. Susceptibility: Sensitivity
Because of the fundamental role played by the AHR in toxicity caused by dioxin-like
chemicals, presence of the AHR is an important indicator of an organism's potential
susceptibility to toxicity from these chemicals. One or more forms of the AHR have been
identified in numerous mammalian, avian, and fish species (for a review, see Hahn, 1998,
2002a). Accordingly, dioxin-like toxicity is clearly elicited by various PCDDs, PCDFs, and
PCBs in a variety of mammals, birds, and fish (Peterson etal., 1993; Theobald etal., 2003; U.S.
EPA, 1993; U.S. EPA, 200Ib). Species- and class-specific differences in AHR number and
function have been identified in fish and birds (Hahn et a/., 1997; Karchner et a/., 1999; Abnet et
a/., 1999; Hansson et a/., 2003) illustrating that the issue regarding presence or absence of an
AHR homolog is complex. For example, different fish species can have multiple AHR
homologs; zebrafish and Atlantic killifish both have AHR1 and AHR2. However, while both
homologs are functional in killifish {i.e., bind 2,3,7,8-TCDD and cause effects), only AHR2 is
active in zebrafish. It is not yet clear whether these differences in AHR diversity and function
play a role in species differences in sensitivity to dioxin-like toxicity.
Homologs of the AHR have also been identified in other classes of organisms, including
one reptile and one amphibian species (Hahn, 2002a). It has been demonstrated that several
marine species have cytosolic proteins that bind a dioxin analog (Brown et a/., 1997). Further
analysis reveals that the amino acid sequence of these proteins is closely related to vertebrate
AHRs. However, these binding proteins do not bind the prototypical vertebrate AHR ligands,
2,3,7,8-TCDD and p-naphthoflavone, which distinguishes them from vertebrate AHRs (Butler et
a/., 2001). It is not yet known whether any of these invertebrate proteins have any role in
producing toxic responses. Effects data, described below, are extremely limited for amphibians,
reptiles, and invertebrates and resulted from exposure to relatively high chemical concentrations.
A summary of effects that have been observed in various animal species is presented in Table 3.
Reproductive and developmental effects are generally among the most sensitive toxicity
endpoints elicited by dioxin-like PCDDs, PCDFs, and PCBs in mammals, birds, and fish.
Developmental effects are manifested in embryonic or early life stages and hence these life
stages are generally more sensitive than juvenile or adult stages in susceptible mammals, birds,
and fish. In addition, reproductive and developmental effects are often considered among the
most relevant toxicity endpoints in ecological risk assessment as these toxicity endpoints may
lead to adverse impacts on wildlife populations (U.S. EPA, 1993, U.S. EPA, 1995a).
The relative sensitivity to dioxin-like toxicity among species that possess the AHR varies
greatly, even within taxonomic class. Inter-species differences in sensitivity exist even when
considering only developmental toxicity or mortality endpoints. A variety of mammals,
including laboratory rodents, monkeys, and mink, have been shown to be sensitive to 2,3,7,8-
TCDD-induced reproductive and developmental toxicity and prenatal or early life stage
mortality, although it is often difficult to quantify the cross-species range in sensitivity in
mammals due to differences in exposure regimens (Peterson, etal., 1993). When administered
doses are converted to body burden concentrations to facilitate cross-species and cross-endpoint
comparisons among mammals, the lowest doses resulting in significant effects on a variety of
non-cancer endpoints are quite similar among rodents and monkeys, with only an approximately
10-fold range in LOAELs (DeVito, etal, 1995; WHO, 1998; van Leeuwen, 2000). The
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Table 3. Effects of 2,3,7,8-TCDD and related chemicals in different animal
species
Effect
Presence of AHR
Binding of 2,3,7,8- TCDD:AHR
Complex to the DRE (enhancer)
Enzyme induction
Acute lethality
Wasting syndrome
Hepatotoxicity
(pathology, hyperplasia,
hypertrophy, porphyria)
Endocrine effects
Immunotoxicity
Carcinogenicity
Developmental and Reproductive
Toxicity
(mortality, teratogenesis, embryo-
fetal toxicity, including
neurological, immunological and/or
endocrine effects during perinatal
period)
Fish
+ [1,2]
+ [3-6]
+ [7-11]
[12,13]
+ [14]
+ [13,15-
17]
+ [18,19]
+ [20]
+ [21]
[15,16,2
2-26]
Birds
Avian
Wildlife
[1,2,27]
+ [28]
33]
+ [34]
+ [34]
+/- [35-
37]
+/- [38-
40]
+ [22,
31,32,41,
42]
Chicken
+ [1,2]
[3,43,44]
+ [29-
31,44-
49]
+ [50]
+ [50]
+ [50-
53]
+ [22,
32, 50,
54-59]
Mammals
Aquatic
Mammals
+ [1,2]
+ [60]
+ [61]
0[62]
Mink
+ [63]
+ [64]
+ [64-
66]
+ [66-
70]
Laboratory
Mammals*
+ [1,2]
+ [3,71]
+ [72-80]
+ [81-89]
+ [90,91]
+ [92-101]
+ [102-103]
+ [104-107]
+ [92,
108,109]
+ [22, 110-
117]
+ = observed; +/- = observed to limited extent, or +/- results; 0 = not observed; blank cell = no data.
* Selected references representative of some effects in various laboratory mammals are provided in the table. Health
effects of 2,3,7,8-TCDD and related chemicals are more comprehensively reviewed in U.S. EPA (2003a).
[1] Hahn, 1998; [2] Hahn, 2002a; [3] Banketal., 1992; [4] Abnet, etal., 1999; [5] Karchner, etal., 1999; [6]
Tanguay, et al., 1999; [7] Janz and Metcalfe, 1991; [8] Parrott et al., 1995; [9] demons et al., 1994; [10] demons et
al., 1996; [11] Andreasen et al., 2002; [12] Kleeman, etal., 1988; [13] Spitsbergen et al., 1988; [14] Carvalho etal.,
2004; [15] Spitsbergen, et al., 1991; [16] Henry, et al., 1997; [17] Hahn and Chandran, 1996; [18] Adams, et al.,
2000; [19] Palace, et al., 2001; [20] Duffy, et al., 2002; [21] Johnson, et al., 1992; [22] Peterson, et al., 1993; [23]
Walker, et al., 1991 [24] Walker and Peterson, 1991; [25] Elonen, et al., 1998; [26] Hill, et al., 2003; [27] Yasui, et
al., 2004; [28] Yasui, etal., 2007; [29] Sanderson and Bellward, 1995; [30] Kennedy etal., 1996; [31] Brunstrom
and Halldin, 1998; [32] Hoffman, et al., 1998; [33] Kennedy et al., 2003; [34] Nosek et al., 1992; [35] Elliott, et al.,
20
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1990; [36] Elliott, et al., 1991; [37] Elliott, et al., 1997; [38] Janz, and Bellward, 1997; [39] Janz and Bellward,
1996a; [40] Janz and Bellward, 1996b; [41] Nosek, et al., 1993; [42] Powell, et al., 1997; [43] Denison, et al., 1988;
[44] Walker etal., 2000; [45] Hamilton, etal., 1983; [46] Brunstrom and Andersson, 1988; [47] Brunstrom, 1991;
[48] Bentivegna, et al., 1998; [49] Gilday, et al., 1998; [50] El-Sabeawy, et al., 2001; [51] Sano, et al., 1985; [52]
Sinclair, et al., 1986; [53] Lambrecht, et al., 1988; [54] Powell, et al., 1996a; [55] Powell, et al., 1996b; [56]
Blankenship, et al., 2003; [57] Bruggeman, et al., 2003; [58] Goff, et al., 2005; [59] Henshel, et al., 1998; [60]
Jensen and Hahn, 2001; [61] Garrick, et al., 2006; [62] DeGuise, et al., 1998; [63] Gillette et al., 1987; [64]
Hochstein, et al., 1988; [65] Hochstein, et al., 1998; [66] Hochstein, et al., 2001; [67] Aulerich, et al., 1988; [68]
Render, et al., 2000; [69] Render, et al., 2001; [70] Beckett, et al., 2008; [71] Zhou, et al., 2003; [72] Kitchin, et al.,
1979; [73] Nebert, 1989; [74] Poland, et al., 1982; [75] Hook, et al., 1975; [76] Liem, et al., 1980; [77] Beatty and
Neal, 1977; [78] Hakansson, et al., 1994; [79] Gasiewicz, et al., 1986; [80] Kruger, et al., 1990; [81] Beatty, et al.,
1978; [82] Neal, etal., 1982; [83] Chapman and Schiller, 1985; [84] Schwetz, etal., 1973; [85] McConnell, etal.,
1978b; [86] DeCaprio, etal., 1986; [87] Henck, etal., 1981; [88] Olson, etal., 1980; [89] McConnell, etal., 1978a;
[90] Peterson etal., 1994; [91] Kelling, etal., 1985; [92] Kociba, etal., 1978; [93] Cantoni, etal., 1981; [94]
Goldstein, etal., 1982; [95] vanBirgelen, etal., 1996b; [96] Jones and Sweeney, 1980; [97] vanBirgelen, etal.,
1996a; [98] Shen, etal., 1991; [99] Birnbaum, etal., 1990; [100] Seefeld, etal., 1980; [101] Bombick, etal., 1985;
[102] vanBirgelen, etal., 1995a; [103] vanBirgelen, etal., 1995b; [104] Smialowicz, etal., 1994; [105]
Smialowicz, etal., 1996; [106] Hong, etal., 1989; [107] Thomas and Hinsdill, 1978; [108] National Toxicology
Program, 1982; [109] Rao, etal., 1988; [110] Roman and Peterson, 1998; [111] Couture, etal., 1989; [112] Abbott,
etal., 1987b; [113] Abbott etal., 1987a; [114] Eriksson, etal., 1998; [115] Giavini, etal., 1982; [116] Wolf, etal.,
1999; [117] Arnold, etal., 1997.
reduction in variability realized by conversion of administered dose to body burden
concentrations demonstrates how analyses based on internal dose or concentration can reduce
variability among toxicity data. Accordingly, development and application of risk assessment
approaches based on internal dose or concentration (body burdens) would also reduce
uncertainties associated with extrapolating data, such as relative potency estimates (van den Berg
etal., 2005).
Although data for 2,3,7,8-TCDD-induced reproductive and developmental toxicity are
lacking for mammalian wildlife species, mink are considered to be among the most sensitive
mammals to dioxin-like toxicity based on studies with adult animals, PCBs, and endpoints other
than reproduction/development (Hochstein etal, 1998; Aulerich etal, 1988; U.S. EPA, 2001b).
The sensitivity of tested bird species to 2,3,7,8-TCDD-induced embryo mortality varies by about
200-fold, with the domestic chicken generally more sensitive than wildlife species (Hoffman et
al, 1996). Of purely aquatic species, fish are more sensitive than other aquatic species. Among
tested freshwater fish species, sensitivity to 2,3,7,8-TCDD-induced early life stage toxicity
ranges approximately 50-fold, with salmonids being the most sensitive and zebrafish the least
sensitive species (Walker and Peterson, 1994; Henry et al, 1997; Elonen et al, 1998; Tanguay et
al, 2003).
The relative sensitivity of animal classes is not constant across chemical classes. For
example, fish are generally quite sensitive to PCDD and PCDF toxicity, as are birds and
mammals. However fish are very insensitive, if sensitive at all, to mono-ort/70-substituted PCBs,
whereas these PCB congeners are toxic to birds and mammals. These differences in species
sensitivity to particular dioxin-like chemicals may create differences in exposure susceptibility
associated with variations in the chemical mixture composition in food webs and demonstrates
the utility of congener-specific site characterization data during problem formulation.
Although AHR homologs have been identified in amphibians and primitive fish (Hahn,
1998), their toxicological significance is uncertain. Amphibians, reptiles, and primitive fish (e.g.,
lamprey, hagfish) are relatively insensitive to dioxin-like chemicals. Frogs and toads are at least
100- to 1000-fold less sensitive to 2,3,7,8-TCDD-induced early life stage mortality than fish
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(Jung and Walker, 1997; U.S. EPA, 1993). A very limited number of studies demonstrating that
PCBs induce dioxin-like biochemical effects (e.g., CYP1A induction) in a few frog and turtle
species (Huang et a/., 1998; Yawetz et a/., 1997) provide some evidence that the AHR-mediated
toxicity pathway is nominally functional in some amphibians and reptiles. Gutleb et al. (1999)
have reported effects of PCBs on development in two frog species, but it is unclear whether these
effects are mediated via the AHR. In summary, data demonstrating dioxin-like effects in
amphibians and reptiles are extremely limited, and effects have been observed at relatively high
concentrations.
It has been demonstrated that a wide variety of invertebrates including amphipods,
cladocerans, midges, mosquito larvae, sandworms, oligochaete worms, snails, clams, and grass
shrimp are insensitive to 2,3,7,8-TCDD-induced toxicity (West etal., 1997; Barber etal., 1998;
Van Beneden et a/., 1998; see U.S. EPA, 1993 and 2001b for summaries and references prior to
1998). Likewise, dioxin-like PCBs (e.g., congeners 77 and 118) generally have little effect on
survival, growth, and reproduction in the cladoceran, Daphnia magna, and the purple sea urchin
(U.S. EPA, 2001b). The insensitivity of invertebrates to dioxin-like toxicity is consistent with the
recent finding that several invertebrate AHR homologs lack the ability to bind the prototypical
AHRligands, 2,3,7,8-TCDD and p-naphthoflavone (Powell-Coffman, etal., 1998; Butler et al,
2001). Given these data, the toxic equivalence methodology is generally not applicable to
invertebrates. However, invertebrates may be vulnerable to these chemicals via other non-dioxin-
like toxicological effects. It is notable, for example, that PCBs measured as Aroclors have been
shown to be chronically toxic to daphnids at low ppb levels (Maki and Johnson, 1975; Nebeker
andPuglisi, 1974).
Limited data indicate that freshwater plants likewise are relatively insensitive to 2,3,7,8-
TCDD. Despite significant accumulation of 2,3,7,8-TCDD in algae and duckweed (i.e., //g/g
concentrations), no adverse effects were observed (U.S. EPA, 1993).
Given the known differences in sensitivity among species and endpoints, risk assessors
should consider the uncertainty introduced when extrapolating from a species or endpoint for
which sensitivity has been established to a species or endpoint of unknown sensitivity. (See U.S.
EPA, 1998 and Section 3.4.3 for a discussion of dealing with uncertainty.) This uncertainty,
which will affect the choice of the threshold or action level to which the calculated TEC is
compared (effects characterization), should be handled in a manner similar to any other chemical
for which interspecies extrapolations need to be performed (e.g., consideration of taxonomic
relatedness).
3.2.1.2. Susceptibility: Exposure
When evaluating the relative susceptibility of species on the basis of exposure, risk
assessors may need to consider three alternative expressions of exposure: (1) concentrations of
PCDDs, PCDFs, and PCBs in water, sediment, and diet associated with the species; (2)
concentrations of PCDDs, PCDFs, and PCBs in the whole body of the species; or (3)
concentrations of PCDDs, PCDFs, and PCBs in specific tissues of the species. As indicated in
Section 3.2.1.1, the relative sensitivity of species is better measured on the basis of
concentrations of PCDDs, PCDFs, and PCBs in tissue(s) of the species than on an external
concentration or administered dose. Thus, assessment endpoints should include species that are
not only susceptible on the basis of sensitivity, but are exposed through bioaccumulation of
dioxin-like PCDDs, PCDFs, and PCBs. Species with greatest bioaccumulation of dioxin-like
chemicals are generally those located at higher trophic levels because these hydrophobic
chemicals have a strong potential for biomagnification (i.e., the increase in concentration of a
22
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chemical in the tissue of organisms along a series of predator-prey associations, primarily
occurring through the mechanism of dietary accumulation).
Temporal and spatial aspects of exposure should also be considered when selecting
species with the highest exposure and bioaccumulation. For example, many PCDDs, PCDFs, and
PCBs are known to biomagnify such that their concentrations in the tissues offish-eating birds
and mammals may be greater than their concentrations in the tissues of the fish that the birds or
mammals eat. However, if birds and mammals move in and out of contaminated areas this
exposure scenario may actually result in bioaccumulation that is less than would be observed for
animals that feed exclusively from the contaminated area.
PCDDs, PCDFs, and PCBs are not metabolized to a large extent by invertebrates.
Therefore, invertebrate tissues tend to be at equilibrium with the water or sediments in which
they live (Thomann, 1989; Gobas, 1993). The strong propensity of PCDDs, PCDFs, and PCBs to
partition to organic carbon, combined with the fact that their freely dissolved concentration in the
water column is extremely low, results in the concentration in sediments usually exceeding the
concentration in surface waters (i.e., sediment concentrations are not at equilibrium with surface
water concentrations). Thus, organisms whose food chains are linked to contaminated sediments
through benthic invertebrates will have greater exposures than those with food chains linked to
surface water through pelagic invertebrates (Burkhard et a/., 2003a).
Unlike invertebrates, vertebrates metabolize PCDDs, PCDFs, and to a limited extent
some PCBs. PCDDs and PCDFs that do not possess chlorines at all four 2, 3, 7 and 8 positions
do not bioaccumulate significantly in vertebrates. Although metabolism of PCDDs and PCDFs
with chlorine substitution at the 2, 3, 7, and 8 positions (the most toxic congeners) occurs to a
lesser extent than those without, this metabolism of PCDDs and PCDFs results in significantly
less bioaccumulation in comparison to PCBs with the same degree of chlorination (Endicott and
Cook, 1994). See Section 3.3.1 for discussion of BAFs and food chain models that are needed to
account for competing mechanisms of bioaccumulation and metabolism.
In addition, since the ability to metabolize dioxin-like chemicals, which would enhance
elimination and thus reduce bioaccumulation, varies across species and by dioxin-like chemical,
the differences in TECs for different species can depend on the relative composition of the
PCDD-PCDF-PCB mixture to which each species is exposed. Thus, selection of susceptible
species should be specific to the exposure conditions associated with each ecological risk
assessment. Examples of how EPA has previously identified predaceous fish (lake trout),
piscivorous birds (belted kingfisher, herring gull, bald eagle), and mammals (river otter, mink) as
appropriate species in regional (i.e., Great Lakes) and national assessments of potential risks
posed by 2,3,7,8-TCDD to aquatic life and associated wildlife can be found in EPA reports (U.S.
EPA, 1993; U.S. EPA, 1995a, U.S. EPA, 1995b).
3.2.1.3. Susceptibility: Integration of Sensitivity and Exposure Considerations
Susceptibilities related to species sensitivity and exposures are not independent.
Generally, species that are highly sensitive and experience high exposure and bioaccumulation
will generally be species at greatest risk. However, as explained in Section 3.2.1.2, species with
the greatest dietary exposure do not always achieve the greatest tissue concentrations of PCDDs,
PCDFs, and PCBs because of inter-species differences in bioaccumulation and metabolism. For
example, species with high exposure may be less vulnerable to toxicity than species with lower
exposure if the latter is more sensitive and/or has higher levels of bioaccumulation. Hence, it is
important to consider both sensitivity and exposure in determining species susceptibility.
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Spatial and temporal gradients in environmental concentrations of PCDDs, PCDFs, and
PCBs can complicate determinations of species at greatest risk, especially when both species
sensitivity and potential population effects are being considered. Timing of exposure with
respect to sensitive life stages may make a difference. Fish and bird embryos with maternal
exposures that occur outside areas of contamination are probably at greatly reduced risk of early
life stage mortality despite subsequent rearing in contaminated ecosystems.
Variations in the composition of dioxin-like chemical mixtures across sites can influence
relative susceptibilities of phyla. Sensitive fish species tend to be more vulnerable at sites with
large PCDD and PCDF concentrations, whereas birds and mammals are relatively more sensitive
than fish at sites with large dioxin-like PCB concentrations. Even within sites, differences in the
relative concentration of PCDD, PCDF, and PCB in chemical mixtures in food chains may
influence which species are at greatest risk. When overall susceptibility is unclear, determination
of TECs and consequent levels of risk for multiple species is advisable.
3.2.1.4. Ecological Relevance
The Guidelines for Ecological Risk Assessment define ecologically relevant assessment
endpoints as those that reflect important characteristics of an ecosystem and are functionally
related to other endpoints (U.S. EPA, 1998). Given the taxonomic diversity and number of
species that have been shown to be sensitive to dioxin-like toxicity, it is likely that most
ecological risk assessments would include multiple "dioxin-sensitive" species. Since ecologically
relevant endpoints may be represented at any level of biological organization, many, if not all, of
the species or groups of species that are sensitive to dioxin-like toxicity may also be relevant to
sustaining the natural structure, function, and biodiversity of the ecosystems under consideration.
For example, in aquatic ecosystems, fish may represent an important class of ecologically
relevant species, owing either to their role as keystone species or because they serve as a
functional link between trophic levels within the food web. Hence, fish would represent both a
sensitive and an ecologically relevant assessment endpoint in many, if not most, aquatic
ecological risk assessment scenarios.
The ecological relevance of an assessment endpoint is further defined by the potential for
adverse effects as a result of exposure to one or more stressors (i.e., susceptibility). The
Guidelines for Ecological Risk Assessment identify five criteria for evaluating adverse changes in
assessment endpoints: nature of effects, intensity of effects, spatial scale of effects, temporal
scale of effects, and potential for recovery (U.S. EPA, 1998). With respect to the effects of
dioxin-like chemicals on wildlife, each of these criteria is meaningful. As summarized in Table
3, 2,3,7,8-TCDD and related chemicals are known to cause, among other effects, reproductive
toxicity, developmental toxicity, and mortality in a wide variety of species. These effects are
particularly relevant ecologically because they have the potential to lead to reduced populations
offish, birds, and mammals and to subsequent changes in the structure, function, and
biodiversity of ecosystems. 2,3,7,8-TCDD and related chemicals are also particularly significant
ecologically because they are among the most, if not the most, potent reproductive and
developmental toxicants known (i.e., the intensity of effects has the potential to be great).
Further, dioxin-like PCDDs, PCDFs, and PCBs are found ubiquitously in environmental
matrices, and they persist in the environment for ecologically relevant time periods, making the
spatial and temporal scale for effects potentially large. Finally, because the critical effects of
AHR agonists occur during developmental stages of sensitive organisms, there is little or no
opportunity for recovery.
24
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3.2.2. Conceptual Model
A conceptual model in problem formulation is a written description and visual
representation of predicted relationships between ecological entities and the chemical stressors to
which they may be exposed (U.S. EPA, 1998). In the case of ecological risk assessments
involving 2,3,7,8-TCDD and related chemicals, a conceptual model might depict the
hypothesized movement of these chemicals from a source into the environment; the subsequent
exposure of ecological entities from media such as soils, sediments, or the water column; further
exposure through the food web (bioaccumulation); and finally the hypothesized direct and
secondary ecological effects from these exposures. Figure 4 illustrates exposure to these
chemicals through sediment and the water column and resulting exposure through an aquatic
food web (source and effects information are omitted for simplicity).
Dissolved &
parti culate
organic mattei
Figure 4. An aquatic food web depicting hypothesized bioavailability and
trophic transfer of 2,3,7,8-TCDD through sediment and the water column.
The toxicity equivalence methodology fits well within such a conceptual model. The
methodology serves as a bridge between exposure and effects by accumulating exposures to a
number of different chemicals into a single value (expressed as a 2,3,7,8-TCDD toxicity
equivalence concentration, TEC). A hypothetical model for exposure to PCDDs, PCDFs, and
PCBs is illustrated in Figure 5, with areas of application for the toxicity equivalence
methodology noted. The items in the boxes making up the flow diagram (left-side) represent the
measured or calculated values that will be necessary to perform a toxicity equivalence-based
assessment. The items listed on the right side of the diagram are pertinent issues that should be
25
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Dose Metric-Specific Calculation of TEC
Administered Dose/Diet
Tissue Concentration
Characterization of Exposure
Soil/Sediment/Water
Congener Concentration
Soi l/Sed im en t/Water
Congener Concentration
Chemical Specific
Species Specific
Trophic Level Specific
Site Specific
BAF/BSAF
Diet
Congener Concentration
Tissue
Congener Concentration
TEF/RPF
Chemical Specific
Species/Class Specific
Choice influenced by &
determines level of
uncertainty
TEF/RPF
Characterization of Effects
/ TCDD Dose-Response
/ 'Appropriate Species
\ 'Appropriate Endpoint
\ 'Appropriate Dose Metric
TCDD Dose-Response
Administered Dose
TCDD Dose-Response
Tissue Concentration
Risk Characterization
Figure 5. Application of the toxicity equivalence methodology in ecological
risk assessment for exposure to PCDDs, PCDFs, and PCBs.
26
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considered in selecting or obtaining the values in the flow diagram. The elements of Figure 5 are
discussed in more detail in Section 3.3.
3.2.3. Analysis Plan
The methods for conducting the analysis phase of the risk assessment and estimating
risks are described in the analysis plan (U.S. EPA, 1998). The analysis plan provides the risk
assessor the opportunity to review for managers and other interested individuals the methods that
will be used to complete the risk assessment. The plan includes an assessment of the available
data, additional data needs, the methods for collecting these data (including analytical methods),
and the method for estimating risks. The uncertainties associated with the data gaps are also
described to provide decision makers with a means of determining the resources needed to
complete the assessment or realistic expectations about the likely outcome of the assessment.
In the application of the toxicity equivalence methodology to risk assessment, the
analysis plan should describe, at a minimum, the method(s) for:
1. Measuring PCDD, PCDF, and PCB concentrations in media and/or biota and how to
account for non-detects.
2. Estimating or measuring exposure (duration, frequency, and intensity).
3. Selecting consensus TEFs or deriving assessment-specific RPFs.
4. Estimating or measuring toxicity effects (laboratory or field studies).
5. Estimating risk.
6. Characterizing uncertainties.
The analysis plan should give
anyone involved in the risk assessment a
clear understanding of the strengths and
limitations associated with each of the
methods, as well as a clear and
transparent description of the
assumptions inherent in each of the
methods.
3.3. CONSIDERATIONS IN
ANALYSIS
Analysis is a process that
examines the two primary components
of risk (i.e., exposure and effects), and
their relationships between each other
and ecosystem characteristics (U.S.
EPA, 1998). Important considerations
for characterizing exposure to PCDDs,
PCDFs, and PCBs are described in
Section 3.3.1. The selection of TEFs or
Text Box 4. Questions for analysis.
• Have I selected appropriate analytical methods and
data quality objectives for measuring individual
dioxin-like chemical concentrations in the media of
interest?
• Do I have environmental fate and transport
information for the PCDDs, PCDFs, and PCBs
known or believed to be present?
• Have I determined a method for determining
bioaccumulation for individual PCDDs, PCDFs, and
PCBs that are relevant to the assessment
endpoints?
• Am I applying the TEFs or RPFs to the appropriate
tissues or dietary components?
• Are the reasons for selection of TEFs or RePs and
derivation of RPFs for the assessment clear and
well-supported?
• Are effects of PCDDs, PCDFs, and PCBs in the
target or related species of interest documented?
27
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RPFs, which is important in linking exposure and effects, is described in Section 3.3.2. Aspects
of the characterization of effects relevant to the toxicity equivalence methodology are presented
in Section 3.3.3. Questions to consider during analysis are provided in Text Box 4.
3.3.1. Characterization of Exposure
Characterization of exposure (U.S. EPA, 1998) includes a description of the actual or
potential contact of a receptor species with chemical stressors or co-occurring chemical stressors,
as in chemical mixtures. The objective of an exposure characterization is to produce a summary
exposure profile that identifies the exposed ecological entity (organism), describes the exposure
pathway, and estimates the dose of each chemical received by the organism. Important
components of an exposure profile for dioxin-like chemicals include: (1) measurements and/or
predictions of individual chemical concentrations in water, sediment, soil, and diet; (2) an
accounting for the differential fate and transport of PCDDs, PCDFs, and PCBs in the ecosystem;
(3) measurements and/or predictions of the bioaccumulation for individual dioxin-like chemicals;
and (4) calculation of TECs that are consistent with the dose metrics of the toxicity data being
used to determine risks (refer to Figure 5).
3.3.1.1. Congener-Specific Analyses
The toxicity equivalence methodology is inherently congener-specific. That is, RePs,
RPFs, and TEFs are derived from and applied to data for individual and specific dioxin-like
chemicals rather than to homolog groups or commercial mixtures (e.g., Aroclors). Effects,
bioaccumulation, and chemical fate and transport models all require input and output of
congener-specific data. Only the species-specific, effect endpoint-specific, spatially and
temporally-specific toxicity equivalence exposure values which result from the completion of the
analysis may be expressed as a TEC. Thus, a prerequisite for using the methodology is chemical
characterization that is high-quality and congener-specific. The toxicity equivalence
methodology cannot be directly applied to homolog groups or to total PCBs. Uncertainty for
application of TEFs to PCB congener concentrations estimated from Aroclor or homolog
analyses is probably large because of the wide range of possible congener mixtures, even within
homolog groups.
Analytical detection levels for dioxin-like chemicals should be lower than concentrations
at which important biological effects may occur. In some cases analytical detection limits for
specific chemicals may be too high to allow measurement of concentrations which would
significantly add to the TEC. In such cases, options exist for calculating the TEC. For example,
concentrations for undetected chemicals may be set equal to zero (no contribution to TEC) or
calculated based on either one half the detection limit or the whole detection limit. Alternatively,
the TEC may be reported as the range of possible values based on the options. If the TECs are
reported in a manner that is transparent to the risk managers, the uncertainties associated with
undetected chemicals will be understood. The best method for handling non-detects in a
particular risk assessment should be determined through consultation between risk assessors and
risk managers early in the risk assessment process (planning/problem formulation phase).
3.3.1.2. Chemical Fate of PCDDs, PCDFs, and PCBs
As indicated in Section 3.3.1.1, modeling or monitoring the environmental fate and
transport of PCDDs, PCDFs, and PCBs requires chemical-specific data and models. PCDDs,
PCDFs, and PCBs are persistent in the environment because they are resistant to chemical and
biological degradation. Affinity for organic carbon and lipids, and relatively low volatility,
28
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allows these chemicals to be retained in soils, sediments, and biota for long periods of time.
Transport on particles through the atmosphere or waterways are important mechanisms for
redistribution of PCDDs, PCDFs, and PCBs and temporal and spatial changes in mixture
composition. PCBs tend to partition from water to air to a greater extent than PCDDs and
PCDFs, which are more subject to photodegradation (U.S. EPA, 2001c). Hydrophobicity is the
most important chemical property that controls bioavailability from water, sediment, and soils,
and can be related to measurements of the octanol-water partition coefficient, Kow. PCDDs,
PCDFs, and PCBs for which dioxin-like toxicity is established have log Kow values that increase
with the degree of chlorination from approximately 6 to 9. This high degree of hydrophobicity
makes measurement of concentrations in water very difficult, especially for PCDDs and PCDFs,
which are present in the environment in much smaller amounts than PCBs. Conversely,
concentrations in surficial sediments or soils are often measurable and can be used effectively to
reference each chemical's distribution to abiotic and biotic components of the ecosystem.
While physical and chemical properties of PCDDs, PCDFs, and PCBs as a group can be
generalized as above, the differences among the individual chemicals result in different profiles
for distribution, fate, and transport and thus temporal and spatial changes in the composition of
chemical mixtures in the environment. Properties such as bioavailability, bioaccumulation,
metabolism, and biomagnification also differ among PCDDs, PCDFs, and PCBs such that the
relative concentration of the individual chemicals in organisms varies with species and trophic
level. Therefore, concentrations of individual PCDDs, PCDFs, and PCBs in abiotic media
usually do not reflect the chemical concentration profile observed in the tissues of wildlife.
TEFs-WHOgg/os or RPFs should only be applied to the specific chemical mixtures to which the
organisms are exposed. Thus, it is imperative that chemical concentrations in abiotic media be
converted to concentrations in either the tissues of organisms being assessed or their food
through use of appropriate bioaccumulation factors or models prior to applying TEFs-WHOgg/os
or RPFs to calculate TECs (refer to Figure 5). For example, BAFs can be applied to PCDD,
PCDF, and PCB concentrations in media to obtain predicted concentrations in organisms (as
described in the following section and illustrated in Figure 6). It follows that TECs should
generally not be directly based on water, sediment, or soil, since these media are inconsistent
with the dosimetry basis for the toxicity equivalence model. In cases where direct ingestion of
contaminated media (e.g., soil, sediment, or water) is a reasonable and significant exposure
pathway, the appropriate exposure dose metric (i.e., administered dose) as described in Section
3.3.1.3, must be considered.
3.3.1.3. Choices for the Exposure Dose Metric
In any risk assessment the dose metric (i.e., the measurement or prediction of chemical
concentrations) should be consistent between the exposure assessment and the effects
assessment. For example, if the dose-response relationship used in the effects assessment is
based on toxicity as a function of concentrations of 2,3,7,8-TCDD in tissue, exposure estimates
would also need to be based on concentrations in tissue of the species of concern. When
incorporating the toxicity equivalence methodology into an exposure assessment, the dose metric
basis for TEFs-WHOgg/os, RPFs, or RePs should be selected to provide consistency in bridging
the exposure assessment to the effects assessment (i.e., exposure dose metric = TEF-WHOgg/os,
RPF, or ReP dose metric = effects dose metric). When this is not possible (e.g., TEF or RPF is
based on concentration of chemical in tissues, and dose-response for effects is based on
administered dose in the diet), the risk assessor should describe, to the extent possible, the
direction and magnitude of the errors that may be introduced.
29
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X ,,-i^
BAFs: Organism lipid to
water freely dissolved
'BAFs-
BSAF: Organism lipid to sediment TOO
Figure 6. Estimating chemical concentrations in eggs and diet by applying
BAFs and BSAFs for PCDDs, PCDFs, and PCBs.
The TEFs-WHO98 and RePs for fish and birds are generally based on the potencies of
dioxin-like chemicals within cells, organs, or whole organisms, with the concentration in tissue
used as the dose metric. The dose metric for 2,3,7,8-TCDD-induced developmental toxicity in
fish and birds is also often expressed as a concentration in tissue (i.e., egg or embryo), which is
desirable. Hence, the dose metrics for fish and bird TEFs-WHO98, RPFs, and RePs are often
consistent with the dose metrics used for the toxicity relationship and allow for an internally
consistent exposure and effects assessment based on concentration of chemicals in the
organism's tissues. TECs based on measurements or estimates of PCDD, PCDF, and PCB
concentrations in tissues are presently most accurate for assessment of effects in fish and birds,
with concentrations in whole embryos used to assess early life stage effects. If concentrations in
tissue are unavailable, they may be estimated from environmental media based on BAFs or
models (as described in Section 3.3.1.4 and Cook etal., 2003) or bioaccumulation from the diet
if dietary intake and concentrations can be estimated.
In contrast to fish and birds, the dose metric used for mammalian TEFs-WHOgg/os and
RePs is generally administered dose. Application of the mammalian TEFs-WHOgg/os to dietary
exposures, rather than concentrations measured or predicted for specific tissues, is presently
more accurate and will minimize uncertainty associated with the risk assessment. While data are
30
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available for derivation of RePs or RPFs based on potencies of dioxin-like chemicals in
mammalian cells or organs (e.g., CYP1A induction), such relative potency data are subject to
variability associated with toxicokinetic differences between chemicals for absorption,
distribution, metabolism, and elimination. For example, the mouse hepatic ethoxyresorufm-O-
deethylase (EROD) RePs based on administered doses for 2,3,7,8-TCDF and 1,2,3,7,8-PeCDF
are less than the RePs based on concentration of the chemicals in the mouse liver that result from
the administered dose (DeVito et a/., 1997). The difference in RePs occurs because both 2,3,7,8-
TCDF and 1,2,3,7,8-PeCDF are more rapidly metabolized than 2,3,7,8-TCDD, and greater
administered doses are required to attain 2,3,7,8-TCDD equivalent concentrations in the liver
(DeVito etal., 1997, Santostefano etal., 1998). Until tissue concentration-based RePs for
mammals are fully developed for application to dietary or in vivo dose metrics, potential
systematic errors associated with using such relative potency estimates in conjunction with
exposure and effects data, based on an administered dose metric, should be recognized and
documented in the risk assessment.
3.3.1.4. Bioaccumulation ofPCDDs, PCDFs, andPCBs
Because TECs should be based on concentrations in tissues of organisms (or their diet)
rather than in abiotic media, risk assessors should consider how they will measure or predict
concentrations of PCDDs, PCDFs, and PCBs in tissues or diets. If measured concentrations in
tissues of the species associated with assessment endpoints are available for all dioxin-like
chemicals of concern, then TECs may be calculated directly as presented in Equation 2-1. In
many cases, however, measured concentrations in organisms will not be available. Furthermore,
even if concentrations in organisms have been measured, there may be a need to relate them to
ambient concentrations of PCDDs, PCDFs, and PCBs in water, sediment, or soil over time in
order to quantify the connections between contaminant sources and exposure as is necessary to
meet remedial action goals. Therefore, it will frequently be necessary to estimate or measure
bioaccumulation for PCDDs, PCDFs, and PCBs in risk assessments involving the toxicity
equivalence methodology.
One traditional method for estimating bioaccumulation is through the use of
bioconcentration factors (BCFs), but BCFs have poor applicability to PCDDs, PCDFs, and
PCBs. BCFs, which are measured under laboratory conditions, describe uptake of the chemical
by aquatic organisms only from water through respiration (i.e., via the gills). Thus, for very
hydrophobic chemicals, BCFs tend to underestimate bioaccumulation, which is the net uptake
and retention of a chemical through all routes of exposure, uptake, and elimination. Additional
complicating factors for PCDDs and PCDFs in aquatic food chains are: (1) uncertainty/difficulty
associated with measuring or estimating the fraction bioavailable in water; (2) strong influence
of benthic (sediment associated) food chains; (3) metabolism rates in vertebrates that may be
sufficient to greatly reduce the impact of dietary exposure; and (4) significant time periods
required to reach approximate steady-state levels in tissues as occurs with most environmental
exposures.
Alternatives to BCFs, water-based BAFs and biota-sediment accumulation factors
(BSAFs) are obtained from direct measurements in the environment or prediction of uptake and
elimination rates of each chemical as a result of all routes of exposure. As shown in Figures 5
and 6, BAFs and BSAFs are the essential connectors of concentrations of PCDDs, PCDFs, and
PCBs in the environment with concentrations in the diet or relevant tissues of organisms of
concern. Typically, BAFs and BSAFs are determined and applied for conditions that
31
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approximate steady-state of the organism with respect to water and sediments, respectively.
Fluctuating concentrations of these chemicals in water can often be effectively handled by using
average concentrations over time because their bioaccumulation rates are relatively insensitive to
short term fluctuations in water (Burkhard, 2003a). Thus, BAFs and BSAFs are the appropriate
quantitative expressions for the relationships between concentrations of PCDDs, PCDFs, and
PCBs in the environment (water, sediment, soil) and concentrations in an organism's tissues.
BAFs have been used explicitly to define water quality standards, as in the Great Lakes Water
Quality Initiative (GLWQI) (U.S. EPA, 1995a) and the Methodology for Deriving Ambient
Water Quality Criteria for the Protection of Human Health (U.S. EPA, 2000b).
Text Box 5. Key to symbols and notations used in equations 3-1 to 3-9.
n = i
superscript fd
superscript t
subscript w
subscript soc
subscript t
subscript i
subscript r
subscript /'
BAF
BAFff
BSAF
Cw
c,
^soc
c,
ct
//
Jsoc
Kow
1 Iso
Representation
toxicity equivalence factor
concentration
toxicity equivalence concentration
number of chemicals in mixture
individual chemical in mixture
freely dissolved chemical
total chemical
in water
in sediment organic carbon
in tissue
in lipid
reference chemical
individual chemical of interest
bioaccumulation factor
BAF, lipid normalized and based on
freely dissolved chemical in water
biota-sediment accumulation factor
C of total chemical in water
C of chemical freely dissolved in water
C of total chemical in sediment
C of chemical in sediment organic carbon
C of chemical in lipid
C of chemical in tissue
fraction lipid in the organism
fraction organic carbon in sediment
octanol-water partition coefficient
sediment-water concentration quotient
ratio between values of ILocw for
reference chemical and chemical
of interest
Common units
ng TCDD/ng chemical
ng/kg
ng/kg
L water/kg organism
L water/kg lipid
kg sediment/kg organism
ng/L water
ng/L water
ng/kg sediment
ng/kg organic carbon
ng/kg lipid
ng/kg tissue
kg lipid/kg organism
kg oc/kg sediment
L water/L octanol
L water/kg organic carbon
unitless
Concentrations in biota, sediments, and water used to calculate BAFs and BSAFs need to
accommodate variability in bioavailability conditions and express bioaccumulation on a
thermodynamic basis (degree of equilibrium/disequilibrium between biota, water, and
sediments). Thus, the concentration of the chemical in the organism's tissues (Ct) is normalized
to lipid content (C) with the fraction lipid (//) in the organism's tissues, and the concentration of
32
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the chemical in sediment (Cs) is normalized to organic carbon content (Csoc) with the fraction of
organic carbon in the sediment (fSOc)- The concentration of the bioavailable chemical in water is
defined as the concentration of freely dissolved chemical (Qf), which is calculated with the
fraction of chemical that is freely dissolved (ffd) as estimated from concentrations of paniculate
organic carbon (POC) and dissolved organic carbon (DOC) in the water (U.S. EPA, 1995a,
2000b, and 2003b). Thus there are two basic forms of bioaccumulation factors in current use: for
water, the bioaccumulation factor, BAFf, and for sediment, the biota sediment accumulation
factor, BSAF:
fd Cl Ct
ct ffd
w J
(3-1)
BSAF =-^- = ctli/ft
(3-2)
For a visualization and sensitivity analysis of the critical determinants of site-specific
BAF and BSAF values and their connection to tissue-based toxicity risk criteria, see Burkhard et
al. (2003a). If tissue concentrations are not available for the species and/or ecosystem of concern
in a risk assessment, it may be possible to estimate BAFs and BSAFs by extrapolation from other
species or ecosystems, as discussed in Section 3.3.1.5 and Burkhard et al. (2006), which
describes a hybrid modeling approach for extrapolating BAFs and BSAFs. A high quality BSAF
data set for PCDDs, PCDFs, and PCBs has been reported for lake trout in Lake Michigan
(Burkhard et al., 2004). In addition, EPA has recently compiled an extensive data set of
approximately 20,000 biota-sediement accumulation factors (BSAFs) from 20 locations (mostly
Superfund sites) for nonionic organic chemicals (e.g., PCBs, PCDDs, PCDFs). This data set can
be downloaded at http://www.epa.gov/med/Prods Pubs/bsaf.htm.
While TEFs-WHOgg/os (or RPFs/RePs) cannot be used to calculate TECs directly from
concentrations of PCDDs, PCDFs, and PCBs in water or sediments, they may be combined with
BAFf s or BSAFs and the fraction lipid in the organism (fe) to determine a wet weight TEC for
an organism as shown in the following two equations:
k
n=\
(3-3)
TEC = £ (Csoc \ (BSAF\ (fe lTEFn)
n=l
(3-4)
Risk assessments, which are concerned with ecological effects as a consequence of
loadings of PCDDs, PCDFs, and PCBs to aquatic ecosystems, must be designed to consider the
33
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masses, and thus the concentrations, of these chemicals in both water and sediments. In these
cases the risk analysis will, either directly or indirectly, involve specific values of BAFf and
BSAF for each chemical. BAFf values can be measured for many PCB congeners but are
difficult to measure directly for PCDDs, PCDFs, and the most toxic PCB congeners because
concentrations in water fall below detection limits. Nevertheless, it may be necessary to calculate
BAFf values, such as for water quality criteria development and application, even if the
BAF f values are not needed for calculating TECs. Any risk management decision based on
future chemical mass balances associated with reducing concentrations of chemicals in
sediments and/or external sources has to address concentration changes in biota, water, and
sediment compartments, regardless of whether measured concentrations are available for each
compartment at any point in time.
To calculate BAFf values for such purposes, EPA presently uses measured BSAFs for
PCDDs, PCDFs, and co-planar PCBs, combined with estimates of sediment-water concentration
quotients (Hsocw as defined by equation 3-5) for reference chemicals which have measurable
concentrations in water (U.S. EPA, 1995a; 2000b). The BSAF method, as described by equation
3-6, has provided accurate predictions of BAF f values for PCBs in several different ecosystems
(L. Burkhard, personal communication; U.S. EPA, 2003b). Since (Hsocw); is difficult to measure
for PCDDs, PCDFs, and PCBs assigned TEFs, a measured value for a reference chemical r
(usually a PCB congener of similar Kow) may be used to estimate (Hsocw);. The ratio of (Hsocw);
for the PCDD, PCDF, or PCB of interest to (Jlsocw)r for the reference chemical (factor Di/r in
equation 3-6) accounts for observed or predicted chemical specific differences in Hsocw. High
quality measurements of FJsocw values for PCBs, PCDDs, and PCDFs in Lake Michigan have
been obtained (Burkhard et a/., 2006) and can be used as a source of measured values for D;/r.
Even with Di/r set as 1.0, the BSAF method robustly captures congener-specific differences in
bioavailability and metabolism in the food chain through use of BSAFs as indicators of relative
bioaccumulation potentials for the dioxin-like chemicals. The method also highlights the
necessity of linking biota to both water and sediment when quantitative ecological risk
assessments are required. For more details see U.S. EPA (2000b).
C BAF/d
_ soc _ I
cfd BSAF
w
(3-5)
(K
\ ow/r
(3-6)
BSAFs are advantageous for describing and predicting bioaccumulation of PCDDs,
PCDFs, and PCBs because they can be measured at a site to capture effects of food web
structure, bioavailability, and metabolism. BSAFs also tend to integrate fluctuations of chemical
concentrations in the water and accommodate spatial gradients in sediment (Burkhard et a/.,
2003b; U.S. EPA, 2003b). When risks are to be assessed and managed on the basis of
approximate steady state conditions expected in the future, the predictive power of BSAFs
34
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depends on adjustments to account for expected changes in these conditions. In reality, these
adjustments are relatively small and similar for each AHR agonist (Cook etal., 2003). Because
BSAFs are very good quantitative measures of the relative bioaccumulation potentials of AHR
agonists in aquatic ecosystems, they are especially useful for calculating TECs. Note that use of
a measured or extrapolated set of BSAFs for a specific site and trophic level in calculating a TEC
under equation 3-4 accommodates chemical-specific differences in bioaccumulation in a manner
that often may equal or exceed the specificity and accuracy for the relative potencies available.
That AHR-mediated toxicity risks can be predicted accurately and relate to the historical
elimination of lake trout as a keystone species in Lake Ontario (Cook etal., 1997; 2003)
demonstrates that the net risks associated with both bioaccumulation and relative potency
differences can be effectively assessed through input of appropriate values into equation 3-4.
3.3.1.5. Examples of TEC Calculations for Fish, Birds, and Mammals
Calculations of TECs are conceptualized in Figure 7. Examples of estimating 2,3,7,8-
TCDD TECs in fish eggs and bird eggs (TECeggs) from average values of measured PCDD,
PCDF, and PCB congener concentrations in sediments are presented in Tables 4 and 5. Table 6
presents an example of estimating 2,3,7,8-TCDD TECs in mink diet. The tables are followed by
detailed descriptions of the calculations. The hypothetical sediments are representative of a
moderately contaminated ecosystem.
\Early life stage mortality\
TEC = 2.3.7,8 TCDD np/q wet
*diet may include incidental
sediment
Figure 7. PCDDs, PCDFs, and PCBs: effects on vertebrates. TECs are
calculated from concentrations in bird eggs, fish eggs, or mammal diet.
The important risk question associated with these examples is whether the chemicals
have accumulated sufficiently to cause significant mortality of lake trout and herring gulls during
early life stages. BSAFs, based on Lake Ontario data for sediments (U.S. EPA, 1995a), lake trout
eggs (Guiney etal., 1996), and herring gull eggs (Government of Canada, 1991), are used here to
35
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illustrate how concentrations of the dioxin-like chemicals in biota may be estimated from
contaminated sediment data. The following relationships are used to calculate concentrations in
trout and gull embryos from BSAFs (for an actual, more specific, and rigorous assessment
example involving lake trout, see Cook etal., 2003):
S~1
• BSAFtroutegg • (fe )troute
trout egg ~ r *~trout egg \I trout egg
/„
(3-7)
s~i ^-^ £ T) ry i -j—r / _/* \
^ gull egg ~ r gull egg V I /gull egg
J soc
(3-8)
The fraction of organic carbon (fsoc) is measured for sediments, in association with
concentrations of each dioxin-like chemical in sediments (Cs\ and the fraction of lipid (/}) in
trout or gull eggs that would exist at the site is predicted from literature values. Finally,
concentrations of PCDDs, PCDFs, and PCBs in tissue are multiplied by the appropriate fish and
bird TEFs-WHOgg (see Table 2) and the products summed to estimate total TECs for trout and
gull embryos, respectively, as indicated by equation 2-1 (note that this is equivalent to use of
equation 3-4). As summarized in Table 4, the trout egg TEC is reported as a range (3.82-10.46
ng/kg trout egg) reflecting the use of both 0 and 0.000005 as the TEF for the mono-ortho PCBs
(TEF<0.000005). Table 5 reports the gull egg TEC as a single value of 703.2 ng/kg gull egg
because the avian TEFs for mono-ortho PCBs biphenyls are discrete values. In this hypothetical
example the non-ortho PCBs contribute 2.06 ng/kg trout egg and 419.62 ng/kg gull egg in
contrast to 1.76 ng/kg trout egg and 10.58 ng/kg gull egg for PCDDs and PCDFs.
Figures 8 and 9 illustrate the relative contributions to the TECs made by PCDDs and PCDFs in
comparison to PCBs for trout and gull eggs, respectively. In this example PCDDs and PCDFs
make approximately equal contributions with PCBs to the trout egg TEC, whereas the PCBs
make a much greater contribution to the gull egg TEC. This is a consequence of both PCB TEFs
and BSAFs being greater for birds than fish. The right half of the graphs also illustrate the
consequences of calculating a TEC based on concentrations in sediments rather than in the eggs.
In the fish example, the sediment-based TEC is somewhat greater than the egg-based TEC, but
the PCDD/PCDF contribution is magnified greatly in comparison to the PCB contribution
because the sediment-based TEC does not account for effects that impact bioaccumulation (e.g.,
bioavailability, food chains, biomagnification, and metabolism). In the gull egg example, the
sediment-based TEC is much less than the egg-based TEC because the effects associated with
exposure and bioaccumulation are not included when TEFs are applied to concentrations in
sediment. More importantly, the gull egg TEC, which is approximately one hundred times
greater than the trout egg TEC, does not necessarily indicate that the gulls are at greater risk than
trout. The risk for lake trout can be greater if the trout are more than one hundred fold more
sensitive to 2,3,7,8-TCDD than herring gulls on the basis of TECegg values. This in fact appears
to have been the case for lake trout and herring gull populations in Lake Ontario during the last
century (Cook et al, 2003).
36
-------
Table 4. An example of estimating TECs in fish eggs from average
concentrations of PCDD, PCDF, and PCB congeners measured in surface
sediment samples of a reservoir.
Note: All data (with the exception of TEFs) in this table are for illustrative
purposes only. They are not recommended default values for all risk assessments.
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-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
OCDF
Sum PCDD and PCDF
3,4,4',5-TCB (81)
3,3',4,4'-TCB (77)
3,3',4,4',5-PeCB (126)
3,3',4,4',5,5'-HxCB (169)
2,3,3',4,4'-PeCB (105)
2,3,4,4',5-PeCB(114)
2,3',4,4',5-PeCB(118)
2',3,4,4',5-PeCB (123)
2,3,3',4,4',5-HxCB (156)
2,3,3',4,4',5'-HxCB (157)
2,3',4,4',5,5'-HxCB (167)
2,3,3',4,4',5,5'-HpCB (189)
Sum PCB
Sum all
Concentration
in Sediment
(ng/kg)
0.30
1.20
1.10
4.70
2.90
78.20
530.00
1.10
0.92
1.40
4.10
1.60
0.30
1.00
2.70
133.00
2.40
Trout Egg
BSAF1
0.149
0.121
0.018
0.007
0.010
0.002
0.0007
0.069
0.009
0.162
0.0045
0.007
0.020
0.002
0.001
0.023
0.001
Concentration
in Trout Egg
(ng/kg egg)
0.22
0.73
0.10
0.17
0.15
0.78
1.96
0.38
0.04
1.13
0.09
0.06
0.03
0.01
0.01
15.30
0.01
TEFs-WHO98
Fish TEF
1
1
0.5
0.01
0.01
0.001
0.0001
0.05
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
O.0001
Trout Egg
TEC
Predicted
Concentration
(ng/kg egg)
0.22
0.73
0.05
0.002
0.002
0.0008
< 0.002
0.02
0.002
0.57
0.009
0.006
0.003
0.001
0.0001
0.15
O.00001
1.76
60
1623
16
4.8
5370
4170
35658
538
8413
917
705
1876
0.95
0.29
4.18
5.58
2.54
5.22
4.66
3.80
5.87
7.89
2.03
2.07
285
2353
334
134
68199
108837
830831
10222
246921
36175
7156
19416
0.0005
0.0001
0.005
0.00005
0.000005
0.000005
0.000005
0.000005
0.000005
0.000005
0.000005
0.000005
0.14
0.24
1.67
0.007
< 0.341
< 0.544
<4.154
0.051
<1.2346
0.1809
0.0358
0.0971
2.06 - 8.70
3.82 - 10.46
'BSAFs for trout eggs are based on 7% lipid in eggs and 1.4% organic carbon in sediment.
37
-------
Table 5. An example of estimating TECs in bird eggs from average
concentrations of PCDD, PCDF, and PCB congeners measured in surface
sediment samples of a reservoir.
Note: All data (with the exception of TEFs) in this table are for illustrative
purposes only. They are not recommended default values for all risk assessments.
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-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
OCDF
Sum PCDD and PCDF
3,4,4',5-TCB (81)
3,3',4,4'-TCB (77)
3,3',4,4',5-PeCB (126)
3,3',4,4',5,5'-HxCB (169)
2,3,3',4,4'-PeCB (105)
2,3,4,4',5-PeCB (114)
2,3',4,4',5-PeCB(118)
2',3,4,4',5-PeCB (123)
2,3,3',4,4',5-HxCB (156)
2,3,3',4,4',5'-HxCB (157)
2,3',4,4',5,5'-HxCB (167)
2,3,3',4,4',5,5'-HpCB (189)
Sum PCB
Sum all
Concentration
in Sediment
(ng/kg)
.30
1.20
1.10
4.70
2.90
78.20
530.00
1.10
0.92
1.40
4.10
1.60
0.30
1.00
2.70
133.00
2.40
Gull Egg
BSAF1
1.2188
1.0313
0.0368
0.2321
0.0102
0.0016
0.0018
0.0250
0.0221
0.3068
0.0181
0.0893
0.0174
0.1200
0.0001
0.0027
0.0002
Concentration
in Gull Egg
(ng/kg egg)
1.83
6.19
0.20
5.46
0.15
0.63
4.75
0.14
0.10
2.15
0.37
0.71
0.03
0.60
0.001
1.78
0.002
TEFs-WHO98
Avian TEF
1.0
1.0
0.05
0.01
0.1
<001
0.0001
1.0
0.1
1.0
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Gull Egg TEC
Predicted
Concentration
(ng/kg egg)
1.83
6.19
0.01
0.055
0.015
< 0.0006
0.0005
0.14
0.01
2.15
0.04
0.07
0.003
0.06
0.00001
0.02
0.0000002
10.58
60
1623
16
4.8
5370
4170
35658
538
8413
917
705
1876
3.41
0.178
30.6
15
15.9
24
46.4
22.3
18.8
32.1
24.8
19.4
1024
1445
2446
360
426118
505919
8270925
60000
790822
147148
87420
181972
0.1
0.05
0.1
0.001
0.0001
0.0001
0.00001
0.00001
0.0001
0.0001
0.00001
0.00001
102.40
72.24
244.62
0.36
42.61
50.59
82.71
0.60
79.08
14.72
0.87
1.82
692.62
703.20
'BSAFs for gull eggs are based on 7% lipid in eggs and 1.4% organic carbon in sediment.
38
-------
Table 6. An example of estimating TECs in the diet of otter from average
concentrations of PCDD, PCDF, and PCB congeners measured in surface
sediment samples of a reservoir.
Note: All data (with the exception of TEFs) in this table are for illustrative
purposes only. They are not recommended default values for all risk assessments.
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-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
OCDF
Sum PCDD and PCDF
3,4,4',5-TCB (81)
3,3',4,4'-TCB (77)
3,3',4,4',5-PeCB (126)
3,3',4,4',5,5'-HxCB (169)
2,3,3',4,4'-PeCB (105)
2,3,4,4',5-PeCB(114)
2,3',4,4',5-PeCB (118)
2',3,4,4',5-PeCB (123)
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB (157)
2,3',4,4',5,5'-HxCB (167)
2,3,3',4,4',5,5'-HpCB (189)
Sum PCB
Sum all
Concentration
in Sediment
(ng/kg)
0.30
1.20
1.10
4.70
2.90
78.20
530.00
1.10
0.92
1.40
4.10
1.60
0.30
1.00
2.70
133.00
2.40
Forage Fish
BSAF1
0.20
0.18
0.03
0.02
0.02
0.008
0.0005
0.12
0.01
0.33
0.01
0.01
0.04
0.05
0.001
0.03
0.001
Concentration
in Otter Diet
(ng/kg fish)
0.133
0.479
0.073
0.209
0.129
1.389
0.588
0.293
0.020
1.026
0.091
0.036
0.027
0.111
0.006
8.858
0.005
TEFs-WHOos
Mammalian
TEF
1
1
0.1
0.1
0.1
0.01
0.0003
0.1
0.03
0.3
0.1
0.1
0.1
0.1
0.01
0.01
0.0003
Otter Diet
TEC
Predicted
Concentration
(ng/kg fish)
0.1330
0.4790
0.0073
0.0209
0.0129
0.0139
0.0002
0.0293
0.0006
0.3078
0.0091
0.0036
0.0027
0.0111
0.0001
0.0886
0.000002
1.1200
60
1623
16
4.8
5370
4170
35658
538
8413
917
705
1876
0.35
0.25
0.92
1.08
0.85
1.41
1.57
1.02
1.66
2.08
1.09
1.26
46.6
901
32.7
11.5
10133
13052
124282
1218
31004
4234
1706
5248
0.0003
0.0001
0.1
0.03
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.00003
0.0140
0.0901
3.2678
0.3450
0.3040
0.3916
3.7285
0.0365
0.9301
0.2170
0.0512
0.1574
9.4454
10.5654
'BSAFs for forage fish in diet of otter are based on 3.11 % lipid in forage fish and 1.4% carbon in sediment.
39
-------
trout eggs
]PCDD+PCDF
1.76
5.06
1PCB
2.06
0.27
DTEC (Total)
3.82
5.34
Figure 8. Fish TECs calculated with TEFs-WHO98 appropriately from
concentrations in eggs versus inappropriately from concentrations in
sediment.
800
600
400
200
0
gull eggs
sediment
DPCDD+PCDF
10.58
6.59
DPCB
692.62
91.03
I TEC (Total)
703.2
97.62
Figure 9. Bird TECs calculated with TEFs-WHO98 appropriately from
concentrations in eggs versus inappropriately from concentrations in
sediment.
A third example of a TEC calculation, also conceptualized in Figure 7, is based on
chemical concentrations in the mammalian diet associated with contaminated sediment rather
than chemical concentrations in the mammal's tissue. The forage fish-based dietary exposure
calculation utilizes equation 3-9, and the TEC calculations are reported in Table 6. Although the
concentrations of individual PCDDs, PCDFs, and PCBs in vulnerable tissues are the most
relevant dose metric for understanding biological responses, it is often impractical or impossible
to define dose on a tissue-specific basis. Thus, the mammalian TEFs-WHOos are largely based
on relative potency data associated with the administered doses in the diet of test animals.
Section 3.3.1.3 provides further discussion.
40
-------
c
„ ,.
o/fer diet
, , ,
forage jish
•fc)
soc
forage fish
(3-9)
Unlike the gull egg example (Figure 9), Figure 10 shows that calculation of the TEC
based on sediments using mammalian TEFs-WHOos does not necessarily significantly
underestimate the value of the TEC calculated based on the otter diet. However, note that,
although the diet- and sediment-based otter TECs are similar, the relative contributions of
PCDDs and PCDFs versus PCBs would significantly impact the extent to which these two TEC
calculations differ at a particular site due to differential bioaccumulation among PCBs, PCDDs,
and PCDFs.
Figure 10. Mammal TECs calculated with TEFs-WHO0s appropriately from
concentrations in diet versus inappropriately from concentrations in
sediment.
TEC calculations for terrestrial birds and mammals exposed through food chains
connected to contaminated soils should proceed in a manner parallel to the aquatic examples in
Tables 5 and 6. The principal exposure pathway is soil to insect to mammal/bird through diet.
Dietary uptake from ingestion of plant foods or soil through preening may in some cases provide
important exposures. However, unlike aquatic systems in which respiration from water is an
important exposure route, for terrestrial mammalian species, by analogy with humans, respiration
of air is unlikely to be a significant direct exposure route for terrestrial organisms (ATSDR,
1998; http://www.cfsan.fda.gov/~lrd/dioxinqa.htmltfg4).
41
-------
Although the TEC calculations are straightforward and fairly simple, multiple decisions
need to be made beforehand. Some of these are described in Text Box 6. Decisions and
assumptions used in the examples described in Tables 4, 5, and 6 include using measured BSAFs
for Great Lakes trout and gulls (which assumes Great Lakes exposure and food web conditions
are sufficiently representative of the
aquatic system to be assessed), and
selecting values for percent lipid for
organisms and percent organic
Text Box 6. Questions when calculating TECs.
Have I selected the appropriate species and identified
a percent lipid for the whole organism, specific tissues
of the organism, and/or the diet of the organism?
Have I selected appropriate analytical methods for
measuring concentrations of chemicals in sediment or
water?
Have I decided how to handle chemicals that have
concentrations below the detection limit?
Have I selected appropriate methods for measuring or
estimating the fraction of organic carbon in the
sediment at the site of interest?
Have I measured or selected appropriate BAFs or
BSAFs that will be used to estimate concentrations of
each chemical in the organism's tissue or diet?
Have I considered implications of biomagnification for
higher trophic level organisms?
Have I selected and applied the TEFs, RPFs, or RePs
in a transparent fashion? (See Sections 3.3.1.3.)
carbon for sediments.
Measured BAFs from one
site, such as the Lake Ontario values
used in the GLWQI (U.S. EPA,
1995a), the high-quality BSAF
values from Lake Michigan
(Burkhard et al, 2004), or EPA's
BSAF data set (available at
http ://www. epa. gov/med/Prods_Pub s
/bsafhtm) may be extrapolated to
another assessment site where
similar measurements are either not
possible (e.g., chemicals not
detectable in water) or feasible (e.g.,
insufficient time, resources)
(Burkhard et al, 2006). When the
trophic level, food web, and the
sediment-water concentration
quotient, Hsocw, are similar for two
ecosystems, direct extrapolation of
BAF f values or BSAFs from one
ecosystem to the other can be
accurate if concentrations of chemicals in water or sediments are defined and measured in a
consistent way for both sites. When conditions are not comparable, as often is the case,
BAF f values or BSAFs can be adjusted, using a basic food chain model, such as that of Gobas et
al. (1993; 1998), for known differences in trophic level, food web, and/or Hsocw This should
increase accuracy of measured BAF^values or BSAFs when applied to an unmeasured system.
An initial demonstration of such a "hybrid modeling approach" appears promising (Burkhard et
al., 2006).
The case studies used for the 1998 EPA/DOI workshop (U.S. EPA, 200la) present
additional and more detailed examples of exposure characterizations. Many practical exposure
and bioaccumulation assessment concerns were incorporated into these case studies, including
how to employ the toxicity equivalence methodology in setting total maximum daily loads
(TMDLs).
42
-------
3.3.2. Three Dimensional Relative Potency Matrix - A Tool for Visualization and Selection
of RePs or Derivation of RPFs
When applying the toxicity equivalence methodology an important consideration to be
made is what relative potency factors to use for each dioxin-like chemical. One expected
approach is to use the TEF-WHOgg/os values, unless there is a need for more site- or species-
specific calculations. When confronted with a lack of ReP data for the specific species and
endpoint of concern, choices from alternative RePs or RPFs and the TEFs must be made.
The ideal RPF is species-specific for the effect endpoint of concern and based on dose
metrics that best describe the toxicity data available, while effectively relating the dose-response
relationship to environmental exposures. Data limitations do not negate the need to consider
uncertainties and make optimum ReP/RPF/TEF-WHOgg/os selection decisions for the particular
problem formulation, species, and effects of concern. To this end, the three dimensional matrix
depicted in Figure 11 provides risk assessors with a conceptual tool for selecting ReP values for
derivation of assessment-specific RPFs. It provides an approach for evaluating the applicability
of different ReP data associated with either the TEFs-WHOgg/os or other RPFs that may be
available (or that could be derived from the ReP data) and the types of uncertainty inherent to
each. The rationale behind this hierarchal methodology is the mechanistic understanding of
AHR-mediated toxicity as well as empirical data that support the extrapolation of relative
potency data across endpoints and species. Using this concept, selection of RePs or derivation of
RPFs can be based on a three-dimensional hierarchal approach involving use of the best
available information relative to the ideal choice - a species-specific RPF for the endpoint of
concern based on optimum dose metrics. Currently, the primary value of the three-dimensional
matrix is to allow a visualization of the complex factors that influence the applicability of
potentially diverse relative potency data for specific risk assessment scenarios. It could also
facilitate efforts to describe uncertainties associated with ReP selections and/or RPF derivations.
The matrix may also be helpful in describing and guiding research needs, and ultimately may
lead to the development of more quantitative methods and further guidance for selecting RePs
and deriving RPFs or proposing revisions to the TEFs-WHOgg/os-
The issues of species, endpoint, or dose metric differences in ReP data are separate from
that of species differences in sensitivity to 2,3,7,8-TCDD. Two species that differ widely in their
sensitivity to 2,3,7,8-TCDD can have relatively similar RePs for most dioxin-like chemicals. For
example, chickens are 119-fold more sensitive than ducks to in vitro effects of 2,3,7,8-TCDD,
yet for TCDF and PCB congeners 126 and 81, the in v/Yro-based RPFs differ less than 5-fold
between these species (Kennedy et a/., 1996). Similarly among fish, salmonids are the most
sensitive species and zebrafish are the least sensitive species to the early life stage toxicity
caused by 2,3,7,8-TCDD, with salmonids approximately 40-fold more sensitive than zebrafish
(Elonen et a/., 1998), yet RePs based on zebrafish in vitro endpoints {i.e., CYP1A induction in
liver) are generally within 5-fold of RePs determined in a variety of rainbow trout in vitro
systems when the same endpoint in the same tissues are compared (Henry et a/., 2001). Limited
ReP data for fish embryos (bull trout, lake trout, rainbow trout, and medaka) suggest that species
sensitivity to 2,3,7,8-TCDD is associated with small differences in RePs for PCB 126 when
based on early life stage mortality. These differences in RePs are less than proportional to the
differences in species sensitivity. Analysis of rainbow trout and zebrafish RePs suggests that
uncertainties surrounding application of the toxicity equivalence methodology are likely to be
greater when applying TEFs-WHOgg values or RPFs across tissues or endpoints than across fish
species (Henry et a/., 2001). At this time, data are lacking for making RPF comparisons between
43
-------
sensitive and insensitive species based on in vivo toxicity of greatest concern. In summary, there
are presently insufficient data to determine definitively if there is any association between
sensitivity to 2,3,7,8-TCDD and RePs for different species.
8
0)
Effect of
Concern
in vivo
Other effect
in vivo
Biochemical
in vivo
'5 Biochemical
Q. In vitro
•O
W AHR binding
QSAR
Same Related Same
Species Species Class
Species Similarity
Unrelated
Species
WORST
Nominal
8 Predicted
6 Administered
organism Dose Relevance
and Consistency*
2 Tissue * Net level'Sum of dose
relevance and consistency
Figure 11. Three dimensional relative potency matrix for selection of RePs
and derivation of RTFs for risk assessment.
Selection involves consideration of how relevant a toxicity test endpoint is to
the endpoint of concern (y-axis); how similar a tested species is to the species of
concern (x-axis); and how relevant the dose metric for an ReP value is to the
optimum dose metric, while being consistent with the dose metric of 2,3,7,8-
TCDD dose-response relationship to be used (z-axis). ReP values with the closest
association to the species and toxic effect of concern, and based on doses
measured in the tissues that are targets for toxic effects, should best minimize
uncertainty while maximizing relevance ("BEST" cube).
3.3.2.1. Endpoint Relevance
The y-axis of the matrix represents six levels that correspond to the various in vivo, in
vitro, and biochemical endpoints used currently to determine relative potency of dioxin-like
chemicals. The levels from bottom to top represent a preferential ranking of endpoint categories
based on probable increasing relevance to the species of concern for which RPFs are to be
derived. The order of preference is similar to that used in deriving the TEFs-WHOgg/os for fish,
birds, and mammals (Van den Berg etal., 1998; 2006). The highest preference is given to RePs
determined for in vivo toxicity endpoints.
44
-------
In this matrix, Level 1 is reserved for in vivo toxicity data for the endpoint of concern
(e.g., early life stage mortality or reproductive failure). Level 2 is for other in vivo toxicity
endpoints that may be less directly connected to the assessment endpoint of concern (e.g., growth
or behavior). Biochemical effect endpoints, such as CYP1A1 induction, are distinguished as
Level 3 for effects in vivo and Level 4 for effects in vitro because in vitro data tend to be less
toxicokinetically realistic than in vivo data. Level 5 is assigned to RPFs based on binding affinity
to the AHR, which is a biochemical endpoint considerably upstream from toxicities of concern,
and thus more distantly related to typical ecological assessment endpoints. Consistent with the
TEFs-WHO98/o5 selection process (Van den Berg etal., 1998; 2006), Level 6 is reserved for
quantitative chemical structure-activity relationships (or QSARs) that may be more or less
quantitative in comparing AHR agonist potencies to 2,3,7,8-TCDD for a variety of endpoints.
3.3.2.2. Species Similarity
The x-axis in the matrix is a scale for the phylogenetic similarity of the species of
concern to the species for which RePs are available. It is divided into four levels, reflecting
different degrees of uncertainty, with uncertainty increasing from left to right. If RePs are
available for the species of concern (Level 1), little uncertainty related to interspecies sensitivity
is involved in using the RePs to calculate an RPF. If ReP data are available for a closely related
species (Level 2), for example, a species within the same genus or family, uncertainty is greater.
The TEFs-WHOgg/os, although based in some cases on species-specific data, are based on class
generalizations and are thus represented in Level 3. In cases when the TEFs-WHOgg/os are based
on a single species the same as or closely related to the species of concern, the TEFs-WHOgg/os
may equate to Level 1 or Level 2, respectively. If ReP data are from a more distantly related
species within the same class, uncertainty increases (Level 4). When ReP data for a dioxin-like
chemical is available for multiple species, the magnitude of the difference or similarity in the
RePs across the levels can be used to gauge the uncertainty associated with using RePs for those
dioxin-like chemicals having only one level of data available.
The basis for reflecting phylogenetic similarity in the matrix is both theoretical and
empirical. The assumption that two species that are more closely related phylogenetically will
have RPFs (determined for the same endpoint) that are similar or even identical is supported by
data. For example, RePs for PCB 126-induced early life stage mortality in lake trout and rainbow
trout vary by less than a factor of two (Zabel et a/., 1995). However, it is clear that more data on
the relative potency of dioxin-like chemicals to produce various effects in additional species are
necessary to more systematically test this assumption.
3.3.2.3. Dose Relevance for Effect and Consistency with Dose-Response Relationship
The z-axis of the matrix represents the degree to which the dose data, associated with
different sets of RePs, are related to the effect of concern (dose relevance) and are consistent
with the specific 2,3,7,8-TCDD dose-response relationship chosen for the assessment (dose
consistency). All effects of concern are assumed to be best related to concentrations of dioxin-
like chemicals, at relevant times, and in specific tissues associated with the mechanism of action
(Level 1). Concentrations in whole bodies of affected organisms (Level 2) are more commonly
the best available and relevant dose metric. Administered doses (Level 3) include injection and
dietary exposures. Level 4 includes doses that are predicted based on mechanisms of fate and
uptake during exposures from water, sediments, or other exposure media from which uptake is
less certain than for Level 3. Level 5 includes doses that are based simply on the nominal mass
of the chemical used in the toxicity test, rather than on measurement during the test. Most in
45
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vitro effects-based ReP dose data probably fall into Level 5 because concentrations of the
chemicals are often not measured in cell cultures.
The extent to which the dose-metric of the 2,3,7,8-TCDD dose-response relationship is
relevant to the endpoint and species associated with the ReP data may be best considered in
tandem with the dose relevance of the ReP data. The matrix illustrates a strategy for doing this
by setting the z-axis scale from 2 to 10 to allow ReP dose relevance and 2,3,7,8-TCDD dose-
response consistency to be summed. For example, if avian ReP data for PCB 126 involve
chicken embryo mortalities based on doses measured as concentrations of PCB 126 or 2,3,7,8-
TCDD in the diets of female chickens (Level 3 - administered dose), but a 2,3,7,8-TCDD dose-
response relationship is available for the risk assessment based on the concentration of 2,3,7,8-
TCDD in embryos associated with embryo mortality for a closely related bird species (Level 1 -
dose measured in tissue), the net dose metric relevance and consistency level for selecting the
ReP data could be set at 4 (3+1) on a scale of 2 to 10. This example should not be regarded as a
prescription, but only as an illustration of how the uncertainties associated with combined
multiple dose expressions associated with the toxicity data might be considered in choosing the
most appropriate ReP values for a particular assessment.
A third dose-related concern is the specificity and accuracy of the analytical methodology
used for the available relative potency data. Because dose metrics impact ReP choices,
evaluation of potential systematic errors associated with the analytical methodology should be
considered as a final quality assurance step in choosing RePs. Dose data suspected of having
significant errors that increase uncertainty associated with RPFs effectively place the RPF in a
lower dose specificity level. An example of data that could fall into this category is relative
potency determined in the presence of potent impurities or synthetic byproducts in test chemicals
that could cause or contribute to the observed effects. For example, certain PCDFs are known to
contaminate PCB congener standards (Goldstein etal., 1978; Elliott etal., 1997; National
Toxicology Program, 2006; U.S. EPA, 2001a). Contamination of test samples usually becomes a
problem when the contaminant causes the relative potency of the test chemicals to be
overestimated. Other sources of dose measurement errors may be related to limitations of
analytical methods.
3.3.2.4. Application of Three Dimensional Relative Potency Matrix - Examples of ReP Data
Prioritization Choices for Deriving RPFs
The matrix (Figure 11) is not a purely quantitative and unambiguous model. Therefore,
any number of questions concerning specific data may arise with its use in risk assessments. A
few examples of such questions are presented here to assist in understanding how the approach
can be used to consider and select RePs or derive RPFs from the types of ReP data available.
The three examples should be regarded as illustrative of the variety of considerations that
may be involved in selecting RePs or deriving RPFs for specific applications. Choices are
suggested primarily to complete the illustrations, not as prescriptions for specific applications.
The complexities involved in evaluating RPFs as alternatives or adjuncts to TEFs illustrate the
value of using TEFs-WHOgg/os, which are based on expert opinion, in an assessment.
3.3.2.4.1. Example 1: Incomplete ReP data sets.
As ReP data sets are often incomplete, it is appropriate to derive RPFs from different ReP
data sets in order to calculate a TEC for a specific species. For example, in performing an
ecological risk assessment for lake trout based on early life stage mortality, the only ReP that
exists specifically for lake trout is for PCB 126. For other dioxin-like chemicals, RePs exist only
46
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for rainbow trout or other fish species. The PCB 126 ReP for lake trout is based on early life
stage mortality, with the dose measured as the concentration in the embryo. Therefore, it is
appropriate to choose the lake trout ReP for PCB 126 and rainbow trout RePs for the other
congeners. In this specific case, since PCB 126 is the most potent PCB, choosing a more species-
specific ReP probably increases accuracy of the TEC for lake trout, at least in situations where
PCBs are a predominant proportion of the TEC. Insufficient data exist to determine if use of
rainbow trout based TEFs for the other congeners may over- or underestimate the TEC for lake
trout (with respect to the 2,3,7,8-TCDD dose-response relationship based on lake trout).
3.3.2.4.2. Example 2: Species similarity versus endpoint similarity.
Selecting RePs or deriving RPFs on the basis of species similarities versus endpoint
similarities, in the absence of data that would allow one to quantify the uncertainty in each,
creates difficult questions. For example, early life stage mortality risks for Caspian terns, using
measured, congener-specific concentrations of PCDDs, PCDFs, and PCBs in tern eggs, cannot
be assessed with RePs specifically based on early life stage mortality in Caspian terns because
such RePs do not exist. The only bird early life stage mortality data for 2,3,7,8-TCDD (i.e., dose-
response data for conducting the effects assessment) are for chickens and pheasants. It is well
established that chickens are exceptionally sensitive to 2,3,7,8-TCDD induced embryo mortality
relative to other bird species. Assume, based on knowledge of population responses of Lake
Ontario Caspian terns to historical 2,3,7,8-TCDD exposures, that the terns are significantly less
sensitive than chickens. Therefore pheasant, rather than chicken, early life stage mortality data
for 2,3,7,8-TCDD was chosen for application in the effects assessment for Caspian terns.
Assume there are RePs for (A) in vitro CYP1A induction in liver cells of Caspian terns,
(B) in vivo early life stage mortality in domestic chickens (used to establish the TEFs-WHOgg)
and (C) in vivo CYP1A induction in embryos of common terns, a closely related species. Table 7
illustrates the positions these three types of data would have in the species-endpoint specificity
matrix. Which of these three sets of ReP data would provide the most accurate estimate of the
embryo TEC for a population of Caspian terns? The TEFs-WHOgg, based largely on chicken
embryo mortality, might be regarded as preferable because the endpoint used is more relevant to
the effect of concern. However, differences between TEFs-WHOgg and tern RePs could indicate
some fundamental difference between terns and chickens in the relative potencies of dioxin-like
chemicals. Under these conditions, the greater species specificity of tern CYP1A induction based
RePs might be considered more relevant than the higher endpoint specificity of most of the
chicken based TEFs. Since Caspian terns are very closely related to common terns, RePs or
RPFs based on in vivo CYP1A induction in embryos of common terns should be preferred over
the RePs or RPFs based on in vitro CYP1A induction in liver cells of Caspian terns due to
greater endpoint relevance. One option when confronted with such difficult choices is to
calculate TECs with both sets of RePs or RPFs. The comparison may indicate both the
magnitude and sources of the uncertainty (e.g., specific dioxin-like chemicals with large
differences in RePs).
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Table 7. ReP selection matrix for Caspian terns (example 2).
In this example, the risk assessor is faced with choosing from (A) RePs based
on in vitro effects in the species of concern, (B) RePs based on in vivo effects of
concern in an unrelated species, or (C) RePs based on in vivo effects in a related
species.
Endpoint
Effect of Concern in vivo
Other Toxic Effect in vivo
CYP1A induction in vivo
CYP1A induction in vitro
AHR binding
Structure Similarity
Taxonomic Relationshi
Same Species
No data
(A) Caspian Tern
data
Related Species
(e.g., same genus
or family)
(C) Common
Tern data
p to Species of Concern
Class-Specific
TEFs-WHO98
(B) Chicken early
life stage
mortality data
Unrelated
Species
3.3.2.4.3. Example 3: Dose-response and exposure relationships.
As described in Section 3.3.1.3 of this report, the dose metric used in an exposure
analysis should be consistent with the dose metric associated with the dose-response relationship
chosen for the risk assessment. It follows that the dose metric basis for the ReP (RPF or TEFs)
selected in an assessment should be as consistent as possible with the dose metrics for both the
exposure analysis, as reflected in the dose specificity axis of Figure 11, and the dose-response
relationship. Example 3 illustrates how the choice of a dose-response relationship and options for
the exposure assessment may influence the choice of RePs.
The case is founded on a study by Tillitt et al. (1996), who assessed risk of reduced mink
kit survival as a consequence of exposure of female mink through a diet of contaminated fish.
Concentrations of PCDDs, PCDFs, and PCBs in both the fish fed to mink and in the livers of the
exposed mink dams were measured as alternative exposure expressions.
Two sets of RPFs, the TEFs-WHO94 (the TEFs-WHO94 for mammals are essentially the
same as the TEFs-WHOgg) and a set of RPFs based on rat liver H4IIE cell CYP1A induction,
were used to estimate alternative TECs that represent kit survival thresholds. The result was four
separate kit survival threshold TECs:
Diet-Based TECs:
• 1.9 pg 2,3,7,8-TCDD equivalence/g diet based on TEFs-WHO94.
• 4.4 pg 2,3,7,8-TCDD equivalence/g diet based on rat liver H4IIE cell-RPFs.
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Tissue-Based TECs:
• 60 pg 2,3,7,8-TCDD equivalence/g mink dam liver based on TEFs-WHC>94.
• 70 pg 2,3,7,8-TCDD equivalence/g mink dam liver based on rat liver H4IIE cell-
based RPFs.
Note that the dose-response relationship between exposure to 2,3,7,8-TCDD alone and kit
survival was not examined in the Tillitt etal. (1996) study. Only the mixture of PCDDs, PCDFs,
and PCBs present in the fish diet and mink livers were evaluated.
Consider a risk assessment that involves the effects offish contamination on mink kit
survival based on a field data set that includes concentrations of PCDDs, PCDFs, and PCBs both
in several species offish and in livers of mink from the area. The Tillitt et al. (1996) paper is the
logical source for the dose-response relationship because it involves both the species of concern
and the endpoint of concern, particularly given that no reproductive effects data for 2,3,7,8-
TCDD have been reported for mink or any other mammalian wildlife species. Selection of both
the exposure metric and the RePs for the assessment should be consistent with the dose-response
relationship used. Hence, if a TEC based on mink dam liver is selected from the study by Tillitt
et al, then clearly using the field data set from the mink liver would be a more comparable
exposure dose metric or diet data. Conversely, if a mink diet TEC from the Tillitt et al. study is
chosen for the effects characterization, then exposure should be based on the field data set based
on fish contamination and RPFs based on dietary administration.
Which exposure metric would be preferable, the fish diet or the mink dam liver
concentrations? In this case the mink dam liver chemical residue data probably provide a more
direct and precise measure of exposure than would reconstruction of the average dietary
exposure from the fish monitoring data. Theoretically, the net effect of metabolism and
biomagnification on the mixture composition in vivo is better accommodated by basing the TEC
on concentrations in the mink dam liver, rather than as administered in the diet. The question
then becomes, which ReP set has the greater dose specificity if mink dam liver based exposure
data are chosen? Both TEFs-WHOos and rat liver H4IIE cell-RePs are based on administered
doses and thus cannot be used in a manner completely consistent with the dose metric (measured
concentrations in liver tissue) for the liver dose-response relationships available (Tillitt et al.,
1996). However, since the rat liver H4IIE cell-RePs are based on administered dose to liver cells,
they circumvent potential errors associated with biomagnification that would affect RePs based
on doses administered through diet. If rat liver H4IIE cell-RePs are used to derive a TEC for this
risk assessment, then they should also be used in deriving the threshold TEC from the Tillitt et
al. study (i.e., the selected threshold TEC would be 70 pg 2,3,7,8-TCDD equivalence/g mink
dam liver).
A third choice of liver exposure RePs exists: a partial set of RePs based on hepatic EROD
induction in female mice following sub-chronic exposures characterized as measured
concentrations in liver of PCDDs and PCDFs (DeVito et al, 1997) and PCBs (DeVito et al,
2000). The mouse liver EROD-based ReP data for PCDDs and PCDFs are similar to both TEFs-
WHOos and rat liver H4IIE cell-RPFs. However, the mouse liver EROD-based ReP data for
PCBs are more similar to the rat liver H4IIE cell-RePs than TEFs-WHOos. Since the mouse liver
EROD RePs are based on measured concentrations in the livers as well as in vivo responses, they
are more dose-specific than TEFs-WHOos or the rat liver H4IIE cell-RePs to the chemical
concentrations measured in mink dam livers. Therefore, the best choice for RePs in this case is
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probably those based on mouse liver EROD RePs, supplemented with rat liver H4IIE cell RePs
for dioxin-like chemicals without mouse liver EROD RePs.
If the risk assessor chooses to use fish diet as the exposure measure, it would be more
consistent to employ RePs or RPFs based on administered dose. In that case, the TEFs-WHOos
probably would be preferable to the rat liver H4IIE cell-RPFs or mouse liver EROD-RePs. This
in turn would necessitate selection of the threshold TEC of 1.9 pg 2,3,7,8-TCDD equivalence/g
diet based on TEFs-WHO94 from Tillitt et al. (1996).
When choices for RePs or RPFs must be made for alternative dose-response relationships
as well as alternative dose expressions for ReP data (as summarized for example 3 in Table 8) to
what extent can one determine which set of RePs or RPFs is the most accurate? Lacking a site-
specific mink bioassay, there is insufficient information to be sure which set provides a more
accurate result, but maintaining consistency in the selection of the dose-response relationship, the
exposure metrics, and the RePs reduces the potential for systematic errors. As pointed out in
example 2, comparison of calculations using the alternative RePs may be helpful in describing
the range of possible risk values. In the case of Tillitt et al. (1996), differences between the
alternative RePs for the PCBs were most responsible for the differences in TECs for the TEFs-
WHO05 versus the rat liver H4IIE cell-RePs (PCBs were responsible for about 60% of the TECs
for the TEFs-WHC-94 compared with 10% for the rat liver H4IIE cell-RePs). Therefore,
applications of the RePs or RPFs that are inconsistent with the choice of TEC-effect relationship
would likely have a more significant effect on the final risk estimates at sites where PCBs are
present at high concentrations, relative to PCDD and PCDF concentrations.
Table 8. ReP selection matrix for mink (example 3).
The risk assessor is seeking to select RePs, RPFs, or TEFs that are most
consistent with the species, endpoint, and dose metrics used for each of four
possible dose-response relationships from Tillitt et al. (1996). The advantages and
disadvantages of alternative sets must be considered.
TEFor
RPF
Species
End
point
Dose
Characteristics of optimal mink
RPFs
If using the dose-response
relationships and exposure metrics
presented in Tillitt et al. (1996)
Mink
Kit survival
TEC in diet based on
concentrations in
fish
TEC in mink
dams based on
concentrations
in liver
Characteristics of available TEFs/RePs from which to
select
TEFs-WHO94
Mammals as a class
(based primarily on
rodents)
Vary depending on the
dioxin-like chemical;
includes subchronic or
chronic effects in vivo and
in vitro
For in vivo endpoints,
based on concentrations in
diet
Rat liver
H4IIE cell
Rats
EROD
induction
in vitro
As added to
cell culture
Mice liver
(partial set)
Mice
EROD
induction
in vivo
Measured in
liver tissue
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3.3.2.5. Summary of Selection ofTEFs, RPFs, or RePs
When applying the toxicity equivalence methodology an important consideration to be
made is what relative potency factors to use for each dioxin-like chemical. One expected
approach is to use the TEF-WHOgg/os values, unless there is a need for more site- or species-
specific calculations. When confronted with a lack of ReP data for the specific species and
endpoint of concern, choices from alternative RePs or RPFs and the TEFs must be made. This
necessary choice may be used to minimize uncertainty based on differences in species,
endpoints, and/or dosimetry associated with specific relative potencies. Uncertainties associated
with the use ofTEFs and RPFs or RePs are separate from the species differences in sensitivity to
2,3,7,8-TCDD. The former affects the accuracy associated with exposure characterization (i.e.,
the 2,3,7,8-TCDD TEC to which the species is exposed), whereas the latter impacts the effects
characterization (i.e., the species-specific dose response for 2,3,7,8-TCDD). While data are
currently insufficient to determine definitively the type of uncertainty that is greater, a larger
uncertainty for species response to 2,3,7,8-TCDD does not reduce the need to minimize
uncertainties associated with calculation of exposure and, therefore, the selection of RePs, RPFs,
and TEFs.
A best available information methodology using the three dimensional matrix (Figure 11)
is recommended for ReP selection. Species specificity, endpoint specificity, and dose
specificity/consistency are the three factors to consider when creating a hierarchy of possible
ReP data for each chemical. To the extent dose specificity is related to the endpoint and species
associated with each candidate set of RePs, it may be best considered after characterizing the
endpoint and species specificity of available RePs. When relative potency data for a mixture of
chemicals lack consistency for species, endpoint, or dose metric, systematic errors associated
with excluding chemicals with inconsistent RePs from the TEC analysis may well exceed any
errors associated with use of the weak relative potency data. However, in the absence of more
specific RePs or RPFs for the species and endpoint of concern, the vertebrate class-specific
TEFs-WHOgg/os are expected, in most cases, to be used for the assessment. In other cases with
more ReP data choices, final selection of ReP may involve use of sensitivity analysis based on
TECs calculated using alternative RePs.
Through the three examples that illustrate application of the ReP matrix, several
additional considerations were identified:
• Species specificity for ReP/RPF selection/derivation should be based on the species
being assessed, not the species on which the dose-response relationship is based.
• RePs/RPFs based on in vivo CYP1A induction in a closely related species may be
preferable to RePs/RPFs based on a more endpoint-specific effect in an unrelated
species, especially when significant differences in the RePs/RPFs may be attributable
to differences in toxicokinetic or toxicodynamic factors in the species.
• The dose metrics for the RePs, RPFs, or TEFs used should be as consistent as
possible with the dose metrics for both the dose-response relationship and the
exposure analysis.
• Accuracy of TECs is probably increased when more species-specific and endpoint-
specific RePs/RPFs are used for a key chemical.
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• In some cases the most applicable dose-response relationship may be based on TECs,
determined with a specific set of RePs for a complex mixture, rather than
concentration of 2,3,7,8-TCDD alone (e.g., the Tillitt, etal. (1996) study derived a
TEC-based toxicity reference value).
• The choice of a specific dose-response relationship may be influenced by the ReP
data available and the nature of exposure measurements available.
3.3.3. Characterization of Ecological Effects
An ecological effects analysis includes an examination of all data describing the effects
of the specific chemicals of concern. This analysis concludes with a stressor-response profile.
PCDDs, PCDFs, and PCBs present in the environment are generally found as complex mixtures.
An assessment of their ecological risk requires both quantifying their individual exposures and
developing a stressor-response profile for their cumulative effects. Figure 7 includes a dose-
response curve illustrating the relationship between early life stage mortality and exposure to
2,3,7,8-TCDD, one example of a relationship that can be used in developing a stressor-response
profile.
Demonstrated toxic effects of 2,3,7,8-TCDD in wildlife species include adverse effects
on reproduction, development, cardiovascular, and endocrine functions; wasting syndrome;
immunotoxicity; and mortality. Effects in fish larvae exposed to 2,3,7,8-TCDD include
pericardial, yolk sac, and meningeal edema; impaired jaw development; impaired heart
development and function; reduced trunk blood flow; anemia; hemorrhage; growth retardation;
and mortality. While 2,3,7,8-TCDD is by far the most studied of the dioxin-like chemicals, a
number of other PCDDs, PCDFs, and PCBs have been shown to cause toxic responses similar to
2,3,7,8-TCDD in both laboratory and field situations. A summary of effects associated with
exposure to 2,3,7,8-TCDD and related chemicals in different fish, bird, and mammalian species
is presented in Table 3. For further information regarding effects observed specifically in
wildlife, refer to U.S. EPA (1993, 2001b) and references therein. Many of the toxicological
studies used in generating RePs, RPFs, and TEFs are also the critical studies that provide a basis
for evaluating the causal connection between exposure to dioxins and potential effects.
A stressor-response profile for the cumulative effects of PCDD, PCDF, and PCB
mixtures is typically based on the stressor-response profile for 2,3,7,8-TCDD. This is because it
is often the only or best available data for endpoints of concern for this chemical. Recall that in
applying the toxicity equivalence methodology, TEFs or RPFs 'convert' the various dioxin-like
chemical concentrations into a 'common currency,' the TEC, which is a 2,3,7,8-TCDD
equivalent concentration. If sufficient data are available, however, it may be possible to develop
stressor-response profiles for chemicals other than 2,3,7,8-TCDD. Such an approach has been
employed when particular dioxin-like chemicals other than 2,3,7,8-TCDD dominate the
estimated TEC (e.g., PCBs).
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3.4. CONSIDERATIONS IN RISK CHARACTERIZATION
In risk characterization, the final phase of ecological risk assessment, the exposure profile
and stressor-response profile developed during the analysis phase are combined to realize the
final estimate of risk. Development of a risk estimate using the toxicity equivalence methodology
is described in Section 3.4.1. Lines of
evidence including field and laboratory
studies and process models are discussed in
Section 3.4.2. The uncertainties in the
methodology and its application to
ecological risk assessment are summarized
• c ±- i A -> T i. u T-J ±-c preparing the risk estimates based on TECs?
in Section 3.4.3. Text box 7 identities
important questions to consider for risk
characterization.
surveys, or other relevant RPFs?
3.4.1. Risk Estimation
When the toxicity equivalence
methodology is used, exposure is expressed associated with each line of evidence?
Text Box 7. Questions for risk characterization.
Have I clearly presented the assumptions and
uncertainties associated with applying the
toxicity equivalence methodology and in
Have I considered multiple lines of evidence,
such as bioanalytical tools, bioassays, field
Have I considered the evidence for causality
by the TEC, which reflects the combined
contribution of the individual dioxin-like chemicals that comprise the mixture. Effects are
usually estimated based on studies of the toxicity of 2,3,7,8-TCDD. TEC values for the
ecological risk assessment are compared to available 2,3,7,8-TCDD toxicity values to estimate
the likelihood and magnitude of effects. The type of comparison depends on the nature of both
exposure and effects information. The simplest risk estimation method is the quotient method. It
is the ratio of the toxicity equivalence exposure point concentration divided by a toxicity
reference value; with quotients exceeding "one" qualitatively suggesting an increased likelihood
for effects:
TEC
Risk Estimate = •
2,3, 7,8- TCDD Toxicity Reference Value
(3-10)
The quotient method for estimating risk has a number of limitations. As a single point
estimate of risk for one species or endpoint, it does not provide a means of quantitatively
expressing the probability of risk or uncertainty. Numerous approaches for estimating risk and
describing uncertainty are available and should be examined before selecting one method for
combining exposure and effects data. For example, more sophisticated models may be used to
combine the exposure and toxicity information into distributions that may allow for the
development of probability density functions, if data are adequate. Additional discussion of
stressor-response profiles and methods for risk estimation in ecological risk assessment are
available in the Guidelines for Ecological Risk Assessment (U.S. EPA, 1998).
3.4.2. Lines of Evidence
This framework presents considerations for the application of RePs, RPFs, and TEFs-
WHO98/05 in the development of a line of evidence to complete an ecological risk assessment for
dioxin-like chemicals. Risk assessments may, however, also include other lines of evidence
derived from bioanalytical tools, field surveys, or similar data that can be incorporated into the
risk characterization (Text Box 7). For example, field studies may be available that evaluate
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mortality and reproductive success offish, birds, and mammals likely to be affected by dioxin-
like chemicals, thereby offering a means to compare risks estimated using the toxicity
equivalence methodology to observed field effects. The toxicity equivalence methodology has
recently been applied using both historical field data and laboratory toxicity data in a
retrospective assessment of risks posed by dioxin-like chemicals to lake trout in Lake Ontario
(Cook etal, 2003).
Additional lines of evidence that may be appropriate for evaluating TECs in
environmental samples may be derived from a variety of bioanalytical tools developed for this
purpose. For example, measurement of chemically activated gene expression via CYP1A1 (e.g.,
EROD) or luciferase [e.g., chemical-activated luciferase gene expression (CALUX)] activity
(Garrison et al., 1996; Sanderson et al., 1996; Richter et al., 1997) in a variety of wild-type or
recombinant mammalian (e.g., H4IIE rat hepatoma, Hepa Iclc? mouse hepatoma) and fish
(RTH-149 rainbow trout hepatoma) cell lines has been used to characterize total dioxin-like
activity in environmental samples. Examples include:
1) bird eggs (Tillitt etal., 1991; Williams et al., 1995);
2) mink liver (Tillitt et al., 1996);
3) sediments and pore water (Murk et al., 1996);
4) newspapers (Seidel et al, 2000); and
5) combustion gas, fly ash, PCB oil, and animal feed (Behnisch et al., 2002).
Several reviews summarize the strengths and limitations associated with these
bioanalytical tools (Behnisch etal, 2001; Seidel etal, 2000; Denison etal, 1999; Giesy etal,
2002; Hahn, 2002b). These bioanalytical tools have the advantage of integrating the total activity
of complex mixtures of AHR agonists. Also, bioanalytically derived TECs can typically be
obtained more quickly and at a lower cost than TECs obtained by chemical analysis. Several
potential problems are associated with these tools, however (see Behnisch etal, 2001 for
detailed discussion). They may overestimate the toxic potency of chemicals that are rapidly
metabolized in vivo and are therefore not a replacement for in vivo tests (Van den Berg et al,
1998; 2006). Experts at the EPA/DOI workshop (U.S. EPA, 2001a) concluded that the potential
for generating false positive responses was high in situations where potent EROD-inducing, non-
dioxin-like chemicals (e.g., PAHs) are abundant. Another important shortcoming of these
bioanalytical tools is that they are not chemical specific (Schmitz et al, 1996) and so cannot be
used to show causality for individual chemicals or classes of chemicals in environmental samples
nor can the results derived from them be used in fate and transport or food chain modeling.
Due to current technical limitations, lack of standard testing procedures, and lack of
established quality criteria associated with existing bioanalytical tools (for summary see
Behnisch et al, 2001), the experts at the EPA/DOI workshop concluded that such bioanalytical
tools should not be used as an alternative to congener-specific analysis and the toxicity
equivalence methodology. Rather, these bioanalytical tools may be considered as additional lines
of evidence for characterizing ecological effects of PCDDs, PCDFs, and PCBs.
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The availability and utility of additional lines of evidence, whether they be bioanalytical
tools, field data or surveys, or other relevant information, should be discussed and described
during the planning and problem formulation phases of the ecological risk assessment.
3.4.3. Summary of Uncertainties
One of the components of a successful risk assessment is identifying and quantifying
uncertainties. This section provides a summary of both the uncertainties inherent to the toxicity
equivalence methodology and the uncertainties associated with the application of the
methodology in ecological risk assessment. Uncertainties associated with the TEFs-WHOgg/os
and their application to ecological risk assessment are only briefly discussed here, but are
described in detail in Van den Berg etal. (1998; 2006) and U.S. EPA (200la). Uncertainties
associated with interpreting the ecological significance of toxicity from dioxin-like chemicals are
not discussed in this framework, but may be found in U.S. EPA (1993; 1995b, c; 2001b).
3.4.3.1. Uncertainty Associated With the Toxicity Equivalence Methodology
While there are uncertainties associated with the application of the toxicity equivalence
methodology, they are believed to be, in aggregate, less significant than those associated with
other aspects of the risk assessment process and those associated with other approaches for
assessing risks of dioxin-like chemicals. Uncertainties in the toxicity equivalence methodology
are related to the assumptions and procedures used to derive the TEFs-WHOgg/os, RPFs, or RePs,
as well as the relative potency data underlying these values.
3.4.3.1.1. AHR ligands.
The TEFs-WHO98/o5 include only those PCDDs, PCDFs, and PCBs known to elicit AHR-
mediated responses. Currently there are consensus TEFs for 29 PCDD, PCDF, and PCB
congeners. Derivation of RPFs for other dioxin-like chemicals is possible based on existing or
emerging ReP values (Villeneuve et al., 2000). Field surveys or bioanalytical tools may provide
another line of evidence regarding whether dioxin-like toxicity risks are fully represented by the
TEFs-WH098/o5.
3.4.3.1.2. Additivity assumption.
The fundamental assumption of the toxicity equivalence methodology is that exposure
concentrations of PCDDs, PCDFs, and PCBs are additive when expressed as toxicity
equivalence concentrations. Section 2.1 describes the theoretical and empirical basis for the
assumption of additivity. Van den Berg etal. (1998; 2006) and theNRC (2006) concluded that
use of an additive toxicity model is the most plausible approach for assessing combined risks
from dioxin-like chemicals, despite the fact that some non-additive interactions among chemicals
have been reported (Van Birgelen etal., 1996b). Antagonistic effects are usually seen above
environmentally relevant doses. Therefore, the assumption of additivity in the toxicity
equivalence methodology is unlikely to result in large errors when antagonists are present (Van
den Berg, 1998; 2006). Considerable experimental data for ecologically relevant exposures and
toxicity endpoints support the additivity assumption, with no evidence of antagonism or
synergism (Walker and Peterson, 1991; Walker et al, 1996; Zabel et al, 1995; Tillitt et al,
1996). The assumption of additivity was further supported by recent experimental data from
Walker et al. (2005) that showed that the TEFs-WHOgg adequately predicted the increased
incidence of liver tumors in mammals with exposure to a mixture of TCDD, 2,3,7,8- PeCDF, and
PCB 126. Likewise, the NRC review of the draft Exposure and Health Reassessment of 2,3,7,8-
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TCDD and Related Compounds (U.S. EPA 2003a), included an evaluation of the additivity
assumption (NRC, 2006). The NRC Committee concluded that "from an overall perspective, this
assumption appears valid, at least in the context of risk assessment" (NRC, 2006).
3.4.3.1.3. Relative potency data.
Inaccuracies in individual dose-response studies used to determine relative potencies of
dioxin-like chemicals, as well as the variability among alternative ReP values, are sources of
uncertainty in TEFs-WHOgg/os, RPFs, and RePs. Accuracy of relative potency estimates may be
attributed to factors such as purity of the test chemicals, study design (e.g., exposure regimens
and endpoints measured), and measurement errors. Variability in relative potency data may be
attributable to factors such as precision of dose and effects measurements, the calculation
technique (e.g., EDso or LDso ratios, LOEL or NOEL ratios, NOEC or LOEC ratios, benchmark
dose ratios) used and the natural variability among organisms of the same species in their
response to dioxin-like chemicals. In deriving the TEFs-WHOgg/os, the expert panel preferred
RePs derived from EDso or LD50 ratios; when full dose-response relationships were not available
(precluding calculation of EDso or LDso), RePs based on LOELs or benchmark doses were
deemed usable, but were considered to have more uncertainty associated with them (van den
Berg etal., 2006). Because relative potency data sets are inherently heterogeneous, uncertainties
in the data used to select TEFs-WHOgg/os, RPFs, or RePs should be analyzed on a case-by-case
basis.
The use of TEFs-WHOgg/os, RPFs, or RePs introduces extrapolation uncertainties that are
common to all ecological risk assessments (e.g., inter-species, endpoint, dosimetry). Sections
3.3.1.3 and 3.2 provide detailed presentation of the considerations to be made to select TEFs-
WHO98/05, RPFs, or RePs that introduce the least amount of uncertainty when incorporating the
toxicity equivalence methodology into a risk assessment. Furthermore, the three dimensional
matrix introduced in this framework (Figure 11) provides an approach for careful selection of the
ReP, RPF, or TEF-WHOgg/os based on the most appropriate studies. Gaps encountered in the
matrix illustrate the areas where site-specific data or additional research may be needed to reduce
uncertainty.
3.4.3.1.4. Point estimates.
The TEFs-WHOgg/os and RPFs are point estimates even though the experimental data
from which they are derived may range over several orders of magnitude. Hence, TEFs-
WHO98/05 and RPFs include uncertainty in the individual RePs, as well as the uncertainty in the
method used to aggregate the data to derive the TEF-WHOgg/os or RPF. Because of the multiple
biological models used for deriving ReP values for a particular chemical, it is difficult to
estimate the variability or uncertainty of a TEF-WHOgg/os or RPF point estimate. However a
qualitative assessment of uncertainties associated with the use of TEFs-WHOgg/os or RPFs is
possible. When evaluating uncertainties associated with use of TEFs-WHOgg/os or RPFs the
following should be considered:
• Qualitative judgments, based on expert opinion, of data quality and confidence in ReP
values are embodied in establishment of the TEFs-WHOgg/os.
• Rounding TEFs to harmonize results across vertebrate classes (Van den Berg et a/.,
1998; 2006) may have introduced systematic errors in the TEFs-WHOgg/os (U.S. EPA,
200 la).
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• Multiple RePs will provide a means of assessing the uncertainty associated with the
ReP, and by extension, with an RPF derived from multiple RePs.
• In a few cases, standard errors associated with RePs (i.e.., variability around ReP
estimates) have been reported in the literature (Henry et a/., 2001). To date they have
not been widely reported in ReP publications or routinely carried over to the TEFs-
WHOgg/os, but if available, could be used to describe variability around point
estimates.
• Meta-analyses or Monte Carlo techniques have been proposed as methods for
providing quantitative uncertainty descriptors for certain TEFs-WHOgg or RPFs
(Finley et a/., 1999). However, these approaches deal only with uncertainties
associated with the precision of the data. They do not address the gap in knowledge
regarding the toxicity of these chemicals.
A recent review by Haws et al. (2006) of the underlying mammalian data for 28 of the 29
dioxin-like chemicals contained in the relative potency database (RePi99? database) used to
derive the TEFs-WHOgg/os provides suggestions for refinements of this database that could
support quantitative uncertainty analyses. Haws et al. (2006) acknowledge that the mammalian
data in the RePiggy database are based on qualitative or subjective judgment. Therefore, Haws et
al. (2006) and others (Van den Berg et a/., 1998) conclude that the mammalian data are not
amenable to determining percentiles or distributions of RePs. However, using a set of criteria for
excluding data (duplicate study, duplicate endpoint, single dose level, etc.), Haws et al. (2006)
selected those RePs that could be included in a new improved database (ReP2oo4 database) that
would be amenable to quantitative uncertainty analysis. Haws et al. (2006) illustrate their
proposal with percentiles (10th, 25th, etc.) to characterize the mammalian data distributions. It is
anticipated that continued refinement of existing ReP data, as well as the addition of new studies,
will provide more data for future iterations that could include distributions of RePs and
eventually probability density distributions. If such approaches are to be employed, existing
guidance on the application of probabilistic analysis in risk assessment should be consulted (U.S.
EPA, 1997a, b).
In the interim, the three dimensional matrix provided of this framework (Figure 11) for
assessing the quality of the mammalian, fish, or bird ReP, RPF, and TEFs-WHOgg/os data is an
existing tool that can be used to identify data that could be included in quantitative uncertainty
analyses.
3.4.3.2. Uncertainty Associated With Application of the Toxicity Equivalence Methodology in
Ecological Risk Assessment
In addition to uncertainties inherent in the toxicity equivalence methodology, application
of the methodology involves a number of uncertainties common to any ecological risk
assessment. This section provides a summary of these uncertainties. In general, uncertainties in
any risk assessment include natural variability in chemical concentrations, interspecies
differences in sensitivity to exposure, errors in field and laboratory measurements of exposure
and effects, lack of knowledge regarding pathways and routes of exposure, and errors in models
of effects and exposure. Quantifying uncertainties in ecological risk assessments for PCDDs,
PCDFs, and PCBs is not discussed in great detail in this framework. It is clearly a challenge with
multiple chemical exposures. Each chemical must be treated as a discrete entity with its own
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variance. This requires high-quality data relevant to exposure, toxicity, and the derivation of the
TEFs-WH098/o5.
3.4.3.2.1. Other methods.
Other methods for addressing risks from exposure to PCDDs and PCDFs include
assuming TCDD is the only toxic congener or assuming that all AHR-agonists are equipotent to
TCDD. These approaches underestimate or overestimate risks, respectively. Methods for
assessing risks from PCBs are more complicated (U.S. EPA, 2005). There are 209 PCB
congeners, but only 12 are AHR-agonists. Therefore, the risk assessor should utilize multiple
methods to address all the risks due to PCB exposure. In addition, the methods for measuring
PCBs in the environment are based on the original formulations of Aroclors. Much of the
historical data on PCB toxicity and exposure for mammals is based on studies of Aroclors. While
there are more laboratory studies with PCB congeners for fish and birds, most of the field
surveys are based on Aroclor or total PCB measurements. The uncertainty associated with these
chemical mixture techniques may result in an overestimation or underestimation of the AHR-
mediated effects depending on the site-specific chemical matrix. Currently, many site-specific
risk assessments rely on measurements of individual dioxin-like chemicals with some type of
chemical mixture. This provides the risk assessor with a body of evidence that is comparable to
past assessments and contributes to developing a more robust congener-specific database.
3.4.3.2.2. Uncertainties in characterization of exposure.
Measurements of chemical concentrations and fate and transport modeling of individual
dioxin-like chemicals are essential for application of the toxicity equivalence approach. The risk
assessor needs to be aware that appropriate data need to be collected for each dioxin-like
chemical considered in the risk assessment, and appropriate models modified to include each
dioxin-like chemical. Variability in chemical concentrations may appear to be a concern with the
toxicity equivalence methodology because of the number of dioxin-like chemicals involved.
However, this same variability occurs when any group of chemicals are considered in estimating
exposures. Furthermore, the incremental contribution of each chemical to overall variability in a
TEC is proportional to the fraction of the TEC associated with the chemical. Analytical
measurement errors associated with current chemical-specific methods, if conducted to meet
appropriate data quality objectives, need not be a major source of uncertainty associated with the
exposure assessment (U.S. EPA, 2001a).
Because there are multiple chemicals involved in the toxicity equivalence methodology,
minimizing the uncertainty associated with detection limits for each chemical will add some
complexity to the risk assessment. It is important that appropriate detection limits are selected for
each individual chemical during the planning and problem formulation phase of the risk
assessment. Detection limits that are relevant to the toxicity endpoint are important, since this
will reduce the uncertainty associated with risk estimates that are driven by chemicals that were
"not detected." There are numerous procedures for estimating concentrations that are below the
detection limit (U.S. EPA, 2006).
The bioaccumulation potential of PCDDs, PCDFs, and PCBs is influenced by several
site- and species-specific factors (e.g., trophic level, benthic/pelagic food chain, sediment organic
carbon, organismal lipid, and sediment-water concentration quotient) as discussed in detail in
Section 3.3.1.4. Hence, extrapolation of BAFs or BSAFs from one ecosystem to another is a
source of uncertainty. When BAFs or BSAFs must be extrapolated, the uncertainty associated
with this approach can be reduced by selecting factors for conditions that are most similar to the
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species and ecosystem of interest. Adjustments for lipid and organic carbon are built into BAFs
and BSAFs. Adjustments for other key differences can be made on the basis of food chain
parameters (see Burkhard et a/., 2006). Uncertainties for the actual site-specific point estimates
for each chemical can be reduced by determining BAFs or BSAFs that are specific for the risk
assessment being conducted. Choosing fixed reference sites for sampling organisms, sediment,
and water for all aspects of the risk assessment and future monitoring is an important step in
reducing uncertainty in relating risks to concentrations in water and sediments over time.
Consistency in applying the correct dose metric for estimating the exposure point
concentration is critical. Applying TEFs-WHOgg, RPFs, or RePs directly to concentrations of
chemicals in abiotic media for fish and birds introduces significant errors and uncertainties into
risk assessments (see Section 3.3.1.5). Since the TEFs-WHOgg for fish and birds are based on
tissue measurements, concentrations in abiotic media should be converted to concentrations in
tissue using bioaccumulation factors and models as discussed in Section 3.3.1.4. Risk
assessments for mammalian species may be derived directly from diet that may include water or
soils, since the mammalian TEFs-WHOgg/os are based on administered dose.
3.4.3.2.3. Uncertainties in characterization of ecological effects.
Use of the toxicity equivalence methodology in ecological risk assessments requires that
2,3,7,8-TCDD dose-response relationships be used to characterize adverse effects. An impetus
for development of the toxicity equivalence approach is the fact that 2,3,7,8-TCDD has been the
most well-studied, dioxin-like chemical and, hence, dose-response relationships for a number of
effects have been well characterized. Some uncertainty may be introduced in using 2,3,7,8-
TCDD dose-response relationships to characterize toxicity of all dioxin-like chemicals. For
example, it is well established that fish are less sensitive than birds and mammals to ortho-
substituted PCBs. Species differences in sensitivity to 2,3,7,8-TCDD are also sources of
uncertainty in the measures of effect (i.e., extrapolating from species of known sensitivity to
2,3,7,8-TCDD to a species of unknown sensitivity). However, reduction of this type of
uncertainty was the impetus for deriving class-specific TEFs-WHOgg/os (Van den Berg etal.,
1998; 2006).
3.4.3.2.4. Uncertainties in risk estimation.
The risk estimate, which is derived from a toxicity equivalence concentration, has similar
uncertainties to other methods of estimating risks for multiple chemicals. The inherent
uncertainties in the methods of estimating risks such as the quotient method (see Section 3.4.1)
are not unique to the application of the toxicity equivalence methodology to risk assessment.
If data are sufficient, the uncertainty in the risk estimate may be quantified. The
reliability of the data distributions should be clearly described. In particular, the uncertainty
describing the variability in the data should be distinguished from the uncertainty due to lack of
knowledge. The risk assessment should include a complete disclosure of all the assumptions and
the statistical conventions that were used to define the uncertainty associated with the risk
estimates.
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4. CONCLUSIONS
A number of PCDDs, PCDFs, and PCBs have been shown to cause toxicity to mammals,
birds, and fish through a common mechanism of action mediated by the AHR. Although these
chemicals can be collectively described as persistent and bioaccumulative in the environment,
their specific environmental profiles and potencies relative to 2,3,7,8-TCDD differ, in some
cases substantially. PCDDs, PCDFs, and PCBs frequently occur in the environment as mixtures;
hence, ecological risk assessments involving these chemicals should consider their cumulative
impacts. As described in this framework, the toxicity equivalence methodology offers a means to
derive a single exposure estimate, the TEC, from multiple chemical concentrations found in such
environmental mixtures. Although not without uncertainties, the toxicity equivalence
methodology has several advantages compared with alternative methods for estimating risks
from mixtures of these chemicals.
There is a growing body of evidence that the use of congener-specific analyses decreases
the overall uncertainty associated with assessing the risks posed by mixtures of PCDDs, PCDFs,
and PCBs (U.S. EPA, 2005). Certainly, a congener-specific approach is far less uncertain
compared to assessment methods based only on 2,3,7,8-TCDD that were used previously. For
example, assessing only 2,3,7,8-TCDD does not take into account the effects of the various other
dioxin-like chemicals often found in environmental mixtures and therefore would underestimate
risk. Alternatively, assuming that all dioxin-like chemicals found in the environment have
toxicity potency equal to 2,3,7,8-TCDD would significantly overestimate risk posed by
environmental mixtures of dioxin-like chemicals. In the assessment of PCBs, a congener-specific
approach, including the toxicity equivalence methodology, is more accurate than either an
Aroclor- or homolog-based approach for a number of reasons (U.S. EPA, 2005). A significant
uncertainty associated with Aroclor analysis is that environmental PCB mixtures often cannot be
adequately described by reference Aroclor standards due to the subjective assignment of Aroclor
congeners. In addition to these analytical uncertainties, there is great uncertainty introduced in
assuming that Aroclors or homolog groups are representative of environmentally weathered PCB
profiles. Hence, measurements of PCB concentrations, bioaccumulation model predictions, and
estimates of exposures (using the toxicity equivalence methodology) are all likely to be more
accurate if based on congener-specific data, rather than total PCBs as determined by either
Aroclor or homolog methods.
The use of the toxicity equivalence methodology has several implications for ecological
risk assessment. The primary implication is that the ecological risk assessor must select
appropriate relative potency factors for PCDDs, PCDFs, and PCBs. As demonstrated in this
framework, practical approaches exist for selecting relative potency factors. International TEFs-
WHO98/05 have been established for mammals, birds, and fish vertebrate classes, and they
represent reasonable values for estimating the TEC. This framework also presents a matrix to
facilitate the selection of assessment-specific RePs or RPFs as alternatives or adjuncts to TEFs
that may enhance the accuracy of risk estimates using the toxicity equivalence methodology. The
selection matrix is a useful tool in optimizing the application of the toxicity equivalence
methodology and encouraging the appropriate use of new relative potency information as it
becomes available.
The relative importance of the uncertainties inherent to the toxicity equivalence
methodology versus those endemic to all risk assessments depends on the particular assessment.
For example, inaccuracies among individual dose-response studies used to determine relative
potencies of dioxin-like chemicals, as well as the variability among alternative ReP values, are
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sources of uncertainty in TEFs and RPFs. Section 3.4.3 summarizes uncertainties inherent to the
toxicity equivalence methodology and the uncertainties associated with applications in ecological
risk assessment. The decision matrix for selection of RePs, described in Section 3.3.2 and Figure
11, provides some considerations for ordering the uncertainties underlying particular elements of
the methodology.
While there are uncertainties associated with the application of the toxicity equivalence
methodology, they are believed to be in aggregate less significant than those associated with
other aspects of the risk assessment process (U.S. EPA, 2001a). Furthermore the NRC has
concluded that even with the inherent uncertainties, the toxicity equivalence methodology
provides a reasonable, scientifically justifiable, and widely accepted method to estimate the
relative potency of dioxin-like chemicals (NRC, 2006). Nonetheless, it is important to note that
the methodology should only be applied in a manner consistent with its underlying assumptions;
that is, it should only be used for the appropriate chemicals, media, and target species.
Furthermore, since the toxicity equivalence methodology is applied by combining toxicity data
for specific effects, exposure relationships involving different media, and species-related
toxicokinetic and toxicodynamic factors, it is important to ensure (to the extent possible) that the
data and calculations are consistent through each step.
In summary, the benefits of the toxicity equivalence methodology can best be realized by
understanding its strengths, limitations, and its role as one of several methods within the broader
context of ecological risk assessment. The goal of this framework has been to foster such
understanding and to encourage future developments in the assessment of ecological risks from
exposure to PCDDs, PCDFs, and PCBs.
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REFERENCES
Abbott, BD; Birnbaum, LS; Pratt, RM. (1987a) TCDD-induced hyperplasia of the ureteral epithelium produces
hydronephrosis in murine fetuses. Teratology 35(3):329-334.
Abbott, BD; Morgan, KS; Birnbaum, LS; Pratt, RM. (1987b) TCDD alters the extracellular matrix and basal lamina
of the fetal mouse kidney. Teratology 35(3):335-44.
Abnet, CC; Tanguay, RL; Hahn, ME; Heideman, W; Peterson, RE. (1999) Two forms of aryl hydrocarbon receptor
type 2 in rainbow trout (Oncorhynchus mykiss): Evidence for differential expression and enhancer specificity. JBiol
Chem 274:15159-15166.
Adams, BA; Cyr, DG; Bales, JG. (2000) Thyroid hormone deiodination in tissues of the American plaice,
Hippoglossoidesplatessoides'. characterization and short-term responses to polychlorinated biphenyls (PCBs) 77 and
126. Comp Biochem Physiol CPharmacol Toxicol Endocrinol 127(3):367-378.
ATSDR. (1998) Toxicological profile for chlorinated dibenzofurans (CDFs). Agency for Toxic Substances and
Disease Registry, Atlanta, GA.
Ahlborg, MG; Becking, GC; Birnbaum, LS; Brouwer, A; Derks, HJGM; Feeley, M; Golog, G; Hanberg, S; Larsen,
JC; Liem, AKD; Safe, S; Schlatter, C; Waern, F; Younes, M; Yrjanheikki, E. (1994) Toxic equivalency factors for
dioxin-like PCBs: report on a WHO-ECEH and IPCS consultation, December 1993. Chemosphere 26(6): 1049-1067.
Alford-Stevens, AL; Bellar, TA; Eichelberger; Budde, WL. (1986) Accuracy and Precision of Determination of
Chlorinated Pesticides and Polychlorinated Biphenyls with Automated Interpretation of Mass Spectrometric Data
Anal Chem 58(9):2022-2029.
Alford-Stevens, AL; Budde, WL; Bellar, TA. (1985) Interlaboratory Study on Determination of Polychlorinated
Biphenyls in Environmentally Contaminated Sediments Anal Chem 57(13):2452-2457.
Andreason, EA; Hahn, ME; Heideman, W; Peterson, RE; Tanguay, RL. (2002) The zebrafish (Danio rerio) aryl
hydrocarbon receptor type 1 is a novel vertebrate receptor. Mol Pharmacol 62(2):234-249.
Arnold, DL; Nera, EA; Stapley, R; Bryce, F; Fernie, S; Tolnai, G; Miller, D; Hayward, S; Campbell, JS; Greer, I.
(1997) Toxicological consequences of Aroclor 1254 ingestionby female rhesus (Macaca mulatta) monkeys and their
nursing infants. Part 3: post-reproduction and pathological findings. Food Chem Toxicol 35(12): 1191-207.
Aulerich, RJ; Bursian, SJ; Napolitano, AC. (1988) Biological effects of epidermal growth factor and 2,3,7,8-
tetrachlrodibenzo-p-dioxin on developmental parameters of neonatal mink. Arch Environ Contain Toxicol 17:27-31.
Bank, PA; Yao, EF; Phelps, CL; Harper, PA; Denison, MS. (1992) Species-specific binding of transformed Ah
receptor to a dioxin responsive transcriptional enhancer. Eur JPharmacol 228(2-3):85-94.
Bank, PA; Yao, EF; Swanson, HI; Tullis, K; Denison, MS. (1995) DNA binding of the transformed guinea pig
hepatic Ah receptor complex identification and partial characterization of two high-affinity DNA-binding forms.
Arch Biochem Biophys3n(2):439-48.
Barber, TR; Chappie, DJ; Duda, DJ; Fuchsman, PC; Finley, BL. (1998) Using a spiked sediment bioassay to
establish a no-effect concentration for dioxin exposure to the amphipodAmpelisca abdita. Environ Toxicol Chem
17:420-424.
Barnes, D; Alford-Stevens, A; Birnbaum, L; Kutz, FW; Wood, W; Patton, D. (1991) Toxicity equivalency factors
for PCBs? QualAssur 1(1):70-81.
Beatty, P; Neal, RA. (1977) Factors affecting the induction of D-5-diaphorase by 2,3,7,8-tetrachlorodibenzo-/>-
dioxin. Biochem Pharmacol 27:505.
62
-------
Beatty, PW; Vaughn, WK; Neal, RA. (1978) Effect of alteration of rat hepatic mixed-function oxidase (MFO)
activity on the toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Toxicol Appl Pharmacol 45:513-519.
Beckett, KJ; Yamini, B; Bursian, SJ. (2008) The Effects of 3,3',4,4',5-Pentachlorobiphenyl (PCB 126) on Mink
(Mustela vison) Reproduction and Kit Survivability and Growth. Arch Environ Contain Toxicol 54(1): 123-129.
Behnisch, PA; Hosoe, K; Brouwer, A; Sakai, S. (2002) Screening of dioxin-like toxicity equivalents for various
matrices with wildtype and recombinant rat hepatoma H4IIE cells. Toxicol Sci 69:125-130.
Behnisch, PA; Hosoe, K; Sakai, S. (2001) Bioanalytical screening methods for dioxins and dioxin-like compounds -
a review of bioassay/biomarker technology. Environ Int 27:413-439.
Bellman, D; Anderson, M; Barren, M. (1997) Assessment of PCB ecological effects: congener-based vs. Aroclor-
based approaches. Society of Environmental Toxicology and Chemistry, 18th Annual Meeting Abstracts, p. 119.
Bentivegna, CS; Ihnat, MA; Baptiste, NS; Hamilton, JW. (1998) Developmental regulation of the 3-
methylcholanthrene- and dioxin-inducible CYP1-A5 gene in chick embryo liver in vivo. Toxicol Appl Pharmacol
151:166-173.
Birnbaum, LS. (1994) The mechanism of dioxin toxicity: relationship to risk assessment. Environ Health Perspect
102 Suppl 9:157-67.
Birnbaum, LS; McDonald, MM; Blair, PC; Clark, AM; Harris, MW. (1990) Differential toxicity of 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) in C57BL/6J mice congenic at the Ah locus. Fundam Appl Toxicol 15:186-
200.
Blankenship, AL; Hilscherova, K; Nie, M; Coady, KK; Villalobos, SA; Kannan, K; Powell, DC; Bursian, SJ; Giesy,
JP. (2003) Mechanisms of TCDD-induced abnormalities and embryo lethality in white leghorn chickens. Comp
Biochem Physiol C Toxicol Pharmacol 136(l):47-62.
Bombick, DW; Madhukar, BV; Brewster, DW; Matsumura, F. (1985) TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin)
causes increases in protein kinases particularly protein kinase C in the hepatic plasma membrane of the rat and the
guinea pig. Biochem BiophysRes Commun 127(1):296-302.
Brown, DJ; Clarke, GC; VanBeneden, RJ. (1997) Halogenated aromatic hydrocarbon-binding proteins identified in
several invertebrate marine species. Aquatic Toxicol 37:71-78.
Bruggeman, V; Swennen, Q; De Ketelaere, B; Onagbesan, O; Tona, K; Decuypere, E. (2003) Embryonic exposure
to 2,3,7,8-tetrachlorodibenzo-p-dioxin in chickens: effects of dose and embryonic stage on hatchability and growth.
Comp Biochem Physiol C Toxicol Pharmacol. 136(1): 17-28.
Brunstrom, B. (1991) Embryolethality and induction of 7-ethoxyresorufin-O-deethylase in chick embryos by
poly chlorinated biphenyls and poly cyclic aromatic hydrocarbons having Ah receptor affinity. Chem Bio I Interact
81:69-77.
Brunstrom, B; Andersson, L. (1988) Toxicity and 7-ethoxyresorufin-O-deethylase-inducing potency of coplanar
polychlorinated biphenyls (PCBs) in chick embryos. Arch Toxicol 62, 263-266.
Brunstrom, B; Halldin, K. (1998) EROD induction by environmental contaminants in avian embryo livers. Comp
Biochem Phys Part C 121:213-219.
Brunstrom, B; Halldin, K. (2000) Ecotoxicological risk assessment of environmental pollutants in the Arctic.
Toxicol Lett 112-113:111-118.
Burkhard, LP; (2003) Factors influencing the design of BAF and BSAF field studies. Environ Toxicol Chem
16:1677-1686.
63
-------
Burkhard, LP; Cook, PM; Mount, DR. (2003a) The relationship of bioaccumulative chemicals in water and sediment
to residues in fish: a visualization approach. Environ Toxicol Chem 22:2822-2830.
Burkhard, LP; Endicott, DD; Cook, PM; Sappington, KG, Winchester, EL. (2003b) Evaluation of two methods for
prediction of bioaccumulation factors. Environ Sci Technol 37:4626-4634.
Burkhard, LP; Cook, PM; Lukasewycz, MT. (2004) Biota-sediment accumulation factors for polychlorinated
biphenyls, dibenzo-p-dioxins, and dibenzofurans in southern Lake Michigan lake trout (salvelinus namaycush).
Environ Sci Technol 38:5297-5305.
Burkhard, LP; Cook, PM; Lukasewycz, MT. (2006) A hybrid empirical/modeling approach for extrapolating BSAFs
across species, time, and/or ecosystems. Environ Toxicol Chem 25: 1946-1952.
Butler, RA; Kelley, ML; Powell, WH; Halm, ME; Van Beneden, RJ. (2001) An aryl hydrocarbon receptor (AHR)
homologue from the soft-shell clam, Mya arenaria'. evidence that invertebrate AHR homologues lack 2,3,7,8-
tetrachlorodibenzo-p-dioxin and beta-naphthoflavone binding. Gene 278:223-234.
Cantoni, L; Salmona, M; Rizzardini, M. (1981) Porphyrogenic effect of chronic treatment with 2,3,7,8-
tetrachlorodibenzo-p-dioxin in female rats. Dose-effect relationship following urinary excretion of porphyrins.
Toxicol Appl Pharmacol 57:156-163.
Carvalho, PS; Noltie, DB, Tillitt, DE. (2004) Intra-strain dioxin sensitivity and morphometric effects in swim-up
rainbow trout (Oncorhynchus mykiss). Comp Biochem Physiol C Toxicol Pharmocol 137(2): 133-142.
Chapman, DE; Schiller, CM. (1985) Dose-related effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in
C57BL/6J and DBA/2J mice. Toxicol Appl Pharmacol 78:147-157.
demons, JH; Lee, LEJ; Myers, CR; Dixon, DG; Bols, NC. (1996) Cytochrome P4501A1 induction by
polychlorinated biphenyls (PCBs) in liver cell lines from rat and trout and the derivation of toxic equivalency
factors. Can JFish AquatSci 53:1177-1185.
demons JH; van den Heuvel, MR; Stegeman, JJ; Dixon, DG; Bols, NC. (1994) A comparison of toxic equivalent
factors for selected dioxin and furan congeners derived using fish and mammalian liver cell lines. Can J Fish Aquat
Sci 51:1577-1584.
Cook, PM; Robbins, J; Endicott, DD; Lodge, KB; Walker, MK; Zabel, EW; Guiney, PD; Peterson, RE. (2003)
Effects of aryl hydrocarbon receptor mediated early life stage toxicity on lake trout populations in Lake Ontario
during the 20th century. Environ Sci Technol 37:3864-3877.
Cook, PM; Zabel, EW; Peterson, RE. (1997) The TCDD toxicity equivalence approach for characterizing risks for
early life stage mortality in trout. In: Rolland, R; Gilbertson, M; Peterson R, eds. Chemically-Induced Alterations in
the Functional Development and Reproduction of Fishes. SET AC Technical Publications Series. Pensacola, FL:
SETAC Press, pp. 9-27.
Couture, LA; Harris, MW; Birnbaum, LS. (1989) Developmental toxicity of 2,3,4,7,8-pentachlorodibenzofuran in
the Fischer 344 rat. FundamAppl Toxicol 12(2):358-66.
DeCaprio, AP; McMartin, DN; O'Keefe, PW; Rej, R; Silkworth, JB; Kaminsky, LS. (1986) Subchronic oral toxicity
of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the guinea pig: comparisons with a PCB-containing transformer fluid
pyrolysate. FundamAppl Toxicol6:454-463.
DeGuise, S ; Bernier, J ; Dufresne, MM ; Martineau, D ; Beland, P ; Fournier M. (1996) Immune functions in beluga
whales (Delphinapterus leucas)'. evaluation of mitogen-induced blastic transformation of lymphocytes from
peripheral blood, spleen and thymus. Vet Immunol Immunopathol 50(1-2):! 17-26.
Denison, MS; Fisher, HM; Whitlock, JP Jr. (1988) The DNA recognition site for the dioxin-Ah receptor complex.
Nucleotide sequence and functional analysis. J Biol Chem 263(33): 17221-17224.
64
-------
Denison, MS; Nagy, SR. (2003) Activation of the aryl hydrocarbon receptor by structurally diverse exogenous and
endogenous chemicals. Annu Rev Pharmacol Toxicol 43:309-334.
Denison, MS; Seidel, SD; Ziccardi, M; Rogers, WJ; Brown, DJ; Clark, GC. (1999) Ah receptor based bioassays:
applications and limitations. Organohalogen Compd 40:27-30.
DeVito, MJ; Birnbaum, LS. (1995) Dioxins: model chemicals for assessing receptor-mediated toxicity. Toxicology
102:115-123.
DeVito, MJ; Birnbaum, LS; Farland, WH; Gasiewicz, TA. (1995). Comparisons of estimated human body burdens
of dioxinlike chemicals and TCDD body burdens in experimentally exposed animals. Env Health Perspect 103:820-
831.
DeVito, MJ; Diliberto, JJ; Ross, DG; Menache, MG; Birnbaum, LS. (1997) Dose-response relationships for
polyhalogenated dioxins and dibenzofurans following subchronic treatment in mice. I. CYP1A1 and CYP1A2
enzyme activity in liver, lung, and skin. Toxicol Appl Pharmacol 147: 267-280.
DeVito, MJ; Menache, MG; Diliberto, JJ; Ross, DG; Birnbaum, LS. (2000) Dose-response relationships for
induction of CYP1 Al and CYP1A2 enzyme activity in liver, lung, and skin in female mice following subchronic
exposure to polychlorinated biphenyls. Toxicol Appl Pharmacol 167:157-172.
DiToro, DM; McGrath, JA. (2000) Technical basis for narcotic chemicals and polycyclic aromatic hydrocarbon
criteria. II. Mixtures and sediments. Environ Toxicol Chem 19:1971-1982.
DiToro, DM; McGrath, JA; Hansen, DJ. (2000) Technical basis for narcotic chemicals and polycyclic aromatic
hydrocarbon criteria. I. Water and tissue. Environ Toxicol Chem 19:1951-1970.
Duffy, JE, Carlson, E; Li, Y; Prophete, C; Zelikoff, JT. (2002) Impact of polychlorinated biphenyls (PCBs) on the
immune function offish: age as a variable in determining adverse outcome. Mar Environ Res 54(3-5):559-563.
Eadon, G; Kaminksy, L; Silkworth, J; Aldous, K; Hilker, D; O'Keefe, P; Smith, R; Gierthy, J; Hawley, J; Kin, N;
DeCaprio, A. (1986) Calculation of 2,3,7,8-TCDD equivalent concentrations of complex environmental contaminant
mixtures. Environ Health Perspect 70:221-227.
Endicott, DD; Cook, PM. (1994) Modeling the partitioning and bioaccumulation of TCDD and other hydrophobic
organic chemicals in Lake Ontario. Chemosphere 28:75-87.
Elliott, JE; Kennedy, SW; Jeffrey, D; Shutt, L. (1991) Polychlorinated biphenyl (pCB) effects on hepatic mixed
function oxidases and porphyria in birds. II. American kestrel. Comp Biochem Physiol Part C 99(1-2): 141-145.
Elliott, JE; Kennedy SW; Lorenzen A (1997) Comparative toxicity of poly chlorinated biphenyls to Japanese quail
(Coturnix c. japonica) and American kestrels (Falco sparverius). J Toxicol Environ Health 51:57-75.
Elliott, JE; Kennedy, SW; Peakall, DB; Won, H. (1990) Polychlorinated biphenyl (pCB) effects on hepatic mixed
function oxidases and porphyria in birds. I. Japanese quail. Comp Biochem Physiol Part C96(l):205-210.
Elonen, GE; Spehar, RL; Holcombe, GW; Johnson, RD; Fernandez, JD; Erickson, RJ; Tietge, JE; Cook, PM. (1998)
Comparative toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin to seven freshwater fish species during early-life-stage
development. Environ Toxicol Chem 17:472-483.
El-Sabeawy, F; Enan, E; Lasley, B. (2001) Biochemical and toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin in
immature male and female chickens. Comp Biochem Physiol C Toxicol Pharmacol 129(4):317-27.
Environment Canada and Health Canada. (2001) Priority substances list assessment report: nonylphenol and its
ethoxylates. Available from: www.ec.gc.ca/substances/ese/eng/psap/final/npe.cfm.
65
-------
Eriksson, P; Fredriksson, A. (1998) Neonatal exposure to 2,2',4,4',5,5'-hexachlorobiphenyl or 3,3',4,4',5,5'-
hexachlorobiphenyl causes behavioral derangements in mouse that deteriorate with age. Organohalogen Compounds
37:117-119.
Finley, B; Kirman, C; Scott, P. (1999) Derivation of probabilistic distributions for the WHO mammalian toxic
equivalency factors. Organo Halogen 42:225-228.
Finley, BL; Trowbridge, KR; Burton, S; Proctor, DM; Panko, JM; Paustenbach, DJ. (1997) Preliminary assessment
of PCB risks to human and ecological health in the lower Passaic River. J Toxicol Environ Health 53:95-118.
Garrick, RA; Woodin, BR; Wilson, JY; Middlebrooks, BL; Stegeman, JJ. (2006) Cytochrome P4501A is induced in
endothelial cell lines from the kidney and lung of the bottlenose dolphin, Turslops truncatus. Aquat Toxicol 76(3-
4):295-305.
Garrison, PM; Tullis, K; Aarts, JMMJG; Brouwer, A; Giesy, JP; Denison, MS. (1996) Species-specific recombinant
cell lines as bioassay systems for the detection of 2,3,7,8-tetrachlorodibenzo-p-dioxin-like chemicals. Fundam Appl
Toxicol 30:194-203.
Gasiewicz, TA; Rucci, G; Henry, EC; Baggs, RB. (1986) Changes in hamster hepatic cytochrome P-450,
ethoxycoumarin o-deethylase, and reduced NAD(P): menadione oxidoreductase following treatment with 2,3,7,8-
tetrachlorodibenzo-p-dioxin. Partial dissociation of temporal and dose-response relationships from elicited toxicity.
Biochem Pharmacol 35:2737-2742.
Giavini, E; Prati, M; Vismara, C. (1982) Rabbit teratology study with 2,3,7,8-tetrachlorodibenzo-p-dioxin. Environ
Res 27(l):74-8.
Giesy, JP; Hilscherova, K; Jones, PD; Kannan, K; Machala, M. (2002) Cell bioassays for detection of aryl
hydrocarbon (AHR) and estrogen receptor (ER) mediated activity in environmental samples. Mar Poll Bull 45:3-16.
Giesy, JP; Kannan, K. (1998) Dioxin-like and non-dioxin-like toxic effects of poly chlorinated biphenyls (PCBS):
implications for risk assessment. CritRev Toxicol 28:511-569.
Gilday, D; Bellward, GD; Sanderson, JT; Janz, DM; Rifkind, AB. (1998) 2,3,7,8-Tetrachlorodibenzo-p-dioxin
(TCDD) induces hepatic cytochrome P450-dependent arachidonic acid epoxygenation in diverse avian orders:
regioisomer selectivity and immunochemical comparison of the TCDD-induced P450s to CYP1A4 and 1A5. Toxicol
Appl Pharmacol 150:106-116.
Gillette, DM; Corey, RD; Helferich, WG; McFarland, JM; Lowenstine, LJ; Moody, DE; Hammock, BD; Shull, LR.
(1987) Comparative toxicology of tetrachlorobiphenyls in mink and rats. I. Changes in hepatic enzyme activity and
smooth endoplasmic reticulum volume. Fundam Appl Toxicol 8(1):5-14.
Gobas, FAPC. (1993) A model for predicting the bioaccumulation of hydrophobic organic chemicals in aquatic
food-webs: applications to Lake Ontario. EcolMod 69:1-17.
Gobas, FAPC; Pasternak, JP; Lien, K; Duncan, RK. (1998) Development and field validation of a multimedia
exposure assessment model for waste load allocation in aquatic ecosystems: application to 2,3,7,8-
tetrachlorodibenzo-p-dioxin and 2,3,7,8-tetrachlorodibenzofuran in the Fraser River watershed. Environ Sci Technol
32: 2442-2449.
Goff, KF; Hull, BE; Grasman, KA. (2005) Effects of PCB 126 on primary immune organs and thymocyte apoptosis
in chicken embryos. J Toxicol Environ Health A 68(6):485-500.
Goldstein, JA; Hass, JR; Linko, P; Harvan, DJ. (1978) 2,3,7.8-Tetrachlorodibenzofuran in a commercially available
99% pure polychlorinated biphenyl isomer identified as the inducer of hepatic cytochrome P448 and aryl
hydrocarbon hydroxylase in the rat. DrugMetab Dispos 6:258-264.
Goldstein, JA; Linko, P; Bergman, H. (1982) Induction of porphyria in the rat by chronic versus acute exposure to
2,3,7,8-tetrachlorodibenzo-/>-dioxin. Biochem Pharmacol 31:1607-1613.
66
-------
Government of Canada. (1991) Toxic chemicals in the Great Lakes and associated effects. Volume 1: Contaminant
levels and trends. Environment Canada, Department of Fisheries and Oceans, Health and Welfare Canada, Minister
of Supply and Services, Ottawa. 488 pp.
Guiney, PD; Cook, PM; Casselman, JM; Fitzsimmons, JD; Simonin, HA; Zabel, EW; Peterson, RE. (1996)
Assessment of 2,3,7,8-tetrachlorodibenzo-f-dioxin induced sac fry mortality in lake trout (Salvelinus namaycush)
from different regions of the Great Lakes. Can JAquat Sci 53:2080-2092.
Gutleb, AC; Appelman, J; Bronkhorst, MC; van den Berg, JHJ; Spenkelink, A; Brouwer, A; Murk, AJ. (1999)
Delayed effects of pre- and early-life time exposure to polychlorinated biphenyls on tadpoles of two amphibian
species (Xenopus laevis andRana temporaria). Environ Toxicol Pharmacol 8:1-14.
Hahn, ME. (1998) The aryl hydrocarbon receptor: a comparative perspective. Comp Biochem Physiol C Pharmacol
Toxicol EndocrinolUl(l-3):23-53.
Hahn, ME. (2002a) Aryl hydrocarbon receptors: diversity and evolution. Chem BiolInteract 141:131-160.
Hahn, ME. (2002b) Biomarkers and bioassays for detecting dioxin-like compounds in the marine environment. Sci
Total Environ 289:49-69.
Hahn, ME; Chandran, K. (1996) Uroporphyrin accumulation associated with cytochrome P4501A induction in fish
hepatoma cells exposed to aryl hydrocarbon receptor agonists, including 2,3,7,8-tetrachlorodibenzo-/>-dioxin and
planar chlorobiphenyls. Arch Biochem Biophys 329(2): 163-174.
Hahn, ME; Karchner, SI; Shapiro, MA; Perera, SA. (1997) Molecular evolution of two vertebrate ary hydrocarbon
(dioxin) receptors (AHR1 and AHR2) and the PAS family. Proc NatlAcad Sci U.S.A. 94:13743-13748.
Hakansson, H; Johansson, L; Manzoor, E; Ahlborg, UG. (1994) Effect of 2,3,7,8-tetrachlorodibenzo-/>-dioxin on the
hepatic 7-ethoxyresorufin O-deethylase activity in four rodent species. Eur J Pharmacol 270(4):279-284.
Hamilton, JW; Denison, MS; Bloom, SE. (1983) Development of basal and induced aryl hydrocarbon
(benzo[a]pyrene) hydroxylase activity in the chick embryo in ovo. Proc NatlAcad Sci USA 80:3372-3376.
Hankinson, O. (1995) The aryl hydrocarbon receptor complex. Annu Rev Pharmacol Toxicol 35:307-340
Hansson, MC; Wittzell, H; Persson, K; von Schantz, T. (2003) Characterization of two distinct aryl hydrocarbon
receptor AhR2 gene lineages in salmonid fish. Gene 303:197-206.
Haws, LC; Su, H; Harris, M; Devito, MJ; Walker, NJ; Farland, WH; Finley, B; Birnbaum, LS. (2006) Development
of a refined database of mammalian relative potency estimates for dioxin-like compounds. Tox Sci 89:4-30.
Henck, JM; New, MA; Kociba, RJ; Rao, KS. (1981) 2,3,7,8-Tetrachlorodibenzo-p-dioxin: Acute oral toxicity in
hamsters. ToxicolAppl Pharmacol 59(2):405-407.
Henry, TR; Nesbit, DJ; Peterson, RE. (2001) Relative potency values (REPs) for polychlorinated dibenzo-p-dioxin,
dibenzofuran and biphenyl congeners based on induction of cytochrome P4501A mRNA in a zebrafish liver cell line
(ZFL). Environ Toxicol Chem 20:1053-1058.
Henry, TR; Spitsbergen, JM; Hornung, MW; Abnet, CC; Peterson, RE. (1997) Early life stage toxicity of 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) in zebrafish (Danio rerio). Toxicol Appl Pharmacol 142:56-68.
Henshel, DS. (1998) Developmental neurotoxic effects of dioxin and dioxin-like compounds on domestic and wild
avian species. Environ Toxicol Chem 17(l):88-98.
Hill, A; Howard, CB; Strahle, U; Cossins, A. (2003) Neurodevelopmental defects in zebrafish (Danio rerio) at
environmentally relevant dioxin (TCDD) concentrations. Toxicol Sci 76(2)392-399.
67
-------
Hochstein, JR; Aulierich, RJ; Bursian, SJ. (1988) Acute toxicity of 2,3,7,8-tetrachlorodibenzo-/>-dioxinto mink.
Arch Environ Contain Toxicol 17:23-27.
Hochstein, JR; Bursian, SJ; Aulerich, RJ. (1998) Effects of dietary exposure to 2,3,7,8-tetrachlorodibenzo-/>-dioxin
in adult female mink (Mustela vison). Arch Environ Contain Toxicol 35:348-353.
Hochstein, JR; Render, JA; Bursian, SJ; Aulerich, RJ. (2001) Chronic toxicity of dietary 2,3,7,8-tetrachlorodibenzo-
p-dioxinto mink. Vet Hum Toxicol 43:134-139.
Hoffman, DJ; Melancon, MJ; Klein, PN; Eisemann, JD; Spann, JW. (1998) Comparative developmental toxicity of
planar polychlorinated biphenyl congeners in chickens, American kestrels, and common terns. Environ Toxicol
Chem 17(4):747-757.
Hoffman, DJ; Rice, CP; Kubiak, TJ. (1996) PCBs and dioxins in birds. In: Beyer, WN; Heinz, GH; and Redmon-
Norwood, AW; eds. Environmental contaminants in wildlife: interpreting tissue concentrations. Boca Raton, FL;
Lewis Publishers.
Hong, R; Taylor, K; Abonour, R (1989) Immune abnormalities associated with chronic TCDD exposure in rhesus.
Chemosphere 18:313-320.
Hook, GER; Haseman, JK; Lucier, GW. (1975) Induction and suppression of hepatic and extrahepatic microsomal
foreign-compound-metabolizing enzyme systems by 2,3,7,8-tetrachlorodibenzo-/>-dioxin. Chem-BiolInteract
10:199.
Huang, Y-W; Melancon, MJ; Jung, RE; Karasov, WH. (1998) Induction of cytochrome P450-associated
monooxygenase in northern leopard frogs, Ranapipiens, by 3,3'4,4',5-pentachlorobiphenyl. Environ Toxicol Chem
17:1564-1569.
Janz, DM; Bellward, GD. (1996a) In ovo 2,3,7,8-tetrachlorodibenzo-/>-dioxin exposure in three avian species. 1.
Effects on thyroid hormones and growth during the perinatal period. ToxAppl Pharm 139:281-291.
Janz, DM; Bellward, GD. (1996b) In ovo 2,3,7,8-tetrachlorodibenzo-p-dioxin exposure in three avian species. 2.
Effects on estrogen receptor and plasma sex steroid hormones during the perinatal period. ToxAppl Pharm 139:292-
300.
Janz, DM; Bellward, GD. (1997) Effects of acute 2,3,7,8-tetrachlorodibenzo-/>-dioxin exposure on plasma thyroid
and sex steroid hormone concentrations and estrogen receptor levels in adult blue herons. J Toxicol Environ Health
16:985-989.
Janz DM; Metcalfe CD. (1991) Relative induction of aryl hydrocarbon hydroxylase by 2,3,7,8-TCDD and two
coplanar PCBs in rainbow trout (Oncorhynchus mykiss). Environ Toxicol Chem 10:917-923.
Jensen, BA; Hahn, ME. (2001) cDNA cloning and characterization of a high affinity aryl hydrocarbon receptor in a
cetacean, the beluga, Delphinapterus leucas. Toxicol Sci 64(l):41-56.
Johnson, R; Tietge, J; Bolts, S. (1992) Carcinogenicity of 2,3,7,8-TCDD to medaka. Toxicologist 12(1): 138.
Jones, KG; Sweeney, GD. (1980) Dependence of the porphyrogenic effect of 2,3,7,8-tetrachlorodibenzopdioxin
upon inheritance of aryl hydrocarbon hydroxylase responsiveness. Toxicol Appl Pharmacol 53:42-49.
Jung, RE; Walker, MK. (1997) Effects of 2,3,7,8-tetrachlorodibenzo-/>-dioxin (TCDD) on development of anuran
amphibians. Environ Toxicol Chem 16:230-240.
Karchner, SI; Powell, WH; Hahn, ME. (1999) Identification and functional characterization of two highly divergent
aryl hydrocarbon receptors (AHR1 and AHR2) in the teleost Fundulus heteroclitus. Evidence for a novel subfamily
of ligand-binding basic helix-loop-helix Per-ARNT-Sim (bHLH-PAS) factors. JBiol Chem 274:15159-15166.
68
-------
Kelling, CK; Christian, BJ; Inborn, SL; Peterson, RE. (1985) Hypophagia-induced weight loss in mice, rats, and
guinea pigs treated with 2,3,7,8-tetrachlorodibenzo-/>-dioxin. Fundam Appl Toxicol 5:700-712.
Kennedy, SW; Jones, SP; Elliott, JE. (2003) Sensitivity of bald eagle (Haliaeetus leucocephalus) hepatocyte
cultures to induction of cytochrome P4501Aby 2,3,7,8-tetrachlorodibenzo-p-dioxin. Ecotoxicology 12(1-4): 163-
170.
Kennedy, SW; Lorenzen, A; Jones, SP; Hahn, ME; Stegeman, JJ. (1996) Cytochrome P4501A induction in avian
hepatocyte cultures: a promising approach for predicting the sensitivity of avian species to toxic effects of
halogenated aromatic hydrocarbons. Toxicol Appl Pharmacol 141:214-230.
Kitchin, KT; Woods, JS. (1979) 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) effects on hepatic microsomal
cytochrome P-448-mediated enzyme activities. Toxicol Appl Pharmacol 47:537-546.
Kleeman, JM; Olson, JR; Peterson, RE. (1988) Species differences in 2,3,7,8-tetrachlordibenzo-/>-dioxin toxicity
and biotransformation in fish. Fundam Appl Toxicol 10:206-213.
Kociba, RJ; Keyes, DG; Beyer, JE; Carreon, RM; Wade, CE; Dittenber, DA; Kalnins, RP; Frauson, LE; Park, CN;
Barnard, SD; Hummel, RA; Humiston, CG. (1978) Results of a two-year chronic toxicity and oncogenicity study of
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in rats. Toxicol Appl Pharmacol 46:279-303.
Kriiger, N; Neubert, B; Helge, H; Neubert, D. (1990) Induction of caffeine-demethylations by 2,3,7,8-TCDD in
marmoset monkeys measured with a 14CO2-breath test. Chemosphere 20:1173-1176.
Kutz, FW; Barnes, DG; Bretthauer, EW; Bottimore, DP; Greim, H. (1990) The International Toxicity Equivalency
Factor (I-TEF) method for estimating risks associated with exposures to complex mixtures of dioxins and related
compounds. Toxicol Environ Chem 26:99-109.
Lambrecht, RW; Sinclair, PR; Bement, WJ; Sinclair, JF. (1988) Uroporphyrin accumulation in cultured
chickembryo hepatocyte: comparison of 2,3,7,8-tetrachlorodibenzo-/>-dioxin and 3,4,3',4'-tetrachlorobiphenyl.
Toxicol Appl Pharm 96:507-516.
Leece, B; Denomme, MA; Towner, R; Li, SMA; Safe, S. (1985) Poly chlorinated bipheny Is: correlation between in
vivo and in vitro quantitative structural-activity relationships (QSARs). J Toxicol Environ Health 16:379-388.
Liem, HH; Muller-Eberhard, U; Johnson, EF. (1980) Differential induction by 2,3,7,8-tetrachlorodibenzo-/>-dioxin
of multiple forms of rabbit microsomal cytochrome P-450: evidence for tissue specificity. Mol Pharmacol 18:565.
Maki, AW; Johnson, HE. (1975) Effects of PCB (Aroclor 1254) and p, p'-DDT on production and survival of
Daphnia magna Strauss. Bull Environ Contam Toxicol 13:412-416.
McConnell, EE; Moore, JA; Dalgard, DW. (1978a) Toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin in rhesus
monkeys (Macaca mulatta) following a single oral dose. Toxicol Appl Pharmacol 43:175-187.
McConnell, EE; Moore, JA; Haseman, JK; Harris, MW. (1978b) The comparative toxicity of chlorinated dibenzo-p-
dioxins in mice and guinea pigs. Toxicol Appl Pharmacol 44:335-356.
Murk, AJ; Legler, J; Denison, MS; Giesy, JP; van de Guchte, C; Brower, A. (1996) Chemical-activated luciferase
gene expression (CALUX): a novel in vitro bioassay for Ah receptor active compounds in sediment and pore water.
Fundam Appl Toxicol 33:149-160.
National Toxicology Program. (1982) Carcinogenesis Bioassay of 2,3,7,8-Tetrachlorodibenzo-p-dioxin (CAS
No. 1746-01-6) in Osborne-Mendel Rats and B6C3F1 Mice (Gavage Study). Natl Toxicol Program Tech Rep Ser
209:1-195.
69
-------
National Toxicology Program. (2006) Toxicology and carcinogenicity studies of a binary mixture of 3,3',4,4',5-
pentachlorobiphenyl (PCB 126) (CAS No. 57465-28-8) and 2,3',4,4',5-pentachlorobiphenyl (PCB 118) (CAS No.
31508-00-6) in female Harlan Sprague-Dawley rats (gavage studies). U.S. Department of Health and Human
Services, Research Triangle Park, NC.
NRC (Committee on EPA's Exposure and Human Health Reassessment of TCDD and Related Compounds,
National Research Council of the National Academies). (2006) Health Risks from Dioxin and Related Compounds:
Evaluation of the EPA Reassessment. National Academies Press, Washington, DC.
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 ofdioxins 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 Factor (I-TEF), method of risk assessment
for complex mixtures ofdioxins and related compounds. Report No. 178.
Neal, RA; Olson, JR; Gasiewicz, TA; Geiger, LE. (1982) The toxicokinetics of 2,3,7,8-tetrachlorodibenzo-p-dioxin
in mammalian systems. DrugMetab Rev 13:355-385.
Nebeker, AV; Puglisi, FA. (1974) Effects of poly chlorinated biphenyls (PCBs) on survival and reproduction of
Daphnis, Gammaurs, and Tanytarsus. Trans Fish Soc 103:722-728.
Nebert, DW. (1989) The Ah locus: genetic differences in toxicity, cancer, mutation, and birth defects. CritRev
Toxicol 20:137-152.
Nosek, JA; Craven, SR; Sullivan, JR; Hurley, SS; Peterson, RE. (1992) Toxicity and reproductive effects of 2,3,7,8-
tetrachlorodibenzo-p-dioxin in ring-necked pheasant hens. J Toxicol Environ Health 35(3): 187-198.
Nosek, JA; Sullivan, JR; Craven, SR; Gendron-Fitzpatrick, A; Peterson, RE. (1993) Embryotoxicity of 2,3,7,8-
tetrachlorodibenzo-p-dioxin in the ring-necked pheasant. Environ Toxicol Chem 12:1215-1222.
Okey, AB; Riddick, DS; Harper, PA. (1994) The Ah receptor: mediator of the toxicity of 2,3,7,8-tetrachlorodibenzo-
/>-dioxin (TCDD) and related compounds. Toxicol Lett 70:1-22.
Olson, JR; Holscher, MA; Neal, RA. (1980) Toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxininthe Golden Syrian
hamster. ToxicolApplPharmacol 55:67-78.
Palace, VP; Allen-Gil, SM; Brown, SB; Evans, RE; Metner, DA; Landers, DH; Curtis, LR; Klaverkamp, JF; Baron,
CL; Lockhart, WL. (2001) Vitamin and thyroid status in arctic grayling (Thymallus arcticus) exposed to doses of
3,3',4,4'-tetrachlorobiphenyl that induce the phase I enzyme system. Chemosphere 45:185-193.
Parrott JL; Hodson PV; Servos MR; Huestis SL; Dixon DG. (1995) Relative potency of polychlorinated dibenzo-p-
dioxins and dibenzofurans for inducing mixed-function oxygenase activity in rainbow trout. Environ Toxicol Chem
14:1041-1050.
Peterson, RE; Seefeld, MD; Christian, BJ; Potter, CL; Kelling, K; Keesey, R. (1984) The wasting syndrome in
2,3,7,8-tetrachlorodibenzo-p-dioxin toxicity: basic features and their interpretation. In: Poland, A; Kimbrough, R,
eds. Banbury Report: Biological Mechanisms of Dioxin Action, Vol. 18. Plainview, NY: Cold Spring Harbor
Laboratory, pp. 291-308.
Peterson, RE; Theobald, HM; Kimmel, GL. (1993) Developmental and reproductive toxicity ofdioxins and related
compounds: cross-species comparisons. CritRev Toxicol 23:283-335.
Poland, A; Knutson, JC. (1982) 2,3,7,8-Tetrachlorodibenzo-p-dioxin and related halogenated aromatic
hydrocarbons: examination of the mechanisms of toxicity. Ann Rev Pharmacol Toxicol 22:517-554.
70
-------
Powell, DC; Aulerich, RJ; Meadows, JC; Tillitt, DE; Giesy, JP; Stromborg, KL; Bursian, SJ. (1996a) Effects of
3,3',4,4',5-pentachlorobiphenyl (PCB 126), 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) injected into the yolks of
chicken (Gallus deomesticus) eggs prior to incubation. Arch Environ Contain Toxicol 31:404-409.
Powell, DC; Aulerich, R; Meadows, JC; Tillitt, DE; Powell, JF; Restrum, JC; Stromborg, KL; Giesy, JP; Bursian,
SJ. (1997) Effects of 3,3',4,4',5-pentachlorobiphenyl (PCB 126), 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), or an
extract derived from field-collected cormorant eggs injected into double-crested cormorant (Phalacrocorax auritus)
eggs. Environ Toxicol Chem 16(7): 1450-1455.
Powell, DC; Aulerich, RJ; Stromborg, KL; Bursian, SJ. (1996b) Effects of 3,3',4,4'-pentachlorobiphenyl,
2,3,3',4,4'-pentachlorobiphenyl, (PCB 126), and 3,3',4,4',5-pentachlorobiphenyl on the developing chicken embryo
when injected prior to incubation. J Toxicol Environ Health 49:319-338.
Powell-Coffman, JA; Bradfield, CA; Wood, WB. (1998) Caenorhabditis elegans orthologs of the aryl hydrocarbon
receptor and its heterodimerization partner the aryl hydrocarbon receptor nuclear translocator. Proc NatlAcad Sci
95:2844-2849.
Rao, MS; Subbarao, V; Prasad, JD; Scarpelli, DG. (1988) Carcinogenicity of 2,3,7,8-tetrachlorodibenzo-p-dioxinin
the Syrian golden hamster. Carcinogenesis 9(9): 1677-9.
Render, JA; Aulerich, RJ; Bursian, S J; Nachreiner, RF. (2000) Proliferation of maxillary and mandibular periodontal
squamous cells in mink fed 3,3',4,4',5-pentachlorobiphenyl (PCB 126). J VetDiagn Invest 12:477-479.
Render, JA; Bursian, S J; Rosenstein, DS; Aulerich, RJ. (2001) Squamous epithelial proliferation in the jaws of mink
fed diets containing 3,30,4,40,5-pentachlorobiphenyl (PCB 126) or 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD).
Vet Hum Toxicol 43:22-26.
Restum, J; Bursain, S; Giesy, J; Render, J; Helferich, W; Shipp, E; Verbrugge, D; Aulerich, R. (1998)
Multigenerational study of the effects of consumption of PCB-contaminated carp from Saginaw Bay, Lake Huron,
on mink. I. Effects on mink reproduction, kit growth and survival, and selected biological parameters. Toxicol
Environ Health 54:343-375.
Rice, CP; O'Keefe, P; Kubiak, TJ. (2002) Sources, pathways and effects of PCBs, dioxins, and dibenzofurans. In:
Hoffman, DJ; Rattner, BA; Burton, GA, Jr; Cairns, J, Jr, eds. Handbook ofEcotoxicology. 2nd ed. Boca Raton, FL:
CRC Lewis Publishers, pp. 499-571.
Richter, CA; Tieber, VL; Dension, MS; Giesy, JP. (1997) An in vitro rainbow trout cell bioassay for aryl
hydrocarbon receptor-mediated toxins. Environ Toxicol Chem 16:543-550.
Roman, BL; Peterson, RE. (1998) Developmental male reproductive toxicology of 2,3,7,8-tetrachlorodibenzo-p-
dioxin and PCBs. ReprodDevel Toxicol 24:593-624.
Ross, P. (2000) Marine mammals as sentinels in ecological risk assessment. Hum Ecol Risk Assess 6: 29-46.
Ross, P; De Swart, R; Addison, R; VanLoveren, H; Vos, J; Osterhaus, A. (1996) Contaminant-induced
immunotoxicity in harbour seals: wildlife at risk? Toxicology 112(2): 157-69.
Safe, S. (1990) Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and related
compounds: environmental and mechanistic considerations which support the development of toxic equivalency
factors (TEFs). CritRev 7b;dco/21(l):51-88.
Safe, S. (1994) Polychlorinated biphenyls (PCBs): environmental impact, biochemical and toxic responses, and
implications for risk assessment. CritRev Toxicol 24(2):87-149.
Sanderson, JT; Aarts, JM; Brouwer, A; Froese, KL; Denison, MS; Giesy, JP. (1996) Comparison of Ah receptor-
mediated luciferase and ethoxyresorufin-O-deethylase induction in H4IIE cells: implications for their use as
bioanalytical tools for the detection of polyhalogenated aromatic hydrocarbons. Toxicol Appl Pharmacol
137(2):316-25.
71
-------
Sanderson, JT; Bellward, GD. (1995) Hepatic microsomal ethoxyresorufinO-deethylase-inducing potency in ovo
and cytosolic Ah receptor binding affinity of 2,3,7,8-tetrachlorodibenzo-/>-dioxin: comparison of four avian species.
ToxApplPharm 132:131-145.
Sano, S; Kawanishi, S; Seki, Y. (1985) Toxicity of poly chlorinated biphenyl with special reference to porphyrin
metabolism. Environ Health Perspect 59:137-43.
Santostefano, MJ; Xiaofeng, W; Richardson, VM; Ross, DG; DeVito, MJ; Birnbaum, LS. (1998) A
Pharmacodynamic Analysis of TCDD-Induced Cytochrome P450 Gene Expression in Multiple Tissues: Dose- and
Time-Dependent Effects. Toxicol Appl Pharmacol 151(2):294-310.
Sather, PJ; Newman, JW ; Ikonomou, MG. (2003) Congener-based Aroclor quantification and speciation
techniques: a comparison of the strengths, weaknesses, and proper use of two alternative approaches.E'wv/row Sci
Technol 37(24):5678-86.
Schmitz, H-J; Behnisch, P; Hagenmaier, A; Hagenmaier, H; Bock, KW; Schrenk, D. (1996) CYP1A1-inducing
potency in H4IIE cells and chemical composition of technical mixtures of poly chlorinated biphenyls. Environ
Toxicol Pharmacol 1:73-79.
Schwetz, BA; Norris, JM; Sparschu, GL; Rowe, VK; Gehring, PJ; Emerson, JL; Gehring, CG (1973) Toxicology of
chlorinated dibenzo-p-dioxins. Environ Health Perspect 5:87-99.
Seefeld, MD; Albrecht, RM; Gilchrist, KW; Peterson, RE. (1980) Blood clearance tests for detecting 2,3,7,8-
tetrachlorodibenzo-p-dioxin hepatotoxicity in rats and rabbits. Arch Environ Contam Toxicol 1980;9(3):317-327.
Seidel, SD; Li, V; Winter, GM; Rogers, WJ; Martinez, El; Denison, MS. (2000) Ah receptor-based chemical
screening bioassays: application and limitations for the detection of Ah receptor agonists. Toxicol Sci 55:107-115.
Sewall, CH; Lucier, GW. (1995) Receptor-mediated events and the evaluation of the Environmental Protection
Agency (EPA) of dioxin risks. MutatRes 333:111-122.
Shen, ES; Gutman, SI; Olson, JR. (1991) Comparison of 2,3,7,8-tetrachlorodibenzo-p-dioxin-mediated
hepatotoxicity in C57BL/6J and DBA/2J mice. J Toxicol Environ Health 32:367-381.
Shipp, E; Restum, J; Giesy, J; Bursain, S; Aulerich, R; Helferich, W. (1998a) Multigenerational study of the effects
of consumption of PCB-contaminated carp from Saginaw Bay, Lake Huron, on mink. II. Liver PCB concentration
and induction of hepatic cytochrome P-450 activity as a potential biomarker for PCB exposure. Toxicol Environ
Health 54:377-401.
Shipp, E; Restum, J; Giesy, J; Bursain, S; Aulerich, R; Helferich, W. (1998b) Multigenerational study of the effects
of consumption of PCB-contaminated carp from Saginaw Bay, Lake Huron, on mink. III. Estrogen receptor and
progesterone receptor concentrations, and potential correlation with dietary PCB consumption. Toxicol Environ
Health 54:403-420.
Sinclair, PR; Bement, WJ; Bonkovsky, HL; Sinclair, JF. (1986) Uroporphyrin accumulation produced by
halogenated biphenyls in chick-embryo hepatocytes — reversal of the accumulation by piperonyl butoxide. Biochem
.7237:63-71.
Smialowicz, RJ; Riddle, MM; Williams, WC; Diliberto, JJ (1994) Effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) on humoral immunity and lymphocyte subpopulations: differences between mice and rats. Toxicol Appl
Pharmacol 124:248-256.
Smialowicz, RJ; Williams, WC; Riddle, MM (1996) Comparison of the T cell-independent antibody response of
mice and rats exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. FundamAppl Toxicol 32:293-297.
Spitsbergen, JM; Kleeman, JM; Peterson, RE. (1988) Morphologic lesions and acute toxicity in rainbow trout
(Salmo gairdneri) treated with 2,3,7,8-tetrachlorodibenzo-p-dioxin. J Toxicol Environ Health 23(3):333-358.
72
-------
Spitsbergen, JM; Walker, MK; Olson, JR; Peterson, RE. (1991) Pathologic alterations in early life stages of lake
trout, Salvelinus namaycush, exposed to 2,3,7,8-tetrachlorodibenzo-/>-dioxin as fertilized eggs. Aquat Toxicol 19:41-
72.
Summer, C; Giesy, J; Bursian, S; Render, J; Kubiak, T; Jones, P; Verbrugge, D; Aulerich, R. (1996a) Effects
induced by feeding organochlorine-contaminated carp from Saginaw Bay, Lake Huron, to laying white leghorn
hens. I. Effects on health of adult hens, egg production, and fertility. Toxicol Environ Health 49:389-407.
Summer, C; Giesy, J; Bursian, S; Render, J; Kubiak, T; Jones, P; Verbrugge, D; Aulerich, R. (1996b) Effects
induced by feeding organochlorine-contaminated carp from Saginaw Bay, Lake Huron, to laying white leghorn
hens. II. Embryotoxic and teratogenic effects. Toxicol Environ Health 49:409-438.
Tanguay, RL; Andreasen, EA; Walker, MK; Peterson, RE. (2003) Dioxin toxicity and aryl hydrocarbon receptor
signaling in fish. In: Schecter, A; Gasiewicz, TA, eds. Dioxins and Health, 2nd ed. John Wiley & Sons, Inc. pp. 603-
628.
Theobald, HM; Kimmel, GL; Peterson, RE. (2003) Developmental and reproductive toxicity of dioxins and related
compounds. In: Schecter, A; Gasiewicz, TA, eds. Dioxins and Health, 2nd ed. John Wiley & Sons, Inc. pp. 329-431.
Thomann, RV. (1989) Bioaccumulation model of organic chemical distribution in aquatic food chains. Environ Sci
Technol 23:699-707.
Thomas, PT; Hinsdill, RD (1978) Effect of poly chlorinated biphenyls on the immune responses of rhesus monkeys
and mice. Toxicol Appl Pharmacol 44:41 -51.
Tillitt, DE; Ankley, GT; Verbruggee, DA; Giesy, JP; Ludwig, JP; Kubiak, TJ. (1991) H4IIE rat hepatoma cell
bioassay-derived 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalents in colonial fish-eating waterbird eggs from the
Great Lakes. Arch Environ Contain Toxicol 21:91-101.
Tillitt, DE; Gale, RW; Meadows, JC; Zajicek, JL; Peterman, PH; Heaton, SN; Jones, PD; Bursian, SJ; Kubiak, TJ;
Giesy, JP; Aulerich, RJ. (1996) Dietary exposure of mink to carp from Saginaw Bay. 3. Characterization of dietary
exposure to planar halogenated hydrocarbons, dioxin equivalents and biomagnification. Environ Sci Technol
30:283-291.
Tillitt, D; Wright, P. (1997) Dioxin-like embryotoxicity of a Lake Michigan lake trout extract to developing lake
trout. Organohalogen Cmpds 34:221-225.
U.S. EPA. (Environmental Protection Agency). (1987) Interim Procedures for Estimating Risks Associated with
Exposures to Mixtures of Chlorinated Dibenzo-p-Dioxins and -Dibenzofurans (CDDs and CDFs). Risk Assessment
Forum, Washington, DC; EPA/625/3-87/012.
U.S. EPA. (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.
Risk Assessment Forum, Washington, DC; EPA/625/3-89/016.
U.S. EPA. (Environmental Protection Agency). (1991) Workshop Report on Toxicity Equivalency Factors for
Poly chlorinated Biphenyl Congeners. Risk Assessment Forum, Washington, DC; EPA/625/3-91/020.
U.S. EPA. (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. Office of Research and
Development, Duluth, MN; EPA/600/R-93/055.
U.S. EPA. (Environmental Protection Agency). (1995a) Great Lakes Water Quality Initiative: Technical Support
Document for the Procedure to Determine Bioaccumulation. Office of Water; EPA/820/B-95/005.
U.S. EPA. (Environmental Protection Agency). (1995b) Great Lakes Water Quality Initiative: Criteria Documents
for the Protection of Wildlife: DDT, Mercury, 2,3,7,8-TCDD, PCBs. Office of Water; EPA/820/B-95/008.
73
-------
U.S. EPA. (Environmental Protection Agency). (1995c) Great Lakes Water Quality Initiative: Technical Support
Document for Wildlife Criteria. Office of Water; EPA/820/B-95/009.
U.S. EPA. (Environmental Protection Agency). (1996) PCBs: Cancer Dose-Response Assessment and Application
to Environmental Mixtures. Office of Research and Development, National Center for Environmental Assessment,
Washington, DC; EPA/600/P-96/001F. Available from: http://www.epa.gov/pcbs/pubs/pcb.pdf.
U.S. EPA (Environmental Protection Agency). (1997a) Policy for Use of Probabilistic Analysis in Risk Assessment.
U.S. EPA, Washington, DC; Available from: http://www.epa.gov/osa/spc/pdfs/probpol.pdf.
U.S. EPA (Environmental Protection Agency). (1997b) Guiding Principles for Monte Carlo Analysis. Risk
Assessment Forum, Washington, DC; EPA/630/R-97/001. Available from:
http://www.epa.gov/ncea/raf/montecar.pdf.
U.S. EPA. (Environmental Protection Agency). (1998) Guidelines for Ecological Risk Assessment. Risk Assessment
Forum, Washington, DC; EPA/630/R-95/002F. Available from:
http://cfpub.epa. gov/ncea/raf/recordisplav .cfm?deid= 12460.
U.S. EPA. (Environmental Protection Agency). (2000a) Supplementary Guidance for Conducting Health Risk
Assessments of Chemical Mixtures. Risk Assessment Forum, Washington, DC; EPA/630/R-00/002. Available from:
http://cfpub.epa.gov/ncea/raf/recordisplav.cfm?deid=20533.
U.S. EPA. (Environmental Protection Agency). (2000b) Methodology for Deriving Ambient Water Quality Criteria
for Protection of Human Health. Office of Water; EPA/822/B-00/004. Available from:
www.epa.gov/waterscience/humanhealth/method.
U.S. EPA. (Environmental Protection Agency). (2001a) Workshop Report on the Application of 2,3,7,8-TCDD
Toxicity Equivalence Factors to Fish and Wildlife. Risk Assessment Forum, Washington, DC; EPA/630/R-01/002.
Available from: http://cfpub.epa. gov/ncea/raf/recordisplav.cfm?deid=23763.
U.S. EPA. (Environmental Protection Agency). (2001b) Dose-Response Assessment from Recently Published
Research of the Toxicity of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin and Related Compounds to Aquatic Wildlife -
Laboratory Studies. Office of Research and Development, National Center for Environmental Assessment,
Cincinnati, OH; NCEA-C-0649. Revised after external peer review.
U.S. EPA. (Environmental Protection Agency). (2001c) Database of Sources of Environmental Releases ofDioxin-
Like Compounds in the United States. Office of Research and Development, Washington, DC; EPA/600/C-01/012.
Available from: http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=20797.
U.S. EPA. (Environmental Protection Agency). (2002) Guidance on Cumulative Risk Assessment of Pesticide
Chemicals that have a Common Mechanism of Toxicity. Office of Pesticide Programs, Washington, DC.
U.S. EPA. (Environmental Protection Agency). (2003a) Exposure and Human Health Reassessment of 2,3,7,8-
Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (External Review Draft). National Center for
Environmental Assessment, Office of Research and Development, Washington, DC; EPA/600/P-00/001B(a-f).
Available from: http://www.epa.gov/ncea/pdfs/dioxin/nas-review/.
U.S. EPA. (Environmental Protection Agency). (2003b) Methodology for Deriving Ambient Water Quality Criteria
for the Protection of Human Health 2000. Technical Support Document Volume 2: Development of National
Bioaccumulation Factors. Office of Water, Washington, DC; EPA/822/R-03-030. Available from:
www.epa.gov/waterscience/humanhealth/method.
U.S. EPA (Environmental Protection Agency). (2005) Memorandum: Response to Ecological Risk Assessment
Forum Request for Informationon the Benefits ofPCB Congener-Specific Analyses. Office of Research and
Development, Cincinnati, OH; NCEA-C-1315/ERASC-002F. Available from:
http://www.epa.gov/oswer/riskassessment/pdf/1315-erasc-002f.pdf.
74
-------
U.S. EPA. (Environmental Protection Agency). (2006) Guidance on Systematic Planning Using the Data Quality
Objectives Process. Office of Environmental Information, Washington, DC; EPA/240/B-06/001. Available from:
http://www.epa.gov/qualitv/qs-docs/g4-final.pdf.
Van Beneden, RJ; Rhodes, LD; Gardner GR. (1998) Studies of the molecular basis of gonadal tumors in the marine
bivalve, Mya arenaria. Mar Environ Res 46:209-213.
VanBirgelen, AP; DeVito, MJ; Akins, JN; Ross, DG; Diliberto, JJ; Birnbaum, LS. (1996a) Relative potencies of
polychlorinated dibenzo-p-dioxins, dibenzofurans and biphenyls derived from hepatic porphyrin accumulation in
mice. ToxicolAppl Pharmacol 138:98-109.
Van Birgelen, AP; Fase, KM; van der Kolk, J; Poiger, H; Brouwer, A; Seinen, W; van den Berg, M. (1996b)
Synergistic effect of 2,2',4,4'5,5'-hexachlorobiphenyl and 2,3,7,8-tetrachlorodibenzo-/>-dioxin on hepatic porphyrin
levels in the rat. Environ Health Perspect 104(5):550-557.
Van Birgelen, AP; Smit, EA; Kampen, DVI; Groeneveld, CN; Fase, KM; Van der Kolk, J; Poiger, H; Van den Berg,
M; Koeman, JH; Brouwer, A. (1995a) Subchronic effects of 2,3,7,8-TCDD orPCBs on thyroid hormone
metabolism - use in risk assessment. Eur J Pharmacol- Environ Toxicol Pharmacol Sec 293:77-85.
Van Birgelen, AP; van der Kolk, J; Faze, KM; Bol, I; Poiger, H; Brouwer, A; Vandenberg, M. (1995b) Subchronic
dose-response study of 2,3,7,8-tetrachlorodibenzo-/>-dioxin in female Sprague-Dawley rats. Toxicol Appl Pharmacol
132:1-13.
VanLeeuwen, FXR; Feely, M; Schrenk, D; Larsen, JC; Farland, W; Younes, M. (2000) Dioxins: WHO's tolerable
daily intake (TDI) revisited. Chemosphere 40:1095-1101.
Van den Berg, M; Birnbaum, L; Bosveld, ATC; Brunstrom, B; Cook, P; Feeley, M; Giesy, JP; Hanberg, A;
Hasegawa, R; Kennedy, SW; Kubiak, T; Larsen, JC; van Leeuwen, FX; Liem, AK; Nolt, C; Peterson, RE;
Poellinger, L; Safe, S; Schrenk, D; Tillitt, D; Tysklind, M; Younes, M; Waern, F; Zacharewski, T. (1998) Toxic
equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife. Environ Health Perspect
106(12):775-792.
Van den Berg, M; Birnbaum, LS; Denison, M, DeVito, M, Farland, W, Feeley, M; Fiedler, H; Hakansson, H;
Hanberg, A; Haws, L; Rose, M; Safe, S; Schrenk, D; Tohyama, C; Tritscher, A; Tuomisto, J; Tysklind, M; Walker,
N; Peterson, RE. (2006) The 2005 World Health Organization Reevaluation of Human and Mammalian Toxic
Equivalency Factors for Dioxins and Dioxin-Like Compounds. Toxicol Sci 93:223 -241.
Villeneuve, DL; Kannan, K; Khim, JS; Falandysz, J; Nikiforov, VA; Blankenship, AL; Giesy, JP. (2000) Relative
potencies of individual polychlorinated naphthalenes to induce dioxin-like responses in fish and mammalian in vitro
bioassays. Arch Environ Contain Toxicol 39:273-281.
Walker, MK; Cook, PM; Butterworth, BC; Zabel, EW; Peterson, RE. (1996) Potency of a complex mixture of
polychlorinated dibenzo-p-dioxin, dibenzofuran, and biphenyl congeners compared to 2,3,7,8-tetrachlorodibenzo-/>-
dioxin in causing fish early life stage mortality. Fundam Appl Toxicol 30:178-186.
Walker, NJ; Crockett, PW; Nyska, A; Brix, AE; Jokinen, MP; Sells, DM; Hailey, JR; Easterling, M; Haseman, JK;
Yin, M; Wyde, ME; Bucher, JR; Portier, CJ. (2005) Dose-additive carcinogenicity of a defined mixture of dioxin-
like compounds. Environ Health Perspect 113:43-48.
Walker, MK; Heid, SE; Smith, SM; Swanson, HI. (2000) Molecular characterization and developmental expression
of the aryl hydrocarbon receptor from the chick embryo. Comp Biochem Physiol C Toxicol Pharmacol 126(3):305-
19.
Walker, MK; Peterson, RE. (1991) Potencies of polychlorinated dibenzo-p-dioxin, dibenzofuran, and biphenyl
congeners, relative to 2,3,7,8-tetrachlorodibenzo-/>-dioxin for producing early life stage mortality in rainbow trout
(Oncorhynchus mykiss). Aquatic Toxicol 21:219-238.
75
-------
Walker, MK; Peterson, RE. (1994) Aquatic toxicity of dioxins and related chemicals. In: Schecter, A, ed. Dioxins
and health. New York, NY: Plenum Press; pp. 347-387.
Walker, MK; Spitsbergen, JM; Olson, JR; Peterson, RE. (1991) 2,3, 7,8-Tetrachlorodibenzo-p-dioxin toxicity during
early life stage development of lake trout (Salvelinus namaycush). Can J Fish Aquat Sci 48:875-883.
West, CW; Ankley, GT; Nichols, JW; Elonen, GE; Nessa, DE. (1997) Toxicity and bioaccumulation of 2,3,7,8-
tetrachlordibenzo-p-dioxin in long-term tests with the freshwater benthic invertebrates Chironimus tentans and
Lumbriculus variegatus. Environ Toxicol Chem 16:1287-1294.
WHO (World Health Organization). (1998) Assessment of the health risk of dioxins: re-evaluation of the Tolerable
Daily Intake (TDI). WHO European Centre for Environment and Health, International Programme on Chemical
Safety. Available from: http://www.who.int/ipcs/publications/en/exe-sum-final.pdf.
Williams, LL; Giesy, JP; Verbrugge, DA; Jurzysta, S; Stromborg, K. (1995) Fob/chlorinated biphenyls and 2,3,7,8-
tetrachlordibenzo-p-dioxin equivalents in eggs of double-crested cormorants from a colony near Green Bay,
Wisconsin, USA. Arch Environ Contain Toxicol 29:327-333.
Wolf, CJ; Ostby, JS; Gray, LE Jr. (1999) Gestational exposure to 2,3,7,8-tetrachlordibenzo-p-dioxin (TCDD)
severely alters reproductive function of female hamster offspring. Toxicol Sci 51(2):259-264.
Yasui, T; Kim, EY; Iwata, H; Franks, DG; Karchner, SI; Hahn, ME; Tanabe, S. (2007) Functional characterization
and evolutionary history of two aryl hydrocarbon receptor isoforms (AhRl and AhR2) from avian species. Toxicol
Yasui, T; Kim, EY; Iwata, H; Tanabe, S. (2004) Identification of aryl hydrocarbon receptor 2 in aquatic birds;
cDNA cloning of AHR1 and AHR2 and characteristics of their amino acid sequences. Mar Environ Res 58(2-
5):113-118.
Yawetz, A; Benedek-Segal, M; Woodin, B. (1997) Cytochrome P4501A immunoassay in freshwater turtles and
exposure to PCBs and environmental pollutants. Environ Toxicol Chem 16:1802-1806.
Yrjanheiki, EJ. (1992) Review of the models for TEFs in assessing health risks of PCDDs and PCDFs. Toxic Sub J
12:283-288.
Zabel, EW; Cook, PM; Peterson, RE. (1995) Toxic equivalency factors of polychlorinated dibenzo-p-dioxins,
dibenzofuran and biphenyl congeners based on early life stage mortality in rainbow trout (Oncorhynchus mykiss).
Aquat Toxicol 31:315-328.
Zhou, JG; Henry, EC; Palermo, CM; Dertinger, SD; Gasiewicz, TA. (2003) Species-specific transcriptional activity
of synthetic flavonoids in guinea pig and mouse cells as a result of differential activation of the aryl hydrocarbon
receptor to interact with dioxin-responsive elements. Mol Pharmacol 63(4):915-24.
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GLOSSARY OF TERMS
Administered Dose: External to the whole organism; concentrations in the diet of test
animals rather than concentrations in cells or tissues
Aroclor: PCB mixtures manufactured in the United States carried the trademark
"Aroclor" followed by a four digit number. The first two digits are 12 and the last two digits
indicate the percent chlorine content by weight. Aroclor 1016 is an exception to the rule. It
contains approximately 41 percent chlorine. The chemical characterization of PCB mixtures as
Aroclors is an imprecise method. Human error (qualitative) and quantitative errors can arise from
judgments used in interpreting results from gas chromatography/mass spectrometry (GC/MS)
analysis. Specifically, GC/MS methods involve comparing chromatographic peaks from
environmental mixtures to "standard" Aroclor peaks. If there is no comparable peak in the
environmental mixture, the sample is assumed to be without Aroclors, even though congeners
may be present. PCB determination by the Aroclor method is subject to systematic and
computational errors that may result in over or under estimation of the true PCB concentration
(Alford-Stevens etal, 1985; Alford-Stevens etal, 1986; Sather etal, 2003).
Aryl Hydrocarbon Receptor (AHR): A ligand-activated transcription factor involved in
the regulation of several genes, including those for xenobiotic-metabolizing enzymes such as
cytochrome P450 1A and IB. Ligands for the AHR include a variety of halogenated aromatic
hydrocarbons including chlorinated dioxins, furans, and biphenyls. The endogenous function and
ligand(s) for the AHR have not been fully elucidated at this time.
AHR Homolog: A protein with structure similar to the AHR.
AHR Ligand: A chemical that stereo-specifically binds to the AHR.
Bioaccumulation: The net accumulation of a substance by an organism as a result of
uptake from all environmental sources.
Bioconcentration: The net accumulation of a substance by an aquatic organism as the
result of uptake directly from the ambient water, through gill membranes or other external body
surfaces.
Biomagnification: The increase in tissue concentration of a chemical in organisms at
successive trophic levels through a series of predator-prey associations, primarily through the
mechanism of dietary accumulation.
Bioaccumulation Factor (BAF): The ratio of the concentration of a substance in tissue
of an organism to its concentration in the ambient exposure media (e.g., water or soil) in
situations where both the organism and its food are exposed and the ratio does not change
substantially over time. For aquatic organisms, the BAF is the ratio of the concentration of
chemical in the organism to its concentration in water, expressed in L/kg. For terrestrial
organisms, the BAF is the ratio of the concentration of chemical in the organism to its
concentration in soil.
Biota-Sediment Accumulation Factor (BSAF): A specific type of bioaccumulation
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factor, defined as the ratio of the lipid-normalized concentration of a substance in tissue of an
aquatic organism to its organic carbon-normalized concentration in surface sediment (expressed
as kg of sediment organic carbon per kg of lipid).
Chlorine Substitution: Each biphenyl molecule consists of two benzene (6-carbon) rings
with one carbon-carbon chemical bond joining each ring. Dibenzo-p-dioxin and furan have two
ortho bridging oxygen atoms joining two benzene rings to form the central p-dioxin ring.
Dibenzofurans have one bridging oxygen plus one carbon-carbon bond joining each benzene ring
to form the central furan ring. Chlorine can be bound (substituted for a hydrogen atom) to any of
the other 10 carbons for biphenyl or other 8 carbons for dibenzo-p-dioxin and dibenzofuran.
Coplanar: A molecule's two rings can twist on the bond joining them; the molecule is
coplanar if the two benzene rings are aligned in the same plane. See Planar.
Cytochrome P450 1A (CYP1A): An enzyme (of the cytochrome P450 family) found in
a variety of tissues, predominantly liver, that metabolizes xenobiotic (foreign) chemicals in
addition to numerous endogenous chemicals; because its production is induced by exposure to
dioxin-like chemicals, CYP1A induction can be used to estimate potency of various dioxin,
furan, and PCB congeners relative to 2,3,7,8-TCDD.
Effective Concentration (ECso): The concentration of a substance required to produce
50% of maximal effect in an individual test unit (e.g., cell culture) or to produce a response in
50% of a population of test organisms.
Effective Dose (ED50): The dose of a substance required to produce 50% of maximal
effect in an individual test unit (e.g., cell culture) or to produce a response in 50% of a
population of test organisms.
Isomers: Molecules that have the same formula but may have different structures.
Lethal Concentration (LC50): The concentration of a substance required to cause
lethality in 50% of test units (e.g., cells in a culture; organisms in a population).
Lethal Dose (LD50): The dose of a substance required to cause lethality in 50% of test
units (e.g., cells in a culture; organisms in a population).
Polychlorinated Biphenyls (PCBs): A family of 209 congeners, the poly chlorinated
biphenyls, of which 13 (listed in Table 2) are thought to have dioxin-like toxicity. PCBs are no
longer manufactured in the United States but formerly were widely used as coolants and
lubricants in electrical equipment.
PCB Congeners: Chemicals with a common carbon molecular structure such as
chlorinated biphenyls, dibenzo-^-dioxins, or dibenzofurans, regardless of exact molecular
formula. There are 209 possible arrangements of chlorines attached to the ten available carbons
on the biphenyl molecule. The International Union of Pure and Applied Chemists (IUPAC) has
adopted a system for numbering PCB congeners sequentially from 1 to 209, starting with a single
chlorine and proceeding to ten chlorines on a biphenyl molecule.
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Polychlorinated dibenzo-p-dioxins (PCDDs): A family of 75 congeners of which 7
(listed in Table 2) are thought to have dioxin-like toxicity. The polychlorinated dibenzo-^-dioxin
structure consists of two benzene rings joined by two ortho oxygen atoms and varying degrees of
chlorine atom substitution on the remaining carbon atoms in the rings. 2,3,7,8-
Tetrachlorodibenzo-^-dioxin (2,3,7,8-TCDD) is the prototypical chemical in this class. PCDDs
have not been commercially produced but are produced inadvertently by a number of industrial
chemical processes and combustion of waste materials.
Polychlorinated dibenzofurans (PCDFs): A chemical class containing 135 congeners
of which 10 (listed in Table 2) are thought to have dioxin-like toxicity. The polychlorinated
dibenzofuran structure consists of two benzene rings joined by one oxygen atom ortho to a
carbon-carbon bond linkage and has varying degrees of chlorine atom substitution on the
remaining carbon atoms in the rings. PCDFs, like the PCDDs, are not produced intentionally but
occur as inadvertent by-products in chemical production processes as well as waste combustion
and PCB degradation reactions.
Planar: Relating to or lying in a plane two-dimensional in quality. See Coplanar.
Polarity: The particular degree to which a molecule's electron density is anisotropically
distributed between two opposing poles.
Quantitative Structure-Activity Relationship (QSAR): Mathematical models that use
non-empirical structural descriptors (e.g., stereoelectronic indices) and/or empirical parameters
(e.g., octanol/water partition coefficients) to estimate biological activity (e.g., toxicity, enzyme
induction, lethality, etc.). QSARs are context specific, i.e., chemical structural similarity is
defined in the context of a well-defined biological endpoint that is being modeled.
Relative Potency (ReP): Estimate based on a single study of the potency, relative to
2,3,7,8-TCDD, of an individual chemical to cause a particular AHR-mediated toxicity or
biological effect in an individual organism, cellular, or biochemical assay.
Relative Potency Factor (RPF): Estimate based on one or more studies of the potency,
relative to 2,3,7,8-TCDD, of an individual chemical to cause AHR-mediated toxicity or
biological effects. The ReP database used to derive an RPF for a chemical may include multiple
endpoints, species, and in vitro or in vivo studies. RPFs may be used as alternatives to TEFs
when more specific data for the species, endpoint, and/or site conditions are judged to improve
the accuracy of the risk assessment. If the RPF is based on a single ReP, the RPF is equal to the
ReP.
2,3,7,8-Tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD): The PCDD congener that has
been most extensively studied and is used as the prototypical AHR agonist. Also commonly
referred to simply as TCDD, it is the congener to which all other dioxin-like congeners (dioxin,
furan, and PCB) are compared to determine their ReP for producing a particular AHR-mediated
toxicity or biological effect. When this is done, the ReP of 2,3,7,8-TCDD is assigned a value of
1.0.
s: Toxicity Equivalence Factors established at a WHO-ECEH consultation
(Van den Berg et a/., 1998); the TEFs scheme built upon previous international efforts
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establishing TEFs for humans and added TEFs-WHOgg values for fish and birds.
TEFs-WHOos: Toxicity Equivalence Factors established at a WHO-IPCS consultation
(Van den Berg et a/., 2006); based on review of only the mammalian TEFs-WHOgg this
consultation resulted in revision to the TEF values for one dioxin (OCDD), three furans
(2,3,4,7,8-PeCDF, 1,2,3,7,8-PeCDF, and OCDF), two non-ort/zo-substitutedPCBs (PCB 81 and
PCB 169), and all relevant mono-ort/zo-substituted PCBs (Van den Berg et al., 2006).
Toxicity Equivalence: The concept of translating the concentrations of dioxin-like
congeners (dioxin, furan, PCB) in fish, birds, or mammals to a 2,3,7,8-TCDD equivalence
concentration. This is done by multiplying the vertebrate class-specific and congener-specific
RPFs or TEFs by whole body or tissue concentrations of the individual dioxin-like congeners in
a fish, bird, or mammal, respectively, to give a corresponding 2,3,7,8-TCDD equivalence
concentration for each congener. These concentrations are then summed for all dioxin-like
congeners present in the fish, bird, or mammal to yield a total 2,3,7,8-TCDD equivalence
concentration.
Toxicity Equivalence Factor (TEF): Estimate of the potency, relative to 2,3,7,8-TCDD,
of an individual poly chlorinated dibenzo-/?-dioxin, dibenzofuran or biphenyl congener, using
careful scientific judgment after considering all available relative potency data. EPA presently
applies this term only to relative potency factors derived through an international scientific
consensus-building process supported by the World Health Organization (Van den Berg et al.,
1998; 2006).
Toxicity Equivalence Concentration (TEC): The TEC is the product of the TEF or
RPF multiplied by the concentration for an individual congener. The total TEC for a mixture is
calculated as the sum of 2,3,7,8-TCDD equivalence concentrations of all congeners present in
the mixture.
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United States
Environmental Protection
Agency
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