EPA/100/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 

i 
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.

ii 
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 

3.3.1.2. Chemical Fate of PCDDs, PCDFs, and PCBs.............................................. 28 

3.3.1.3. Choices for the Exposure Dose Metric .......................................................... 29 

3.3.1.4. Bioaccumulation of PCDDs, PCDFs, and PCBs .......................................... 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. Endpoint Relevance ....................................................................................... 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 of TEFs, RPFs, or RePs............................................. 51 

iii 
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 Methodology........... 55 

3.4.3.1.1. AHR ligands...................................................................................... 55 

3.4.3.1.2. Additivity assumption........................................................................ 55 

3.4.3.1.3. Relative potency data........................................................................ 56 

3.4.3.1.4. 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 
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-WHO98 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 polychlorinated 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 

v 
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 et al., 1998, 2006; U.S. EPA, 2001a; NRC, 2006). Consultations 

between EPA and the Department of Interior (DOI) 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 DOI sponsored a workshop that recommended the development of 

further guidance on application of the toxicity equivalence methodology (U.S. EPA, 2001a). 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 
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. 

vii 
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 

viii 
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 

K1A 0H3 

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), NL3508 TD Utrecht, The Netherlands 

ix 
LIST OF ABBREVIATIONS AND ACRONYMS 

2,3,7,8-TCDD 2,3,7,8-tetrachlorodibenzo-p-dioxin 

AHR aryl hydrocarbon receptor 

BAF bioaccumulation factor 

BCF bioconcentration factor 

BSAF biota-sediment accumulation factor 

DOI U.S. Department of Interior 

EC effective concentration 

ECO-TEF ecological toxicity equivalence factor 

ED effective dose 

EPA U.S. Environmental Protection Agency 

EROD ethoxyresorufin-O-deethylase 

HpCB heptachlorinated biphenyl 

HpCDD heptachlorinated dibenzo-p-dioxin 

HpCDF heptachlorinated dibenzofuran 

HxCB hexachlorinated biphenyl 

HxCDD hexachlorinated dibenzo-p-dioxin 

HxCDF hexachlorinated dibenzofuran 

IPCS International Programme on Chemical Safety 

LD lethal dose 

LOAEL lowest observed adverse effect level 

NATO/CCMS North Atlantic Treaty Organization/Committee on the Challenges of Modern 

Society 

NOAEL no-observed adverse effect level 

NRC National Research Council of the National Academies 

OCB octachlorinated biphenyl 

OCDD octachlorinated dibenzo-p-dioxin 

OCDF octachlorinated dibenzofuran 

PCBs polychlorinated biphenyls 

PCDDs polychlorinated dibenzo-p-dioxins 

PCDFs polychlorinated dibenzofurans 

PeCB pentachlorinated biphenyl 

PeCDD pentachlorinated dibenzo-p-dioxin 

PeCDF pentachlorinated dibenzofuran 

QSAR quantitative structure-activity relationship 

ReP relative potency 

RPF relative potency factor 

TCB tetrachlorinated biphenyl 

TCDD tetrachlorinated dibenzo-p-dioxin 

TCDF tetrachlorinated dibenzofuran 

TEC toxicity equivalence concentration 

TEF toxicity equivalence factor 

TEFs-NATO89 TEFs (sometimes also referred to as I-TEFs) adopted by the NATO/CCMS in 

1989 

TEFs-WHO94 TEFs adopted by the WHO in 1994 

x 
TEFs-WHO98/05 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). 

TMDL total maximum daily load 

WHO World Health Organization 

WHO-ECEH WHO European Centre for Environmental Health 

WHO-IPCS 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-p-dioxin 

PeCDD pentachlorinated dibenzo-p-dioxin 

HxCDD hexachlorinated dibenzo-p-dioxin 

HpCDD heptachlorinated dibenzo-p-dioxin 

OCDD octachlorinated dibenzo-p-dioxin 

PCDDs polychlorinated dibenzo-p-dioxins 

PCDF abbreviations:

TCDF tetrachlorinated dibenzofuran 

PeCDF pentachlorinated dibenzofuran 

HxCDF hexachlorinated dibenzofuran 

HpCDF heptachlorinated dibenzofuran 

OCDF octachlorinated dibenzofuran 

PCDFs polychlorinated dibenzofurans 

xi 
xii 
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 of fish, birds, and mammals. The most well-studied 

chemical in this group is 2,3,7,8-tetrachlorodibenzo-p-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, 2001a). 

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. 

1 
General Structure Representative Examples 

O

Cl O

Cl

Cl

Cl

9 1

O

O

Polychlorinated dibenzo-p-dioxins (PCDDs) 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) 

7

8

9

6 O 4

3

2

1

O

Cl

Cl

Cl

Cl

Polychlorinated dibenzofurans (PCDFs) 2,3,7,8-Tetrachlorodibenzofuran (TCDF) 

6 5

4

2 2 3 ' 3'

4'

5' 6'

Cl

Cl

Cl

Cl

Cl

Polychlorinated biphenyls (PCBs) 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. 

8

7

6 4

3

2

2 
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, 

2001a). 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). 

Text Box 1. Clarification of terminology. 

Acronym used Analogous acronyms 

in this framework found in the literature

ReP REP, ReP, RP, RPF, TEF 

RPF REP, ReP, RP, RPF, TEF 

TEF IEF, I-TEF, TEF-WHO, 

REP, RPF, RP 

TEC TEqC, TEQ, TEq 

Term used Analogous terms 

in this framework found in the literature

Toxicity equivalence Toxicity Equivalency, 

 Toxicity Equivalent, 

 Toxic Equivalency, 

 Toxic Equivalent 

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 AHRmediated 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. 

3 
Toxicity Equivalence Factor (TEF) – Estimate of the potency, relative to 2,3,7,8-

TCDD, of an individual polychlorinated 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 WHOECEH 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 dioxinlike 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 

4 
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 et al., 

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) (Yrjänheiki, 1992). 

Table 1. Number of polychlorinated dioxin, furan, and biphenyl congeners 

Chemical Class Number of Congeners Dioxin-like Chemicals

Dioxins (PCDDs) 075 07 

Furans (PCDFs) 135 10 

Biphenyls (PCBs) 209 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 et al., 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). 

5 
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-WHO94). 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 al., 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 al., 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 al., 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 WHOECEH 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 major objective 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: 

6 
• 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 (2001a). 

In 2005, the WHO International Programme on Chemical Safety held another expert 

meeting during which the 1998 mammalian TEFs-WHO98 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 reevaluation 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 TEFsWHO98 are reflected in Table 2 and designated as TEFs-WHO05. 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). 

7 
Table 2. World Health Organization TEFs for mammals, birds, and fish 

TEF Congener

Mammals1 Birds2 Fish2

Dioxins

2,3,7,8-TCDD 1 1 1

1,2,3,7,8-PeCDD 1 1 1

1,2,3,4,7,8-HxCDD 0.1 0.05 0.5

1,2,3,6,7,8-HxCDD 0.1 0.01 0.01

1,2,3,7,8,9-HxCDD 0.1 0.1 0.01

1,2,3,4,6,7,8-HpCDD 0.01 <0.001 0.001

OCDD 0.0003 0.0001 <0.0001

Furans

2,3,7,8-TCDF 0.1 1 0.05

1,2,3,7,8-PeCDF 0.03 0.1 0.05

2,3,4,7,8-PeCDF 0.3 1 0.5

1,2,3,4,7,8-HxCDF 0.1 0.1 0.1

1,2,3,6,7,8-HxCDF 0.1 0.1 0.1

1,2,3,7,8,9-HxCDF 0.1 0.1 0.1

2,3,4,6,7,8-HxCDF 0.1 0.1 0.1

1,2,3,4,6,7,8-HpCDF 0.01 0.01 0.01

1,2,3,4,7,8,9-HpCDF 0.01 0.01 0.01

OCDF 0.0003 0.0001 <0.0001

Non-ortho PCBs

3,3',4,4'-TCB (77) 0.0001 0.05 0.0001

3,4,4',5-TCB (81) 0.0003 0.1 0.0005

3,3',4,4',5-PeCB (126) 0.1 0.1 0.005

3,3',4,4',5,5'-HxCB (169) 0.03 0.001 0.00005

Mono-ortho PCBs

2,3,3',4,4'-PeCB (105) 0.00003 0.0001 <0.000005

2,3,4,4',5-PeCB (114) 0.00003 0.0001 <0.000005

2,3',4,4',5-PeCB (118) 0.00003 0.00001 <0.000005

2',3,4,4',5-PeCB (123) 0.00003 0.00001 <0.000005

2,3,3',4,4',5-HxCB (156) 0.00003 0.0001 <0.000005

2,3,3',4,4',5'-HxCB (157) 0.00003 0.0001 <0.000005

2,3',4,4',5,5'-HxCB (167) 0.00003 0.00001 <0.000005

2,3,3',4,4',5,5'-HeCB (189) 0.00003 0.00001 <0.000005

Source: 1Van den Berg et al., 2006; 2Van den Berg et al., 1998. 

8 
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

et al., 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 

9 
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 et al., 1996a, b; Giesy and Kannan, 1998; Restum et al., 1998; Shipp et al., 1998a, b; 

Ross, 2000). More recently, the 2005 WHO-IPCS expert panel (Van den Berg et al. 2006) revisited 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 et al., 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-WHO98 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-ortho-substituted PCBs (Van den Berg et al., 2006). These recent 

changes in the mammalian TEFs (TEFs-WHO98/05) 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 
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 al., 1997; Brunstrom 

and Halldin, 2000; Finley et al., 1997; Giesy and Kannan, 1998; U.S. EPA 2005; note, however, 

that only Giesy and Kannan incorporated the 1998 taxa-specific TEFs-WHO98 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 polycyclic 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 al., 1998), and the 2005 WHO-ECEH meeting came to 

similar conclusions (Van den Berg et al., 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 al., 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-WHO98/05. 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 
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 EC50, ED50, or LD50 of 2,3,7,8-TCDD divided by 

the EC50, 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 WHOTEF, 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-WHO98/05 reproduced in Table 2 were determined based on the 

consensus judgment of the experts present at the WHO consultations (Van den Berg et al., 1998; 

2006). The TEFs-WHO98/05 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 al., 

1998) and one order of magnitude for mammal TEFs (Van den Berg et al., 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 al., 

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 dioxinlike chemical concentrations in tissues of organisms at risk, in ecological risk assessments it has 

often been based on concentrations in the diet. 

 

TEC =

n=1

k

∑ Cn * TEFn

(2-1) 

Where: Cn = concentration of dioxin-like chemical n in an organism or its diet 

Where: TEFn = toxicity equivalence factor for dioxin-like chemical n

Where: TEFn = (Note: An RPF can replace the TEF term) 

Where: 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 
(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 
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 

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. 

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-WHO98/05 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? 

14 
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 
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 dioxinlike 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 
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, 2001a). 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 al., 1994; Barnes

et al., 1991; Eadon et al., 1986; U.S. EPA, 1987; 1989; 1991; 2001a; NATO, 1988a, b; NRC, 

2006; Van den Berg et al., 1998; 2006; Yrjänheiki, 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-WHO98/05 or more specific RPFs or RePs. Use of the 

TEFs-WHO98/05 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-WHO98/05 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 assessmentspecific 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 
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 2001a). 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 et al., 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 

consequence of exposure to 

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. 

Text Box 3. Questions for problem formulation. 

• Are the chemicals of concern dioxin-like PCDDs, PCDFs, 

and PCBs? 

• 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? 

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 
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 et al., 1993; Theobald et al., 2003; U.S. 

EPA, 1993; U.S. EPA, 2001b). Species- and class-specific differences in AHR number and 

function have been identified in fish and birds (Hahn et al., 1997; Karchner et al., 1999; Abnet et 

al., 1999; Hansson et al., 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 al., 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 ∃-naphthoflavone, which distinguishes them from vertebrate AHRs (Butler et 

al., 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, et al., 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, et al., 1995; WHO, 1998; van Leeuwen, 2000). The 

19 
Table 3. Effects of 2,3,7,8-TCDD and related chemicals in different animal 

species 

Birds Mammals

Effect Fish Avian 

Wildlife Chicken Aquatic 

Mammals Mink Laboratory 

Mammals*

Presence of AHR + [1,2] + 

[1,2,27] + [1,2] + [1,2] + [1,2] 

Binding of 2,3,7,8- TCDD:AHR 

Complex to the DRE (enhancer) + [3-6] + [28] + 

[3,43,44] + [60] + [3,71] 

Enzyme induction + [7-11] + [29-

33] 

+ [29-

31,44-

49] 

+ [61] + [63] + [72-80] 

Acute lethality [12,13] + [34] + [50] + [64] + [81-89] 

Wasting syndrome + [14] + [34] + [50] + [64-

66] + [90,91] 

Hepatotoxicity 

(pathology, hyperplasia, 

hypertrophy, porphyria) 

+ [13,15-

17] 

+/- [35-

37] 

+ [50-

53] + [92-101] 

Endocrine effects + [18,19] +/- [38-

40] + [102-103] 

Immunotoxicity + [20] 0 [62] + [104-107] 

Carcinogenicity + [21] + [92, 

108,109] 

Developmental and Reproductive 

Toxicity 

(mortality, teratogenesis, embryofetal toxicity, including 

neurological, immunological and/or 

endocrine effects during perinatal 

period) 

+ 

[15,16,2

2-26] 

+ [22, 

31,32,41,

42] 

+ [22, 

32, 50, 

54-59] 

+ [66-

70] 

+ [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] Bank et al., 1992; [4] Abnet, et al., 1999; [5] Karchner, et al., 1999; [6] 

Tanguay, et al., 1999; [7] Janz and Metcalfe, 1991; [8] Parrott et al., 1995; [9] Clemons et al., 1994; [10] Clemons et 

al., 1996; [11] Andreasen et al., 2002; [12] Kleeman, et al., 1988; [13] Spitsbergen et al., 1988; [14] Carvalho et al., 

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, et al., 2007; [29] Sanderson and Bellward, 1995; [30] Kennedy et al., 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 
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 et al., 2000; [45] Hamilton, et al., 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] Håkansson, et al., 1994; [79] Gasiewicz, et al., 1986; [80] Kruger, et al., 1990; [81] Beatty, et al., 

1978; [82] Neal, et al., 1982; [83] Chapman and Schiller, 1985; [84] Schwetz, et al., 1973; [85] McConnell, et al., 

1978b; [86] DeCaprio, et al., 1986; [87] Henck, et al., 1981; [88] Olson, et al., 1980; [89] McConnell, et al., 1978a; 

[90] Peterson et al., 1994; [91] Kelling, et al., 1985; [92] Kociba, et al., 1978; [93] Cantoni, et al., 1981; [94]

Goldstein, et al., 1982; [95] van Birgelen, et al., 1996b; [96] Jones and Sweeney, 1980; [97] van Birgelen, et al., 

1996a; [98] Shen, et al., 1991; [99] Birnbaum, et al., 1990; [100] Seefeld, et al., 1980; [101] Bombick, et al., 1985; 

[102] van Birgelen, et al., 1995a; [103] van Birgelen, et al., 1995b; [104] Smialowicz, et al., 1994; [105]

Smialowicz, et al., 1996; [106] Hong, et al., 1989; [107] Thomas and Hinsdill, 1978; [108] National Toxicology 

Program, 1982; [109] Rao, et al., 1988; [110] Roman and Peterson, 1998; [111] Couture, et al., 1989; [112] Abbott, 

et al., 1987b; [113] Abbott et al., 1987a; [114] Eriksson, et al., 1998; [115] Giavini, et al., 1982; [116] Wolf, et al., 

1999; [117] Arnold, et al., 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 

et al., 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 et al., 1998; Aulerich et al., 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-ortho-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 

21 
(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 al., 1998; Yawetz et al., 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 et al., 1997; Barber et al., 1998; 

Van Beneden et al., 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 

AHR ligands, 2,3,7,8-TCDD and ∃-naphthoflavone (Powell-Coffman, et al., 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-dioxinlike 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 

and Puglisi, 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., Φg/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 

22 
chemicals have a strong potential for biomagnification (i.e., the increase in concentration of a 

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 of fish-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 al., 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. 

23 
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 

of fish, 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 
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). 

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 
Risk Characterization Risk Characterization

Soil/Sediment/Water

Congener Concentration

Soil/Sediment/Water

Congener Concentration

BAF/BSAF

BAF/BSAF

•Chemical Specific

•Species Specific

•Trophic Level Specific

•Site Specific

Dose Metric-Specific Calculation of TEC

Administered Dose/Diet Tissue Concentration

Diet

Congener Concentration

Diet TEC Tissue TEC

Tissue

Congener Concentration

TCDD Dose-Response

Tissue Concentration

TCDD Dose-Response

Administered Dose

TCDD Dose-Response

•Appropriate Species

•Appropriate Endpoint

•Appropriate Dose Metric

Characterization of Effects Characterization of Effects

Characterization of Exposure Characterization of Exposure

TEF/RPF

TEF/RPF

•Chemical Specific

•Species/Class Specific

•Choice influenced by &

determines level of 

uncertainty

Risk Characterization Risk Characterization

Soil/Sediment/Water

Congener Concentration

Soil/Sediment/Water

Congener Concentration

BAF/BSAF

BAF/BSAF

•Chemical Specific

•Species Specific

•Trophic Level Specific

•Site Specific

Dose Metric-Specific Calculation of TEC

Administered Dose/Diet Tissue Concentration

Diet

Congener Concentration

Diet TEC Tissue TEC

Tissue

Congener Concentration

TCDD Dose-Response

Tissue Concentration

TCDD Dose-Response

Administered Dose

TCDD Dose-Response

•Appropriate Species

•Appropriate Endpoint

•Appropriate Dose Metric

Characterization of Effects Characterization of Effects

Characterization of Exposure Characterization of Exposure

TEF/RPF

TEF/RPF

•Chemical Specific

•Species/Class Specific

•Choice influenced by &

determines level of 

uncertainty

Figure 5. Application of the toxicity equivalence methodology in ecological 

risk assessment for exposure to PCDDs, PCDFs, and PCBs. 

26 
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. 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? 

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 

27 
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 
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-WHO98/05 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-WHO98/05

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-WHO98/05, RPFs, or RePs should be selected to provide consistency in bridging 

the exposure assessment to the effects assessment (i.e., exposure dose metric = TEF-WHO98/05, 

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 
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 et al., 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-WHO98/05 and 

RePs is generally administered dose. Application of the mammalian TEFs-WHO98/05 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 
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 ethoxyresorufin-Odeethylase (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 al., 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 et al., 1997, Santostefano et al., 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 of PCDDs, PCDFs, and PCBs

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 
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. 

Symbol Representation Common units

TEF toxicity equivalence factor ng TCDD/ng chemical 

C concentration ng/kg 

TEC toxicity equivalence concentration ng/kg 

k number of chemicals in mixture 

n = i individual chemical in mixture 

superscript fd freely dissolved chemical 

superscript t total chemical 

subscript w in water 

subscript soc in sediment organic carbon 

subscript t in tissue 

subscript λ in lipid 

subscript r reference chemical 

subscript i individual chemical of interest 

BAF bioaccumulation factor L water/kg organism 

BAF BAF, lipid normalized and based on L water/kg lipid l

fd

freely dissolved chemical in water 

BSAF biota-sediment accumulation factor kg sediment/kg organism 

Cw C of total chemical in water ng/L water 

Cw

f d C of chemical freely dissolved in water ng/L water 

Cs C of total chemical in sediment ng/kg sediment

Csoc C of chemical in sediment organic carbon ng/kg organic carbon 

Cλ C of chemical in lipid ng/kg lipid 

Ct C of chemical in tissue ng/kg tissue 

fλ fraction lipid in the organism kg lipid/kg organism 

fsoc fraction organic carbon in sediment kg oc/kg sediment 

Kow octanol-water partition coefficient L water/L octanol 

∏socw sediment-water concentration quotient L water/kg organic carbon 

Di/r ratio between values of ∏socw for unitless 

reference chemical and chemical 

 of interest 

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 (Cλ) with the fraction lipid (fλ) in the organism’s tissues, and the concentration of 

32 
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 , which is calculated with the 

fraction of chemical that is freely dissolved ( as estimated from concentrations of particulate 

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, , and for sediment, the biota sediment accumulation 

factor, BSAF: 

(Cw ) fd

f fd )

fd BAFl

 

l

fd

BAF = Cl

C

w

fd = Ct •1/ f

l

C

w

t • f fd

(3-1) 

 

BSAF = Cl

C

soc

= Ct •1/ f

l

C

s •1/ fsoc

(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-WHO98/05 (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 

BAF l s or BSAFs and the fraction lipid in the organism (f fd λ) to determine a wet weight TEC for 

an organism as shown in the following two equations: 

( )( ) ( )( n n

fd

n

fd

w

k

n

TEC ∑ λλ TEFfBAFC == 1

)

)

(3-3) 

( ) ( ) ( )( soc n n n

k

n

TEC ∑ C λ TEFfBSAF == 1

(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 
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 BAF and 

BSAF for each chemical. BAF l 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 

BAF values, such as for water quality criteria development and application, even if the 

BAF l 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. 

l

fd

fd

l

fd

fd

To calculate BAF l values for such purposes, EPA presently uses measured BSAFs for 

PCDDs, PCDFs, and co-planar PCBs, combined with estimates of sediment-water concentration 

quotients (∏

fd

socw 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 values for PCBs in several different ecosystems 

(L. Burkhard, personal communication; U.S. EPA, 2003b). Since (∏

l

fd

socw)i 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 (∏socw)i. The ratio of (∏socw)i

for the PCDD, PCDF, or PCB of interest to (∏socw)r for the reference chemical (factor Di/r in 

equation 3-6) accounts for observed or predicted chemical specific differences in ∏socw. High 

quality measurements of ∏socw values for PCBs, PCDDs, and PCDFs in Lake Michigan have 

been obtained (Burkhard et al., 2006) and can be used as a source of measured values for Di/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). 

 

∏socw = C

soc

C

w

fd = BAFl

fd

BSAF

(3-5) 

 

BAFl

fd ( )i=( ) BSAF

i

D( ) i/r Π( ) socw r K( ) ow i

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 al., 

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 
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 et al., 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 et al., 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. 

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 et al., 1996), and herring gull eggs (Government of Canada, 1991), are used here to 

35 
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 et al., 2003): 

( ) eggtrout eggtrout

soc

s

eggtrout BSAF f f

C

C λ •= •

(3-7) 

( ) egggull egggull

soc

s

egggull BSAF f f

C C λ •= •

(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 (fΡ) 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-WHO98 (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. 

Concentration

in Sediment 

(ng/kg) 

Trout Egg 

BSAF1

Concentration

in Trout Egg 

(ng/kg egg) 

TEFs-WHO98

Fish TEF 

Trout Egg 

TEC 

Predicted 

Concentration 

(ng/kg egg) 

2,3,7,8-TCDD 0.30 0.149 0.22 1 0.22

1,2,3,7,8-PeCDD 1.20 0.121 0.73 1 0.73

1,2,3,4,7,8-HxCDD 1.10 0.018 0.10 0.5 0.05

1,2,3,6,7,8-HxCDD 4.70 0.007 0.17 0.01 0.002

1,2,3,7,8,9-HxCDD 2.90 0.010 0.15 0.01 0.002

1,2,3,4,6,7,8-HpCDD 78.20 0.002 0.78 0.001 0.0008

OCDD 530.00 0.0007 1.96 <0.0001 < 0.002

2,3,7,8-TCDF 1.10 0.069 0.38 0.05 0.02

1,2,3,7,8-PeCDF 0.92 0.009 0.04 0.05 0.002

2,3,4,7,8-PeCDF 1.40 0.162 1.13 0.5 0.57

1,2,3,4,7,8-HxCDF 4.10 0.0045 0.09 0.1 0.009

1,2,3,6,7,8-HxCDF 1.60 0.007 0.06 0.1 0.006

1,2,3,7,8,9-HxCDF 0.30 0.020 0.03 0.1 0.003

2,3,4,6,7,8-HxCDF 1.00 0.002 0.01 0.1 0.001

1,2,3,4,6,7,8-HpCDF 2.70 0.001 0.01 0.01 0.0001

1,2,3,4,7,8,9-HpCDF 133.00 0.023 15.30 0.01 0.15

OCDF 2.40 0.001 0.01 <0.0001 <0.00001

Sum PCDD and PCDF 1.76

3,4,4',5-TCB (81) 60 0.95 285 0.0005 0.14

3,3',4,4'-TCB (77) 1623 0.29 2353 0.0001 0.24

3,3',4,4',5-PeCB (126) 16 4.18 334 0.005 1.67

3,3',4,4',5,5'-HxCB (169) 4.8 5.58 134 0.00005 0.007

2,3,3',4,4'-PeCB (105) 5370 2.54 68199 <0.000005 < 0.341

2,3,4,4',5-PeCB (114) 4170 5.22 108837 <0.000005 < 0.544

2,3',4,4',5-PeCB (118) 35658 4.66 830831 <0.000005 <4.154

2',3,4,4',5-PeCB (123) 538 3.80 10222 <0.000005 <0.051

2,3,3',4,4',5-HxCB (156) 8413 5.87 246921 <0.000005 <1.2346

2,3,3',4,4',5'-HxCB (157) 917 7.89 36175 <0.000005 <0.1809

2,3',4,4',5,5'-HxCB (167) 705 2.03 7156 <0.000005 <0.0358

2,3,3',4,4',5,5'-HpCB (189) 1876 2.07 19416 <0.000005 <0.0971

Sum PCB 2.06 - 8.70

Sum all 3.82 - 10.46

1

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. 

Concentration

in Sediment 

(ng/kg) 

Gull Egg 

BSAF1

Concentration 

in Gull Egg 

(ng/kg egg) 

TEFs-WHO98

Avian TEF 

Gull Egg TEC 

Predicted 

Concentration

(ng/kg egg) 

2,3,7,8-TCDD .30 1.2188 1.83 1.0 1.83

1,2,3,7,8-PeCDD 1.20 1.0313 6.19 1.0 6.19

1,2,3,4,7,8-HxCDD 1.10 0.0368 0.20 0.05 0.01

1,2,3,6,7,8-HxCDD 4.70 0.2321 5.46 0.01 0.055

1,2,3,7,8,9-HxCDD 2.90 0.0102 0.15 0.1 0.015

1,2,3,4,6,7,8-HpCDD 78.20 0.0016 0.63 <.001 < 0.0006

OCDD 530.00 0.0018 4.75 0.0001 0.0005

2,3,7,8-TCDF 1.10 0.0250 0.14 1.0 0.14

1,2,3,7,8-PeCDF 0.92 0.0221 0.10 0.1 0.01

2,3,4,7,8-PeCDF 1.40 0.3068 2.15 1.0 2.15

1,2,3,4,7,8-HxCDF 4.10 0.0181 0.37 0.1 0.04

1,2,3,6,7,8-HxCDF 1.60 0.0893 0.71 0.1 0.07

1,2,3,7,8,9-HxCDF 0.30 0.0174 0.03 0.1 0.003

2,3,4,6,7,8-HxCDF 1.00 0.1200 0.60 0.1 0.06

1,2,3,4,6,7,8-HpCDF 2.70 0.0001 0.001 0.01 0.00001

1,2,3,4,7,8,9-HpCDF 133.00 0.0027 1.78 0.01 0.02

OCDF 2.40 0.0002 0.002 0.0001 0.0000002

Sum PCDD and PCDF 10.58

3,4,4',5-TCB (81) 60 3.41 1024 0.1 102.40

3,3',4,4'-TCB (77) 1623 0.178 1445 0.05 72.24

3,3',4,4',5-PeCB (126) 16 30.6 2446 0.1 244.62

3,3',4,4',5,5'-HxCB (169) 4.8 15 360 0.001 0.36

2,3,3',4,4'-PeCB (105) 5370 15.9 426118 0.0001 42.61

2,3,4,4',5-PeCB (114) 4170 24 505919 0.0001 50.59

2,3',4,4',5-PeCB (118) 35658 46.4 8270925 0.00001 82.71

2',3,4,4',5-PeCB (123) 538 22.3 60000 0.00001 0.60

2,3,3',4,4',5-HxCB (156) 8413 18.8 790822 0.0001 79.08

2,3,3',4,4',5'-HxCB (157) 917 32.1 147148 0.0001 14.72

2,3',4,4',5,5'-HxCB (167) 705 24.8 87420 0.00001 0.87

2,3,3',4,4',5,5'-HpCB (189) 1876 19.4 181972 0.00001 1.82

Sum PCB 692.62

Sum all 703.20

1

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. 

Concentration 

in Sediment 

(ng/kg) 

Forage Fish 

BSAF1

Concentration 

in Otter Diet 

(ng/kg fish) 

TEFs-WHO05

Mammalian 

TEF 

Otter Diet 

TEC 

Predicted 

Concentration 

(ng/kg fish) 

2,3,7,8-TCDD 0.30 0.20 0.133 1 0.1330

1,2,3,7,8-PeCDD 1.20 0.18 0.479 1 0.4790

1,2,3,4,7,8-HxCDD 1.10 0.03 0.073 0.1 0.0073

1,2,3,6,7,8-HxCDD 4.70 0.02 0.209 0.1 0.0209

1,2,3,7,8,9-HxCDD 2.90 0.02 0.129 0.1 0.0129

1,2,3,4,6,7,8-HpCDD 78.20 0.008 1.389 0.01 0.0139

OCDD 530.00 0.0005 0.588 0.0003 0.0002

2,3,7,8-TCDF 1.10 0.12 0.293 0.1 0.0293

1,2,3,7,8-PeCDF 0.92 0.01 0.020 0.03 0.0006

2,3,4,7,8-PeCDF 1.40 0.33 1.026 0.3 0.3078

1,2,3,4,7,8-HxCDF 4.10 0.01 0.091 0.1 0.0091

1,2,3,6,7,8-HxCDF 1.60 0.01 0.036 0.1 0.0036

1,2,3,7,8,9-HxCDF 0.30 0.04 0.027 0.1 0.0027

2,3,4,6,7,8-HxCDF 1.00 0.05 0.111 0.1 0.0111

1,2,3,4,6,7,8-HpCDF 2.70 0.001 0.006 0.01 0.0001

1,2,3,4,7,8,9-HpCDF 133.00 0.03 8.858 0.01 0.0886

OCDF 2.40 0.001 0.005 0.0003 0.000002

Sum PCDD and PCDF 1.1200

3,4,4',5-TCB (81) 60 0.35 46.6 0.0003 0.0140

3,3',4,4'-TCB (77) 1623 0.25 901 0.0001 0.0901

3,3',4,4',5-PeCB (126) 16 0.92 32.7 0.1 3.2678

3,3',4,4',5,5'-HxCB (169) 4.8 1.08 11.5 0.03 0.3450

2,3,3',4,4'-PeCB (105) 5370 0.85 10133 0.00003 0.3040

2,3,4,4',5-PeCB (114) 4170 1.41 13052 0.00003 0.3916

2,3',4,4',5-PeCB (118) 35658 1.57 124282 0.00003 3.7285

2',3,4,4',5-PeCB (123) 538 1.02 1218 0.00003 0.0365

2,3,3',4,4',5-HxCB 8413 1.66 31004 0.00003 0.9301

2,3,3',4,4',5'-HxCB (157) 917 2.08 4234 0.00003 0.2170

2,3',4,4',5,5'-HxCB (167) 705 1.09 1706 0.00003 0.0512

2,3,3',4,4',5,5'-HpCB (189) 1876 1.26 5248 0.00003 0.1574

Sum PCB 9.4454

Sum all 10.5654

1

BSAFs for forage fish in diet of otter are based on 3.11% lipid in forage fish and 1.4% carbon in sediment. 

39 
0

1

2

3

4

5

6

PCDD+PCDF 1.76 5.06

PCB 2.06 0.27

TEC (Total) 3.82 5.34

trout eggs sediment

Figure 8. Fish TECs calculated with TEFs-WHO98 appropriately from 

concentrations in eggs versus inappropriately from concentrations in 

sediment. 

0

200

400

600

800

PCDD+PCDF 10.58 6.59

PCB 692.62 91.03

TEC (Total) 703.2 97.62

gull eggs sediment

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-WHO05 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 
 

Cotter diet = C

s

f

soc

• BSAFforage fish • f( )l 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-WHO05 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. 

0

2

4

6

8

10

12

PCDD+PCDF 1.12 5.93

PCB 9.44 3.65

TEC (Total) 10.56 9.58

otter diet (fish) sediment

Figure 10. Mammal TECs calculated with TEFs-WHO05 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.html#g4). 

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 

carbon for sediments. 

Text Box 6. Questions when calculating TECs. 

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_Pubs

/bsaf.htm) 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, ∏socw, are similar for two 

ecosystems, direct extrapolation of 

BAF l 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 l 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 ∏

fd

fd

socw. This should 

increase accuracy of measured BAF l values or BSAFs when applied to an unmeasured system. 

An initial demonstration of such a “hybrid modeling approach” appears promising (Burkhard et 

al., 2006). 

fd

• 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.) 

The case studies used for the 1998 EPA/DOI workshop (U.S. EPA, 2001a) 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-WHO98/05 values, unless there is a need for more site- or speciesspecific 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-WHO98/05 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-WHO98/05 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-WHO98/05. 

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 vitro-based RPFs differ less than 5-fold 

between these species (Kennedy et al., 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 al., 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 al., 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-WHO98 values or RPFs across tissues or endpoints than across fish 

species (Henry et al., 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. 

Figure 11. Three dimensional relative potency matrix for selection of RePs 

and derivation of RPFs 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-WHO98/05 for fish, 

44 
birds, and mammals (Van den Berg et al., 1998; 2006). The highest preference is given to RePs 

determined for in vivo toxicity endpoints. 

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-WHO98/05 selection process (Van den Berg et al., 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-WHO98/05 , 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-WHO98/05 are based 

on a single species the same as or closely related to the species of concern, the TEFs-WHO98/05 

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 al., 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 dioxinlike 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 

45 
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 

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 doseresponse 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 doseresponse 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 et al., 1978; Elliott et al., 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-WHO98/05, 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 

46 
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 

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 speciesspecific 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., doseresponse 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-WHO98) 

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-WHO98, 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-WHO98 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). 

47 
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. 

Taxonomic Relationship to Species of Concern

Same Species

Related Species 

(e.g., same genus 

or family)

Class-Specific 

TEFs-WHO98

Unrelated 

Species

Endpoint

No data

(B) Chicken early 

life stage 

mortality data 

Effect of Concern in vivo

Other Toxic Effect in vivo

CYP1A induction in vivo (C) Common 

Tern data 

(A) Caspian Tern 

data CYP1A induction in vitro

AHR binding 

Structure Similarity 

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-WHO98) 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. 

48 
Tissue-Based TECs: 

• 60 pg 2,3,7,8-TCDD equivalence/g mink dam liver based on TEFs-WHO94. 

• 70 pg 2,3,7,8-TCDD equivalence/g mink dam liver based on rat liver H4IIE cellbased 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 et al. (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 of fish contamination on mink kit 

survival based on a field data set that includes concentrations of PCDDs, PCDFs, and PCBs both 

in several species of fish 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-WHO05 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 TEFsWHO05 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-WHO05. 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-WHO05 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 

49 
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-WHO05

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 sitespecific 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 TEFsWHO05 versus the rat liver H4IIE cell-RePs (PCBs were responsible for about 60% of the TECs 

for the TEFs-WHO94 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. 

Characteristics of available TEFs/RePs from which to 

select

Characteristics of optimal mink 

RPFs

If using the dose-response 

relationships and exposure metrics 

presented in Tillitt et al. (1996) 

TEFs-WHO94

Rat liver 

H4IIE cell 

Mice liver 

(partial set) 

TEF or 

RPF

Mink 

Mammals as a class 

(based primarily on 

rodents) 

Species Rats Mice 

End 

point Kit survival 

Vary depending on the 

dioxin-like chemical; 

includes subchronic or 

chronic effects in vivo and

in vitro

EROD 

induction 

in vitro

EROD 

induction 

in vivo

Dose

TEC in diet based on 

concentrations in 

fish 

TEC in mink 

dams based on 

concentrations 

in liver 

For in vivo endpoints, 

based on concentrations in 

diet 

As added to 

cell culture 

Measured in 

liver tissue 

50 
3.3.2.5. Summary of Selection of TEFs, 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-WHO98/05 values, unless there is a need for more site- or speciesspecific 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 of TEFs 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-WHO98/05 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 endpointspecific RePs/RPFs are used for a key chemical. 

51 
• 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, et al. (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 doseresponse 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). 

52 
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 

in Section 3.4.3. Text box 7 identifies 

important questions to consider for risk 

characterization. 

Text Box 7. Questions for risk characterization. 

3.4.1. Risk Estimation 

When the toxicity equivalence 

methodology is used, exposure is expressed 

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: 

• Have I clearly presented the assumptions and 

uncertainties associated with applying the 

toxicity equivalence methodology and in 

preparing the risk estimates based on TECs? 

• Have I considered multiple lines of evidence, 

such as bioanalytical tools, bioassays, field 

surveys, or other relevant RPFs? 

• Have I considered the evidence for causality 

associated with each line of evidence? 

 

Risk Estimate = TEC

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 TEFsWHO98/05 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 

53 
mortality and reproductive success of fish, birds, and mammals likely to be affected by dioxinlike 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 et al., 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 1c1c7 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 et al., 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 et al., 2001; Seidel et al., 2000; Denison et al., 1999; Giesy et al., 

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 et al., 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, nondioxin-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. 

54 
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-WHO98/05

and their application to ecological risk assessment are only briefly discussed here, but are 

described in detail in Van den Berg et al. (1998; 2006) and U.S. EPA (2001a). 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-WHO98/05, RPFs, or RePs, 

as well as the relative potency data underlying these values. 

3.4.3.1.1. AHR ligands.

The TEFs-WHO98/05 include only those PCDDs, PCDFs, and PCBs known to elicit AHRmediated 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-WHO98/05. 

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 et al. (1998; 2006) and the NRC (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 et al., 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-WHO98 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-

55 
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-WHO98/05, 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., ED50 or LD50 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-WHO98/05, the expert panel preferred 

RePs derived from ED50 or LD50 ratios; when full dose-response relationships were not available 

(precluding calculation of ED50 or LD50), RePs based on LOELs or benchmark doses were 

deemed usable, but were considered to have more uncertainty associated with them (van den 

Berg et al., 2006). Because relative potency data sets are inherently heterogeneous, uncertainties 

in the data used to select TEFs-WHO98/05, RPFs, or RePs should be analyzed on a case-by-case 

basis. 

The use of TEFs-WHO98/05, 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 TEFsWHO98/05, 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-WHO98/05 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-WHO98/05 and RPFs are point estimates even though the experimental data 

from which they are derived may range over several orders of magnitude. Hence, TEFsWHO98/05 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-WHO98/05 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-WHO98/05 or RPF point estimate. However a 

qualitative assessment of uncertainties associated with the use of TEFs-WHO98/05 or RPFs is 

possible. When evaluating uncertainties associated with use of TEFs-WHO98/05 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-WHO98/05. 

• Rounding TEFs to harmonize results across vertebrate classes (Van den Berg et al., 

1998; 2006) may have introduced systematic errors in the TEFs-WHO98/05 (U.S. EPA, 

2001a). 

56 
• 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 al., 2001). To date they have 

not been widely reported in ReP publications or routinely carried over to the TEFsWHO98/05, 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-WHO98 or RPFs 

(Finley et al., 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 (ReP1997 database) used to 

derive the TEFs-WHO98/05 provides suggestions for refinements of this database that could 

support quantitative uncertainty analyses. Haws et al. (2006) acknowledge that the mammalian 

data in the ReP1997 database are based on qualitative or subjective judgment. Therefore, Haws et 

al. (2006) and others (Van den Berg et al., 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 (ReP2004 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-WHO98/05 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 

57 
variance. This requires high-quality data relevant to exposure, toxicity, and the derivation of the 

TEFs-WHO98/05. 

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 AHRmediated 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 

58 
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 al., 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-WHO98, 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-WHO98 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-WHO98/05 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 orthosubstituted 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-WHO98/05 (Van den Berg et al., 

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. 

59 
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 TEFsWHO98/05 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 

60 
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. 

61 
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76 
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 et al., 1985; Alford-Stevens et al., 1986; Sather et al., 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 1B. 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 

77 
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 (EC50): 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 polychlorinated 

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-p-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-p-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-p-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. 

TEFs-WHO98: Toxicity Equivalence Factors established at a WHO-ECEH consultation 

(Van den Berg et al., 1998); the TEFs scheme built upon previous international efforts 

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establishing TEFs for humans and added TEFs-WHO98 values for fish and birds. 

TEFs-WHO05: Toxicity Equivalence Factors established at a WHO-IPCS consultation 

(Van den Berg et al., 2006); based on review of only the mammalian TEFs-WHO98 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-ortho-substituted PCBs (PCB 81 and 

PCB 169), and all relevant mono-ortho-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 polychlorinated dibenzo-p-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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