EPA/630/R-01/002

August 2001

www.epa.gov/ncea/raf

Workshop Report on the Application of 

2,3,7,8-TCDD Toxicity Equivalence Factors 

to Fish and Wildlife

Risk Assessment Forum

U.S. Environmental Protection Agency

Washington, DC
ii

DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection

Agency policy and approved for publication. Mention of trade names or commercial products

does not constitute endorsement or recommendation for use.
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CONTENTS

CONTRIBUTORS TO WORKSHOP DESIGN AND REPORT . . . . . . . . . . . . . . . . . . . . . . . . iv

1. INTRODUCTION ..........................................................1

2. OBJECTIVES AND DESIGN OF THE EPA/DOI WORKSHOP ......................4

3. WORKSHOP FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4. CONCLUSIONS OF THE EPA/DOI WORKSHOP PLANNING GROUP . . . . . . . . . . . . . . 11

5. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

APPENDICES:

A. Workshop Case Study: A Preliminary Problem Formulation for a Retrospective Ecological

Risk Assessment Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

B. Workshop Case Study: A Preliminary Problem Formulation for a Prospective Ecological

Risk Assessment Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

C. Report from the Workshop on the Application of 2,3,7,8-TCDD Toxicity Equivalence

Factors to Fish and Wildlife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-i

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

II. Opening Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-2

III. Workshop Proceedings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-27

IV. Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-60

Appendices

A. Workshop Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-A-1

B. Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C–B-1

C. Premeeting Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-C-1

D. Detailed Summaries of Expertise Group Discussions . . . . . . . . . . . . . . C-D-1

E. Detailed Summaries of Case Study Discussions . . . . . . . . . . . . . . . . . . . C-E-1

F. Written Comments from Observers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-F-1
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CONTRIBUTORS TO WORKSHOP DESIGN AND REPORT

EPA Regional Offices EPA Office of Research and Development

Patricia Cirone (Region 10) Steven Bradbury

Robert Pepin (Region 5) Philip Cook

Steve Wharton (Region 8) Michael Devito

Tala Henry

Cynthia Nolt-Helms

U.S. Department of Interior

Tim Kubiak (U.S. Fish and Wildlife Service)

Donald Tillitt (U.S. Geological Survey)

Lisa Williams (U.S. Fish and Wildlife Service)

Workshop Case Studies (Appendix A and Appendix B)

 EPA U.S. Department of Interior

Steven Bradbury Tim Kubiak

Philip Cook Donald Tillitt

Lisa Williams

Report from the Workshop on the Application of 2,3,7,8-TCDD 

Toxicity Equivalence Factors to Fish and Wildlife (Appendix C)

Prepared by Eastern Research Group, Inc., an EPA Contractor.

Risk Assessment Forum Staff

William Wood

Scott Schwenk
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1. INTRODUCTION

On January 20-22, 1998, a workshop was held on “The Application of 2,3,7,8-TCDD

Toxicity Equivalence Factors to Fish and Wildlife.” The workshop was developed by a joint

planning committee of the U.S. Environmental Protection Agency (EPA) and the U.S.

Department of the Interior (DOI) and hosted with the support of the Eastern Research Group,

Inc. (ERG), a contractor. This introduction provides background about polychlorinated

dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls (PCBs) and the

development of toxicity equivalence factors (TEFs) to assess their risks. This section is followed

by sections that describe the design of the workshop and its results.

PCDD, PCDF, and PCB congeners that elicit toxicity through the same aryl hydrocarbon

receptor (AhR) mediated mechanisms as 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD)

are found in a variety of aquatic and terrestrial ecosystems. Exposures of these compounds to

fish, and avian and mammalian wildlife have been well documented in a number of settings;

examples include the Great Lakes (Gilbertson and Fox, 1977; Kubiak et al., 1989; Yamashita et

al., 1993; Devault et al., 1996); the St Lawrence Estuary (Beland et al., 1993); many rivers and

lakes of the U.S. (U.S. EPA, 1992a), the arctic (Norstrom et al., 1990), and, similarly, many

locations outside of North America. The documented ecological risks associated with exposures

of fish and wildlife to 2,3,7,8-TCDD (U.S. EPA, 1993) are magnified through concomitant

exposures to other AhR agonists. Because these compounds are typically found as complex

mixtures, assessments of ecological risks require both the evaluation of their individual

exposures and combined effects. Thus, the need to assess the risks of these classes of

compounds may require evaluations of congeners other than 2,3,7,8-TCDD alone or evaluations

that are more specific than “total PCBs” or “total PCDDs.” To address the combined

AhR-mediated effects of PCDDs, PCDFs, and PCBs, the concept of 2,3,7,8-TCDD toxicity

equivalence factors (TEFs) has been proposed to facilitate both human health and ecological risk

assessments (e.g., U.S. EPA, 1989; U.S. EPA, 1991; van den Berg et al., 1998).

A TEF is a best estimate, from available data for an individual chemical that behaves as

an AhR agonist, of the chemical’s potency relative to the potency of the reference chemical,

2,3,7,8-TCDD. Specific relative potency (ReP) data used to determine TEF values generally

consist of the ED50, LD50, or LC50 of 2,3,7,8-TCDD divided by the respective ED50, LD50, or LC50

of the chemical. The ReP data base for a chemical may include multiple species, in vitro or in

vivo studies, and endpoints ranging from mortality to protein induction (e.g., the enzyme

CYP1A1). The World Health Organization (WHO) has defined TEFs as order to half-order of
2

magnitude estimates of relative potency derived from an assessment of available RePs (van den

Berg et al., 1998).

Initially, TEFs were developed primarily to address human health effects. In 1987, the

EPA published an interim report on the toxicity equivalence methodology and proposed TEF

values for fifteen 2,3,7,8-chlorine substituted dibenzo-p-dioxin and dibenzofurans for human

health risk assessment (U.S. EPA, 1987). At the time the initial TEF values were presented, few

in vivo experimental data were available, particularly for the dibenzofurans. Following the

EPA-TEF report, the North Atlantic Treaty Organization Committee on the Challenges of

Modern Society (NATO-CCMS) also published TEFs for dioxin and dibenzofurans based on

scientific consensus (I-TEFs; NATO-CCMS, 1988a,b). The I-TEFs were adopted by EPA in

1989 (U.S. EPA, 1989). TEFs were subsequently expanded to include dioxin-like PCBs

(Ahlborg et al., 1994; Safe, 1994; van den Berg et al., 1998). 

The criteria to include a compound required a structural relationship to the PCDFs and

PCDDs, binding to the AhR receptor, and expression of dioxin-specific biochemical or toxicity

responses (Ahlborg et al., 1994). TEFs were derived using all the available data and a tier

approach to data selection. While the actual TEFs proposed by these reports vary due to data

availability and differences in assessing RePs (dose measures, species, end points, in vitro/in

vivo, etc.), greater reliance was usually placed on RePs derived from chronic or subchronic in

vivo exposures and endpoints associated with overt toxicity, followed by biochemical effects

derived from in vivo exposures, followed by biochemical effects derived from in vitro studies,

and finally quantitative structure activity relationships.

Rather than employing “human-health” TEFs to evaluate risks of PCDDs, PCDFs, and

PCBs to fish and wildlife, the need to establish TEFs more appropriate for ecological risk

assessments and associated management decisions has been recognized (van den Berg et al.,

1998; U.S. EPA, 1992b). In many cases these needs require a greater capability than that used in

screening-level risk assessments. Class-specific TEFs are needed to establish aquatic life and

wildlife water quality criteria and remediation targets at contaminated sites. For example,

development of wildlife criteria for PCBs in the Great Lakes Water Quality Guidance (GLWQG)

initially proposed the use of TEFs calculated by Safe (1990). Based on recommendations from

the EPA Science Advisory Board (U.S. EPA, 1992b), this approach was not included in the final

rule because it was concluded that, although the conceptual basis for the use of TEFs was

reasonable, there were insufficient data available to determine the extent to which the TEFs

available for human health are appropriate for the species and endpoints of concern in ecological
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analyses. Currently, EPA and the U.S. Department of Interior (DOI) are jointly addressing the

possibility of advancing the use of TEFs in the Great Lakes Water Quality Guidance as new data

and methods become available (U.S. Department of the Interior, 1995). In terms of assessing

impacted sites, virtually all of the Great Lakes Areas of Concern have PCB- and PCDFcontaminated sediments that require assessment of loading and resultant effects to facilitate risk

management decisions. Regulatory decisions for situations with PCDDs, PCDFs, and PCBs that

have relied on single chemical estimates for 2,3,7,8-TCDD or total PCBs have been criticized

because of the limitations of these approaches.

Due to the increasing need for development of TEFs appropriate for use in ecological risk

assessments, the WHO convened a meeting of scientific experts in 1997 to derive TEFs for

mammals, birds and fish (van den Berg et al., 1998). The goals of this meeting were to establish

a process for deriving TEFs, to facilitate the collection and evaluation of available RePs, and to

establish consensus TEFs. The derivation of TEFs involved a tiered evaluation of RePs similar

to that described previously, in which overt toxicities from in vivo studies were preferentially

used when available. RePs were evaluated and a TEF was derived from available RePs. The

TEF values were assigned one significant figure and then rounded to half-order of magnitude

point estimates. Through these efforts, mammalian TEFs for PCDDs, PCDFs, and PCBs were

reevaluated, and in some cases revised, and TEFs established for fish and birds (Appendix C,

Figure 2, p. C-8). While human TEFs were considered applicable for mammalian wildlife

species, use of bird- and fish-specific TEFs was considered more credible than application of the

mammalian TEFs across all taxa. Thus, while harmonization of specific TEFs across taxa were

attempted to the extent possible, it was concluded this could not be accomplished in some cases. 

For example, fish are relatively insensitive to mono-ortho PCBs compared to mammals and

birds.

The proposed WHO TEFs clearly establish a major advancement in the evaluation of

existing mammalian, fish and bird ReP data for PCDDs, PCDFs, and PCBs. In addition, the

resulting TEFs advance the quality of screening-level ecological risk assessments in cases where

less quantitative assessments are required (e.g., ranking sites for further action). Building on this

effort, EPA and DOI convened a workshop to evaluate the use of the toxicity equivalence

methodology in ecological risk assessments.
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2. OBJECTIVES AND DESIGN OF THE EPA/DOI WORKSHOP

The January 20-22, 1998 workshop held on “The Application of 2,3,7,8-TCDD Toxicity

Equivalency Factors to Fish and Wildlife” was developed by a joint planning committee of the

U.S. Environmental Protection Agency (EPA) and the U.S. Department of the Interior (DOI)

(Appendix C, pp. C-A-3 - C-A-4). The 20 invited workshop participants, seven of whom were

from other countries, came from academia, industry, public interest groups, and government (see

Appendix C, pp. C-A-1 - C-A-2). In addition, eight EPA and four DOI Workshop Planning

Group members participated, and 37 observers attended the meeting. Areas of expertise

represented by the workshop participants included TEF derivation, chemical fate and transport,

bioaccumulation, population modeling, and ecological risk assessment.

The primary objective of the workshop was to identify, document, and compare

uncertainties (lack of knowledge, systematic errors, incompatibility between dose metrics, and

variability) in TEF development and their impact in ecological risk assessments. To achieve this

objective, two case studies (see Appendices A and B) based on hypothetical situations for

prospective and retrospective ecological risk assessments were prepared by the joint EPA and

DOI Workshop Planning Group. The group also prepared a series of questions (see Charge to

Reviewers, Appendix C, pp. C-C-5 - C-C-8) that workshop participants were asked to answer in

writing before the workshop (see Appendix C, pp. C-C-9 - C-C-170). Specific questions were

associated with four general topics: (1) stress-response profile relative to the derivation of TEF

values; (2) stress-response profile relative to application of the toxicity equivalence

methodology; (3) exposure profile; and (4) risk characterization.

Realistic environmental conditions and problems were incorporated into the case studies. 

The retrospective case emphasized assessment of a contaminated lake in which restoration of

self-sustaining populations of fish, birds, and mammals was the management objective. The

prospective case emphasized assessment of a lake ecosystem for which chemical loadings were

to be managed in order to prevent adverse dioxin toxicity effects in fish, birds and mammals

exposed through the food web.

Each case study incorporated a series of issues to focus discussions. Although many

issues and related questions involved effects characterization topics, issues and questions

concerning exposure characterizations, which directly impact the effective application of TEFs,

were also raised for discussion. Implicit in the case studies were proposed models and

approaches which were subject to evaluation at the workshop. For example, the prospective case
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proposed a model for incorporation of the toxicity equivalence model into total maximum daily

loading (TMDL) process. This not only created a realistic and challenging application of the

methodology for the participants to evaluate, but highlighted problems associated with ascribing

toxicity equivalence to abiotic media (e.g., sediments, water, effluents).

The workshop convened with a plenary session for introductions, a workshop overview,

an overview of the June 1997 WHO Meeting on the Derivation of TEFs for Dioxin-like

Compounds for Humans and Wildlife, and an overview of the two ecological risk assessment

case studies (see the Workshop Agenda, Appendix C, pp. C-B-1 - C-B-3). Following the plenary

session, the invited participants were assigned to one of three concurrent expertise group

sessions to allow individuals with specific expertise to come to a common understanding of the

issues related to their area of expertise. The three expertise groups were: (1) Toxicity

Equivalence Factors; (2) Fate and Transport and Bioaccumulation; and, (3) Risk Assessment and

Population Modeling. Expertise groups shared their findings in a plenary session. Then the

participants were distributed among three new groups so that each group had members from each

of the expertise groups. The three new groups independently discussed the application of the

toxicity equivalence methodology, first to the retrospective case study and then to the

prospective study. At the end of the workshop, the participants met in a final plenary session to

share and summarize the findings of the individual groups and of the workshop as a whole.
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3. WORKSHOP FINDINGS

The experts at the workshop reached consensus on 16 general conclusions and on

answers to most of the specific charge questions, which are reproduced below in their entirety

(from Appendix C, Section IV, pp. C-60 - C-65). The specific conclusions related to the

workshop charge questions may be found in Appendix C, pp. C-65 - C-68.

Conclusions and Recommendations of the Workshop:

1. The TEF/TEQ [toxicity equivalence factor/toxicity equivalence] methodology is

technically appropriate for evaluating risks to fish, birds, and mammals associated with

AhR agonists. The methodology can support risk analyses beyond screening-level

assessments. Examples of possible applications include the evaluation of point source

discharges (within the framework of the Clean Water Act) and the evaluation of

contaminated sites (within the framework of the Comprehensive Environmental

Remediation and Compensation Liability Act). The applicability of the methodology is

situation-specific. As with any method, appropriate caution should be exercised to avoid

misuse or application of the methodology to situations where the underlying assumptions

are known not to be valid. When applying the method, it should be recognized that there

may be effects associated with the chemicals of concern that are unrelated to AhR and,

therefore, may need to be evaluated under a separate methodology. These possibilities

should be considered during the planning stage of an assessment.

2. The TEF/TEQ methodology reduces uncertainties associated with developing

dose-response information for AhR agonists that exist with methods that rely on a single

compound (e.g., TCDD) or on compounds evaluated as an aggregate (e.g., total PCBs). 

Specifically, because the methodology takes into account the possible effects of the suite

of chemicals that act as AhR agonists, it is less likely to underestimate risks than are

methods based on only one of these compounds (i.e., TCDD). Further, because total

PCBs in the environment can be comprised of many compounds that vary in

concentration and potency as AhR agonists, the TEF/TEQ methodology provides a

means for accounting for these variables.

3. The uncertainties associated with using RePs or TEFs are not thought to be larger than

other sources of uncertainty within the risk assessment process (e.g., dose-response

assessment, exposure assessment, and risk characterization). However, these
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uncertainties should be quantified better.

4. As is the case with any ecological risk assessment, the nature and magnitude of

uncertainties should be identified and carried through the ecological risk assessment

process (dose-response assessment, effects assessment and risk characterization). This

could involve a number of different approaches, including qualitative analyses,

assignment of ordinal rankings to sources of uncertainty, presentation of ranges, fuzzy

arithmetic, and probabilistic analyses. Information on the sensitivity of the risk estimates

to the uncertainties associated with the TEQ methodology (as well as other ERA

components) should be identified and quantified (if possible). This knowledge can be

used to communicate the range of possible results to the decision maker and to identify

what additional information would be the most useful for decision making. Specific

examples of approaches are provided in the summaries of the workshop breakout group

sessions on the case studies (Appendix C-E).

5. Workshop participants supported the use of a hierarchical procedure for selecting ReP or

TEF values for use in risk assessment. In general, the most appropriate values are those

that are closely related to the taxa and endpoints being evaluated. Workgroup

participants agreed that uncertainties are introduced with increasing taxonomic and

endpoint extrapolation. The workgroups suggested schemes for selecting ReP or WHO

TEF values, as well as schemes for considering how uncertainties associated with

selecting values can be identified and tracked. These are identified in the workgroup

summaries (Appendix C-E).

6. A database of ReP and TEF values should be maintained in order to facilitate the

application of the hierarchical procedure and to enable the conduct of sensitivity and

uncertainty analyses. The appropriate regulatory agencies will need to consider how to

insure the quality of the data in the database, document the values and the procedures

used to derive them, make the database accessible, and provide guidance for its use.

7. The derivation of ReP and WHO TEF values needs to be adequately documented

(including specific citations) in order to support the use of these values in regulatory risk

assessments. The WHO TEF documents and data provided to the 1997 WHO expert

panel did not include documentation for the mammalian TEF values. This was viewed as

a major limitation on the use of the document for risk assessment purposes.
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8. The TEF/TEQ methodology requires analytical methods to identify and quantify the

individual dioxin, furan, and PCB compounds. The accuracy and precision of available

methods are considered acceptable for risk assessment purposes. The analytical

measurement errors are not considered to be a large source of uncertainty within the

assessment. A few of the workshop participants familiar with the analytical methods

reported measurement errors in the range of five to 30%.

9. The costs for analyzing the suite of individual dioxin, furan, and PCB compounds are

greater than those associated with analyzing an individual compound (e.g., TCDD) or for

measuring "total PCBs." Workshop participants agreed that it may be possible to focus

the analytical effort at different stages of the assessment, thereby reducing costs. For

example, investigations may indicate that risks are due to a few of the compounds or to a

particular class and these may form the basis for subsequent evaluation. Further, it may

be possible to complement detailed analyses of individual compounds with simpler and

cheaper analytical methods (e.g., to provide information on spatial extent of

contamination).

10. Analytical detection levels for congeners need to be lower than concentrations at which

important biological effects might occur. Workshop participants agreed that this can be

achieved with available methods. As with any analytical program where data will be

used in risk assessments, data quality objectives should be specified and care taken to

insure that they are met.

11. Because physical, chemical, and biological properties vary among the individual dioxin,

furan, and PCB compounds, exposure assessments that complement the TEF/TEQ

methodology may require more information and resources (i.e., effort) than exposure

assessments for an individual compound (e.g., TCDD) or a class of compounds (e.g.,

total PCBs). Fate and transport models used to support the exposure assessment will

need to account for individual compounds through the various modeled components. In

some cases, it may be possible to model groups of compounds with similar fate and

transport properties.

12. Information on the environmental behavior of individual chemical congeners is needed to

understand and use the congener-specific information in a modeling effort. With

increasing use of a TEF/TEQ methodology, gaps in knowledge on chemical-specific

environmental behavior will become evident. Regulatory agencies will need to consider
9

how best to acquire this information or develop exposure assessment tools that can

complement the use of TEF/TEQ for specific regulatory applications.

13. Application of a TEF/TEQ methodology could be considered within the framework of a

"lines of evidence" approach as described within the EPA's guidance for ecological risk

assessment. As such, additional field and laboratory information could corroborate or

improve the results of an assessment that is based, in part, on the application of the

TEF/TEQ methodology. Use and integration of various lines of evidence in ecological

risk assessment can often strengthen the analysis and provide a greater degree of

confidence in the results than can be achieved from relying only on a single line of

evidence. Each piece of information will have inherent strengths and limitations, and the

amount of confidence placed on the information will also reflect the technical

background of the individuals using the methodology and their experience with it.

14. Several workshop participants stressed the value of applying population-level assessment

tools and obtaining population-level information in support of assessments (i.e., as a line

of evidence). These included methods by which risks to individuals could be described

in terms of potential risks to local populations. In addition, a few participants gave

examples of tools that could be helpful for assessing whether population-level effects

were being manifested (for retrospective assessments). Examples included direct

observations of hatching success, the condition of fledgling birds, and the age structure of

populations.

15. Participants also discussed the use of bioassay tools to support the assessment. These

methods could complement assessments that rely upon the TEF/TEQ methodology. One

participant summarized the strengths and limitations of these tools as follows. In vitro

TEQ bioassays have the advantage of measuring the integrated effects of complex

mixtures of Ah receptor agonists. In addition, such assays have the potential of

identifying compounds that act via the Ah receptor which would not be identified by a

chemical residue approach that measures only dioxins, furans and PCBs. In vitro

bioassay-derived TECs [toxicity equivalence concentrations] can be obtained at a lower

cost than TECs obtained by analysis of chemical residues. One potential problem with in

vitro bioassays is that they can overestimate the toxic potency of compounds which are

rapidly metabolized in vivo (e.g., PCB 77). However, recent research has shown that

such problems can likely be circumvented. Various in vitro bioassays have considerable

potential for predicting TECs which are relevant to whole organisms.
1

 EPA/DOI clarification: this conclusion does allow application of TEFs/RePs to

concentrations of PCDD, PCDF, and PCB congeners in water, sediments, or soils if and only if

the appropriate bioaccumulation factors are used to transform the concentrations in the media to

concentrations of congeners in the organisms, or the food of the organisms, that are specific

subjects of an ecological risk assessment.

10

16. Participants adopted the language given in the WHO document cautioning against the

potential misapplication of the TEF/TEQ methodology to environmental media (e.g.,

sediments or soils). Specifically, the participants indicated that it is not appropriate to

derive TEQs for these media.1 TEQs are relevant only with respect to specific ecological

receptors. The methodology can be used to support decisions concerning the regulation

of point source discharges and environmental cleanups that involve chemicals in

environmental media. However, in these cases, the decision involves identifying

concentrations of chemicals or the composition of mixtures that would yield acceptable

TEQ with respect to specified ecological receptors.
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4. CONCLUSIONS OF THE EPA/DOI WORKSHOP PLANNING GROUP

At the end of the workshop the EPA/DOI Workshop Planning Group (Appendix C, pp.

C-A-3 - C-A-4) met to reflect on the outcome of the meeting, to consider the workshop

conclusions, to consider supplementary conclusions, and to recommend future actions. The

following observations and conclusions resulted from the post-workshop discussion. 

A. The design and execution of the workshop provided a sustained high level of

participant involvement from start to finish.

Participants performed as critical problem solvers rather than simply as critical peer

reviewers. ERG, the Workshop Chair, and the Discussion Group Leaders provided excellent

organization and leadership. This and the experience and quality of the participants, focused

with the use of the case studies, contributed to an excellent evaluation of the use of the toxicity

equivalence methodology in ecological risk assessments.

B. The objectives of the workshop were met and the overall conclusions support the

use of the toxicity equivalence methodology in quantitative risk assessments, especially

given the alternative of continuing to separately assess risks from individual compounds

which share an AhR-mediated mechanism of action.

C. The documentation of which and how specific studies were ultimately used for

setting mammalian TEFs in the WHO 1997 report should be improved. This will aid in

determining how or whether to modify TEFs to reflect new data.

Because the development of a procedure such as a decision matrix for selecting

appropriate TEFs or alternative RePs for specific ecological risk assessment was a critical

concept that emerged from the workshop, documentation of the data basis for the TEF values is

important. The EPA and DOI Planning Group felt that for those chemicals that were reexamined at the WHO 1997 workshop based on new data, the estimated mammalian TEF values

were reasonably-well documented. These TEFs were based on multiple studies examining

multiple endpoints of concern, and key papers which either confirmed or altered the relative

potency for each endpoint were cited. Most of the papers cited are subchronic studies using

either enzyme induction, tumor promotion or nonspecific biological endpoints such as alterations

in organ and body weight. In addition, for many of the chemicals, RePs for developmental

toxicities in mice were also considered in the derivation of the TEF values. For PCBs 77, 126,
2

 However, a review following the workshop indicates that U.S. EPA (1989) does cite the

studies used in the development of TEFs for PCDDs and PCDFs in the NATO-CCMS effort and

provides a rationale for each value.

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105, 118, and 169, the TEF values are based on a variety of endpoints including enzyme

induction, immunotoxicity, teratogenicity, developmental toxicities and tumor promotion. The

Planning Group recommended that future efforts to establish or revise existing mammalian TEFs

continue to provide transparency and documentation as reflected in van den Berg et al. (1998).

The Planning Group believed that the basis for the establishment of certain of the

mammalian TEFs could have been better documented. The experts at the WHO 1997 meeting

relied heavily on prior international efforts (NATO-CCMS in 1989 and WHO-IPCS published in

Ahlborg et al., 1994) establishing TEFs for humans. Although these earlier international TEF

consensus efforts describe their methods, the Planning Group felt that they did not indicate

which studies received the most weight and how each study affected the derivation of the TEF

value for a particular congener2

. Because the data sets used for the estimation of the mammalian

TEF values are quite large, it is difficult to deduce the reasoning used in these previous

workshops absent such documentation. For example, the WHO-IPCS effort that estimated TEF

values for 12 PCBs examined over 120 studies consisting of over 270 relative potency values

(Ahlborg et al., 1994), the vast majority of which involved in vivo studies of enzyme induction in

rodents. They were based on rodent studies because the large majority of mammalian studies

have involved rodents, though a few studies have examined the effects of dioxin-like chemicals

in wildlife species such as mink (e.g., Hochstein et al., 1998; Patnode and Curtis, 1994; Tillitt et

al., 1996) or seals (e.g., Ross et al., 1996). Determining how to use new data to modify TEF

values in the future will be made more complicated in those cases where the basis for the current

TEF values is not fully documented. Future revisions to TEFs based on reviews of new data in

the context of all of the available data for each congener should eliminate or reduce this

complication.

D. Because mammalian TEFs reflect a degree of public health protection, they could

lead to an overestimation of risk to wildlife.

Because of allowances for uncertainty in species extrapolation, TEFs for humans have

been described as “order of magnitude” estimates, with TEF values rounded in a direction that

provides a degree of pubic health protection. (This means, for example, that an estimated TEF of

0.04 for a given congener would be rounded up to 0.1 rather than down to 0.01.) The Planning
13

Group believed that this rounding could lead to an overestimation of adverse effects when

applied to wildlife. However, use of the WHO TEFs for mammalian risk assessments involving

toxicological testing of complex mixtures with environmentally relevant exposures suggests that

the potential overestimation of risk is small. In the case of reproductive impairment in a

sensitive mammalian test species such as the mink, the toxicity equivalence methodology

accurately and consistently explains the reproductive measurement endpoints in a dose-response

manner (Tillitt et al., 1996). Risk assessors should keep in mind that this rounding approach

differs from that used for fish and birds, which were simply rounded to the nearest order or half

order of magnitude. Additionally, it means that the uncertainty of a given TEF value for

mammals cannot be expressed as quantitatively as for fish and birds. At best, the uncertainty can

be described qualitatively by highlighting which studies contributed the most to the professional

judgment used to derive a TEF. 

E. The documentation of studies on which RePs and TEFs for fish and birds are based

can be improved to evaluate better the associated uncertainties.

The 1997 WHO expert panel also included scientific experts for the purpose of assigning

TEFs for fish and birds to allow evaluation of risks to fish and avian wildlife. The EPA/DOI

Planning Group affirmed the need to evaluate relative uncertainties associated with use of the

TEF for a congener versus a potentially more relevant ReP value. In some cases uncertainty may

be reduced by using specific ReP data for one or more congeners with TEFs for other congeners

in the exposure being assessed. In order to make such decisions, risk assessors need to consider

the data and procedural basis for selecting each TEF value as well as the specificity of alternative

ReP values.

The TEFs for fish and birds were assigned on the basis of a four-tiered ReP data

interpretation approach (van den Berg et al., 1998). The experts at the 1997 WHO consultation

also looked at consistency across endpoints for RePs. They considered information on

endpoints, species, or studies which may have been inconsistent with others (i.e., appeared to be

outliers) when selecting RePs for the TEF. Tier one data involved toxicity observed in

developing embryos. Tier two data involved biochemical effects (induction of CYP1A)

observed in developing embryos. Tier three involved biochemical effects (induction of CYP1A)

in in vitro systems. Tier four involved use of Quantitative Structure Activity Relationship

(QSAR) studies when no adequate effects data were available for the chemical. Most of the

TEFs for fish resulted from early life stage mortality from injection exposure of each chemical to

trout eggs whereas most of the avian TEFs were based on induction of ethoxyresorufin-O-
14

deethylase (EROD) activity or lethality in chicken embryos following egg injections.

Despite the methodological rigor documented for selection of TEFs for fish and birds, the

Planning Group felt that some inconsistencies and uncertainties appear in van den Berg et al.

(1998). These are noted here for the purpose of supporting future efforts to clarify the basis for

individual TEF decisions and to refine TEF values in the future as needed. Three issues the

Planning Group discussed were: (1) apparent deviations from the WHO decision framework (for

example, 1,2,3,4,7,8-HxCDF TEF for birds was set as 0.1 [the value used for mammals]

although egg injection in chicken embryos gave 0.01); (2) systematic errors associated with

rounding TEFs to the nearest factor of ten or five; and (3) uncertainties introduced by

“harmonizing” some fish and bird values with mammalian TEFs. Systematic errors associated

with rounding TEFs to the nearest factor of ten or five occur because rounding up causes a

smaller change than rounding down. For example, rounding 0.06 to 0.1 results in a TEF that is a

factor of 1.67 larger; whereas “rounding down” from 0.04 to 0.01 results in a TEF that is a factor

of 4.0 smaller. Additionally, the final step in choosing the WHO fish and bird TEF values

included “harmonization” which involved changing some values to those set for human health

and mammals. Given the uncertainty associated with a particular TEF choice, one could argue

that slight changes are of no consequence. However, these modifications are systematic changes

in a point estimate rather than an expression of variance.

F. Guidance is needed for selecting the class-specific WHO TEFs or

species/endpoint-specific RePs to use in ecological risk assessments.

The selection of TEFs or RePs for mammalian wildlife, avian wildlife and fish should be

based on the assessment endpoints and measures of effect identified in the ecological risk

assessment, and the nature and availability of RePs and TEFs. While the WHO class-specific

TEFs are appropriate for use in risk assessments associated with ranking or prioritizing future

assessment efforts, they may not be the most appropriate values to employ in more quantitative

risk assessments where assessment endpoints, measures of effects, and exposure scenarios have

been more rigorously identified. In these cases, if specific species- or endpoint-based RePs are

available that are more closely related to the measures of effect, their use in an ecological risk

assessment could reduce uncertainties in species or endpoint extrapolations, compared to using

the TEFs prepared by the WHO. It is likely, however, that in many situations the appropriate

RePs will not be available. In these instances the WHO TEFs should be employed.

Given these considerations, the Planning Group recommended that a methodology be
15

developed to facilitate a consistent approach to ReP and TEF selection. A series of species and

endpoint selection issues and criteria should be evaluated to determine which RePs or TEFs

should be employed in a specific ecological risk assessment. The conceptual model for such a

TEF/ReP selection guidance (Appendix C, pages C-50 - C-51) would be based on the tiers used

by the WHO to establish TEFs (van den Berg et al., 1998), where in vivo endpoints are generally

preferred. Using the proposed TEF/ReP selection guidance for a specific risk assessment could

result in the selection of a ReP rather than the class-specific WHO TEF because of greater

relevancy to the measures of effect identified. It may also be possible that the output from the

proposed TEF/ReP selection guidance could result in the selection of two or more RePs that may

be equally credible. For example, this outcome could result in situations where a ReP for an in

vivo endpoint in a species similar to that identified in the assessment endpoint is considered as

credible as a ReP for an in vitro endpoint derived from the species of interest. The use of a

TEF/ReP selection guidance would facilitate a clear and transparent explanation of the

assumptions associated with ReP/TEF selection and their strengths and limitations in the risk

assessment. Use of the guidance would also facilitate a consistent ReP/TEF selection process for

risk assessors across organizations and programs.

Next Steps: The Planning Group recommended that EPA and DOI facilitate the

formation of a workgroup of Federal scientists to establish a draft detailed guidance for ReP/TEF

selection. EPA and DOI should subsequently sponsor a peer-review of the draft guidance. 

Following revisions, the guidance would be incorporated within a draft Framework for the Use

of 2,3,7,8-TCDD Toxicity Equivalence Factors in Ecological Risk Assessments. This framework

would be useful, in the context of ecological risk assessments, to multiple programs engaged in

pollution prevention activities such as water quality standards and NPDES/SPDES permits,

planning activities such as Section 303(d) list generation, total maximum daily loads (TMDLs),

wasteload and load allocations, and remediation activities such as CERCLA cleanups and

Natural Resource Damage Assessments (NRDAs) by natural resource trustees.

G. A single database of reviewed RePs and TEFs should be established and maintained

to facilitate consistent use of RePs/TEFs in ecological risk assessments.

While it is recommended that ecological risk assessors be provided some flexibility in

selecting ReP/TEF values, through the use of the proposed guidance previously described, it is

essential that the knowledge base from which selections are made is restricted to a database that

has been prepared and periodically updated by experts in ReP/TEF development and application. 
3

 This database is available at the WHO website www.who.nl by choosing the item

“Toxicity Equivalency Factors (TEFs) for PCBs, PCDDs, and PCDFs for humans and wildlife”

under the selection “Downloads.”

16

The database developed by the WHO3 serves as an excellent model from which to start. The

establishment and maintenance of a database would ensure that risk assessors have ready access

to high quality RePs, facilitate consistency across risk assessments with similar species,

chemicals and measures of effects (in conjunction with the guidance for ReP/TEF selection), and

ensure that multiple organizations are not expending funds to establish redundant ReP

knowledge bases.

Next Steps: The Planning Group recommended that EPA and DOI initiate discussions

with the WHO to develop and evaluate options for establishing and maintaining a

readily-accessible database of ReP/TEF values. At a minimum, these deliberations should

evaluate time frames for updating a database, formal preparation of review protocols and

associated quality assurance/quality control requirements, the nature of potential distribution

options, and costs.

H. Chemical impurities could affect ReP and TEF calculations.

This issue was not addressed during the 1997 workshop, nor in Appendix C, but is

considered significant enough to warrant raising here. One aspect of data quality that was not

evaluated when using RePs to set the WHO TEF values is the purity of chemical standards

required in ReP studies to assure that more potent congeners are not present as trace

contaminants which cause the observed effects, rather than the chemical being tested. Apparent

AhR agonists with small TEFs are most susceptible to this uncertainty. For example, a dioxin

effect attributed to a chemical with a TEF of 0.0001 could be entirely caused by another

chemical with a TEF of 0.1 when present as 0.1% of the concentration of the test chemical. 

Small TEFs do not mitigate the potential for errors in risk assessments when the chemical is

present in large concentrations as in the case of the mono-ortho PCBs. Unfortunately, most

studies which were used as the basis for the WHO TEFs do not present chemical purity

information sufficient to evaluate this uncertainty.

I. Terms and acronyms need to be standardized.

Acronyms such as “TEF” are important language tools that are intended to allow efficient
17

and accurate communication of scientific information. In the workshop and this report TEF was

defined as “toxicity equivalence factor,” rather than commonly used variations such “toxic

equivalent factor” or “ toxic equivalency factor.” Since the WHO (van den Berg et al., 1998)

distinguished between TEFs and REPs, which were the relative potency data points used to

derive TEFs, there has been a period of accommodation for the new REP acronym. In the

workshop and this report the acronym “ReP” was used, rather than “REP,” in order to be

consistent with use of lower case letters when two or more letters in an acronym represent a

single word. Another acronym for which there has been some degree of ambiguity is TEQ

which is used to represent “toxicity equivalence,” “toxicity equivalent,” “toxic equivalent,”

“toxic equivalent concentration,” etc. In the workshop and this report the acronyms TEQ and

TEC were used to represent “toxicity equivalence ” and “toxicity equivalence concentration,”

respectively. During the writing of this report alternative acronyms such as RPF for “relative

potency factor,” TEq for “toxicity equivalence,” and TEqC for “toxicity equivalence

concentration” were discussed. EPA and DOI should consider further clarification of these key

terms and acronyms for consistent use in risk assessments.
18

5. REFERENCES

Ahlborg, UG; Becking, GC; Birnbaum, LS; Brouwer, A; Derks, HJGM; Feeley, M; Golog, G; Hanberg, S; Larsen,

JC; Liem, AKD; Safe, S; Schlatter, C; Waern, F; Younes, M; Yrjanheikki, E. (1994) Toxic equivalency factors for

dioxin-like PCBs: report on a WHO-ECEH and IPCS consultation, December 1993. Chemosphere 28(6):1049-

1067.

Beland, P; DeGuise, S; Girard, C; Lagace, A; Martineau, D; Michaud, R; Muir, DCG; Norstrom, RJ; Pelletier, E;

Ray, S; Shugart, LR. (1993) Toxic compounds and health and reproductive effects in St. Lawrence Beluga Whales.

J Great Lakes Res 19:766-775.

Devault, DS; Hesselberg, R; Rogers, PW; Feist, TJ. (1996) Contaminant trends in lake trout and walleye from the

Laurentian Great Lakes. J Great Lakes Res 22:884-895.

Gilbertson, M; Fox, GA. (1977) Pollutant-associated embryonic mortality of Great Lakes herring gulls. Environ

Pollut 12:211-216.

Hochstein, JR; Bursian, SJ; Aulerich, RJ. (1998) Effects of dietary exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin

in adult female mink (Mustela vison). Arch Environ Contam Toxicol 35:348-353.

Kubiak, TJ; Harris, HJ; Smith, LM; Schwartz, TR; Stallings, DL; Trick, JA; Sileo, L; Docherty, DE; Erdman, TC.

(1989) Microcontaminants and reproductive impairment of the Forster’s Tern on Green Bay Lake Michigan. Arch

Environ Contam Toxicol 18:706-727.

NATO-CCMS (North Atlantic Treaty Organization-Committee on the Challenges of Modern Society). (1988a)

International Toxicity Equivalency Factor (I-TEF), method of risk assessment for complex mixtures of dioxins and

related compounds. Report No. 176.

NATO-CCMS. (1988b) Scientific basis for the development of International Toxicity Equivalency Factor (I-TEF),

method of risk assessment for complex mixtures of dioxins and related compounds. Report No. 178.

Norstrom, RJ; Simon, M; Muir, DCG. (1990) Chlorinated dioxins and furans in marine mammals from the

Canadian arctic. Environ Pollut 66:1-20.

Patnode, KA; Curtis, LR. (1994) 2,2'4,4'5,5'- and 3,3',4,4',5,5'-Hexachlorobiphenyl alteration of uterine

progesterone and estrogen receptors coincides with embryotoxicity in mink (Mustela vison). Toxicol Appl

Pharmacol 127:9-18.

Ross, PS; De Swart, RL; Timmerman, HH; Reijnders, PJH; Vos, JG; Van Loveren, H; Osterhaus, ADME. (1996)

Suppression of natural killer cell activity in harbour seals (Phoca vitulina) fed Baltic Sea herring. Aquatic Tox

34(1):71-84.

Safe, S. (1990) Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and related

compounds: environmental and mechanistic considerations which support the development of toxic equivalency

factors (TEFs). Crit Rev Toxicol 21(1):51-88.

Safe, S. (1994) Polychlorinated biphenyls (PCBs): environmental impact, biochemical and toxic responses, and

implications for risk assessment. Crit Rev Toxicol 24(2):87-149.

Tillitt, DE; Gale, RW; Meadows, JC; Zajicek, JL; Peterman, PH; Heaton, SN; Jones, PD; Bursian, SJ; Kubiak, TJ;

Giesy, JP; Aulerich, RJ. (1996) Dietary exposure of mink to carp from Saginaw Bay: 3) characterization of dietary

exposure to planar halogenated hydrocarbons, dioxin equivalents, and biomagnification. Eviron Sci Technol 30:283-

291.

U.S. Department of the Interior. (1995) Letter to Mr. Barry DeGraff transmitting the Section 7 Consultation
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Biological Opinion regarding the Great lakes Water Quality Initiative, from Mr. John A. Blankenship. February 21,

1995. 61pp.

U.S. EPA (Environmental Protection Agency). (1987) Interim procedures for estimating risks associated with

exposures to mixtures of chlorinated dibenzo-p-dioxins and -dibenzofurans (CDDs and CDFs). Risk Assessment

Forum. EPA/625/3-87/012.

U.S. EPA. (1989) Interim procedures for estimating risks associated with exposures to mixtures of chlorinated

dibenzo-p-dioxins and -dibenzofurans (CDDs and CDFs) and 1989 update. Risk Assessment Forum. EPA/625/3-

89/016.

U.S. EPA. (1991) Workshop report on toxicity equivalency factors for polychlorinated biphenyl congeners. Risk

Assessment Forum. EPA/625/3-91/020.

U.S. EPA. (1992a) National study of chemical residues in fish. EPA/506/6-90/001a.

U.S. EPA. (1992b) An SAB Report: evaluation of the guidance for the Great Lakes water quality initiative. Science

Advisory Board. EPA-SAB-EPEC/DWC-93-005.

U.S. EPA. (1993) Interim report on data and methods for assessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin risks to

aquatic life and associated wildlife. EPA/600/R-93/055.

van den Berg, M; Birnbaum, L; Bosveld, ATC; Brunstrom, B; Cook, P; Feeley, M; Giesy, JP; Hanberg, A;

Hasegawa, R; Kennedy, SW; Kubiak, T; Larsen, JC; van Leeuwen, FX; Liem, AK; Nolt, C; Peterson, RE;

Poellinger, L; Safe, S; Schrenk, D; Tillitt, D; Tysklind, M; Younes, M; Waern, F; Zacharewski, T. (1998) Toxic

equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife. Environ Health Perspect

106(12):775-792.

Yamashita, N; Tanabe, S; Ludwig, JP; Kurita, H; Ludwig, ME; Tatsukawa, R. (1993) Embryonic abnormalities and

organochlorine contamination in Double-crested Cormorants (Phalacrocorax auritus) and Caspian Terns

(Hydropogne caspia) from the upper Great Lakes, collected in 1988. Environ Pollut 79:163-173.

A-1

APPENDIX A

WORKSHOP CASE STUDY

A PRELIMINARY PROBLEM FORMULATION FOR A 

RETROSPECTIVE ECOLOGICAL RISK ASSESSMENT SCENARIO: 

PROPOSED SEDIMENT REMEDIATION ON A MESOTROPHIC/OLIGOTROPHIC 

NORTHERN LAKE

Note: DOI and EPA scientists have created two exercises for the workshop on risk assessments

for mixtures of PCDDs, PCDFs, and PCBs. This scenario provides background

information on a hypothetical lake and presents issues in three areas (stressor

characterization, ecological effects and endpoint selection, and the conceptual model),

with each area using as a starting point information in the U.S. EPA interim report on

TCDD risks to aquatic life and wildlife (U.S. EPA, 1993a), the Water Quality Guidance

for the Great Lakes System (U.S. EPA, 1996; U.S. EPA, 1993b), and the recent WHO

report on proposed TEFs for aquatic life and wildlife (WHO, 1997). The scenario is

intended to promote discussion of the use of proposed TEFs for ecological risk

assessment in general, not for guidance on how to assess either responsibility for or costs

of remediating PCDD, PCDF, and PCB sediment contamination.

Background

Introduction. Scientists have documented a trail of contaminated sediments from the 

Yuckymuck River into Oneofakind Lake in the northern United States. No industrial

development has ever existed in the area; however, a train derailment along the Yuckymuck

River spilled into the water used hydraulic oils in which the major source of PCBs was Aroclor

1248. The only known source of PCDDs is atmospheric deposition. The sources of PCDFs are

atmospheric deposition and the oil spill, in which PCDF was a microcontaminant in the used

Aroclor 1248.

The lake is an important recreational area, supporting a large sport fishery and a variety

of avian and mammalian wildlife on public land along its shores. On the shore of the lake is a

secondary municipal waste treatment plant that previously discharged to the lake. This discharge

led to eutrophication, low dissolved oxygen levels, and anoxic conditions in some locations

within the lake. To correct the problem, the discharge from the plant was diverted out of the

basin, and anoxic conditions are no longer observed. The adjacent land is mostly forested, but no

logging has occurred during the last 30 years. However, previous forestry practices included the

use of DDT for insect control. 

Low doses of 2,3,7,8-TCDD and other Ah receptor (AhR) agonists like PCDDs, PCDFs,

and certain PCBs can significantly affect egg viability and/or the survival of young fish,

mammals, and birds in laboratory tests. Thus, existing contamination from these compounds has

the potential to adversely affect fish and wildlife populations of the lake. In addition, aquatic life

and wildlife populations may be affected by previous DDT use or eutrophication.
A-2

Risk Management Goals. Under the authority provided by the Comprehensive

Environmental Response and Liability Act (CERCLA), EPA and natural resource trustees,

including federal, state, tribal and foreign governments, can assess spill-related releases of toxic

pollutants to the nation's surface waters. To execute this authority, it is necessary to determine

the risks and possible injuries to natural resources from current sediment contamination and to

determine a level of contamination that will be not be detrimental to the fish and wildlife in the

lake. 

This risk assessment will address the anticipated relationship between the amount of

PCBs, PCDFs, and PCDDs in the sediments and the potential effects on fish and wildlife. The

primary risk assessment goal of this exercise is to assess the TCDD-like toxicity of PCDD,

PCDF, and PCBs present in the lake. Other teams are assessing the risks of non-TCDD-like PCB

activity, residual risks associated with remaining DDE concentrations, and previous

eutrophication. If these initial analyses indicate that these compounds are contributing to adverse

effects, then subsequent analyses must establish target sediment concentrations for evaluating

remediation options.

This retrospective assessment will address only the science aspects of the scenario and

will not judge or weigh the actual economic costs or cost-effectiveness of achieving the resulting

cleanup level for dioxin-like compounds. Results of these assessments are routinely factored into

the record of decision by EPA regarding the need for any sediment remediation. Natural resource

trustees, such as the Department of the Interior (through the Fish and Wildlife Service), may

claim damages for residual injuries following cleanup. 

Ecosystem Description. Oneofakind Lake is composed of a large central oligotrophic

basin that has extensive coastal wetlands important to many species (Figure 1). The shoreline has

numerous coves and inlets of small tributaries. The lake also has several islands.

The lake supports a substantial sport fishery, which includes stocked populations of lake

trout and landlocked Atlantic salmon, plus largemouth bass, catfish, crappie, and bluegills. Other

fish species include emerald and spottail shiners, white sucker, lake sturgeon, and carp. Turtles,

snakes, and amphibians are present in littoral zones. The lake is moderately productive, with

diverse phytoplankton and littoral zone emergent and submerged vegetation. There are healthy

communities of pelagic and benthic invertebrates.

Current populations of lake trout are below levels observed prior to the onset of

eutrophication of the lake and the PCB spill. Previous eutrophication and resulting anoxic

conditions were thought to contribute to declines in lake trout populations before the train

derailment. Diversion of the municipal wastewater discharge has resulted in current dissolved

oxygen levels that should be sufficient to support successful recruitment. While populations are

improving, it is not clear if the current population levels reflect continuing, slow recovery from

the previous anoxic conditions or whether additional stressors may be affecting the lake trout.
A-3

The shoreline supports a variety of avian wildlife, including many species of birds that

are primarily piscivorous (e.g., herons and egrets; grebes, cormorants, and various diving ducks;

osprey; several species of gulls and terns) and others that feed heavily on emergent aquatic

insects (e.g., various fly-catching swallows and warblers). Mammals such as the river otter,

muskrat, and mink are found along the shores of some of the coves and tributaries; however, the

diet of the mink is only partly fish from the lake itself. Trappers have reported that otter numbers

have been declining in recent years. The state wildlife manger is currently planning an extensive

population survey to scientifically evaluate these concerns.

Caspian tern populations were depressed in the past because of effects of DDE on

eggshell thinning. Eggshell thicknesses have returned to pre-DDE exposure averages, and tern

populations appear to be recovering as forestry management practices have changed and

concentrations of DDE have decreased. In fact, current DDE concentrations in eggs are thought

to be consistent with successfully reproducing Caspian tern populations. Nonetheless,

recruitment rates are below what is expected for this ecosystem. It is unknown whether this

depressed recruitment reflects a lag in the recovery from DDE exposures or is the result of

exposures to other stressors.

Stressor Characterization for the Northern Lake

The spill of used hydraulic oils in the Yuckymuck River contained PCDFs, which are

formed during use of the hydraulic oil and occur in the parent oil as a microcontaminant. 2,3,7,8-

TCDD and related PCDDs, as well as PCDFs and PCBs, enter the Oneofakind Lake ecosystem

in atmospheric deposition. All these compounds are highly hydrophobic, associate strongly with

organic matter, and distribute primarily into the sediments, suspended solids, and biota of an

aquatic system. This results in low dissolved concentrations of PCBs, PCDFs, and PCDDs in

water. Other chemicals known to contribute to toxic effects through an AhR-mediated mode of

action (e.g., PAHs) are not known to occur in the sediments above trace concentrations. 

Contaminant surveys indicate that PCBs, PCDFs, and PCDDs within Oneofakind Lake

are fairly evenly distributed. Temporal sampling of surficial sediment indicates that the

concentrations of these AhR agonists have decreased since the time of the spill. The loss of these

compounds has occurred primarily through sediment burial. The rate of decay of concentrations

of PCBs, PCDFs, and PCDDs in surficial sediments has followed first-order loss kinetics, and

the current concentrations appear to be nearing a steady state. The concentrations of these

compounds in the Yuckymuck River have also decreased exponentially, and the loading to

Oneofakind Lake is now thought to have attained a fairly constant rate, within the parameters of

river discharge. Because the system is in a quasi-equilibrium state, using BSAFs and BMFs to

estimate concentrations of these compounds in sediments, fish tissues, and wildlife tissue is

assumed to be reasonable. Current concentrations of PCBs, PCDFs, and PCDDs have been

measured in representative samples of sediments, fish, birds, and mammals from Oneofakind

Lake (Tables 1-4).

Ecological Effects and Endpoint Selection for Oneofakind Lake
A-4

Ecological Effects of 2,3,7,8-TCDD. 2,3,7,8-TCDD has been demonstrated in the

laboratory to be highly toxic to fish and to many warm-blooded vertebrates (EPA, 1993a).

Salmonids, mink, and gallinaceous birds are especially sensitive. The most sensitive endpoints

with ecological relevance to population biology are the survival of early life stages of fish and

reproduction in mammals and birds, although “wasting syndrome” can occur in post-fledging

birds and immune function disorders have been associated with this type of ecosystem

contamination. Survival and growth of adult organisms are significantly less sensitive endpoints.

Other aquatic life (aquatic plants, invertebrates, and amphibians) are much more tolerant of

TCDD than fish, mammals, and birds and thus would not be receptors of concern for this risk

assessment. Ecological effects of greatest concern are the survival of fish fry and the

reproductive success of piscivorous wildlife. Particularly low concentrations in sediments and

water are of concern to piscivorous wildlife because of significant biomagnification of TCDD

and related compounds, even though piscivorous wildlife do not necessarily feed exclusively on

the most contaminated fish in the lake.

The TCDD dose-response curves for early life-stage mortality are so steep for fish and

wildlife that it is likely that the difference between the dose producing no effects on populations

and the dose producing severe effects is quite small. For both fish and wildlife, the most

sensitive and most heavily exposed species appear to be at the top of aquatic food webs.

Therefore, this assessment could largely depend on information about effects on individuals.

Because available toxicity information on early life-stage survival is limited to a few species, a

major uncertainty that must be considered is the variability among species and the extrapolation

of available toxicity information to species of interest. 

For piscivorous wildlife, exposures will be based on concentrations in aquatic organisms

in their diet (oral dose to receptor species) or a combination of diet and biomagnification to a

target organ or tissue of the receptor species (e.g., eggs of a fish or bird, livers of mammals). For

some of the wildlife species, especially the piscivorous mammals, some portion of the diet

comes from terrestrial sources or from aquatic animals in the uncontaminated tributaries. To

estimate expected doses, feeding habits and movements must be considered in relationship to the

expected contamination of food organisms. Dose-response relationships for receptor wildlife

species or surrogates can be applied to assess expected effects on individuals and then

extrapolated as appropriate to expected effects on populations. 

Ecological Effects of Related Compounds. A major consideration of this assessment is

the joint behavior and toxic effects of 2,3,7,8-TCDD and other AhR agonists. Comparative

toxicity information is available for the rainbow trout and for some birds and mammals (WHO,

1997). Based on their relative toxicities and relative concentrations in sediments and biota, other

chemicals of significant concern are 1,2,3,7,8 PeCDD; 2,3,7,8-TCDF; 1,2,3,7,8-PeCDF;

2,3,4,7,8-PeCDF; 3,3',4,4'-TeCB (PCB 77); 3,4,4',5-TeCB (PCB 81); 3,3',4,4',5-PeCB (PCB

126); 2,3,3',4,4'-PeCB (PCB 105); and 2,3',4,4',5-PeCB (PCB 118). The pattern of congeners

will vary in sediment, water, and species of biota and may vary in tissues within a species

because of differences in chemical fate, transport, metabolism, bioavailability, bioaccumulation,

and biomagnification.
A-5

Toxic Equivalency Factors (TEFs) appropriate for assessing the toxic potential of

complex mixtures of PCDDs, PCDFs, and PCBs for aquatic life and wildlife are available

(WHO, 1997). The assessment must address the fate and transport of these chemicals and their

expected accumulation in the food chain in addition to their toxic potential relative to TCDD.

The predicted safe tissue concentration of TCDD for each organism is equal to the total TCDD

toxic equivalent concentration (TEQ) of concern for the organism. The TCDD toxicity

equivalency model assumes that each chemical’s dioxin-like toxicity is additive. The major

source of AhR agonists in this lake is the sediments. Interpreting the TEQs of chemicals in the

sediment is complicated by (1) the influence of bioaccumulation and chemical fate and transport

phenomena on the composition of the chemical mixture; (2) the choice of appropriate TEFs; and

(3) the need to relate congener concentrations and possibly TEQs in sediment to concentrations

and TEQs in biota in the contaminated ecosystem. Note that uncertainties regarding the choice of

TEFs for different endpoints and for PCDF, PCDD, and PCB congeners have been discussed

(WHO, 1997).

Assessment Endpoints. Management goals of concern to the involved risk managers are

self-sustaining populations of lake trout, Atlantic salmon, largemouth bass, catfish, crappie, and

bluegills, which are sought by sport anglers; of lake sturgeon, which are speared in the winter;

and of birds and mammals along the shores of the reservoir, which provide important aesthetic

benefits and serve as important ecosystem health indicators. As stated above, invertebrate and

plant populations are of less concern, at this time, because of their demonstrated tolerance to

TCDD in laboratory studies. Assessment endpoints selected by the risk assessors and managers

from the state are the recruitment of lake trout, Caspian terns, and otter in Oneofakind Lake.

Measures of Effect. Measures of effect most relevant to these assessment endpoints are

the effects of TCDD on reproductive success (e.g., egg production and viability) and/or larval

and offspring survival in laboratory tests. Because of the uncertainties in establishing the

bioavailability of TCDD and related compounds in aqueous solutions, measured TCDD

concentrations in food or in the test organisms themselves, as opposed to aqueous TCDD

concentrations, are a more useful metric for expressing and applying dose-response

relationships. 

Risk assessors and managers have previously determined levels of concern in tissues of

fish and wildlife (Table 5). These values incorporate uncertainty in interspecies extrapolation for

2,3,7,8-TCDD and total PCBs for species in this lake, but do not address uncertainties in

applying TEFs across species or endpoints. [Note: The listed levels of concern are based on the

toxicological literature, but they are not to be used or cited outside this exercise.]

Although several studies show that reproduction and/or survival of early life stages is

sensitive to TCDD, data are available for only a small number of species. Consequently, there

are uncertainties in extrapolating measures of effect from tested species to the species of interest

for the assessment endpoints. As stated previously, few toxicity data are available for these

measures of effect with regard to other PCDDs, PCDFs, or PCB congeners.
A-6

Approach for Oneofakind Lake

The foundation for the conceptual model is the tissue residue approach contained in the

U.S. EPA interim TCDD report (U.S. EPA, 1993a). Chemical residues in organs or tissues of

sensitive aquatic organisms exposed to persistent, hydrophobic organic chemicals, such as those

documented in the sediments and biota, are the exposure metrics upon which the estimation of

the potential for adverse effects to the organism must be based. A simplified model for

applications of water/sediment quality criteria or residue-based tissue criteria to establish

sediment remediation conditions for single chemicals is used. This model can be expanded to

consider multiple stressors and enables consideration of populations of multiple species and their

interactions. Models for relating fish populations to chemical dose-toxic response relationships

(Barnthouse et al., 1987) are adaptable to the tissue residue approach used for TCDD and related

compounds. This same approach can be used for assessing avian populations. Since the known,

most sensitive adverse effects of TCDD and related chemicals on fish and birds are directly

attributable to exposure of the embryo, the chemical concentrations found or predicted in eggs

are presently used as the exposure metric of primary interest. In mammalian wildlife assessment,

the toxicity data available at this time for TCDD and related chemicals are primarily on dietary

doses. However, it has been observed that the reproductive toxicity, and in particular

fetotoxicity, and early life-stage mortality of dioxin-like chemicals in mink correlate well with

maternal liver concentrations (Tillitt et al., 1996).

The pathways for PCDD, PCDF and PCB exposures and bioaccumulation in Oneofakind

Lake biota are illustrated in Figure 2. Fish and wildlife exposure in natural systems is expected

to be primarily via contaminated food, and effects are often best referenced to accumulation in

food or in the receptor organism itself. Accumulation in aquatic organisms and the distribution

and bioavailability of contaminants in sediments are of central concern in this assessment.

Concentrations of chemicals in the sediments, suspended solids, and water in various areas of the

lake, and concentrations in aquatic organisms may be estimated using suitable biota-sediment

accumulation factors (BSAFs). Chemical concentrations in wildlife predators can be estimated

with biomagnification models of forage items and target organisms. The relationships among

these compartments may be described through the use of simplified steady-state factors (U.S.

EPA, 1993a; 1995). The concentration of chemicals predicted to be found in whole organisms

can be related to specific tissue concentrations through lipid normalization or a more specific

toxicokinetic model. Another bioaccumulation approach is to use measured or estimated BSAFs

(U.S. EPA, 1995) to relate chemical concentrations in the surface sediments of the organism’s

habitat to the concentrations of TCDD and other dioxin-like chemicals in sensitive organisms.

The BSAF approach has the advantage of using an accumulation factor that can be directly

measured in contaminated ecosystems.

Biomagnification factors (BMFs) were used in this scenario to estimate magnification of

chemicals through food items to target organs/tissues. Various aquatic trophic levels serve as

important and diverse food sources and thus as chemical pathways to wildlife. Individual

congeners have different degrees of biomagnification due in part to varying pharmacokinetics,

feeding rates, and residence time in the contaminated ecosystem. The model for
A-7

(1) j

n

i'1

[(Coc )i (ffl) (BSAFfish )i (TEFfish )i]' TEQfish wet wt.

biomagnification of dioxin-like chemicals from forage fish to the avian target tissue, the egg, and

thus the developing embryo, comes from Braune and Norstrom (1989). This model was applied

to the avian wildlife target species present at Oneofakind Lake, the Caspian tern. The otter was

selected as the model species for the assessment of mammalian wildlife exposure and potential

adverse effects on reproduction, and the BMFs were those for mink (Tillitt et al., 1996).

When the chemical composition of a sediment is characterized, as in this scenario, the

fate/transport, bioaccumulation, and biomagnification models used for TCDD and related

compounds can be used to predict differences in the chemical mixture among target biota and

sediment. If a BSAF approach is used, the concentrations of TCDD and related chemicals of

surface sediments may be related to a TEQtissue associated with the absence of adverse effects in

populations of sensitive species. The equations used in this scenario to model the pathways of

chemical transport between sources and target organisms are given below. There are three

simplified models that describe the expected relationships between chemical concentrations in

the sediment and (1) the chemical concentrations in forage fish (shiner) and the eggs of a top

predator fish species (lake trout eggs); (2) the chemical concentrations in the eggs of a fisheating bird species (Caspian tern); and (3) the chemical concentrations in a target organ of otter

(liver). All these simplified food chain models are linked to the sediments through the use of the

BSAF approach.

Forage fish (shiner) or fish egg (lake trout eggs) concentrations are related to the surficial

sediment concentrations as:

where i represents the ith congener, (Coc)i

 is the organic carbon-normalized concentration of each

congener in the surficial sediments, ffl is the fractional lipid composition of the whole fish

(defined as 3 percent for the shiners) or fish eggs (defined as 5 percent for the lake trout eggs).

The contaminants in the whole fish and their eggs are assumed to be in equilibrium such that the

relative concentrations of congeners are the some in the two tissues, and the absolute

concentrations are directly proportional to the fractional composition of the lipid. The BSAFfish is

the congener-specific biota-sediment accumulation factor and is normalized for both tissue lipid

and organic carbon content of the sediment. The fish-specific TEFs (TEFfish) for each congener

are used to make the final conversion to a summed wet weight fish dioxin toxic equivalent

(TEQfish wet wt.).

The formula that relates the surficial sediment concentrations of PCDDs, PCDFs, and

PCBs to the expected potency of dioxin-like chemicals in the eggs of fish-eating birds nesting

around Oneofakind Lake is: 
A-8

(2) j

n

i'1

[(Coc )i (BSAFfish )i (fbel) (BMFb&egg,l)i (TEFbird )i]' TEQbird egg wet wt.

(3) j

n

i'1

[(Coc )i (BSAFfish )i (fml) (BMFmink,l)i (TEFmammal)i]' TEQmink wet wt.

where f bel is the fractional lipid composition of the bird eggs (defined as 8 percent for Caspian

tern eggs), BMFb-egg, l is the congener-specific, lipid-normalized biomagnification factor between

fish and fish-eating bird eggs, and TEFbird is the bird-specific TEF. The resultant sum of the

congeners is the total dioxin-like potency expected in the bird eggs on a wet weight basis

(TEQbird egg wet wt.).

The model that relates the sediment concentrations of the chemicals to exposure of otter

through their consumption of fish is:

where fml is the fractional composition of the otter liver, (BMFmink, l )i

 is the congener-specific

BMF between fish and the liver (taken from a mink model, Tillitt et al., 1996), and TEFmammal is

the mammal-specific TEF. The resultant sum of the congeners is the total dioxin-like potency

expected in the otter liver on a wet weight basis (TEQmink wet wt.).

The chemical fate and transport model chosen to relate concentrations of PCBs, PCDDs,

and PCDFs in the sediments of Oneofakind Lake to the concentrations in target resource species

was that of BSAFs and BMFs. The dynamic equilibrium conditions this approach presumes are

thought to be reasonable assumptions for Oneofakind Lake and the contamination by these

persistent, hydrophobic chemicals. The BSAFs and BMFs are measures of differences in

bioaccumulation potential for aquatic organisms/tissues for each chemical and allow estimation

of target tissue or organ exposure.
A-9

References

Barnthouse, L.W., G.W. Suter, A.E. Rosen, and J.J. Beauchamp. 1987. Estimating Responses of

Fish Populations to Toxic Contaminants. Environ. Toxicol. Chem. 6:811-824.

Braune, B.M., and R.J. Norstrom. 1989. Dynamics of organochlorine compounds in herring

gulls. III: Tissue distribution and bioaccumulation in Lake Ontario gulls. Environ. Toxicol.

Chem. 8:957-986.

Elonen, G.E., R.L. Spehar; G.W. Holcombe, R.D. Johnson, J.D. Fernandez, J.E. Tietge, and P.M.

Cook. 1997. Comparative toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) to seven

freshwater species during early-life-stage development. Environ. Toxicol. Chem., accepted for

publication.

Ernst, W. 1984. Pesticides and Technical Organic Chemicals. In O. Kinne, ed., Marine

Ecology 5: 4. New York: John Wiley & Sons, pp. 1617-1709.

Giesy, J.P., W.W. Bowerman, M.A. Mora, D.A. Verbrugge, R.A. Outhout, J.L. Newsted, C.L.

Summer, R.J. Aulerich, S.J. Bursian, J.P. Ludwig, G.A. Dawson, T.J. Kubiak, D.A. Best, and

D.E. Tillitt. 1995. Contaminants in fishes from Great Lakes-influenced sections and above dams

of three Michigan rivers. III: Implications for the health of bald eagles. Arch. Environ. Contam.

Toxicol. 29:309-321.

Hendricks, J.D., T.P. Putman, and R.O. Sinnhuber. 1980. Null effect of Dietary Aroclor 1254 on

hepatocellular carcinoma incidence in rainbow trout (Salmo gairdneri) exposed to aflatoxin B1 

as embryos. J. Environ. Toxicol. Pathol. 4:9.

Hoffman, D.J., C.P. Rice, and T.J. Kubiak. 1996. PCBs and dioxins in birds. In W.N. Beyer,

G.H. Heinz, and A.W. Redmon-Norwood, eds. Environmental Contaminants in Wildlife. 

Interpreting Tissue Concentrations. Boca Raton, FL: CRC/Lewis Publishers, pp. 165-208.

Leonards, P. E. G., T.H. de Vries, W. Minnaard, S. Stuijfzand, S., P. de Voogt, N. van Straalen,

and B. van Hattum. 1995. Assessment of experimental data on PCB-induced reproduction

inhibition in mink, based on an isomer and congener-specific approach using 2,3,7,8-

tetrachlorodibenzo-p-dioxin toxic equivalency. Environ. Toxicol. Chem. 14:639-652.

Niimi, A.J. 1983. Biological and toxicological effects of environmental contaminants in fish and

their eggs. Can. J. Fish Aquat. Sci. 40:306-312.

Powell, D.C., R.J. Aulerich, J.C. Meadows, D.E Tillitt, J.P Giesy, K. Stromborg, and S.J.

Bursian. 1996. Effects of 3,3'4,4',5-pentachlorobiphenyl (PCB 126) and 2,3,7,8-

tetrachlorodibenzo-p-dioxin (TCDD) injected into the yolks of chicken (Gallus domesticus) eggs

prior to incubation. Arch. Environ. Contam. Toxicol. 31:404-409.
A-10

Tillitt, D.E., R.W. Gale, J.C. Meadows, J.L. Zajicek, P.H. Peterman, S.N. Heaton, P.D. Jones,

S.J. Bursian, T.J. Kubiak, J.P. Giesy, and R.J. Aulerich. 1996. Dietary exposure of mink to carp

from Saginaw Bay. 3. Characterization of dietary exposure to planar halogenated hydrocarbons,

dioxin-equivalents, and biomagnification. Environ. Sci. Technol. 30(1):283-291.

U.S. EPA. 1980. Ambient Water Quality Criteria for Polychlorinated Biphenyls. U.S.

Environmental Protection Agency. 440/5-80-068. 211 pp.

U.S. EPA. 1993a. Interim Report on Data and Methods for Assessment of 2,3,7,8-

Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and Associated Wildlife. U.S. EPA, Office of

Research and Development, Washington, D.C., EPA/600/R-93/055.

U.S. EPA. 1993b. Great Lakes Water Quality Initiative Criteria Documents for the Protection of

Wildlife (PROPOSED): DDT, Mercury, 2,3,7,8-TCDD, PCBs. U.S. EPA, Washington D.C.,

EPA-822-R-007 (April 1993).

U.S. EPA. 1996. 32 C.F.R. Part 132—Water Quality Guidance for the Great Lakes System. In

Code of Federal Regulations. U.S. Government Printing Office, Washington, D.C.

World Health Organization. 1997. Draft Report on the Meeting on the Derivation of Toxic

Equivalency Factors (TEFs) for PCBs, PCDDs, PCDFs and Other Dioxin-Like Compounds for

Humans and Wildlife. June 15-18, 1997, Stockholm, Sweden.

Wright, P.J., and D.E. Tillitt. 1997. Embryotoxicity of Great Lakes lake trout extracts to

developing rainbow trout. Aquat. Toxicol. (submitted manuscript).
A-11

Figure 1
A-12

Figure 2
A-13

Table 1. Concentrations of PCBs, PCDDs, and PCDFs and Calculated TEQs in Fish from Oneofakind Lake

Sediment

0-10 cm

n=15

dw

Shiners

(whole)

n=25

ww

Lake Trout

Eggs

n=15

ww

Fish

TEF1

consensus

(note 1)

Shiners

(whole)

TEQ1

ww

Lake Trout

Eggs

TEQ1

ww

Fish

TEF2

unrounded

(note 2)

Shiners

(whole)

TEQ2

ww

Lake Trout

Eggs

TEQ2

ww

% organic carbon 3.5%

% lipid 3.0% 5.0%

Total PCB in ng/g 110 345 938

PCBs in pg/g:

77 390 308 992 0.0001 0.031 0.099 0.0002 0.062 0.198

81 11 19 76 0.0005 0.010 0.038 0.0006 0.012 0.046

105 600 5208 11905 <0.000005 0.000 0.000 <0.000005 0.000 0.000

114 88 250 11806 <0.000005 0.000 0.000 <0.000005 0.000 0.000

118 1800 11042 62202 <0.000005 0.000 0.000 <0.000005 0.000 0.000

123 88 125 337 <0.000005 0.000 0.000 <0.000005 0.000 0.000

126 16 79 116 0.005 0.396 0.580 0.005 0.396 0.580

153 1600 19375 12202 na 0.000 0.000 na na na

156 88 625 1389 <0.000005 0.000 0.000 <0.000005 0.000 0.000

157 88 250 516 <0.000005 0.000 0.000 <0.000005 0.000 0.000

167 350 917 1706 <0.000005 0.000 0.000 <0.000005 0.000 0.000

169 2 7 5 0.00005 0.000 0.000 0.00004 0.000 0.000

189 88 83 208 <0.000005 0.000 0.000 <0.000005 0.000 0.000

PCDD in pg/g:

2378-TCDD 1.1 0.13 0.55 1 0.130 0.552 1 0.130 0.552

12378-PCDD 0.8 0.14 0.21 1 0.140 0.207 0.7 0.098 0.145

12478-PCDD 0.8 0.01 0.01 na 0.000 0.000 na 0.000 0.000

123478-HxCDD 0.5 0.01 0.01 0.5 0.005 0.007 0.3 0.003 0.004

123678-HxCDD 2.7 0.09 0.09 0.01 0.001 0.001 0.02 0.002 0.002

123789-HxCDD 1.4 0.02 0.01 0.01 0.000 0.000 0.02 0.000 0.000

1234678-HpCDD 47.0 0.03 0.07 0.001 0.000 0.000 0.002 0.000 0.000

OCDD 369 0.12 0.12 na 0.000 0.000 na na na

PCDF in pg/g:

2378-TCDF 180 0.92 17.66 0.05 0.046 0.883 0.03 0.028 0.530

12378-PCDF 95 0.33 1.76 0.05 0.017 0.088 0.03 0.010 0.053

23478-PCDF 65 0.96 3.27 0.5 0.479 1.637 0.3 0.288 0.982

123478-HxCDF 95 0.13 0.36 0.1 0.013 0.036 0.2 0.026 0.071

123678-HxCDF 25 0.17 0.46 0.1 0.017 0.046 0.1 0.017 0.046

123679-HxCDF 0 0.00 0.00 na 0.000 0.000 0.000 0.000

123789-HxCDF 0 0.02 0.03 0.1 0.002 0.003 0.1 0.002 0.003

234678-HxCDF 10 0.25 0.55 0.1 0.025 0.055 0.1 0.025 0.055

1234678-HpCDF 300 0.17 0.42 0.01 0.002 0.004 0.01 0.002 0.004

1234789-HpCDF 16 0.02 0.03 0.01 0.000 0.000 0.01 0.000 0.000

OCDF 470 0.33 0.36 0.0001 0.000 0.000 na 0.000 0.000

Total TEQ in pg/g 1.3 4.2 1.1 3.3

TEQ from PCBs 0.44 0.72 0.47 0.82

TEQ from PCDDs 0.28 0.77 0.23 0.70

TEQ from PCDFs 0.60 2.75 0.40 1.74

Note 1: TEF1 values are the consensus values for fish from the Draft Report on TEFs by the World Health Organization (1997). Table 3 of that

report describes the endpoint used to develop each TEF, gives the experimentally-determined TEF, and shows the consensus value which is rounded

to the nearest half order of magnitude. Most of the TEFs are based on mortality following egg injections for rainbow trout.

Note 2: TEF2 values are the experimentally-determined values for fish from the Draft Report on TEFs by the World Health Organization (1997). 

Table 3 of that report describes the endpoint used to develop each TEF, gives the experimentally-determined TEF, and shows the consensus value

which is rounded to the nearest half order of magnitude. Most of the TEFs are based on mortality following egg injections for rainbow trout.
A-14

Table 2. Concentrations of PCBs, PCDDs, and PCDFs and Calculated TEQs in Caspian Tern Eggs from Oneofakind Lake

Sediment

0-10 cm

n=15

dw

Shiners

(whole)

n=25

ww

Caspian

Tern Egg

n=12

ww

Bird

TEF1

consensus

(note 1)

Caspian

Tern Egg

TEQ1

ww

Bird

TEF2

unrounded

(note 2)

Caspian

Tern Egg

TEQ2

ww

% organic carbon 3.5%

% lipid 3.0% 8.0%

Total PCB in ng/g 110 345 5667

PCBs in pg/g:

77 390 308 1083 0.05 54.17 0.03 32.50

81 11 19 458 0.1 45.83 0.2 91.67

105 600 5208 182083 0.0001 18.21 0.0001 18.21

114 88 250 11750 0.0001 1.18 0.00008 0.94

118 1800 11042 516667 0.00001 5.17 <.000001 0.00

123 88 125 2292 0.00001 0.02 0.00002 0.05

126 16 79 2750 0.1 275.00 0.1 275.00

153 1600 19375 475000 na 0.00 na 0.00

156 88 625 39042 0.0001 3.90 0.0001 3.90

157 88 250 11208 0.0001 1.12 0.0001 1.12

167 350 917 33417 0.00001 0.33 0.000008 0.27

169 2 7 321 0.001 0.32 0.002 0.64

189 88 83 6292 0.00001 0.06 na 0.00

PCDD in pg/g:

2378-TCDD 1.1 0.13 4.50 1 4.50 1 4.50

12378-PCDD 0.8 0.14 2.00 1 2.00 1.2 2.40

12478-PCDD 0.8 0.01 0.01 na 0.00 na 0.00

123478-HxCDD 0.5 0.01 0.12 0.05 0.01 0.05 0.01

123678-HxCDD 2.7 0.09 2.20 0.01 0.02 0.01 0.02

123789-HxCDD 1.4 0.02 0.17 0.1 0.02 0.1 0.02

1234678-HpCDD 47.0 0.03 0.48 <0.001 0.00 <0.001 0.00

OCDD 369 0.12 9.90 na 0.00 na 0.00

PCDF in pg/g:

2378-TCDF 180 0.92 2.79 1 2.79 1 2.79

12378-PCDF 95 0.33 1.54 0.1 0.15 0.3 0.46

23478-PCDF 65 0.96 9.58 1 9.58 1.1 10.54

123478-HxCDF 95 0.12 2.38 0.1 0.24 0.01 0.02

123678-HxCDF 25 0.17 4.58 0.1 0.46 0.04 0.18

123679-HxCDF 0 0.00 0.00 na 0.00 na 0.00

123789-HxCDF 0 0.02 0.21 0.1 0.02 na 0.00

234678-HxCDF 10 0.25 5.00 0.1 0.50 na 0.00

1234678-HpCDF 300 0.17 4.04 0.001 0.00 na 0.00

1234789-HpCDF 16 0.02 0.46 0.001 0.00 na 0.00

OCDF 470 0.33 1.71 0.0001 0.00 na 0.00

Total TEQ in pg/g 426 445

TEQ from PCBs 405.3 424.3

TEQ from PCDDs 6.5 6.9

TEQ from PCDFs 13.8 14.0

Note 1: TEF1 values are the consensus values for birds from the Draft Report on TEFs by the World Health Organization (1997). Table 4 of that

report describes the endpoint used to develop each TEF, gives the experimentally-determined TEF, and shows the consensus value which is

rounded to the nearest half order of magnitude. Most of the TEFs are based on EROD induction or mortality following egg injections in chickens.

Note 2: TEF2 values are the experimentally-determined values for birds from the Draft Report on TEFs by the World Health Organization (1997). 

Table 4 of that report describes the endpoint used to develop each TEF, gives the experimentally-determined TEF, and shows the consensus value

which is rounded to the nearest half order of magnitude. Most of the TEFs are based on EROD induction or mortality following egg injections in

chickens.
A-15

Table 3. Concentrations of PCBs, PCDDs, and PCDFs and Calculated TEQs in Otter Livers from Oneofakind Lake

Sediment

0-10 cm

n=15

dw

Shiners

(whole)

n=25

ww

Otter

Liver

n=8

ww

Mammal

TEF1

consensus

(note 1)

Otter

Liver

TEQ1

ww

Mammal

TEF2

1994 TEFs

(note 2)

Otter

Liver

TEQ2

ww

% organic carbon 3.5%

% lipid 3.0% 3.0%

Total PCB in ng/g 110 345 1001

PCBs in pg/g:

77 390 308 46 0.0001 0.00 0.0005 0.02

81 11 19 19 0.0001 0.00 0.0001 0.00

105 600 5208 26042 0.0001 2.60 0.0001 2.60

114 88 250 988 0.0005 0.49 0.0005 0.49

118 1800 11042 47479 0.0001 4.75 0.0001 4.75

123 88 125 94 0.0001 0.01 0.0001 0.01

126 16 79 998 0.1 99.75 0.1 99.75

153 1600 19375 0 na 0.00 na 0.00

156 88 625 5391 0.0005 2.70 0.0005 2.70

157 88 250 2756 0.0005 1.38 0.0005 1.38

167 350 917 5225 0.00001 0.05 0.00001 0.05

169 2 7 91 0.01 0.91 0.01 0.91

189 88 83 756 0.0001 0.08 0.0001 0.08

PCDD in pg/g:

2378-TCDD 1.1 0.13 1.43 1 1.43 1 1.43

12378-PCDD 0.8 0.14 0.88 1 0.88 0.5 0.44

12478-PCDD 0.8 0.01 0.00 na 0.00 na 0.00

123478-HxCDD 0.5 0.01 0.00 0.1 0.00 0.1 0.00

123678-HxCDD 2.7 0.09 3.02 0.1 0.30 0.1 0.30

123789-HxCDD 1.4 0.02 0.00 0.1 0.00 0.1 0.00

1234678-HpCDD 47.0 0.03 1.13 0.01 0.01 0.01 0.01

OCDD 369 0.12 7.48 0.0001 0.00 0.001 0.01

PCDF in pg/g:

2378-TCDF 180 0.92 0.00 0.1 0.00 0.1 0.00

12378-PCDF 95 0.33 0.00 0.05 0.00 0.05 0.00

23478-PCDF 65 0.96 51.85 0.5 25.93 0.5 25.93

123478-HxCDF 95 0.12 8.05 0.1 0.81 0.1 0.81

123678-HxCDF 25 0.17 0.00 0.1 0.00 0.1 0.00

123679-HxCDF 0 0.00 0.00 na 0.00 na 0.00

123789-HxCDF 0 0.02 0.00 0.1 0.00 0.1 0.00

234678-HxCDF 10 0.25 18.95 0.1 1.90 0.1 1.90

1234678-HpCDF 300 0.17 4.58 0.01 0.05 0.01 0.05

1234789-HpCDF 16 0.02 0.00 0.01 0.00 0.01 0.00

OCDF 470 0.33 0.00 0.0001 0.00 0.001 0.00

Total TEQ in pg/g 144 144

TEQ from PCBs 112.7 112.7

TEQ from PCDDs 2.6 2.2

TEQ from PCDFs 28.7 28.7

Note 1: TEF1 values are the consensus values for humans and mammals from the Draft Report on TEFs by the World Health Organization

(1997). Table 2 of that report lists the 1994 WHO TEF, describes any new information, and lists the new consensus value which is rounded to the

nearest half order of magnitude. The TEFs are derived from a variety of endpoints, primarily in rodents.

Note 2: TEF2 values are the old (1994) WHO values for humans and mammals from the Draft Report on TEFs by the World Health Organization

(1997). Table 2 of that report lists the 1994 WHO TEF, describes any new information, and lists the new consensus value which is rounded to the

nearest half order of magnitude. The TEFs are derived from a variety of endpoints, primarily in rodents.
A-16

Table 4. Organic Carbon- and Lipid-Normalized Concentrations of PCBs, PCDDs, and PCDFs in

Samples from Oneofakind Lake

Sediment

0-10 cm

n=15

OC weight

Shiners

(whole)

n=25

lipid weight

Lake Trout

Eggs

n=15

lipid weight

Caspian

Tern Egg

n=12

lipid weight

Otter

Liver

n=8

lipid weight

% organic carbon 3.5%

% lipid 3.0% 5.0% 8.0% 3.0%

Total PCB in ng/g 3,100 12,000 19,000 71,000 33,000

PCBs in pg/g:

77 11,000 10,000 20,000 14,000 1,500

81 310 640 1,500 5,700 640

105 17,000 170,000 240,000 2,300,000 870,000

114 2,500 8,300 240,000 150,000 33,000

118 51,000 370,000 1,200,000 6,500,000 1,600,000

123 2,500 4,200 6,700 27,000 3,100

126 460 2,600 2,300 34,000 33,000

153 46,000 650,000 240,000 5,900,000 0

156 2,500 21,000 28,000 490,000 180,000

157 2,500 8,300 10,000 140,000 92,000

167 10,000 31,000 34,000 420,000 170,000

169 50 220 110 4,000 3,000

189 2,500 2,800 4,200 79,000 25,000

PCDD in pg/g:

2378-TCDD 31 4 11 56 48

12378-PCDD 23 5 4 25 29

12478-PCDD 230000

123478-HxCDD 130020

123678-HxCDD 77 3 2 28 100

123789-HxCDD 401020

1234678-HpCDD 1,300 1 1 6 38

OCDD 11,000 4 2 120 250

PCDF in pg/g:

2378-TCDF 5,100 31 350 35 0

12378-PCDF 2,700 11 35 19 0

23478-PCDF 1,900 32 65 120 1,700

123478-HxCDF 2,700 4 7 30 270

123678-HxCDF 710 6 9 57 0

123679-HxCDF 00000

123789-HxCDF 41130

234678-HxCDF 290 8 11 62 630

1234678-HpCDF 8,600 6 8 51 150

1234789-HpCDF 4601160

OCDF 13,000 11 7 21 0
A-17

Table 5. No-effect thresholds (wet weight basis) for embryo mortality (fish and birds) and fetoand early life-stage toxicity (mink) as reference points for the retrospective case study. The

reference value is followed by the range of reported values.

Target 2,3,7,8-TCDD

(pg/g)

Reference Total

PCBs

(ug/g)

Reference

Fish egg 30 (3-3,000) U.S. EPA, 1993;

Elonen et al., 1997;

Wright and Tillitt,

1997

5 (1-20) Ernst, 1984;

Hendricks, 1980; U.S.

EPA 1980; Niimi,

1983

Bird egg 100 (10-100) Giesy et al. 1995;

Hoffman et al., 1996;

Powell et al. 1997

5 (1 - 5) Giesy et al. 1995;

Hoffman et al., 1996;

Powell et al. 1997

Mink liver 60 (10-200) Tillitt et al., 1996;

Leonards et al., 1995

2 (0.1-2) Tillitt et al., 1996;

Leonards et al., 1995

B-1

APPENDIX B

WORKSHOP CASE STUDY

A PRELIMINARY PROBLEM FORMULATION FOR A PROSPECTIVE

ECOLOGICAL RISK ASSESSMENT SCENARIO:

PROPOSED PAPER MILL ON A RIVER FLOWING INTO A LARGE LAKE

IN THE NORTHWESTERN U.S.

Note: EPA scientists have created the attached scenario for the workshop exercises on risk

assessments for mixtures of PCDDs, PCDFs, and PCBs. This scenario provides

background information for a hypothetical lake and presents issues in three areas

(stressor characterization, ecological effects and endpoint selection, and the conceptual

model), with each area using as a starting point information in the U.S. EPA Interim

Report on TCDD risks to aquatic life and wildlife, the Great Lakes Water Quality

Initiative/Guidance, and the recent WHO report on proposed TEFs for aquatic life and

wildlife. The scenario is presented to promote discussion on the use of proposed TEFs for

ecological risk assessment in general. EPA is not asking for guidance on how to assess

the specific risks of PCDD and PCDF discharges from paper mills.

Background

Introduction. A paper mill is proposed on the Roundtail River, 5 km upstream from

Roundtail Lake in the northwestern United States. The large lake constitutes an important

recreational area, as it supports a sizable sport fishery and a variety of avian and mammalian

wildlife despite increased development along much of its privately owned shoreline. The lake is

not a primary source of drinking water. The river drains a vast forested and mountainous area

that is pristine except for areas of intensive logging in some tributary and headwater regions.

Before reaching the lake, the Roundtail River passes through an agricultural region with a

population of approximately 80,000. With the exception of an aluminum processing facility, a

hydroelectric dam on a tributary of the Roundtail River, and a reconstituted wood products mill,

limited industrial development exists in the upstream area; however, three municipal wastewater

treatment facilities supply effluents to the Roundtail River, and a fourth facility discharges into a

smaller river just before it enters the lake. All four municipal facilities recently were added to

tertiary treatment, so nonpoint sources of nutrients now are the primary concern for

eutrophication of the lake. 

Although the proposed mill will use the minimum amount of chlorine necessary in its

bleaching process, the production and discharge of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)

and other chlorinated dibenzo-p-dioxins and dibenzofurans is expected. Under low-flow

conditions in the Roundtail River (21 m3

/sec), the proposed mill will use up to one-fourth of the

water flow. Given higher flows in May and June due to snowmelt and the contribution of other

streams that enter Roundtail Lake, the mill effluent volume will be about 2 percent of the annual

average water input (255 m3/sec) from all sources to the lake. In laboratory tests low doses of

TCDD have significantly affected egg viability and/or the survival of young fish, mammals, and
B-2

birds; therefore the presence of TCDD and associated organic contaminants in the effluent from

this proposed facility could pose a risk to fish and wildlife populations of the lake when added to

existing background levels of PCDDs, PCDFs, and PCBs.

Risk Management Goals. Under the authority provided by the Clean Water Act (307a)

and federal regulations (49 FR 9016), the state is required to control the discharge of toxic

pollutants to the nation's surface waters through the NPDES permit limit process. This mandate

makes it necessary to keep discharges of chemicals of concern from the proposed facility below

a level that is detrimental to the fish and wildlife of the lake. The state, with EPA regional office

approval, has decided to establish a TCDD toxicity equivalence-based total maximum daily

loading (TEqTMDL) to ensure that both human health (via consumption of fish) and fish and

wildlife populations are protected from the cumulative effects of exposure to TCDD and related

chemicals. A TMDL quantifies the maximum allowable loading of a pollutant to a water body

and allocates the loading capacity to contributing point and nonpoint sources, including natural

background, such that water quality standards for the pollutant will be attained. A TMDL must

incorporate a margin of safety that accounts for uncertainty about the relationship between

pollutant loads and water quality. Because of the differences in assessment endpoints and the

uncertainties inherent in comparing risks to humans versus risks to wildlife, there will be an

independent human health risk assessment. 

The state will allocate a percentage of the TEqTMDL to the proposed mill. This

essentially results in a permit to discharge quantities of individual chemicals that when combined

contribute to a total TCDD toxicity equivalence concentration in lake water (TEqCw

t

) that does

not exceed the percentage of the TEqTMDL allocated to the mill. An ecological risk assessment

will address the anticipated relationship between the amount of chlorinated dibenzodioxins and

dibenzofurans discharged and the potential for effects on fish and wildlife. The exposure

assessment will be based on expected steady-state exposure concentrations in the lake under

average annual inputs of water and solids. The effect of dioxin-like compounds, including coplanar PCBs, from other sources in the system is considered in the TEqTMDL process. Existing

dioxin-like compounds in the system were tentatively assumed to account for less than 20

percent of the TEqTMDL, so that most will be reserved for (1) allocation to new sources in the

future; (2) revision of the TEqTMDL based on a determination of increased or decreased risks;

(3) discovery of presently unknown sources of exposure; or (4) new estimates of fish and

wildlife vulnerability to TCDD. Thus, results of the ecological risk assessment will be used to

determine both the water quality standards and the TEqTMDL, which will use total dioxin

toxicity to shape final permit conditions and effluent treatment standards for the mill. 

Based on the initial ecological risk assessment, the state chose to use the TCDD water

quality criteria for protection of avian and mammalian wildlife contained in the Great Lakes

Water Quality Guidance (GLWQG) (U.S. EPA 1995a). The GLWQG does not include a TCDD

water quality criterion for protecting aquatic life, but the state is particularly concerned with

protection of the bull trout, an endangered species. Therefore, the initial ecological risk

assessment determined a water quality standard for protecting bull trout in the Roundtail Lake

and Roundtail River system. To complete the ecological risk assessment, the state wishes to
B-3

evaluate how to best apply the TCDD toxicity equivalence approach to establish the TEqTMDLs

for protection of fish, avian, and mammalian wildlife in the Roundtail ecosystem and to establish

permit conditions for the proposed mill.

The state adopted a plan, based on recommendations of a consultant, for relating the

individual TCDD water quality standards (WQSs) for protecting fish, birds, and mammals to

maximum allowable loads (MALs) to the lake for each Ah-receptor agonist, as if each chemical

were the only contributor to the overall TCDD toxicity equivalence concentration in the lake

water. Sets of MALs for each chemical are to be determined, independently from WQSs for

fish, birds, and mammals, by (1) defining the WQS for TCDD as the total TCDD toxicity

equivalent concentration in water (TEqCw

t

) standard (assumes additivity); (2) converting the

TEqCw

t

 standard to maximum allowable concentrations of each congener in water (MACw

t

 s) by

accounting for differences in toxicity and bioaccumulation potential in comparison to TCDD

(see equation 2); and (3) using a chemical mass balance model, which considers differences in

chemical partitioning fate, and transport and assumes a steady-state condition between the rate of

chemical input to Roundtail Lake and the rate of loss from all loss mechanisms, to relate each

chemical’s MACw

t

 to a MAL (mass/day) for each chemical. The MALs for individual chemicals

can be used to measure the cumulative contribution of each chemical in an effluent to the overall

TEqTMDL for the ecosystem. For example, if the proposed mill’s wasteload allocation is 25

percent of the total TEqTMDL for Roundtail Lake, the sum of each effluent chemical’s daily

load divided by its MAL should not exceed 0.25. Under this system, the mill would be able to

adjust conditions, if necessary, to meet the overall TCDD toxicity equivalence condition rather

than a set of single chemical mass loading limitations. The state proposed that sets of MALs be

calculated for protection of fish, birds, and mammals, regardless of differences in initial

estimates of which set would be most stringent. This approach was intended to facilitate

informed risk management decision-making in the permit setting and monitoring process.

Ecosystem Description. The 482 km2 lake (Figure 1) was formed by retreat of a glacier

12,000 years ago and is composed of a broad central basin. The Roundtail River is the larger of

two tributaries draining a basin of approximately 50,000 km2

. The lake’s shoreline is fairly

regular, but there are several islands, including one very large island that is a designated wildlife

refuge. The maximum depth of the lake is 120 m, the lake water volume is 2.4x1010 m3, and the

mean water retention time is 3 years. The average concentration of total suspended solids in the

open lake waters is 1.2 mg/L, with an organic carbon content of 15 percent. Dissolved organic

carbon concentrations are approximately 2 mg/L. In addition to an annual increase in turbidity

each spring due to snowmelt-induced high river flows, shoreline erosion due to episodes of high

lake water levels causes increased suspended sediment. The average sedimentation rate in the

deep basin of the lake is approximately 0.4 cm/year. Sediments in depositional basins, which

constitute approximately 50 percent of the bottom area, range from 2 to 4 percent organic carbon

on a dry weight basis, with an average of 2.6 percent. Based on an estimated depositional area

sediment mixed layer depth of 5 cm, the volume of the sediment mixed layer is 1.2x107 m3, with

a bulk density of 2.5x105

 mg/L.

The lake has a substantial coldwater fishery that includes introduced species such as
B-4

kokanee salmon, lake trout, rainbow trout, lake whitefish, and yellow perch plus native species

such as bull trout (a charr, like the lake trout, and recently declared a threatened species under

the Endangered Species Act), westslope cutthroat trout, and mountain whitefish. Other fish

species include shiners, suckers, northern squawfish, pigmy whitefish, peamouth, and sculpins.

Turtles, snakes, and amphibians are present in littoral zones. The lake was historically

oligotrophic but now is considered tending toward mesotrophic, with a diverse phytoplankton

assemblage and some littoral vegetation. There are healthy communities of pelagic and benthic

invertebrates, including mysids, which were accidently introduced around 1980 and have now

significantly altered the food web. They have caused a decline in the kokanee salmon population,

which in turn has been associated with the decline of the bull trout population and reduced

growth of larger lake trout.

The shoreline supports a variety of avian wildlife, including several species of birds that

are primarily piscivorous (e.g., osprey, bald eagle, mergansers, and several species of gulls and

terns) and others that feed heavily on emergent aquatic insects (e.g., various fly-catching

swallows and warblers). Because of numerous smaller lakes and streams nearby, the diet of

eagles and ospreys is estimated to be approximately 50 percent fish from the lake and river when

they are seasonally resident in the area. Mammals such as the river otter and mink are found

along the undeveloped shores of the lake and river; however, their diet consists only partly of

fish from the lake itself. As populations of kokanee and bull trout have declined, fish that

migrate to the headwaters of the Roundtail River have become a minor part of the diet of grizzly

bears.

Stressor Characterization for the Northwestern Lake

The effluent from the proposed paper mill will contain unknown amounts of TCDD and

other chlorinated dibenzodioxins and dibenzofurans, which are formed from chlorine bleaching

during paper production. TCDD is highly hydrophobic and associates strongly with organic

matter, distributing primarily into the sediments, suspended solids, and biota of an aquatic

system. This results in low dissolved concentrations of PCDDs and PCDFs in water. The mill

effluent, following the proposed treatment, is predicted to contain 2,3,7,8-TCDD; 1,2,3,7,8-

PeCDD; 1,2,3,4,7,8-HxCDD; 2,3,7,8-TCDF; 1,2,3,7,8-PeCDF; 2,3,4,7,8-PeCDF; and

1,2,3,4,7,8-HxCDF in the relative mass concentration ratios of 1 : 0.1 : 0.5 : 20 : 0.2 : 0.5 : 5,

respectively. Other chemicals known to contribute to toxic effects through an Ah-receptormediated mode of action are not predicted to occur in significant concentrations in the effluent.

However, the role of other chemicals, including certain PCB congeners present in the ecosystem,

in adding to the effect of TCDD, or modifying it through antagonism or synergism, is uncertain.

There are currently no known significant sources of TCDD and related PCDDs and PCDFs to

this lake, and the TCDD level in a few lake trout analyzed was reported to be nondetectable at

1.0 pg TCDD/g wet weight whole fish. Total PCB levels in large lake trout and bull trout (fR

 ~

0.18) are 0.5 to 1.0 Fg/g wet weight with younger adult trout (fR ~ 0.18) at 0.2 Fg/g wet weight.

The only known source of PCBs is from atmospheric inputs to the watershed. Concentrations of

co-planar PCBs in large trout are 1 and 0.2 ng/g wet weight for PCB 77 and PCB 126,

respectively. Kokanee (fR

 ~ 0.08) , whitefish (fR ~ 0.06) , suckers (fR ~ 0.08), and other fish
B-5

consumed by wildlife have co-planar PCB concentrations that are approximately five times

lower than the large trout. In 1996, surface sediment (average foc = 0.026) concentrations of PCB

congeners 77 and 126 measured in the lake’s central basin averaged 0.25 ng/g and 0.003 ng/g,

respectively. The atmospheric flux of PCBs, PCDDs, and PCDFs into the reservoir is unknown.

Finally, local gull egg concentrations of 1 to 3 Fg/g for total PCBs have been reported. There

are no data for concentrations of bioaccumulative organic chemicals in mammalian wildlife

associated with Roundtail Lake. 

The river has no depositional zones that would result in significant loss of a chemical

before it reached the lake, and essentially 100 percent of the discharged PCDDs and PCDFs will

appear as a point source to the lake (Figure 1). Discharge of the PCDDs and PCDFs will be

continuous and is expected to be relatively constant. The use of steady-state exposure conditions

for the risk assessment was justified on the basis of the long water retention time for the lake and

the relative homogeneity of PCB levels in the sediments and biota throughout the lake. 

Additional data and models for fate and transport of hydrophobic organic chemicals are provided

in a recent EPA report on estimating exposure to dioxin-like compounds (U.S. EPA, 1994) and

the WASP4 model user’s manual (Ambrose, 1988). 

Ecological Effects and Endpoint Selection for the Northwestern Lake

Ecological Effects of TCDD. TCDD has been demonstrated in the laboratory to be highly

toxic to fish and to many warm-blooded vertebrates (see Chapter 4 of EPA interim report).

Lethal dose studies show that a variety of fish, mink, and gallinaceous birds are especially

sensitive. The survival of early life stages of fish and reproduction in mammals and birds have

been shown to be the most sensitive endpoints, with survival and growth of older organisms

being significantly less sensitive. Other aquatic life such as plants, invertebrates, and amphibians

have been shown to be much more tolerant to TCDD than fish and thus would not be endpoints

of concern for this risk assessment. Ecological effects of greatest concern are the survival of fish

fry and the reproductive success of piscivorous wildlife. Doses of concern to piscivorous wildlife

are particularly low because of biomagnification of TCDD, although the wildlife will not

necessarily be feeding exclusively on the most contaminated fish in the reservoir.

The TCDD dose/early life-stage mortality response curves for fish are so steep that it is

likely that a narrow range of exposures exists between no effects on populations and severe

effects. For both fish and wildlife, the most sensitive and most heavily exposed species appear to

be at the top of aquatic food webs. Therefore, this assessment could largely depend on

information on effects to individuals. Because available toxicity information on early life-stage

survival is limited to a few species, a major uncertainty that must be considered is the variability

among species and the extrapolation of available toxicity information to species of interest.

For aquatic associated-wildlife, effects will be based on concentrations in aquatic

organisms in their diet. For some of the wildlife species, especially the piscivorous mammals,

some portion of the diet will be from terrestrial sources or from aquatic animals in the
B-6

uncontaminated tributaries. To estimate expected doses, these feeding habits and movements

must be considered in relationship to the expected contamination of food organisms. Doseresponse relationships for receptor wildlife species or surrogates can be applied to assess

expected effects on individuals and then extrapolated as appropriate to expected effects on

populations. As for fish, extrapolations among species of different sensitivities is a major

uncertainty that must be addressed.

Ecological Effects of Related Compounds. A major consideration of this assessment is

the joint behavior and toxic effects of TCDD and other planar chlorinated aromatic organic

chemicals. Comparative toxicity information is available for the rainbow trout (WHO report) and

for some mammals. Based on their relative toxicities and relative concentrations in paper mill

effluents, other chemicals of significant concern are 1,2,3,7,8 PeCDD; 2,3,7,8-TCDF; 1,2,3,7,8-

PeCDF; and 2,3,4,7,8-PeCDF. Relative concentrations in the effluent will not be quantitatively

repeated in residues in aquatic organisms due to differences in chemical fate, transport,

bioavailability, and bioaccumulation. As stated previously, a number of PCB congeners have

also been monitored in the sediments of the central basin and in a number of fish and wildlife

species.

Toxicity equivalence factors (TEFs) appropriate for assessing the toxic potential of

complex mixtures of PCDDs, PCDFs, and PCBs for fish and wildlife are available (WHO, 1997

and references cited). The assessment will have to address the fate and transport of these

chemicals and their expected accumulation in the food chain, in addition to their toxic potential

relative to TCDD. The predicted safe tissue concentration of TCDD for each organism is equal

to the total TCDD toxicity equivalence concentration (TEqC) of concern for the organism under

the TCDD toxicity equivalence model, which assumes that each chemical’s dioxin-like toxicity

is additive. Relating the TEqC to concentrations of chemicals in the effluent is complicated by

(1) the influence of bioaccumulation and chemical fate and transport phenomena on the

composition of the chemical mixture; (2) the choice of appropriate TEFs; and (3) the need to

relate TEqCs to all sources of TCDD and related chemicals in the ecosystem. Note that

uncertainties regarding the choice of TEFs for different endpoints and for PCDF, PCDD, and

PCB congeners has been discussed (WHO, 1997).

Assessment Endpoints. Assessment endpoints of concern are the productivity of fish,

bird, and mammal populations of sensitive species and the survival of individuals of species

listed as threatened under the Endangered Species Act. Risk managers concerned with

establishing water quality standards for protecting fish and wildlife, as required for the TMDL

and paper mill NPDES permit conditions, chose the three most vulnerable species for protection.

The bull trout, as a potentially very sensitive fish species (probably as sensitive as or more

sensitive than lake trout), was chosen because of its status as a threatened species. The bald eagle

and the river otter were the representative bird and mammal species chosen. Invertebrate and

plant populations were determined not to be of concern because of their demonstrated tolerance

to TCDD in laboratory studies. The state, based on the preliminary phase of the ecological risk

assessment, set alternative TCDD water quality standards for protecting each of the three

species. For protecting bald eagle and river otter populations, the WQSs were tentatively set at
B-7

0.028 pg TCDD/L and 0.0032 pg TCDD/L, respectively, using the data and models established

in the GLWQI (U.S. EPA, 1995b,c). After reviewing the preliminary WQSs for wildlife, the

state decided to withhold use of the WQS for protecting the river otter for TMDL and wasteload

allocation determinations pending further studies of the mammal’s resident status in the vicinity

of Roundtail Lake, despite background PCB concentrations in fish that indicated possible

exceedance of the 0.0032 pg/L. The state’s plan, however, does call for continuing to use all

three WQSs as reference points for assessing the overall ecological risks to be predicted on the

basis of future management decisions and data from monitoring programs.

A tissue residue value of 9 pg TCDD/g wet whole spawning fish was chosen for

protection of bull trout populations based on an assumption that bull trout and lake trout have a

similar sensitivity for early life-stage mortality, a TCDD concentration ratio between whole

female and egg of 3, and the need for an interspecies extrapolation uncertainty safety factor of 10

to ensure protection of a threatened species under the Endangered Species Act. Using the lipidnormalized bioaccumulation factor for freely dissolved TCDD (BAFR

fd = 9 x 106

) developed in

the GLWQG and used for the wildlife WQC, a fraction lipid in female bull trout of 0.18, and an

estimated fraction of TCDD freely dissolved (ffd) in Roundtail Lake of 0.26, a WQS for

protection of bull trout was determined to be 0.021 pg total TCDD/L.

Measures of Effect. Measures of effect that are most relevant to these assessment

endpoints are the effects that TCDD has on reproductive success (e.g., egg production and

viability) and/or larval and offspring survival in laboratory tests. Because of the uncertainties in

establishing the bioavailability of TCDD and related compounds in aqueous solutions, measured

TCDD concentrations in food or in the test organisms themselves, are a more useful metric for

expressing and applying dose-response relationships. 

Although several studies show that reproduction and/or survival of early life stages is

sensitive to TCDD, data are available for only a small number of species. Consequently, there

are uncertainties in extrapolating measures of effect from tested species to the species of interest

for the assessment endpoints. As stated previously, there are limited toxicity data available for

these measurement endpoints with regard to other dibenzodioxins, dibenzofurans, or PCB

congeners.

Conceptual Model for the Northwestern Lake

The foundation for the conceptual model is the tissue residue approach contained in the

U.S. EPA interim TCDD report (U.S. EPA, 1993). Chemical residues in tissues of sensitive

aquatic organisms exposed to persistent, hydrophobic organic chemicals, such as those predicted

for the paper mill effluent and that currently exist in the sediments and biota, are the exposure

metrics upon which the estimate of the potential for adverse effects to the organism must be

based. In this case, Figure 2 shows the logical flow of assessment information when thresholds

for adverse ecological effects or goals for protecting fish and wildlife populations are to be

related to safe chemical loadings to the ecosystem. This is a typical conceptual model for

applying water quality criteria to the establishment of effluent permit conditions for single
B-8

chemicals, except that this model can be expanded to consider multiple stressors and enable

consideration of populations of multiple species and their interactions. Models for relating fish

populations to chemical dose-toxic response relationships (Barnthouse et al., 1987) are adaptable

to the tissue residue approach used for TCDD. The boxes in this conceptual model represent

assessment endpoints, which are generally quantitative. The arrows between boxes are specific

types of models used to interrelate the assessment endpoints. All the models are reversible;

hence the two-way arrows. The conceptual model applies equally to assessments that seek to

determine risks associated with a known or predicted chemical loading (right to left flow of

steps).

Figure 2 shows effects on aquatic organisms linked to exposure levels through chemical

residue-based dosimetry. The same approach could be used for wildlife assessment; however, the

toxicity data available for TCDD and related chemicals at this time relate primarily to dietary

dose. Since the known adverse effects of TCDD and related chemicals for fish are directly

attributable to exposure of the embryo, the chemical residue levels in eggs is presently the

exposure metric of primary interest. If, for example, male fertility should be determined to be a

more sensitive endpoint, chemical residue levels in the testes might become an important

exposure assessment endpoint. Care must be taken to ensure that appropriate exposure and

bioaccumulation models are chosen for relating the residue of concern in the tissue of each

aquatic species to chemical concentrations in the water and sediment of the region they inhabit.

Figure 3 illustrates the pathways for PCDD, PCDF and PCB exposures and

bioaccumulation in Roundtail Lake biota. Fish and wildlife exposure in natural systems is

expected to be primarily via contaminated food, and effects are often best referenced to

accumulation in food or in the receptor organism itself. Bioaccumulation in aquatic organisms,

and the distribution and bioavailability of contaminants in water and sediments, will therefore be

of central concern in this assessment. Based on predicted concentrations of chemicals in the

sediments, suspended solids, and water in the reservoir, concentrations in aquatic organisms

must be estimated using suitable bioaccumulation models. This can be accomplished via a food

chain model, such as that of Thomann et al. (1992) shown in Figure 4, or application of

bioaccumulation factors. Bioaccumulation factors between fish and water (BAFs) are discussed

in Chapter 3 of the interim report (U.S. EPA, 1993), and BAFs for specific PCDD, PCDF, and

PCB congeners are provided in the Great Lakes Water Quality Initiative Technical Support

Document for the Procedure to Determine Bioaccumulation Factors (U.S. EPA, 1995d). The

state set water quality standards by using GLWQG BAFs to provide the essential link between

concentrations of concern in fish tissue and concentrations of prospective concern in water. An

alternative bioaccumulation approach is to use measured biota-sediment accumulation factors

(BSAF, see section 1.E of U.S. EPA, 1995d, to calculate site-specific BAFs). The BSAF

approach has the advantage of using an accumulation factor that can be directly measured in

contaminated ecosystems but requires measurement of BAFs for a few reference congeners, such

as PCB congeners that can be detected in water.

When individual chemical masses in an effluent are to be related to a TCDD water

standard through a TCDD toxicity equivalence approach, as in this scenario, TEFs must be used
B-9

j

n

i'1

[(C fd

w )i (BAF fd

R, tissue )i (fR) (TEFtissue )i

] ' TEqCtissue (1)

(C t

w)tcdd / TEqC tw ' j

n

i'1

[(C t

w)i (BAF t

R, tissue )i (TEFtissue )i

]

(BAF t

R,tissue )tcdd

(2)

Jsocw ' Csoc

C fd

w

' BAF fd

R

BSAF (3)

to predict differences in toxicity; food chain models or BAFs must be used to predict differences

in bioaccumulation, including the impact of widely varying degrees of metabolism for different

congeners; and chemical fate and transport models must be used to predict chemical mass

distribution differences between the water and sediments and the effluent. If a lipid-normalized

bioaccumulation factor approach (BAFR

fd) is used, the freely dissolved (bioavailable)

concentration of each chemical in water (Cw

f d)I

 may be related to the TCDD toxicity equivalence

concentration in tissue (TEqCtissue):

Alternatively, the state water quality standards for fish, birds, and mammals, expressed as

concentrations of total TCDD in water, (Cw

t

)tcdd, may be defined as the corresponding TCDD

toxicity equivalence concentrations for total TCDD in water (TEqCw

t

), which may be calculated

on the basis of n congeners contributing to the (TEqCw

t

):

Note that equation 2 can be transformed to an expression based on freely dissolved chemical

concentrations in water through use of ffds for each chemical. Also note that each chemical’s

contribution to the TEqCw

t

 is a function of its bioaccumulation potential relative to that of TCDD,

just as the TEF represents toxicity potency relative to TCDD. It is likely that BAFR

fd ratios

measured for fish in one ecosystem are more universal across sites and conditions than the

individual BAFR

fds, which may vary somewhat depending on site conditions such as food chain

structure and sediment-water distribution of the chemical. Sediment-based analogues of

equations 1 and 2 can be used for sediment criteria by using biota sediment accumulation factors

(BSAFs). BAFR

fds and BSAFs are related to each other through the sediment organic carbonwater (freely dissolved) chemical concentration quotient (Jsocw):

The chemical fate and transport model chosen to relate steady-state concentrations of

PCDDs and PCDFs in water to mass loadings from the effluent will determine differences in

chemical mixture composition and mass distribution between water, sediment, and effluent.

BAFR

fds (or BSAFs) provide measures of differences in bioaccumulation potential of each

chemical for each whole organism or specific tissue (such as eggs for fish) and are influenced by

the distribution of the chemical between water and sediment in the ecosystem. These
B-10

relationships are to be considered on the basis of a steady-state chemical distribution model for

the purpose of determining the mill effluent permit conditions that will assure a mill contribution

less than or equal to the allocated percentage of the TEqTMDL for Roundtail Lake, based on

consideration of the individual WQCs for protection of bull trout as a threatened species under

the ESA, and eagle and otter populations. Figure 5 provides a process diagram for wasteload

allocation in association with establishing an NPDES permit for the proposed paper mill on the

Roundtail River. Individual wasteload allocations are calculated for protection of fish (j=1),

birds (j=2), and mammals (j=3) based on TEFs, BAFs, and mass balance relationships for i

chemicals that may contribute to the (TEqCw

t

)js. You are invited to evaluate and comment on the

strengths and weaknesses of the state’s application of TEFs in this scenario and to recommend

improvements or alternative approaches, if applicable. The general and scenario-specific

questions are provided to further stimulate your evaluation. 
B-11

References:

Ambrose, R.B., T.A. Wood, J.P. Connolly, and R. W. Schanz. 1988. WASP4, A Hydrodynamic

and Water Quality Model. Model User’s Manual and Programmer’s Guide. EPA/600/3-87/039.

U.S. EPA, Office of Research and Development.

Barnthouse, L.W., G.W. Suter, II, A.E. Rosen, and J.J. Beauchamp. 1987. Estimating responses

of fish populations to toxic contaminants. Environ. Toxicol. Chem. 6:811-824.

Thomann, R.V., J.P. Connolly, and T.F. Parkerton. 1992. An equilibrium model of organic

chemical distribution in aquatic food chains. Environ. Sci. Technol. 23:699-707.

U.S. EPA. 1995a. Final Water Quality Guidance for the Great Lakes System; Final Rule.

60FR15366 (Federal Register), pp. 15366-15425.

U.S. EPA. 1995b. Great Lakes Water Quality Initiative Criteria Documents for the Protection of

Wildlife. EPA/820/B-95/008. Office of Water, Washington, D.C.

U.S. EPA. 1995c. Great Lakes Water Quality Initiative Technical Support Document for

Wildlife Criteria. EPA/820/B-95/009. Office of Water, Washington, D.C.

U.S. EPA. 1995d. Great Lakes Water Quality Initiative Technical Support Document for the

Procedure to Determine Bioaccumulation Factors. EPA/820/B-95/005. Office of Water,

Washington, D.C.

U.S. EPA. 1996. Proposed Guidelines for Ecological Risk Assessment. EPA/630/R-95/002B.

Risk Assessment Forum, Washington D.C. 

U.S. EPA. 1993. Interim Report on Data and Methods for Assessment of 2,3,7,8-

tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and Associated Wildlife. EPA/600/R-93/055.

Office of Research and Development, Washington, D.C.

U.S. EPA. 1992. Framework for Ecological Risk Assessment. EPA/630/R-92/001. Risk

Assessment Forum, Washington, D.C.
B-12

Figure 1
B-13

Toxicity

Hazard

Demographic

and

Social Impacts

Reproduction

Cancer

Imunotox.

Neurotox. etcReproduction

Age-sp. Mortal.

Pred. Vulner.

Disease Resist.

Population

Changes

Residues in

Aquatic

Organisms

Population

Changes

Reproduction

Age-sp. Mortal.

Pred. Vulner.

Disease Resist.

Wildlife

Exposure

Human

Exposure

Chemical 

Loading

Community

Structure and

Function

Toxicity

Hazard

Exposure

BAF

Toxicity

Hazard

Population

Model

Population

Model

Population

Model

Chemica

Fate 

Reproduction

Cancer

Imunotox.

Neurotox. etc.

Food

Water

Sediment

Food ConsumptionFood Consumption

Ecosystem

Model

Risks Hazards Exposures

Conceptual Model for Risk Assessments and Criteria Development Involving Determination of Safe Loadings of Bioaccumulative Chemicals to Aquatic Systems

Figure 2
B-14

Dissolved 

Particulate

Zooplankton

Insectivorous

Birds

Piscivorous

Birds

Mam

Benthic

Invertebrates

System

Inputs

Oth

Pathways of TCDD Accumulation in Roundtail Lake

Phytoplankton

Dissolved

Particulate

Piscivorous

Fish

Forage

Fish

Figure 3
B-15

Figure 4
B-16

STD(Ct )tcdd,j ≡ STD(TEqCt )j w w = w t t

(Ct ) w i,j =Convert each (MACt )i,j to a MALi,j using a system-level mass balance model

S [(Ct )i,j(TEF)i,j(BAFl )i,1 /(BAFl )tcdd,1]

i 1

n

=

∑

•

≤   w allocation M ALi j, 1.0

TCDD-based WQ STD for 

jth Biota Group

STD(Ct )tcdd, j

TCDD Toxicity Eq. Conc.-based 

WQ STD for jth Biota Group 

STD(TEqCt )j

Max. Allowable Conc. of

Each Specific Congener for

jth Biota Group

(MACt )i,j

Max. Allowable Load for 

Each Specific Congener 

MALi,j

The Basic Relationship:

i = congener j = biota group (1 = fish, 2 = birds, 3 = mammals)

Statement of TCDD Toxicity Equivalent Additivity Model

n

i=1

w

w

w

w

Form AllocateMatrix Fraction [MALi,j] fallocation ● [MALi,j] Permit Condition

TEq TMDL

The effluent must meet the permit condition:

Figure 5. Process Diagram for Prospective Waste Load Allocation

Where wi=projected load of ith congener

w

MALi,j

i

fallocation

(TEF)i,j(BAFl )i,1 t

(BAFl ) STD tcdd,1 (TEqCt ) w j t
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APPENDIX C

REPORT

from the

WORKSHOP ON THE APPLICATION OF

2,3,7,8-TCDD TOXICITY EQUIVALENCY FACTORS

TO FISH AND WILDLIFE

Chicago Hilton & Towers

Chicago, Illinois

January 20-22, 1998

Submitted to:

Risk Assessment Forum

U.S. Environmental Protection Agency

401 M Street, SW

Washington, DC 20460

Submitted by:

Eastern Research Group, Inc.

110 Hartwell Avenue

Lexington, MA 02173-3134

March 31, 1998
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NOTE

This report was prepared by Eastern Research Group, Inc., an EPA contractor, as a

general record of discussion during the workshop. As requested by EPA, this report captures the

main points of scheduled presentations, highlights from the group discussion, and a summary of

comments offered by observers attending the workshop; the report is not a complete record of all

details discussed, nor does it embellish, interpret, or enlarge upon matters that were incomplete

or unclear. This report will be used by EPA as a basis for additional study and work on the

application of toxicity equivalency factors (TEFs) in ecological risk assessments. Except as

specifically noted, none of the statements in this report represent analyses or positions of EPA.
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TABLE OF CONTENTS

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-iv 

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

II. Opening Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-2

Synopsis of the WHO Stockholm Meeting . . . . . . . . . . . . . . . . . . . . . C-3

Martin van den Berg, Chair of the WHO Working

Group on TEFs

Overview of the Retrospective Case Study . . . . . . . . . . . . . . . . . . . . . C-9

Donald Tillitt, EPA/DOI Planning Group

Overview of the Prospective Case Study . . . . . . . . . . . . . . . . . . . . . . C-12

Steven Bradbury, EPA/DOI Planning Group

Workshop Structure/Summary of Premeeting Comments . . . . . . . . . C-18

Charles Menzie, Workshop Chair

Observer Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-25

III. Workshop Proceedings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-27

Review of the Total Maximum 

Daily Load (TMDL) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-27

Plenary Session: Discussion of the 

Prospective Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-31

Plenary Session: Discussion of the 

Retrospective Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-43

IV. Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-60

Appendices

A. Workshop Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-A-1

 B. Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-B-1

C. Premeeting Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-C-1

D. Detailed Summaries of Expertise Group Discussions . . . . . . . . . . . C-D-1

E. Detailed Summaries of Case Study Discussions . . . . . . . . . . . . . . . C-E-1

F. Written Comments from Observers . . . . . . . . . . . . . . . . . . . . . . . . . C-F-1
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PREFACE

This workshop was developed by a joint planning group from the U.S. Environmental Protection

Agency (EPA) and the U.S. Department of Interior under the aegis of EPA's Risk Assessment

Forum. One role that the Risk Assessment Forum plays within EPA is to promote consensus on

risk assessment issues and to ensure that this consensus is incorporated into appropriate Agency

risk assessment guidance. In the past, the Forum has issued guidance on the use of toxicity

equivalency factors (TEFs) for assessing the human health risks associated with exposures to

complex mixtures of chlorinated dibenzo-p-dioxins and dibenzofurans (EPA/625/3-87/012 and

EPA/625/3-89/016). This workshop was convened to examine the applicability of recently

developed World Health Organization TEFs for assessing risks to fish and wildlife from

polychlorinated dioxins, furans, and biphenyls.
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I. INTRODUCTION

Many individual members of the family of chemicals known as polyhalogenated aromatic

hydrocarbons have been shown to produce toxic effects that are similar to those associated with

exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Among the classes of environmental

contaminants falling into this general category are polychlorinated biphenyls (PCBs),

dibenzofurans (PCDFs), and dibenzo-p-dioxins (PCDDs), all of which are believed to exert their

toxic effects at least in part as a result of their binding to the aryl hydrocarbon receptor (AhR). 

Based both on their mechanistic similarity to TCDD and on the fact that these chemicals

often exist as complex mixtures in the environment, efforts have been made to derive toxicity

equivalency factors (TEFs) that can be used to express the toxicity of individual PCB, PCDF,

and PCDD congeners relative to the toxicity of TCDD. In two previous workshops, convened by

the World Health Organization (WHO) in August 1996 and June 1997, scientific experts

reviewed the available relative potency data and developed consensus TEF values for use in risk

assessments involving dioxin-like compounds. In addition to updating the existing mammalian

TEFs, the WHO group developed consensus TEFs for birds and fish.

To examine issues associated with the application of TEFs and the related toxicity

equivalents (TEQs) to ecological risk assessments, Eastern Research Group, Inc. (ERG), in

consultation with the U.S. Environmental Protection Agency (EPA) and the U.S. Department of

the Interior (DOI), assembled a group of experts to consider two hypothetical case studies: a

prospective case study involving a risk assessment for a hypothetical point source requiring a

water quality permit and a retrospective case study focusing on a hypothetical freshwater

ecosystem in which reproductive effects have been observed and a remediation effort is being

considered. 
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II. OPENING PRESENTATIONS

To begin the workshop, Dr. Menzie introduced Ms. Christine Boivin, who welcomed

workshop participants on behalf of EPA's Risk Assessment Forum, and Mr. John Blankenship,

who extended a welcome on behalf of the DOI Fish and Wildlife Service. Following these

introductions, Dr. Menzie provided an overview of the overarching goal of the workshop, which

he described as exploring the extent to which a TEF/TEQ approach can be used in risk

assessments that have progressed beyond the screening stage. As such, the focus of the

workshop would be on the application and use of this particular tool rather than on the broader

range of issues associated with the performance of ecological risk assessments. During the

course of discussions, the group would attempt to identify, document, and compare the

uncertainties associated with the derivation of individual TEF values—including both the

uncertainties related to statistical variability and those related to a lack of knowledge—and to

assess the impact of these uncertainties on ecological risk assessments.

Noting that risk assessment almost by definition occupies a position at the interface between

science and policy, Dr. Menzie indicated that it would be most useful for discussions to remain

as focused as possible on the more technical implications of gaps in the TEF knowledge base. 

Thus, he anticipated that discussions over the next few days would center on issues such as the

relative contribution of TEF-related uncertainties to the overall uncertainty of an ecological risk

assessment, additional data requirements and analytical support that might be needed to

implement a TEF approach as opposed to other approaches that might be considered, and the

ability and/or need to support a TEF approach with other lines of evidence. To consider these

and related issues in a real-world context, workshop participants would be asked to apply the

TEF/TEQ methodology to the two case studies developed by the EPA/DOI Planning Group. For

each of these cases, the goal would be to see how application of the TEF/TEQ methodology

might impact the uncertainties associated with the exposure assessment, the effects assessment,

and the overall characterization of risk.

Following Dr. Menzie's opening remarks, the experts heard a series of formal presentations

designed to establish a common frame of reference for subsequent discussions. Brief summaries
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of these presentations are provided below.

Synopsis of the WHO Stockholm Meeting

Dr. Martin van den Berg, Chair of the WHO Working Group on TEFs

Dr. van den Berg began by noting that his presentation would provide an overview of the

issues addressed and the decisions agreed to at the WHO-sponsored meeting on the derivation of

TEFs for dioxin-like compounds in humans and wildlife, which was held in Stockholm, Sweden,

in June of 1997. In contrast with earlier TEF meetings, which had addressed only mammalian

and human TEFs, the Stockholm meeting also undertook an evaluation of TEFs for birds, fish,

and wild mammals. Participants included approximately two dozen experts in wildlife

toxicology and/or in the laboratory determination of TEFs, including several of the experts and

Planning Group members who are also participating in this workshop. The Stockholm meeting

was divided into two sessions—one dealing with human and mammalian TEFs derived from

laboratory experiments, and the other dealing with TEFs for fish and birds. The

human/mammalian session was chaired by Dr. Linda Birnbaum, who is a member of the

EPA/DOI Planning Group, and the wildlife session was chaired by Dr. Richard Peterson, who is

one of the experts at this meeting. Rapporteurs were Drs. Mark Feeley and Sean Kennedy, who

is also an expert at this meeting. Dr. van den Berg served as organizer and overall Chair of the

Stockholm meeting.

Prior to the Stockholm meeting, criteria for including a compound in the WHO TEF scheme had

already been established. To be included in the TEF scheme, a compound must:

• be structurally related to PCDDs and PCDFs;

• bind to the Ah receptor;

• elicit dioxin-specific biochemical and toxic responses; and

• be persistent and accumulate in the food chain.

In its deliberations, the WHO group discriminated between TEFs and relative effect potencies, or

REPs. As defined by WHO, a TEF is an order-of-magnitude estimate of the toxicity of a
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compound relative to the toxicity of TCDD that is derived using careful scientific judgment after

considering all available data. An REP, in contrast, is derived from the results of a single in vivo

or in vitro study, which may be either a biochemical or a toxicological study.

In preparation for the Stockholm meeting, the Karolinska Institute assembled a database

containing the results of thousands of published studies comparing the biochemical or

toxicologic profiles of individual congeners with a reference compound (either TCDD or PCB

126). When PCB 126 was used as the reference compound, a REP of 0.1 was assumed. To be

included in the database, a published study had to meet the following three criteria: 

 • At least one PCDD, PCDF, or PCB congener and a reference compound must be

included in the study.

• The reference compound and the congener(s) must be included in the same

experiment or studied with the same experimental design and by the same authors in

separate experiments.

• The relevant endpoint should be affected by the congener as well as the reference

compound.

Regarding the determination of relative potency values for inclusion in the Karolinska database,

Dr. van den Berg indicated that there were several methods used. If a relative potency value was

reported in a published study, that REP was included in the database without modification. If no

REP was reported, one could be derived by any of the following methods:

• calculated by comparing dose-response curves or by using linear interpolation of log

doses, comparing the same effect level;

• determined from the ratio of reported ED50, LD50, or EC50 values; or

• calculated from tumor promotion indices, Kd values for Ah receptor binding, or

directly estimated from graphs.

The Karolinska database is now part of the public domain and can be accessed by anyone who

applies to use it at the WHO European Center of Environmental Health.

Based on the wide range of REPs reported in the literature, workgroups at the Stockholm

meeting proposed human, wild mammal, bird, and fish TEFs for each individual congener. 
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These proposed values were then the subject of extensive discussion during a plenary session,

and on the last day of the meeting consensus values were derived for each compound.

Turning specifically to the derivation of the human and mammalian TEFs , Dr. van den Berg

noted that meeting participants decided that there was no scientific reason to assign TEFs for

wild mammals that would differ from those derived for humans and laboratory mammals. He

then outlined the criteria used to weight different types of experimental data. In evaluating

toxicity data, meeting participants agreed that in vivo data should be given precedence over in

vitro data, which in turn should be given precedence over data from quantitative structureactivity relationship (QSAR) studies. When more than one in vivo study was available, those

involving chronic exposures were given the highest priority, and progressively lower priority

was given to those involving subchronic, subacute, and acute exposure scenarios. Among

studies using Ah receptor endpoints, toxicity studies were given greater weight than biochemical

studies.

Because mammalian TEFs had previously been assigned by WHO on the basis of work done

by Ahlborg et al. in 1994, participants at the Stockholm meeting had to decide under what

conditions they would incorporate an existing TEF into their scheme. They agreed that if the

available information was insufficient to warrant a change, the existing TEF value for PCDDs,

PCDFs, and PCBs would be adopted. The major changes to existing mammalian TEFs agreed to

at the Stockholm meeting are summarized in Figure 1. Notably, meeting participants agreed that

the di-ortho PCBs, which were assigned TEF values in the earlier WHO effort, should no longer

be included in the TEF scheme. This decision was based on the fact that in vivo data, which

includes both enzyme induction and reproduction studies, do not support the in vitro

observations upon which the initial TEF values were based.
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REVISED MAMMALIAN TEFs

Congener Old TEF New TEF Explanation of Change

1,2,3,7,8-

PeCDD 0.5 1 CYP1A1/A2, tumor promotion

OCDD 0.001 0.0001 misinterpretation of earlier data; exposure

versus tissue concentration

OCDF 0.001 0.0001 similarity to OCDD

PCB 77 0.0005 0.0001 EROD induction

PCB 81 -- 0.0001 similarity to PCB 77

PCB 170 0.0001 -- in vivo data (CYP1A1, repro) do not

support in vitro observations

PCB 180 0.0001 -- in vivo data (CYP1A1, repro) do not

support in vitro observations

Figure 1.

In evaluating the data for fish and birds, the WHO groups used a four-tier approach. In

decreasing priority, the tiers were:

• Tier 1: overt toxicity observed in developing embryos (endpoint = LD50);

• Tier 2: biochemical effects observed in developing embryos (endpoint = CYP1A);

• Tier 3: biochemical effects observed in in vitro systems (endpoint = CYP1A); and

• Tier 4: estimates from QSAR studies.

To simplify matters for risk assessment and management purposes, participants at the WHO

meeting attempted to harmonize the TEFs across the different taxonomic categories. This was

not possible, however, because of clear taxonomic differences in the effects of various

congeners. As an example of these differences, Dr. van den Berg mentioned the responses of

fish and mammals to mono-ortho PCBs.

Another aspect of the harmonization effort involved a decision about whether to report the

consensus TEFs as distinct individual values or to round them as had been done previously. For
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conformity with the existing TEF values, some of which were adopted by the WHO, new TEFs

were rounded to a value of either 1 or 5. In this rounding procedure, Dr. van den Berg said that a

conservative approach was used to provide optimal protection of fish and wildlife.

The consensus TEFs for dioxins, furans, non-ortho PCBs, and mono-ortho PCBs are listed in

Figure 2. In general, fish and birds tend to be less sensitive to hexachloro- and

heptachlorodioxins than are mammals, but there were not enough data to determine the relative

sensitivity of either fish or birds to octachlorodioxins. The most notable taxonomic distinction

for the dibenzofurans is the generally greater sensitivity of birds than either fish or mammals to

TCDF and the two pentachlorodifurans. Among the planar PCBs, birds tended to be more

sensitive than fish, particularly to PCBs 81 and 126. However, PCB 169 was less toxic to fish

and birds than to mammals. For the mono-ortho PCBs, the group felt that it was not possible to

establish TEFs for fish; to accommodate the fact that some regulatory agencies might require

some number to be used, the group decided to assign an upper limit value to the TEFs for fish. 

In most cases, these compounds were also determined to be slightly less toxic to birds than to

mammals.
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WHO CONSENSUS TEFs FOR MAMMALS, FISH, AND BIRDS

HUMANS/

MAMMALS

FISH BIRDS

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 0.5 0.05

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

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

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

OCDD 0.0001 - - 

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

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

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

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 0.0001 0.0001 0.0001

3,4,4',5-TCB (81) 0.0001 0.0005 0.1

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

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

3,3',4,4',5,5'-HxCB (169) 0.01 0.00005 0.001

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

2,3,4,4',5-PeCB (114) 0.0005 <0.000005 0.0001

2,3',4,4',5-PeCB (118) 0.0001 <0.000005 0.00001

2',3,4,4',5-PeCB (123) 0.0001 <0.000005 0.00001

2,3,3',4,4',5-HxCB (156) 0.0005 <0.000005 0.0001

2,3,3',4,4',5'-HxCB (157) 0.0005 <0.000005 0.0001

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

2,3,3',4,4',5,5'-HpCB (189) 0.0001 <0.000005 0.00001

Figure 2.
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Overview of the Retrospective Case Study

Dr. Donald Tillitt, EPA/DOI Planning Group

Dr. Tillitt began his presentation by thanking the experts for the excellent job they did in the

premeeting comments they had submitted prior to the workshop. The goal of the workshop

exercises, he said, was to apply the TEF methodology to a couple of hypothetical cases that are

broadly representative of situations in which the method might be applied, and in so doing to

gain a more complete understanding of the strengths and weaknesses of the approach. 

Dr. Tillitt acknowledged, as some of the experts had pointed out in their premeeting

comments, that the retrospective case study was not a true risk assessment, in that it did not

address the full range of stressors on the system of interest. This limited focus was intentional,

however, as the Planning Group had tried to confine its description of the case only to those

elements that might be relevant to use of the TEF methodology. For the same reason, the

Planning Group had provided a detailed description of mechanisms involved in the transfer of

contaminants up the food chain. By establishing this type of information at the outset, the

Planning Group hoped to steer participants away from discussions about what the correct values

might be so that they could focus more directly on issues associated with application of the TEF

methodology.

The site for the retrospective case study was Oneofakind Lake, a mesotrophic/oligotrophic

freshwater system located in the northern United States (Figure 3). There are no industrial

sources of contamination around the lake. At one time, there were some eutrophication

problems in the lake, but those are now largely resolved. The source of dioxin-like

contamination was a spill that occurred in the Yuckymuck River and subsequently moved into

Oneofakind Lake. Currently, sediments and biota are known to be contaminated with PCBs and

furans from the spill, and temporal sampling of the sediments has suggested a first-order loss of

these compounds which is believed to be occurring primarily through sediment burial. Dioxin

and furan loading to the lake is believed to occur mainly via atmospheric inputs. Previous

logging activity around the lake included the use of DDT for insect control, but no logging has
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Figure 3

occurred for 30 years.

Components of the aquatic ecosystem include lake trout, Atlantic salmon, largemouth bass,

catfish, crappie, and bluegills; the forage fish are emerald and spottail shiners. The waterbird

population is normal for this type of lake; the species that may be of concern to state agencies

include herons, gulls, and terns. The three types of evidence suggesting some sort of disruption

of the ecosystem are decreased Caspian tern reproduction, decreased lake trout recruitment, and

anecdotal reports from trappers that the otter population is declining. For this case study, the

Planning Group selected a tissue residue assessment approach. The target organ for dioxin-like

effects is the developing embryo in the case of birds and fish, and the developing fetus in the

case of mammals.

Figure 4 illustrates the simplified food chain model developed for this case study. 

Contaminated sediments are the primary load to the system. Biota sediment accumulation
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Figure 4.

factors (BSAFs) are used to estimate the trophic transfer of contaminants from the sediments and

up through the food chain and to predict tissue concentrations in the forage and piscivorous fish. 

Biomagnification factors (BMFs) are used to estimate the transfer of contaminants from fish to

piscivorous birds and mammals, and to predict tissue and egg concentrations in the piscivorous

species. Assessment endpoints for this study are lake trout recruitment, Caspian tern

reproduction, and the size of the otter population.

Dr. Tillitt concluded his presentation by noting that, in the workshop exercise, participants

were being asked to apply the TEF/TEQ methodology to determine how contaminant levels in

the species of interest compare to hypothetical no-effect thresholds for fish eggs, bird eggs, and

mink liver. In particular, he said, the Planning Group would be interested in the experts’

thoughts about how a risk assessment based on the use of the TEF model would compare with an

assessment based either on TCDD alone or on total PCBs.
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Overview of the Prospective Case Study

Dr. Steven Bradbury, EPA/DOI Planning Group

 

 Dr. Bradbury began by noting that both the retrospective and prospective case studies were

designed to explore whether it might be possible to move beyond the traditional use of TEFs,

which has been exclusively for screening-level assessments. In the retrospective scenario, for

example, it has already been established that an AhR agonist situation exists, and the question is

whether the TEF methodology can be used to inform a decision about remediation. In the

prospective scenario, the situation is that dioxins and furans are going to be released into an

environment that already contains some PCBs, and the question is whether the TEF approach can

be used to inform a permitting decision. In this sense, he noted, one goal of the workshop is to

determine whether the state of the science is sufficiently advanced to support a different

application of the TEF methodology than has been used in the past.

Regarding the specifics of the prospective case study, Dr. Bradbury noted that the setting for

this case is a lake in the northwestern United States (Figure 5). A new paper mill has been

proposed, and the mill is likely to discharge dioxins and furans into the system. The engineers

associated with the plant may have some flexibility in manipulating the mix of congeners that

will be released, but they need to know what targets they should be aiming to meet. There are

already PCBs in the system, due to atmospheric deposition and other background sources.

In issuing a permit for the new paper mill, the state has decided to use a total maximum daily

load (TMDL) approach. Accordingly, the regulators want to determine the total load of AhR

agonists the system can tolerate and still maintain the productivity of fish, birds, and mammals in

the ecosystem. Based on the current loading of the system from background sources, they will

then be able to decide how much the new plant will be allowed to contribute and how much of

the maximum load to set aside both to provide a margin of safety and to accommodate future

demands on the system.

 
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Figure 5.
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Among the aquatic species present in the ecosystem are salmon, lake trout, and bull trout. 

The bull trout is of particular concern to the risk managers, since it has recently become a listed

species. A variety of piscivorous birds use this system for foraging, relying on Lake Roundtail

for roughly half of their diet and on other lakes and rivers in the area for the remainder. River

otter and mink are found in the system, but there is some question as to the home ranges of these

populations. 

Possible risk assessment endpoints for the prospective scenario include the productivity of

birds, fish, and mammals, and the assessment could focus on the most representative, the most

highly exposed, or the most sensitive species. Although population-level effects are clearly of

concern, the bull trout's status as an endangered species also introduces a need for at least some

attention to individual-level effects. As in the retrospective case study, the Planning Group

provided hypothetical standards for protection of the species of concern, in this case the bull

trout, bald eagle, and river otter.

As Figure 6 illustrates, the conceptual model for the prospective case study is similar to that

used in the retrospective case, except that it relies on either freely dissolved or total

concentrations in the water as a predictor of residues in the organisms and therefore of expected

effects. This approach is necessary because of the prospective nature of the assessment and the

fact that loading of the system is the variable for which the permit is to be written.
C-15

Toxicity

Hazard

Demographic

and

Social Impacts

Reproduction

Cancer

Imunotox.

Neurotox. etc

Reproduction

Age-sp. Mortal.

Pred. Vulner.

Disease Resist.

Population

Changes

Residues in

Aquatic

Organisms

Population

Changes

Reproduction

Age-sp. Mortal.

Pred. Vulner.

Disease Resist.

Wildlife

Exposure

Human

Exposure

Chemical 

Loading

Community

Structure and

Function

Toxicity

Hazard

Exposure

BAF

Toxicity

Hazard

Population

Model

Population

Model

Population

Model

Chemical Transport

Fate Models

Reproduction

Cancer

Imunotox.

Neurotox. etc.

Food

Water

Sediment

Food Consumption

Food Consumption

Ecosystem

Model

Risks Hazards Exposures

Conceptual Model for Risk Assessments and Criteria Development Involving 

Determination of Safe Loadings of Bioaccumulative Chemicals to Aquatic Systems

Figure 6.

Potential routes of exposure to the contaminants of concern are illustrated in Figure 7. As in the

retrospective case study, movement of these chemicals through the various trophic levels of the

ecosystem will determine the doses received by the organisms of concern. Thus, also as in the previous

case, bioaccumulation and biomagnification factors will have to be used to work through the various

exposure scenarios.

To conclude his presentation, Dr. Bradbury presented a methodology the Planning Group had

devised to address the various issues likely to arise in a prospective risk assessment tied to a TMDL

model (Figure 8). The first step, he suggested, is to relate the total concentration of dioxin-like

chemicals in the water to the concentrations of individual congeners, keeping track of both their TEFs 

 and their bioaccumulation potential relative to TCDD. By using TCDD to standardize both the effect
C-16

Figure 7.

and exposure metrics, it should be possible to determine the maximum load an individual

congener could contribute to the system and not exceed the water quality threshold. Repeating

this process for each congener and for each of the species-specific threshold values will generate

a matrix of values that are all normalized to TCDD. When the regulator decides on the

percentage of the maximum load that will be allocated to the plant, the same fraction can be

applied to all elements of the matrix. At the same time, the discharger can use the matrix both to

see which congener is driving the assessment and to determine whether particular combinations

of congeners will or will not exceed the permit level. 
C-17

STD(Ct )tcdd,j ≡STD(TEqCt )j w w = w t t

(Ct

 ) wi,j=

Convert each (MACt )i,j to a MALi,j using a system-level mass balance model

S[(Ct

 )i,j(TEF)i,j(BAFl )i,1 /(BAFl )tcdd,1]

i 1

n

=

∑

•

≤ 



 





w

allocation M ALi j,

1.0

TCDD-based WQ STD for 

jth Biota Group

STD(Ct )tcdd, j

TCDD Toxicity Eq. Conc.-based 

WQ STD for jth Biota Group 

STD(TEqCt)j

Max. Allowable Conc. of

Each Specific Congener for

jth Biota Group

(MACt)i,j

Max. Allowable Load for 

Each Specific Congener 

MALi,j

The Basic Relationship:

i = congener j = biota group (1 = fish, 2 = birds, 3 = mammals)

Statement of TCDD Toxicity Equivalent Additivity Model

n

i=1

w

w

w

w

Form Allocate

Matrix Fraction [MALi,j] fallocation ● [MALi,j] Permit Condition

TEq TMDL

The effluent must meet the permit condition:

Figure 5. Process Diagram for Prospective Waste Load Allocation

Where wi=projected load of ith congener

o/s/g/j/76007001/RSKrev5.ppt

w

MALi,j

i

fallocation

(TEF)i,j(BAFl )i,1

t

(BAFl ) STD tcdd,1 (TEqCt ) w j t

Figure 8.

 At the conclusion of Dr. Bradbury's presentation, Dr. DePinto pointed out that the rationale

for using this approach has to do with the fact that each congener has a different fate and

transport profile in the system. As a result, it is virtually impossible to model the TCDD toxic

equivalency concentration as a single entity. The purpose of the matrix is to account for the

differing fate and transport properties of individual congeners from discharge all the way to the

endpoint or endpoints of concern. This is especially useful in complex ecosystems, since the

suite of congeners released by the paper mill may be very different from the mix already present

in the system, and both will likely differ from the mix of congeners entering the system from
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some other source. With the matrix, however, the issue is reduced to one of simple additivity.

 In response to a question from one of the experts (deFur), Dr. Bradbury indicated that

alternatives to using the TEF methodology are tracking TCDD alone and basing the permit on

that determination, or issuing separate permits for each of the individual congeners. If

workshop participants had other ideas about how to approach the problem, however, Dr.

Bradbury encouraged them to explore these approaches and present them to the Planning Group.

 A member of the Planning Group (Henningsen) questioned the case study's emphasis on daily

loading, when the toxicity of these chemicals is usually more chronic and the sensitivity of the

target organisms varies over different life stages. Dr. Bradbury noted that the TMDL model has

a regulatory underpinning, and indicated that it would be just as useful for the group to think

about total maximum load over some other time frame for risk assessment purposes.

Workshop Structure/Summary of Premeeting Comments

 Dr. Charles Menzie, Workshop Chair

 After the two case studies had been presented, Dr. Menzie reviewed the proposed agenda for

the workshop (Appendix B). He noted that the workshop was designed to follow an iterative

process in which small work group meetings would alternate with plenary sessions at which the

group as a whole would have an opportunity to discuss the various approaches taken and lessons

learned in the smaller work groups. To begin this process, workshop participants had been

assigned to one of three expertise groups:

• Toxic Equivalency Factors, chaired by Dr. Richard Peterson;

• Fate and Transport, chaired by Dr. William Adams; or

• Risk Assessment and Population Modeling, chaired by Dr. Menzie.

The purpose of these groups, Dr. Menzie said, would be for individuals with specific expertise in

each of these areas to come to a common understanding of what the issues are and how they

might be addressed in the context of the two hypothetical case studies. Once this was done,

members of the expertise groups would fan out among the three work groups in which the case

studies themselves were to be reviewed. Thus, each work group would contain some individuals
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from all three expertise groups. In this sense, the work group portion of the workshop could be

thought of as a replication effort to see how three more or less similar groups might address the

issues posed by each of the case studies. Then, in plenary sessions, the efforts of each work

group would be discussed by the group as a whole to identify areas of agreement where they

exist and to illuminate the reasons for any differences of opinion in areas where agreement could

not be reached.

 Having provided this overview of the workshop structure, Dr. Menzie noted that in its charge

to the experts, the Planning Group had emphasized that the primary objective of the workshop

was to identify, document, and compare the uncertainties associated with the use of the

TEF/TEQ approach and to consider the impact of these uncertainties on ecological risk

assessments. Toward this end, the Planning Group had posed a series of questions and issues to

focus the experts’ deliberations. Prior to the workshop, each of the experts submitted written

comments outlining their individual responses to these questions (Appendix C). To provide a

sense of the range of views experts had coming into the workshop, Dr. Menzie offered a general

summary of the commonalities and differences he had noticed in his own review of the

premeeting comments. His observations related to selected charge questions are summarized in

the paragraphs that follow.

• Charge Question I-1: The WHO consensus TEF values are reported as point

estimates and generally rounded off to the nearest order of magnitude. For the risk

assessment case studies, additional background information used in the derivation of

the TEF values is provided. Does this additional information enhance the means of

evaluating uncertainties in the assessments? If so, how? If not, why?

 In general, Dr. Menzie said, most experts agreed that the additional information was an

enhancement. A number of experts indicated that the WHO tier system offers a useful

framework for identifying at least the sources of uncertainty. Some felt that additional

background regarding the derivation of specific TEF values would also be helpful, in that it

would allow uncertainties to be carried along through the risk assessment in a more quantitative

way. One person thought that this information was particularly important for the compounds

that were driving a particular case study, while another suggested that it would be very useful for

someone to take on the task of developing a single document that addresses the uncertainties
C-20

associated with the derivation of each of the consensus TEFs.

 With respect to the rounding procedure used by WHO, Dr. Menzie noted that various

opinions were expressed, but most experts agreed that rounding is probably not an important

contributor to the overall uncertainty in the assessment. The general feeling seemed to be that

the uncertainty associated with rounding would be less than half of an order of magnitude, and at

least one expert noted that this question could be readily addressed by performing a model

sensitivity analysis.

 Finally, Dr. Menzie noted, various commenters had offered specific cautions related to use of

the consensus TEF values. One expressed the view that it is not possible to quantitatively

evaluate the available data and assign valid, comparable uncertainty rankings, and that

qualitative assessment may be possible but may also be misleading. Another suggested that

probabilistic methods could be used to examine uncertainties and limit the illusion of certainty

associated with a point estimate. 

• Charge Question I-2: Some TEFs were determined from several studies,

endpoints, and exposure routes, while other TEFs were based on a single study and

endpoint. Given the range of knowledge associated with specific compounds,

should all TEFs be considered to have similar uncertainties? Why? Or why not?

 In reviewing the individual responses to this question, Dr. Menzie noted that the

overwhelming sense of the group was that uncertainties associated with the TEFs should not be

considered similar, and that the level of uncertainty is related to the weight of evidence used to

derive each of the individual TEF values. Several people noted that uncertainties tended to be

largest for the least potent and most easily metabolized compounds, which are also the

compounds least likely to drive a risk assessment. One expert wondered whether it might be

possible to develop a sliding scale to capture the uncertainty associated with the individual TEF

values. Others raised the possibility that uncertainty in the TEFs could be addressed by adopting

an uncertainty factor similar to those employed to deal with other types of uncertainty in the risk

assessment process.

 Regarding the uncertainty associated with use of the TEF/TEQ approach, some experts felt
C-21

that probabilistic methods could be used to determine the impact of TEF-related uncertainties on

the overall uncertainty associated with the assessment, but others wondered whether even this

level of quantification would be possible using the available data. One person expressed the

view that uncertainties associated with individual TEFs will not be quantifiable until there is a

common experimental basis for derivation of these values, and that attempts to partially quantify

uncertainty could impart a false sense of accuracy. Another expressed particular concern about

the TEFs for birds, which were derived mainly from in vitro assays using endpoints that are only

peripherally related to the effects of interest. 

• Charge Question I-3: The TEF values provided were based on endpoints that

ranged from in vitro biochemical responses (e.g., induction of cytochrome P450

1A1) to in vivo early life stage mortality. To what extent can these endpoints be

extrapolated to the measures of effects that are relevant for the assessment

endpoint for each case study?

 Dr. Menzie noted that in responding to this question a number of experts mentioned that

uncertainty increases as the experimental evidence strays farther from the endpoint of interest. 

Many TEFs, however, are based on biochemical effects rather than toxic injuries, and these

endpoints are poorly linked to survival, growth, and reproduction. In this regard, experts

cautioned that particular care should be taken in applying TEFs derived from in vitro data unless

the laboratory endpoint has been closely correlated to a toxic effect in a relevant species. As an

example, one person commented on the questionable relationship between ethoxyresorufin-odeethylase (EROD) induction and mortality in bird eggs, since in vitro enzyme induction assays

do not take metabolism into account, and since the shape of the dose-response curve for EROD

induction varies from one congener to the next. Another factor that may complicate the use of

TEFs is the paucity of information about compensatory mechanisms that may mitigate the effect

of dioxin-like compounds at the population level. 

• Charge Question II-1. What are the implications, both quantitatively and

conceptually, of assuming no dose-additivity or no interaction among the

components of the mixtures described in the case studies? To what extent would

the risk assessment conclusions differ if stressor response analyses were based on

total PCBs or 2,3,7,8-TCDD alone?

 Although some experts disagreed, the majority opinion was that the assumption of nonadditivity would require a procedure for evaluating each compound separately. If such a
C-22

procedure were used, however, the lack of toxicity data for many compounds would complicate

an assessment of overall risk, which would normally be done by summing the hazard quotients

for individual compounds. Some experts noted that the assumption of additivity was more likely

to result in an overestimation of risk than the TEF/TEQ approach was to result in an

underestimation of risk. Also, most experts felt that assessments based on total PCBs or on

TCDD alone would typically give lower estimates of risk than would the TEQ approach. 

However, some noted that differences among the three approaches would largely disappear if the

results of the assessment were to be judged against an established criterion or other benchmark

value. 

• Charge Question II-2. Many TEFs are based on LC50 or EC50 values. To what

extent should TEF values derived at a median response level be used in risk

assessments where a no adverse effect level is being employed?

 Responses to this question covered a broad range of opinions, most of which had to do with

the shape of dose-response curves for the endpoints of interest. A number of experts felt that the

use of median response values was acceptable, since the goal was to determine relative rather

than absolute potencies. Also, some pointed out that LC50 and EC50 values tended to be more

stable measures within the dose-response curve than either NOAEL or LOAEL values. Other

experts disagreed, however. One suggested using an effect level that is more relevant to the

protection of ecological endpoints, and another suggested that it would be more appropriate to

use a no adverse effect level, particularly for screening-level assessments. A third felt that this

issue was relatively unimportant, since differences between the various metrics would probably

be lost in the noise.

• Charge Question II-3. The TEF values provided were typically based on a single

or limited number of mammal, bird, or fish experiments. To what extent can classspecific TEFs be directly extrapolated to the species identified within each case

study?

 The issue of interspecies extrapolation generated a variety of opinions, Dr. Menzie said, and

most experts believe that this is a matter of substantial concern. In general, the experts felt more

comfortable applying TEFs to organisms that are closely related to the species in which the TEF

was derived, and less comfortable as the taxonomic distance between the reference species and

the species of interest in the risk assessment increased. In the prospective case study, for
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example, most people felt that it was appropriate to apply the fish TEF to the bull trout, since the

data from which the TEF was derived were from another salmonic species; if largemouth bass

had been the species of concern, however, use of the fish TEF would have been more

problematic. A similar situation arises when TEFs based on data collected in chickens are used

to predict the effects of exposure to dioxin-like compounds on eagles. One expert suggested that

if data for the species of interest were available, those data should be used in lieu of the more

generic TEF values.

 Regarding the uncertainty associated with this aspect of the TEF/TEQ approach, some experts

felt that a traditional uncertainty factor could be applied to account for differences between the

reference species and the species of concern. One person pointed out that interspecies

differences in sensitivity to TCDD are so large that they might in fact dwarf the uncertainties

associated with the TEF approach. Dr. Menzie noted that this observation is particularly

germane to the case studies, since the threshold for TCDD toxicity is itself a variable rather than

a fixed value.

• Charge Question III- 1: To what extent does the TEF approach present challenges,

introduce new uncertainties, or modify old uncertainties associated with modeling

the exposure of AhR agonists? To what extent does the availability and quality of

congener-specific physicochemical data limit the means of employing fate and

transport or food chain models?

 In general, Dr. Menzie noted, experts were in agreement that the TEF methodology poses a

number of challenges for modeling, most of which are logistical problems that have to do with

ways of accounting for the differing fate and transport properties of individual congeners and

carrying these differences through the modeling effort. Some experts felt that this problem could

be minimized if the model is focused on those compounds that are driving both the exposure and

the risk. 

 A number of experts cautioned that uncertainties will be magnified in attempts to model

exposure over more than two levels of the ecosystem. As an example, one person noted that

uncertainties would be great in an approach that attempted to model avian exposure on the basis

of sediment levels, since contaminants would be moving through many different trophic levels of

the system and uncertainties would be introduced at each step along this pathway.
C-24

• Charge Question III-3: To what extent does the TEF approach require a more

rigorous analytical design in quantifying sediments, soil, and biota AhR agonist

concentrations than is apparent in other methods which aggregate stressors (e.g.,

total PCBs)?

 In their responses to this question, most experts agreed that the TEF methodology requires a

more rigorous analytical design than other methods, and that analytical costs would probably be

greater as a result of the need to quantify individual congeners. Others, however, felt that this

might not be the case, since congener-based analytical methods are now routinely used by many

agencies and organizations. 

• Charge Question IV-1: In evaluating the case studies, are the uncertainties

associated with TEFs more problematic than other uncertainties of the risk

assessments? Do the uncertainties associated with TEFs limit the means of

performing the assessments, or do the other areas of the effect and exposure

characterization contribute similar or greater levels of uncertainty?

 In general, experts did not feel that uncertainties associated with the TEF methodology would

be any more problematic than other types of uncertainty in the risk assessment process. Indeed,

one person suggested that the TEF-related uncertainties may actually be less problematic, since

people have already worked through them. Others, however, felt that this question could not be

answered a priori, noting that someone would have to go through a TEF exercise and really

think through the issues to make any reasonable statement about the relative magnitude of the

associated uncertainties.

& Charge Question IV-2: Biologically-based TEQ assays on environmental samples

could be employed as an alternative to the TEF-based approach. What would the

strengths and weaknesses of such an approach be? To what extent could these

approaches be integrated?

 Responses to this question were mixed. Several individuals pointed out the advantages of

these methods, which include their ability to focus on an integrated response to a mix of

chemicals in the environment and their lower cost in comparison with chemical-based

approaches. Others, however, focused on the limitations of these methods: they do not account

for metabolism; they can be confounded by other compounds; and they may not identify the

most important compound for control purposes. In general, biologically-based TEQ assays were
C-25

viewed primarily as a research tool at present, with a lack of regulatory acceptance. Some

experts felt that these methods could be very useful, however, particularly as screening tools, and

several suggested that these methods could be used in concert with the TEF/TEQ approach.

Observer Comments

 At the end of his presentation, Dr. Menzie opened the floor to comments from those attending

the workshop as observers. The only observer to take advantage of this opportunity was Dr.

Angelique van Birgelen, who identified herself as a toxicologist with the National Institute for

Environmental Health Sciences (NIEHS). Dr. van Birgelen noted that while it is rewarding to

see how much progress has been made in the development and now the application of TEFs for

dioxin-like compounds, it is also important not to lose sight of other ways in which the TEF

approach can be improved. Toward this end, she suggested that there are three additional

compounds or classes of compounds that should be assigned TEF values and included in the

WHO scheme: 3,3',4,4'-tetrachloroazobenzene (TCAB); hexachlorobenzene (HCB); and several

of the polychlorinated naphthalenes (PCNs). 

 According to Dr. van Birgelen, all of these compounds have been shown to bind to the Ah

receptor, all have been shown to produce dioxin-like effects, and all have been shown to

accumulate or to have a long half-life in certain species. Moreover, each may account for a

substantial fraction of the total TEQ in some environmental settings. 

 Dr. van Birgelen provided the group with an extensive body of published data related to these

three compounds/classes of compounds, which she summarized by briefly describing the AhR

binding properties, effect profiles, physicochemical characteristics, and estimated annual

discharge for each compound or class. Based on this information, she urged the group to

consider recommending that these compounds be included in the TEF scheme, and offered to

provide further information if that would be useful.

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III. WORKSHOP PROCEEDINGS

The second day of the workshop began with concurrent meetings of the Expertise

Groups. Discussions in these groups were organized around question lists assembled by the

Planning Group to raise issues of relevance to the various expertise areas. Each group included a

notetaker from the Planning Group, whose job it was to capture the key points of the discussion. 

Appendix D of this report contains a list of Expertise Group assignments and the discussion

summaries prepared by the notetakers.

Review of the TMDL Model

Before adjourning into breakout groups to discuss the prospective case study, workshop

participants heard a brief presentation by Dr. Philip Cook, of the EPA/DOI Planning Group, who

reviewed key aspects of the TMDL model and worked through a series of calculations related to

that model. Dr. Cook began by discussing some elements of the flow chart originally presented

during the opening plenary session by Dr. Steven Bradbury (see Figure 8, above). He noted that

one can set a water quality standard based on the toxicity of TCDD, and that this standard may

be based on effects observed in birds, fish, or mammals. Such a standard is represented in the

uppermost box of the flow chart, where C represents concentration, the subscript w indicates that

water is the medium of interest, and the superscript t refers to the fact that the standard deals

with the total concentration of the contaminant of interest, in this case TCDD. In the second

box, the same standard is expressed in terms of dioxin toxicity equivalents. Based on the

additivity assumption, this standard can also be expressed in a third way, as the sum of the

toxicity equivalence concentrations of individual congeners. 
C-28

To determine toxicity equivalence concentrations for individual congeners in the system

of interest, each congener's concentration in water must be adjusted to reflect both its toxicity

relative to TCDD and its bioaccumulation potential relative to that of TCDD. This is done by

taking the product of the congener-specific water concentration, the congener-specific TEF, and

the congener-specific bioaccumulation factor, divided by the bioaccumulation factor for TCDD. 

When this process is completed for each congener, the toxicity equivalence concentrations for all

congeners can be added together to determine the total toxicity equivalence concentration for the

system, and this value can be compared with the standard to determine whether the system is or

is not in compliance.

These same relationships underlie the TCDD Toxicity Equivalence Waste Load

Allocation Model selected for the prospective case study. In this model, it is assumed that the

ecosystem has a definable assimilative capacity for chemicals which, if not exceeded, will

provide the desired level of protection. To facilitate waste load allocation for complex mixtures

of AhR agonists, maximum allowable concentrations in water (MACws) and maximum allowable

loads (MALs) to the water body are calculated on the basis of each individual chemical's TEF,

bioaccumulation factor, and fate/transport properties. Because each chemical is modeled

individually, each MACw is equal to the toxicity equivalence concentration of that chemical in

water.

Because of these relationships, the accuracy of the approach depends on how well the

relationships between chemical sources and organisms of interest are modeled for each

individual congener in the ecosystem. An important step in the modeling process, for example,

involves relating the concentration of a contaminant in fish tissues, which can be measured, to a

concentration of concern in water. Ideally, this conversion is achieved by applying a

bioaccumulation factor that is both congener- and organism-specific. Similarly, fate and

transport properties determine the relationship between a mass loading of the chemical to the

system and its ultimate concentration in water, and these properties, too, are congener-specific.
C-29

The purpose of the MACw calculation is to determine the maximum concentration each

congener could have in the water of this system if none of the other congeners were present,

based on its toxicity profile. The MAL, in turn, relates this concentration to the loading of the

congener into the system, based on its fate and transport characteristics. Because MACws and

MALs are normalized values, they can be manipulated to assess the combined impact of

different mixtures of congeners on the system of interest.

To illustrate the application of this methodology, Dr. Cook worked through an example

that showed how the TMDL approach would be applied to the two-chemical mixture described

in Figure 9, assuming fish to be the organisms of interest. 

VARIABLES USED IN A SAMPLE TMDL CALCULATION

FOR A TWO-CHEMICAL MIXTURE

Chemical TEF BAF log Kow Projected Load

X (TCDD) 1.0 107 7 0.1 g/day

Y 0.1 106 6.5 20 g/day

Figure 9.

The two chemicals considered in this example, TCDD and a related congener Y, have

different TEFs, different bioaccumulation factors, and different lipid solubilities. In the

example, the proposed loading of dioxin is 0.1 g/day, and the proposed loading of congener Y is

20 g/day, and the water quality standard for TCDD has been set at 0.02 pg/L.

By definition, the MACw for TCDD is equal to the standard, or 0.02 pg/L. To determine the

MACw for congener Y, the standard must be multiplied by the bioaccumulation factor for TCDD

(107) and divided by the congener-specific TEF (0.1) and bioaccumulation factor (106). This

calculation yields a maximum concentration of 2 pg/L for congener Y, which is, as one would

expect given the lower potency of congener Y, many times higher than the maximum

concentration for TCDD. Using a system-specific mass balance model, the details of which are
C-30

irrelevant to this example, the MACws convert to MALs of 2 g/day for TCDD and 500 g/day for

congener Y.

In the final stage of the TMDL methodology, the total load represented by the two

compounds in the mixture is compared with the load allocated to the discharger under the permit

condition, which in this example is defined as 10% of the total MAL. This is done by dividing

the projected load of each chemical by both the allocation factor and its individual MAL, and

summing the resulting values for all congeners present in the discharge. As long as this sum is

equal to or less than 1, as it is in this case, the discharger is in compliance. Importantly, this is

true regardless of the precise congener composition of the discharge; as long as the sum of their

individual adjusted loads is less than or equal to 1, the permit condition is being met. 

In response to a question from one of the experts, Dr. Cook indicated that the greater

difference between the MALs than MACws for these two chemicals has to do with

physicochemical differences that affect their individual fate and transport profiles. Another

expert asked whether water quality standards are typically based on dissolved or total

concentrations of TCDD, and Dr. Cook said that there are currently no national water quality

criteria for protection of fish and wildlife from the effects of dioxin. Based on what he has seen

within EPA, however, Dr. Cook said that he would expect such standards to focus on the total

concentration of chemical in the water. A third expert said that the example made it clear how to

determine MACws for chemicals in the case study, but that it was not clear how the associated

MALs would be derived. Dr. Cook indicated that this had been a topic of discussion in the Fate

and Transport Expertise Group, and that people from that group would be prepared to address

questions about MAL derivation within the context of each breakout group's analysis of the case.

At the conclusion of Dr. Cook's presentation, workshop participants reported to their

respective breakout groups for discussion of the prospective case study. The three breakout

groups were chaired by Drs. Peter deFur, Janet Burris, and Charles Menzie. On the final day of

the workshop, the same groups met to discuss the retrospective case study. Appendix E of this

report contains a list of breakout group assignments and the detailed summaries prepared by each

of the workgroup facilitators at the conclusion of the workshop. Following each of the
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individual workgroup meetings, participants met in a plenary session to discuss the results of

their deliberations.

Plenary Session: Discussion of the Prospective Case Study

Group #1. Dr. deFur noted that his group began its deliberations by addressing the use of

more general as opposed to site-specific bioaccumulation factors (BAFs) for risk assessment

purposes. The group agreed that site-specific BAFs would be a vast improvement over the more

generic BAFs proposed for use in the case study. At a minimum, the group felt that some effort

should be made to determine whether trophic conditions in the system of interest were or were

not similar to those assumed in the derivation of the generic BAFs. If they were not, various

methods could be used to generate more site-specific values. One method that was suggested

was to develop a site-specific model that would incorporate published data more relevant to the

site; another involved the collection of field data that could be used to develop more site-specific

values.

Regarding uncertainties associated with the use of BAFs, members of the group identified

numerous sources of variability in these values. In general, the group agreed that BAFs are most

applicable in the system where they were developed, and that their reliability decreases as they

are applied to systems that are progressively more different from the original system in terms of

their size, biological and physical complexity, and scope. Indeed, group members felt that the

relationship between the bioaccumulative behavior of TCDD and other congeners was likely to

be more stable than the behavior of TCDD in different systems. As a result, they concluded that

it would be more useful to improve understanding of the bioaccumulative behavior of TCDD

than to improve understanding of the relationships between BAFs for TCDD and other dioxinlike compounds.

Throughout their discussions, Dr. deFur's group encountered a number of issues that

highlighted differences in the European and American approaches to assessments of dioxin-like

compounds. The most striking of these was the fact that in Europe chemical analyses are seldom

if ever done for a single congener, so it simply would not be the case that TCDD would be
C-32

measured alone. As a result, environmental concentrations of the individual congeners are

known, and it is usually possible to determine BAFs for the full suite of dioxin-like congeners. 

Given the obvious importance of BAFs to the TMDL model, the group agreed that wider

adoption of the European practice would substantially reduce the uncertainty associated with

TMDL-based regulatory and management decisions.

Turning to the question of dose-response relationships, the group discussed problems

associated with relying on TEFs that are derived at the cellular or molecular level to predict

effects at the population level. While recognizing that regulatory and management decisions are

often constrained by the legal, policy, or even cultural context within which those decisions are

made, group members felt that the level of uncertainty associated with these types of

extrapolations is large and that this aspect of the assessment paradigm needs to be addressed. 

Particularly when attempting to set regulatory limits such as MACs, information about

population dynamics is a critical component of the knowledge base. Like BAFs and other

elements of the TMDL approach, population data will be most useful if collected on a sitespecific basis, focusing on density-dependent as well as density-independent factors. 

Another element of the group's discussion focused on the relationship between TEQ- and

TEF-based approaches. In general, the group felt that these approaches are complementary, in

the sense that TEQ-based bioassays might serve as a reality check for a TEF-based analyses. If

the results obtained via both methods were concordant, confidence in the TEF-based analysis

would certainly increase. Even non-concordance might be useful in highlighting specific areas

where further investigation is needed.

The group also spent a fair amount of time discussing how the uncertainties associated with

application of the TEF methodology compare to those associated with other elements of the risk

assessment process, including the uncertainty in BAFs, uncertainty in population dynamic

models, and uncertainty in environmental measurements. In addition, the group discussed the

many places within the TMDL model that errors were likely to be propagated and perhaps even

magnified. At the end of this discussion, there was general agreement that no single source
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always generates the greatest amount of uncertainty, and that the relative contribution of

individual sources of uncertainty varies from site to site.

At the end of his summary, Dr. deFur asked whether other group members would like to

comment on any additional issues that came up during the group's deliberations. One member of

the group noted that toward the end of the session there had been some discussion of the need to

identify the uncertainties associated with various elements of the TMDL model, including but

not limited to the uncertainties associated with the derivation of TEFs, and to find appropriate

ways of carrying these uncertainties through the risk assessment process. Although presented as

point estimates, all of the numbers in the case study exercise have some variance associated with

them. To determine the relative contribution of individual uncertainties, therefore, one could use

a Monte Carlo or other probabilistic method to see how each of these uncertainties affects the

values generated via the TMDL process.

In response to a question from one of the other experts, Dr. deFur elaborated on the role that

bioassay-based approaches might play within the TMDL framework. One way that bioassays

could be useful, he said, was in screening-level analyses—for example, to see whether

contaminants actually do accumulate at the predicted rate. Later in the process, bioassays could

be used to determine how rates of enzyme induction, for example, compare with those predicted

at one level of the TMDL model. In this setting, observed values should be fairly close to

predicted values, or there should at least be some way of explaining disparities between the two

approaches. He also noted that the group recognized the difference between their around-thetable discussion and the circumstances under which management decisions generally need to be

made. In this sense, it might not always be possible for confirmatory bioassays to be run, due to

both resource and logistic constraints. The group nevertheless felt that in some situations

bioassays could provide a useful complement to a TEF-based approach.

Group 2. Ms. Burris began by noting that her group spent a good portion of the session

discussing the uncertainties associated with the derivation of TEFs and the effect of these

uncertainties on their application within the prospective case study. Based on this discussion,

the group agreed that a hierarchical approach should be used to select the TEFs applied to a
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particular risk assessment. If a species-specific value is available, for example, that value should

be used in lieu of the WHO consensus TEF. Also preferable to the consensus TEF would be a

value derived for a more closely related species than that used to derive the WHO value. 

However, a sensitivity analysis should be performed to determine whether uncertainty would

actually be reduced by the use of species-specific values. 

Group members felt that more information about the methods used to derive consensus TEFs

would have been helpful, since it would have allowed the uncertainties to be better understood

and carried through the analysis. Their impression was that the process used to derive consensus

values was not consistent from one congener to the next, and that this made it difficult to have

even a qualitative sense of the uncertainties introduced by using the consensus TEFs. Rounding,

in particular, seemed to be a quantifiable source of uncertainty, but information about the

rounding process was too scant to allow a more detailed consideration of this issue.

Despite its shortcomings, the group concluded that the TEF approach is more valid than

approaches using either total PCBs or TCDD alone. However, they thought that there would still

be a need for total PCB-based approaches, since some of the effects of these compounds are not

mediated by the Ah receptor.

Turning to the prospective case study, the group decided to use the consensus avian TEF for

the bald eagle, but to look at the effects of rounding and not rounding the TEF value. In general,

group members were comfortable extrapolating from the endpoint used in deriving the TEF to

the reproductive endpoint in the assessment. For the bull trout, the group elected to use TEFs

derived from rainbow trout data, and they thought that early life stage mortality was the

appropriate endpoint. For the otter, they chose to use the WHO consensus TEF, but there was

some discomfort about extrapolating from the TEF endpoint to the assessment endpoint.

Group members did not feel that the use of median values for deriving TEFs was a

significant source of uncertainty, since the median values tended to be more stable and were

probably more appropriate for looking at relative toxicity.
C-35

Moving on to the exposure assessment, the group felt that use of the TEF approach for this

particular fate and transport modeling exercise was really no different than the use of any other

chemical-specific model. The challenge, however, was in modeling the many different

congeners and in having the data available to complete the modeling exercise.

Looking at the measurements of individual congeners in sediment and fish tissue, the group

felt that the greatest uncertainties were in water measurements, due mainly to limit-of-detection

issues. From a physicochemical perspective, the group had high confidence in the log Kow

values, but the Koc data and Henry's Law constants were considered suspect. Biotransformation

and metabolism of the individual congeners were not as clearly understood; in some cases there

was no knowledge, and in others it is known that there are changes in the composition of

congeners as they move between the different species. PCB 126 is enriched, for example, during

transfers from fish to wildlife species, and this needs to be considered. In general, however, we

have a better understanding of the transfer within fish than we do from fish to wildlife. In order

to be able to appropriately model or understand the fate and transport of various congeners

within the food chain, we need to know more about what the organisms are consuming, since the

composition of congeners is species-specific and will therefore vary from one species to another.

In general, group members felt reasonably confident that they would be able to complete a

worthwhile modeling exercise if they had more information about transfers from sediment to the

sediment-water interface and about sediment transport within the system. Without this

information, however, the modeling exercise would be extremely uncertain. Some members of

the group thought that it would be a good idea to advise the risk managers to substitute a bettercharacterized model for the one proposed in the case study, but there was a divergence of

opinion on this issue.

In terms of the analytical requirements to implement a TEF approach, group members agreed

that the TEF approach would be more costly than the total PCB or TCDD-only approaches, since

the discharger would have to analyze many different congeners. This might turn out to be

beneficial, however, since a better understanding of the toxicity associated with specific

congeners might give the discharger more flexibility in altering the composition of the discharge.
C-36

Overall, group members agreed that the uncertainties associated with the exposure profile

and with projecting exposures in the future under these conditions were at least as great and

possibly greater than those associated with the stress response profile or the use of TEFs. To

gain a better understanding of relative uncertainties, the group recommended a sensitivity

analysis focusing on TEFs, Koc values, and biomagnification factors. Regarding the latter,

group members parenthetically noted that the same dose metric should be used for BMFs and

TEFs.

Regarding the use of biological assays, group members felt that these really were not

applicable to a prospective case study, since it is not yet clear which chemicals will be present in

the system. However, biological assays could be used to document background conditions in the

system before the discharge occurs, particularly since it is already known that PCBs are present.

When the group discussed errors associated with the application of a TCDD-based water

standard, two potential problems were raised: the enrichment of PCB 126 from fish to wildlife

and the observed loss of chlorinated dibenzofurans in some species of birds.

The group concluded its discussion by talking about ways the assessment for this site might

be done better or differently. Group members agreed that it might be useful to put together a

more site-specific model, but there would be no way of knowing whether such a model would be

predictive. Other existing food chain models could be used, but these would have to be modified

to address metabolism issues. Everyone was more comfortable using the TEF/TEQ approach

than using either of the default approaches, but most thought that the assessment would generate

a range of risk estimates that would be perplexing to the risk manager. It was agreed, however,

that this may be the best we can do given the current state of the science.

Following Ms. Burris' presentation of the group's findings and recommendations, there was

considerable discussion of the role that bioassays might play in a prospective case scenario. In

response to a question about how they came to their decision that bioassays would not be useful,

a member of the group explained that there was some concern about how the results of bioassays

could be misleading if appropriate extraction and fractionation steps were not included. Another
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member of the group mentioned studies of Canadian paper mills in which bioassays were applied

directly to the effluent, resulting in a gross overestimation of discharge toxicity. The questioner

agreed that these issues need to be taken into account, but suggested that the wording of the

group's conclusion was overly strong. He noted that there are many different types of bioassays,

and that some would be very useful in a prospective setting. As an example, he suggested a

bioassay that is able to predict the relative potencies of various congeners for relevant endpoints

in a fish species of concern. Such a bioassay could be used to test both how sensitive that system

is to different compounds and how the sensitivity of the target species compares with that of 

other organisms in the system. This information, in turn, might be extremely useful in a

prospective assessment of the impact that further loading of the system might have on the species

of concern.

In response to a question from the Chair, Ms. Burris confirmed that the group's sense had

been that uncertainties associated with the use of TEFs are no greater than those associated with

exposure or response assessments, although the group did not have enough information to

quantify these different types of uncertainty. The group also felt that uncertainties were less

manageable in the context of a prospective case study, since a prospective scenario does not lend

itself to the sorts of approaches that can be used to reduce uncertainty in a retrospective

assessment.

Group 3. Dr. Menzie indicated that the results of his group's deliberations would presented

by group members Donald Tillitt and Wayne Landis. Dr. Tillitt began by noting that Group 3

had begun its analysis of the case study where the other groups had left off, in that this group had

focused almost exclusively on how the various sources of uncertainty might be addressed in a

risk characterization for the prospective case study. For purposes of this exercise, the group

identified five major sources of uncertainty: the derivation of TEFs, the derivation and use of

BAFs, extrapolation of TEFs between species, exposure modeling, and derivation of the

threshold values themselves. For each of these sources of uncertainty, the group developed

specific criteria that could be used to rank degrees of uncertainty on a scale of 1 to 4, which was

chosen because of its rough correspondence to the tier system used in the derivation of TEFs at

the Stockholm meeting.
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Dr. Landis added that the group's intent in developing these criteria was to move from

"feelings" and "senses" of relative uncertainty to a more quantitative expression. While

recognizing that the ranking system is not quantitative in a statistical sense, it does provide a way

of assigning relative values to the differing degrees of qualitative uncertainty that most people

would agree exist in different interspecies extrapolations or in different types of gaps in the

congener-specific data. In addition, this approach allows the uncertainty rankings to be

manipulated arithmetically in ways that provide additional information about the system as a

whole.

To illustrate the results of the group's deliberations, Dr. Landis showed the matrix

reproduced as Figure 10. For each cell in the matrix, the group attempted to rank the uncertainty

associated with a particular variable in either species- or congener-specific terms. For example,

they felt that the uncertainty associated with application of a TEF derived in rainbow trout or

lake trout to bull trout was considerably less than the uncertainty associated with applying a TEF

derived in chickens to bald eagles; as a result, the group gave the TEFs for bull trout an

uncertainty ranking of 1 and the TEFs for bald eagle an uncertainty ranking of 4. In considering

BAFs, the group felt that these were less uncertain for fish than for either birds or mammals, and

rankings were assigned accordingly. Similarly, because the exposure model was developed

around fish, its application resulted in less uncertainty if a fish rather than a bird or mammal was

the species of concern. Also, because of their migratory potential, birds and mammals are much

more likely to have exposures outside the system than are fish.

Once these individual rankings were completed, the group summed all of the species- and

congener-specific values to see how each contributed to overall uncertainty. From this

summation, it became clear that the species-specific uncertainty was greatest for bald eagle,

slightly less for the river otter, and much less for the bull trout. One of the encouraging

conclusions that can be drawn, therefore, is that uncertainty is relatively low for the species that

is endangered. In addition, the group concluded that the species most likely to drive the lower

limit would be the river otter, for which uncertainty was the greatest.
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Another way the group used this matrix was to identify the sources of greatest uncertainty in

the assessment. To a large extent, Dr. Landis said, overall uncertainty was driven by uncertainty

in the modeling. For individual species, however, it was possible to identify specific areas in

which uncertainty was due to a lack of knowledge about the properties and effects of different

congeners. In this sense, the matrix could also be used to identify ways of reducing the

uncertainty in these assessments. For both the bald eagle and river otter, for example, additional

information about species-specific TEF and BAF values would substantially reduce the

uncertainty of the assessment. In this way, Dr. Landis suggested, use of this matrix would allow

the risk assessor to answer a variety of questions that are vitally important to stakeholders,

including how the situation might be improved. In addition, the group felt that this matrix might

be a useful tool in communicating the results of the assessment to risk managers. 

One caveat that the group identified in considering possible uses of the matrix is that the

relative rankings are specific to the system under consideration. Because the rankings reflect

relative rather than absolute measures of uncertainty, different values would have to be generated

for different systems, and the results of site-specific analyses could not be directly compared.
C-40

Relative Uncertainties in the Ecological Risk Assessment Including Use of TEF Values

Ranks for

uncertainty

Species/Congener

TEFs BAFs

Species

Sens./Extrapolation

Exposure

Model

Threshold

concentration Total

Bull

trout 1 2 2 2 2 Species specific 9 30

Bull

Trout

1 2 1 Congener specific 21

221

321

421

521

621

721

Bald

Eagle 4 3 4 4 4 19 55

Bald

Eagle

1 3 1 36

231

331

434

533

633

732

River

Otter 3 3 3 4 3 16 48

River

Otter

1 3 1 32

231

331

432

532

632

732

Criteria are described in the text. This approach and these values are presented for illustration only.

Figure 10.
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Following these presentations, one of the experts from a different work group expressed

some concerns about using a matrix such as this to identify the areas in which additional research

is most needed. The reason for his concern was that the matrix does not address the relative

sensitivity of the model as a whole to specific elements of the matrix. Depending on the model,

it could be more important to reduce the uncertainty in one variable from 2 to 1 than to reduce

the uncertainty in a different variable from 4 to 2. Dr. Landis agreed with this observation,

noting that it would be necessary to combine the matrix with a more conventional sensitivity

analysis to determine precisely where additional research would have the greatest impact on

overall uncertainty. However, he thought that the matrix enables assessors and managers to

better understand those aspects of the uncertainty problem that are not typically addressed in a

sensitivity analysis. A member of the Planning Group suggested that it might be possible to

combine these two approaches by weighting different cells in the matrix to reflect the results of a

sensitivity analysis.

At this point in the discussion, another member of Group 3 noted that the group was

unable to identify any place in the process diagram where this and other information about

relative uncertainties could be incorporated into and carried through the TMDL process. He

thought that this would be an important issue for the modelers to address, since the ultimate

value of quantifying the uncertainties depends on there being a way to bring this information to

bear on the decisionmaking process. One way to do this, he thought, would be to go back and

reframe the question that the model was designed to answer in a way that includes specific

attention to the impact of various types of uncertainty.

In response to a request from Dr. Menzie to describe the group's thoughts about use of

the TEF approach as opposed to one of the defaults, Dr. Tillitt said that there was an agreement

that the use of TEFs does not contribute disproportionately to overall uncertainty, and that the

TEF approach reveals some useful information that would not be apparent if other approaches

were used. As a result, the group felt that something important would be lost if one of the

defaults were used.
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One of the experts noted that it is important to be cautious when using a semiquantitative method as a decisionmaking tool. The reason for his concern was that the weighting

of different variables may reflect subjective biases, and this subjectivity could be obscured by

the quasi-mathematical nature of the method. If this occurred, the method would simply be

validating a conclusion that was essentially predetermined. Dr. Landis agreed, and noted that

this is why it is important for the ranking criteria to be established a priori, before the method is

applied to specific sites. Another group member noted that the ranking criteria themselves

would certainly be open to debate, and might even change over time, as more information

becomes available. Continuing along these same lines, another expert suggested that it would be

an interesting test of the method this group used to see how different groups given the same a

priori criteria and the same data set would rank the relative uncertainties. Finally, a member of

the Planning Group urged that, in the workgroup's more detailed report of its deliberations,

members of the group try to more clearly describe the ranking scheme they used to construct

their matrix, since these a priori criteria represented such a key element of the process.

Summary. To conclude the plenary session, Dr. Menzie provided a brief summary of

what he thought were the major conclusions that could be drawn from the group's consideration

of the prospective case study. In general, all three workgroups felt that the TEF approach could

be applied to a prospective case scenario, but that this approach might be more costly than the

other alternatives. All three groups felt that there needed to be a way to track uncertainties

through the risk assessment process, but that uncertainties associated with the application of

TEFs are no greater than those associated with other elements of the TMDL model, and that they

may in fact be smaller. As a result, all three groups concluded that use of the TEF-based

approach is preferable to use of the traditional TCDD-based methodology, which in comparison

might underestimate risk. There was some discussion of the usefulness of biological assays in

supplementing the TEF approach, and a divergence of opinion regarding the applicability of

these methods to a prospective case scenario. Finally, the group had discussed the need for

better ways of incorporating what we do know about different sources of uncertainty into the

TMDL model and for communicating the results of the assessment to risk managers.
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At the end of Dr. Menzie's summary, one of the Planning Group members asked if any of

the groups had addressed the aspect of the TMDL approach that has to do with issuing a permit 

that is based at least in part on chemicals that are not in the discharger's wastestream. One of the

experts noted that this had been addressed to some extent in the comment that a TEF-based

approach might in some cases actually turn out to be beneficial to the discharger, since only the

subset of AhR agonists would be driving the assessment and therefore the permitting process. 

The questioner noted that this is a departure from the chemical-specific approach that EPA has

traditionally used in regulating environmental contaminants, since it directs the regulator to

mode of action or ecological effect rather than to chemical identity. One of the experts

suggested that if the goal is truly environmental protection, then this is an appropriate refocusing of the regulator's attention. Another expert disagreed, suggesting that further groundtruthing is needed before TEF-based approaches can reasonably be applied in a regulatory

setting.

Plenary Session: Discussion of the Retrospective Case Study

Group #1. Dr. deFur began by noting that his group's approach to the retrospective case

study differed in two important respects from their approach to the prospective case. First, the

group attempted to be as quantitative as possible in addressing the retrospective scenario, as

opposed to the largely conceptual approach they had taken to the prospective case. In addition,

in accordance with guidance the facilitators had been given by members of the Planning Group,

the group agreed to try to make a decision about the site described in the retrospective case

study.

After reviewing the features of the site, the group first talked about what the decision was

that they were trying to make. Rather than a decision about whether to remediate or not to

remediate, the group elected to try and decide whether the data were sufficient to support a

regulatory or management decision. In particular, they agreed to focus on whether the TEF/TEQ

approach offered any advantages over approaches based on total PCBs or on TCDD alone.
C-44

The group's quantitative analysis centered on a graph that one of the members drew to

summarize how the data from the site would look from both a TEQ and total PCB perspective

(Figure 11). In this figure, the left-most bars in each graph represent the species-specific TEQs

for the site, broken down to reflect the contribution of various classes of compounds to the total

TEQ. The vertical line to the right of this bar represents the threshold range for the species of

concern. In the right half of each graph, a similar method is used to depict the site-specific

values and threshold ranges for total PCBs.

Interpretations of this graphic covered a fairly broad range. Some people felt that

conclusions drawn on the basis of the TEQ data would differ from those drawn using total PCBs,

but others felt that there would be no difference in the bottom-line conclusions as to whether

exposures do or do not reach threshold. The group did not try to reach an agreement on this

issue, since it seemed important to note that these data could be interpreted one way by some

people and differently by others.
C-45

Comparison of Site Data from TEQ and Total PCB Perspectives
C-46

The group noted that in two of the three species, PCBs were the main contributors to total

TEQ; TCDD for the most part made a relatively small contribution to the total TEQ, and furans

were similarly minor contributors, except in fish. Clearly, the contribution of various classes

was more obvious using the TEQ approach. Group members felt that this was important, since it

increased people's comfort level about the range of conclusions that could be drawn about the

site. Everyone agreed that the results of the TEQ analysis were sufficient to support screeninglevel decisions. Opinions began to diverge, however, as application of the TEQ approach moved

closer to the regulatory arena.

Group members concluded that the amount of additional information revealed by application

of the TEF approach depends on the mix of congeners present in the system. In at least one case,

moreover, the group agreed that reliance on TCDD alone would alter the outcome of the risk

analysis. In this case as in the prospective case study, group members who were not accustomed

to dealing with the U.S. regulatory system were surprised that anyone would actually go out and

measure TCDD alone, as opposed to the full suite of dioxin-like congeners, and even more

surprised that a regulatory decision might be based on TCDD alone. Group members agreed that

this approach is scientifically unsound.

The group engaged in an extended discussion of uncertainty, and members agreed that it is

important to identify and put bounds on the various sources of uncertainty in the TEQ-based

analysis. In particular, it is important to recognize that some uncertainties are quantitative,

having to do with statistical variability, while others have to do with gaps in the knowledge base. 

Different analytical tools should be used to address these differing types of uncertainty and

different analytical approaches are required to carry them through the assessment.

When it came to the actual decision the group had agreed to make, there was a divergence of

opinion about whether the TEF approach is sufficient. Some people felt that the approach

provided enough information to move forward, and others did not. Everyone agreed that the

approach provides useful information about where the key gaps in the data are, and for that

reason alone there was agreement that the approach should not be turned down. However, some

people felt that the results of the TEF approach would have to be supplemented with more
C-47

information on population dynamics and on the relationship between the biochemical or

molecular endpoints on which the TEFs are based and effects at the population level before the

approach could be used to decide whether to move forward into a regulatory decisionmaking

mode.

Differences in opinion about the sufficiency of the TEF approach were based mainly on the

paucity of information about the uncertainties associated with individual TEF values. Although

group members uniformly felt that the underlying data was probably very robust, some

nevertheless felt that TEF values could not legitimately be used in a risk assessment until and

unless the associated uncertainties were expressed quantitatively and carried through the

analysis. In particular, group members were concerned about uncertainties associated with the

derivation of TEFs, with species differences in responsiveness to the various congeners, and with

the ability of TEF-based methods to predict population-level effects. 

At the end of their deliberations, Dr. deFur's group attempted to identify data gaps that

seemed particularly critical in the context of the retrospective case study. Research efforts that

might be useful in addressing these gaps included:

• testing of the Caspian terns themselves to develop species-specific BAF and BMF

values;

• performing ground-truthing exercises to get a better sense of the relationship between

exposure levels and responses in the tern population;

• gathering population data for the three species of concern;

• examining sediment core samples from the lake as opposed to the river to get a better

sense of the distribution of chemicals in the system as a function of both time and

space;

• determining deposition rates and inputs from sources other than the site of the prior

spill; and

• performing ground-truthing exercises to assess the predictive capability of the

TEF/TEQ approach at sites for which there is already a good body of data.
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In response to a question from Dr. Menzie, who asked whether the group had identified any

specific types of uncertainty in the TEF approach that were particularly problematic, Dr. deFur

indicated that the three major concerns of the group had to do with differences between the

species used to derive the TEF values and the species of concern in the risk assessment, with the

statistical uncertainty in the derivation of a TEF from multiple REP values, and with the

statistical uncertainty in the REP values themselves. Another group member pointed out that the

reason for this concern was that group members were unsure whether the uncertainty in

TEF/TEQ values was high enough to impact conclusions about whether observed levels of

contaminants did or did not exceed the threshold value.

Another member of the expert group commented that the group's reticence to recommend

that the results of the TEF analysis be used as a basis for risk management decisions seemed to

include some presumptions about what those decisions might be. Noting that there was a similar

reticence in his own group, this expert suggested that assessors should be sure they are not

attempting to do the risk manager's job, since the decision could just as easily be whether to

spend an additional $100,000 on research as to embark on a $1 billion remediation effort. If

experts believe the method sufficient to support the former decision—which most seem to—then

it was not clear to him why it wouldn't be sufficient to support the latter, since the validity of the

method would not have changed. The task of the assessor, he noted, is to present the facts and

associated uncertainties in a way that will inform the risk manager's decision, not to determine

which decisions should or should not be made on the basis of the available data. Dr. deFur

responded that there had been some discussion of this in the group, and that no one wanted to go

on the record as recommending remediation even for a fictitious site.

Dr. Menzie asked whether the group felt that the need for supplementary lines of evidence

was critical to adoption of the TEF approach, or whether these additional lines of evidence

would simply increase the group's comfort level. Dr. deFur responded that both views had been

expressed in the group, and that no effort had been made to reach an agreement on this issue.

At this point in the discussion, a member of the Planning Group expressed concern about

recommendations related to the need for further research. He said that it was unclear to him, as
C-49

someone who does not do risk assessments, how likely it is that additional information will

actually be forthcoming. If this is unlikely, as he guessed it would be, then he wondered how

useful these recommendations would actually be. Dr. deFur responded that if groups like the

expert group did not request, insist on, and point the direction for additional research, then the

necessary research certainly would not be forthcoming. In that respect, he felt it very important

for research recommendations to be made, even though he knew from his own experience that

risk managers at Superfund sites and other potential cleanup sites often had to make remediation

decisions based on data sets that were not nearly as rich as those provided for the two case

studies. The original commenter noted that if decisions are being made on such a scant amount

of data, it is hard to imagine how uncertainties in the TEFs could possibly be large enough to

have an impact on the overall uncertainty of the assessment.

Another member of the Planning Group agreed, noting that in the field exposure information

is often much more uncertain that toxicity information, which is what the TEF is providing. 

Even so, he thought that additional data would be particularly valuable in the context of this case

study because the risks are only marginally excessive, if they are excessive at all. If the residues

had been much higher than threshold, he thought that many of the people who were otherwise on

the fence might support the use of this methodology for decisionmaking purposes.

One of the members of Dr. deFur's group thought that some of the research this group called

for could be done relatively easily and probably would be done if an industrial concern were in a

situation similar to that described in the case study. Faced with a $10 million cleanup, industry

scientists would have a strong motivation to fill some of these gaps in the understanding of

uncertainty, precisely because they would not want to be caught in the position of having to

comply with management decisions that were based on back-of-the-envelope risk calculations

that failed to take uncertainty into account. He went on to note that even he and the other people

who were calling for better characterization of the uncertainties like the TEF approach, because

it does have the advantage of bringing different congeners together in an integrated model. The

only question is whether the method is sufficiently well developed to support definitive,

quantitative risk management decisions. Without more precise information about the error in

these values, it is simply not possible to answer this question.
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In response to this comment, one of the experts expressed the opinion that uncertainties in

the method do not mean that the method cannot or should not be used. He noted that decisions

are made every day on the basis of incomplete information; if a decision needs to be made

tomorrow, this incomplete method may represent the best that we can do. Another expert

suggested that, at least from a risk management perspective, the question can also be framed in

terms of the need to select between three different methods that are all incomplete in some way. 

From this perspective, he thought that most people would agree that despite its limitations, the

TEF methodology offers important advantages over those based on total PCBs or on TCDD

alone. 

Group 2. Ms. Burris noted that her group began its deliberations by discussing the effects

portion of the analysis, working through each of the species of concern to determine which TEF

they would use and what level of uncertainty was associated with these selections.

For lake trout, the group decided to use both a TEF derived from the rainbow trout data

(0.005) and an REP for PCB 126 in lake trout (0.003). The group felt that extrapolation from the

trout data to other, non-salmonic species in the lake would introduce uncertainty, but that the

magnitude of this uncertainty is unknown because the data needed to quantify it are not

available.

For the Caspian tern, the group chose to adopt the WHO TEF, mainly because the

information used to derive it was of better quality than the species-specific data that were

available. Based on an EROD assay of PCB 126, the TEF derived for the Caspian tern using

species-specific data was 0.03, and the WHO consensus value was 0.1. Therefore, use of the

WHO value increased the TEQ from 185 to 426.

At this point in their deliberations, the group briefly discussed whether the risk assessor

should be allowed to select a species-specific TEF from the available REPs, or whether that

decision should be left to individuals with a better understanding of the literature. The group did

not reach an agreement on this point, but they did feel that it was important for the assessor to

have the flexibility to use a species-specific value if one was available.
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For the mink, the group elected to use the WHO value. There was some discussion of the

endpoints used in the derivation of this value, but the information needed to resolve this issue

was not available.

Because of the difficulties they had in selecting TEFs for the species of interest, the group

had a general concern about the lack of transparency in the WHO consensus TEF values. The

group also felt that it would be more useful if these values were expressed as ranges, since

management decisions are frequently not based on point estimates. Ranges would also help to

quantify the uncertainty associated with a particular TEF, which would increase overall

confidence in the results of the analysis.

Looking more closely at the issue of using TEFs other than those set forth by the WHO, the

group attempted to develop a TEF selection hierarchy. In decreasing order of preference, the

hierarchy they developed was as follows:

 • a TEF derived using the endpoint of interest in the species of concern;

• a TEF derived on the basis of in vivo toxicity data in the species of concern;

• a TEF derived using the endpoint of concern in a related species;

• a TEF derived on the basis of in vivo toxicity data in a related species;

• a TEF derived from a Tier 2 REP for the species of interest; and

• the WHO consensus TEF.

The group also discussed whether uncertainty in the assessment could be reduced by

performing a full food chain modeling exercise. They decided that such an effort would be

problematic both because of the heterogeneity in the system and a possible lack of equilibrium. 

Members agreed that a full modeling exercise was probably not necessary, but that a partial

modeling exercise could be useful in developing site-specific BSAFs and BMFs. These values,

in turn, would allow the risk manager to examine the tissue level reductions that could be

expected to occur in target species under different management scenarios. However, the model

could probably not be used to predict concentrations over time.
C-52

The group's approach to the risk characterization was similar to that followed by Dr. deFur's

group, and they noted that the TEF methodology yielded a higher estimate of risk that either the

total PCB or TCDD-only methodologies.

A question that came up during the group's discussion of this case was how to account for the

fact that, as a migratory species, the terns might be getting some of their exposure at another site. 

After some discussion, members agreed that the assessor could use a weight of evidence

approach to evaluate the relevant scientific literature and develop an opinion about whether and

to what extent tissue concentrations in the birds should be attributed to the site.

The group developed hazard quotients for individual organisms in each of the species of

interest. In general, these values were borderline. Use of a TEF for common tern data as

opposed to a TEF derived from the Caspian tern data altered the hazard quotient by less than an

order of magnitude. There was some concern within the group about how hazard quotients

should be translated to effects at the population or community level. Because the stated goal of

the assessment was protection at the population level, the group felt that a separate modeling

exercise would be required to better understand the relationship between hazard quotients and

the assessment endpoint. Without this information, some members of the group were concerned

about the advisability of basing a management decision on the results of the TEF-based analysis. 

Regarding issues that should be addressed in the risk characterization, one person suggested

that it would be useful to try to describe how the system might look in one, five, and ten years if

no action was taken. Some members of the group thought that PCB concentrations would

decrease over time, eventually reaching a level that is lower than the action threshold. Others

suggested that a hundred-year flood scenario should be included in the characterization, and that

there should be some discussion of the decrease in reproduction required to produce a population

effect. In view of the borderline condition of the system, some group members also felt that

attention should be focused on the potential effect of additional inputs to the system that might

occur in the future.
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When a vote was taken, two members of the group voted for action and four voted for no

action. In the event that the risk manager decided to pursue a cleanup, the group agreed that the

otter would be the species of concern in setting cleanup levels. The reason for this choice had to

do with the fact that the otter is considerably more sensitive to dioxin-like compounds than the

reference species, so there is reason to believe that the true threshold for toxic effects would be at

the low end of the range established for the mink.

To follow up on this latter point, one of the other members of the group noted that the range

in the threshold for fish covers three orders of magnitude, and that this is a TCDD-based

threshold. Given that the uncertainty in the threshold value for a single congener, particularly

TCDD, is so great, this person wondered how much the estimated order-of-magnitude

uncertainty in TEF values would actually add to the overall uncertainty of the assessment.

Another group member elaborated on the decision not to recommend a food web model for

this system. First, group members had concluded that it would be difficult to obtain credible

water concentrations for the individual congeners, since they are present at such low levels. It

would also be difficult to estimate sediment values, since the distribution of these compounds in

sediment was likely to be heterogeneous. As a result, group members thought that development

of species-specific BAFs and BMFs would be sufficient to reduce the uncertainty without

introducing such formidable analytic challenges.

A member of the expert group raised a general issue related to the use of Ah receptor agonist

levels in the liver as a marker of exposure, since there is a tendency for these chemicals to

accumulate in the liver, and accumulation is itself dependent on the level of exposure. One of

the Planning Group members pointed out that studies addressing this issue have shown no effect

on the BMFs for the various congeners.

Another member of the Planning Group questioned the workgroup's use of a 50% reduction

as a more or less universal population effect of concern, rather than tailoring this threshold to the

local population. He thought that for bald eagles or nesting pairs, for example, a different metric

might be more appropriate. The group member who had originally proposed the 50% value
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agreed, and said that historical records of reproductive performance might also be useful if the

number of individuals or nesting pairs in the system was small. A different member of the

Planning Group suggested that another way to approach this issue would be to simply use

exceedance of the standard as a surrogate for population-level effects, since standards are

developed to protect the most sensitive members of a population.

Group 3. Dr. Menzie said that his group began by revisiting a couple of the topics they had

addressed previously, during consideration of the prospective case study. One member of the

group, for example, had developed a concern that the uncertainty associated with the derivation

of TEFs might be greater than was reflected in the matrix the group presented at the previous

day's plenary session. The group therefore decided that it was important to stress that the matrix

was intended to illustrate a conceptual approach, rather than to present hard and fast descriptions

of the uncertainty in this particular system.

The group also revisited the issue of uncertainty in the water quality standards. Initially, the

group had thought about the uncertainty in these values as having mainly to do with the

interspecies extrapolations required in the application of these values. Subsequently, however,

group members realized that there are probably other uncertainties associated with these values

as well. The lesson, Dr. Menzie suggested, is that it is important to think about uncertainties on

the exposure as well as the effects side of the analysis.

 Like the previous group, Dr. Menzie's workgroup was able to trace the origin of the WHO

consensus TEFs for fish and birds, but not for mammals. The group understood that this

information does exist, but for purposes of this risk assessment the associated uncertainties were

not quantifiable. Given the importance of uncertainty information to the risk assessment

process, the group decided to recommend that some organization make an effort to provide that

level of documentation for the consensus TEF values, so that risk assessors could have a better

understanding of where those values come from. 

One of the lessons the group learned from the case study exercise had to do with the

availability of site-specific measurements in this case study. The group discussed the
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uncertainties associated with the measurements themselves, and concluded the need for

measuring a large number of congeners in the TEF approach did not add appreciably to the

overall uncertainty of the assessment. Assuming that appropriate analytical methods are used,

the group thought that errors in these measurements would fall in the 5% to 30% range. The

effect of these uncertainties might be substantial, however, if there was reason to question the

analytical methods themselves.

Another point of discussion had to do with the potential for uncertainties related to detection

limits for the individual congeners. In some situations, the detection limits of an analytical

method might be well above levels of a congener that are of importance for risk assessment

purposes. Because of this, risk assessors involved in a TEF/TEQ analysis must recognize the

importance of achieving detection levels that correspond to the needs of the assessment process. 

Dr. Menzie noted that the group talked a little bit about whether there are any sampling

issues that are specific to the TEF/TEQ approach. Although they recognized sampling as an

important element of the risk assessment process, group members did not think that sampling

issues associated with the TEF/TEQ approach are any different than those associated with other

methodologies.

Group members thought that the cost of the TEF approach would probably be greater than

the cost of other methods, since the need for multiple-congener analysis translates to a higher

price per sample. Some members predicted, however, that the cost of multi-congener analyses

will decline as this methodology becomes more widely used.

The group also discussed how a risk assessor might use the TEF approach in dealing with a

partial data set—for example, one in which data were available only for PCBs. The group

decided that in such a case it would be very valuable to analyze at least some samples for the full

suite of congeners to get some sense of the relative importance of the different congener groups

and to confirm that the compounds for which data are available are actually the congeners

driving the assessment. 
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As a longer-term improvement to the methodology, the group felt that it might be useful to

see if there is a reliable way of identifying, on a site-specific basis, a simpler measurement that

could be used as a surrogate for TEQs. If so, this surrogate could be used to more costeffectively monitor the effects of a remediation effort over time. 

To address the quantitative aspects of the retrospective case study, Dr. Menzie's group used a

process that was similar to those used in the other two groups, and they arrived at essentially the

same conclusions. One caveat that the group thought it important to mention, however, is that

there could be effects on endpoints other than reproduction that are not specifically being

addressed in the risk assessment, particularly with regard to PCBs.

Regarding the issue of whether the TEF methodology was robust enough to support a

regulatory decision, the group first agreed that the decision might involve a range of options

rather than simply focusing on whether or not to dredge. In thinking about the quality of the

available data, the group felt that it would be very important to have a better sense of what the

background levels of the contaminants typically are in Caspian terns and otters. Without this

information, the group thought that it would be very difficult to recommend that some specific

action be taken. 

The group also considered whether congener levels in the terns and otters, especially, could

be explained by the levels or concentrations of these compounds in the fish and sediments of

Oneofakind Lake. After attempting to develop some rough site-specific BAF and BMF factors,

the group concluded that the observed values are consistent with a sediment to fish to predator

pathway.

To move much forward with the analysis, the group thought that it would be valuable to

develop a better understanding of the system itself, especially with respect to the structure of the

food webs. In particular, they wondered how actions taken in the lake could be expected to

impact contaminant body burdens in the organisms of interest. In the absence of information

about background levels of the contaminants in the species of concern, the group couldn't get too

far with this process, so they decided to assume that the necessary data were available and that
C-57

these data showed a one or two order of magnitude elevation in the tissue levels of these

compounds. The group then attempted to devise a way of working backward from these levels

to a sediment remediation target. What they found was that the logistical challenges of this

approach had mainly to do with the need to carry a number of compounds that differ in their

behavioral characteristics through the process, much as was done in the TMDL model. 

As part of the risk characterization for the retrospective scenario, the group thought that it

might be useful to develop a regression model relating body burdens to contaminant levels in

sediments or water on a TEQ basis, so that risk managers could have a clear sense of the degree

of remediation required to achieve various target levels in the organisms of concern. Like the

previous group, Dr. Menzie's group felt that it would also be important to explore the possibility

of future recovery of the system in the absence of any intervention, and at least some members

thought that such an effort could be informed by sediment core sampling to examine the history

of recovery in the years since the spill. Given its borderline status, some members felt that a

recommendation to simply monitor the system might be appropriate, while others thought that it

would be preferable to formally model what the system was likely to look like in years to come.

Additional lines of evidence that the group thought might be brought to bear on the

remediation decision include more extensive field observations of the current state of the

population, with attention to whether effects predicted by the TEF/TEQ approach are actually

occurring at the individual level. Similarly, they thought that it would be useful to obtain a more

precise understanding of the distribution of contaminants within the sediments, so that

remediation efforts can be directed where they are most needed.

A final point of discussion within the group had to do with the need for a top-down,

population-level analysis of this system. In general, Dr. Menzie said, group members' sense of

the urgency of this need tended to reflect their individual areas of expertise and familiarity with

specific tools. Thus, toxicologists were more comfortable with the idea of collecting and

working with toxicity data, while the population biologists were more comfortable with the use

of specific metrics to describe what is going on in the system at a population level. During the
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course of this discussion, however, all members of the group agreed that it will be important to

find ways of bringing together the lines of evidence that come from these different perspectives.

Following Dr. Menzie's summary of the group's deliberations, Dr. van den Berg noted that

several groups had commented on the lack of transparency in the derivation of WHO consensus

TEF values for mammals. He indicated that the authors of the WHO document had not realized

that these values would be useful, and he said that specific references to the studies driving those

TEF values would be added to the paper, at least for those TEFs that were changed by the

Working Group. Adding this information for the TEFs that were adopted without modification

may be difficult, since documentation as to how those values were derived is scant.

Regarding the issue of expressing the consensus TEFs as ranges rather than point estimates,

Dr. van den Berg said that participants at the Stockholm meeting had decided against this

approach because many of the TEFs were derived from a variety of endpoints and so may have a

range that covers several orders of magnitude. In the past, people have used this fact to wrongly

claim that the TEF system doesn't work. If risk assessors wish to work with ranges instead of

point estimates, Dr. van den Berg suggested that they go back to the studies from which the

TEFs were derived, and develop their own TEF ranges from the ones that are most appropriate to

the site they are assessing.

A member of the Planning Group noted that the 1994 Ahlborg paper does include histograms

describing the studies used to derive mammalian TEFs, and that, contrary to popular belief, a

large number of these values are based on in vivo, Tier 1-level data.

Another member of the Planning Group asked Dr. van den Berg to comment on the

accessibility of the Karolinska database and on how the database would be maintained—whether

anyone had assumed responsibility for keeping it current and/or for assessing the quality of

studies that are included. Dr. van den Berg said that it was his understanding that the database

would be accessible to anyone who wanted to use it, and that the charge for access would be

minimal. Regarding maintenance of the database, he noted that at the time of the Stockholm

meeting the database was two or three months behind the calendar. Although he did not know
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whether the database has been similarly maintained since the meeting, he indicated that the issue

of maintenance is currently being discussed. There are no plans to review the data from a quality

control perspective, but informal guidelines have been established.

After this exchange, another member of the Planning Group commented on the Menzie

group's discussion of detection limits as they relate to use of the TEF approach, noting that one

way to address this problem is to be sure that the concentrations a lab provides are accompanied

by information about the quantitative limits of the detection method. 

One of the experts questioned the group's suggestion that a surrogate such as total PCBs

might be useful for screening or monitoring purposes. He cautioned that this could be

misleading, as it would be in the retrospective scenario, where dibenzofurans, despite being

present at very low levels in the Aroclors, are highly enriched in the sediments of this system. A

member of Dr. Menzie's group pointed out that the group had discussed the possibility of using

total PCBs as a surrogate mainly because of the closed nature of the system in the case study. In

addition, group members agreed that if such a surrogate were used, it would be important to do

some confirmatory studies to validate the presumed correspondence with TEQ values. A

member of the Planning Group noted that confirmatory studies might be particularly important in

a remediation setting, since the method of removal might affect the ratio of congeners in the

system.
C-60

IV. CONCLUSIONS AND RECOMMENDATIONS

The following conclusions and recommendations are the result of the peer consultancy

workshop. As such, they reflect the collective professional judgment of the expert scientists. 

They should not be construed as scientific facts, but as the outcomes of discussions over a threeday period among individuals with varying types of expertise related to the derivation of relative

potency values and toxic equivalency factors, and their application to risk assessment. In these

conclusions, reference to the TEF/TEQ methodology is meant to include both species-specific

REP values as well as WHO TEF values.

The conclusions and recommendations are organized into two parts. The first consists of the

conclusions reached within the plenary session at the close of the workshop. Conclusions have

also been developed around the charge questions provided to the experts prior to the meeting. 

These conclusions were prepared by the work group chair and work group leaders immediately

following the close of the meeting.

Part One: Conclusions Reached by Experts at the Plenary Session

1. The TEF/TEQ methodology is technically appropriate for evaluating risks to fish, birds,

and mammals associated with AhR agonists. The methodology can support risk analyses beyond

screening-level assessments. Examples of possible applications include the evaluation of point

source discharges (within the framework of the Clean Water Act) and the evaluation of

contaminated sites (within the framework of the Comprehensive Environmental Remediation

and Compensation Liability Act). The applicability of the method is situation-specific. As with

any method, appropriate caution should be exercised to avoid misuse or application of the

methodology to situations where the underlying assumptions are known not to be valid. When

applying the method, it should be recognized that there may be effects associated with the

chemicals of concern that are unrelated to AhR and, therefore, may need to be evaluated under a

separate methodology. These possibilities should be considered during the planning stage of an

assessment.
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2. The TEF/TEQ methodology reduces uncertainties associated with developing doseresponse information for AhR agonists that exist with methods that rely on a single compound

(e.g., TCDD) or on compounds evaluated as an aggregate (e.g., total PCBs). Specifically,

because the method takes into account the possible effects of the suite of chemicals that act as

AhR agonists, it is less likely to underestimate risks than are methods based on only one of these

compounds (i.e., TCDD). Further, because total PCBs in the environment can be comprised of

many compounds that vary in concentration and potency as AhR agonists, the TEF/TEQ

methodology provides a means for accounting for these variables. 

3. The uncertainties associated with using REPs or TEFs are not thought to be larger than

other sources of uncertainty within the risk assessment process (e.g., dose-response assessment,

exposure assessment, and risk characterization.) However, these uncertainties should be

quantified better.

4. As is the case with any ecological risk assessment, the nature and magnitude of

uncertainties should be identified and carried through the ecological risk assessment process

(dose-response assessment, effects assessment and risk characterization). This could involve a

number of different approaches, including qualitative analyses, assignment of ordinal rankings to

sources of uncertainty, presentation of ranges, fuzzy arithmetic, and probabilistic analyses. 

Information on the sensitivity of the risk estimates to the uncertainties associated with the TEF

approach (as well as other ERA components) should be identified and quantified (if possible). 

This knowledge can be used to communicate the range of possible results to the decision maker

and to identify what additional information would be the most useful for decisionmaking. 

Specific examples of approaches are provided in the summaries of the workshop breakout group

sessions on the case studies (Appendix E).

 5. Workshop participants supported the use of a hierarchical procedure for selecting REP or

TEF values for use in risk assessment. In general, the most appropriate values are those that are

closely related to the taxa and endpoints being evaluated. Workgroup participants agreed that

uncertainties are introduced with increasing taxonomic and endpoint extrapolation. The

workgroups suggested schemes for selecting REP and/or WHO TEF values, as well as schemes
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for considering how uncertainties associated with selecting values can be identified and tracked. 

These are identified in the workgroup summaries (Appendix E).

6. A database of REP and TEF values should be maintained in order to facilitate the

application of the hierarchical procedure and to enable the conduct of sensitivity and uncertainty

analyses. The appropriate regulatory agencies will need to consider how to insure the quality of

the data in the database, document the values and the procedures used to derive them, make the

database accessible, and provide guidance for its use.

7. The derivation of REP and WHO TEF values needs to be adequately documented

(including specific citations) in order to support the use of these values in regulatory risk

assessments. The WHO TEF document provided to workshop participants did not include

documentation for the mammalian TEF values. This was viewed as a major limitation on the use

of the document for risk assessment purposes.

8. The TEF/TEQ method requires analytical methods to identify and quantify the individual

dioxin, furan, and PCB compounds. The accuracy and precision of available methods are

considered acceptable for risk assessment purposes. The analytical measurement errors are not

considered to be a large source of uncertainty within the assessment. A few of the workshop

participants familiar with the analytical methods reported measurement errors in the range of 5 to

30%.

9. The costs for analyzing the suite of individual dioxin, furan, and PCB compounds are

greater than those associated with analyzing an individual compound (e.g., TCDD) or for

measuring “total PCBs.” Workshop participants agreed that it may be possible to focus the

analytical effort at different stages of the assessment, thereby reducing costs. For example,

investigations may indicate that risks are due to a few of the compounds or to a particular class

and these may form the basis for subsequent evaluation. Further, it may be possible to

complement detailed analyses of individual compounds with simpler and cheaper analytical

methods (e.g., to provide information on spatial extent of contamination).
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 10. Analytical detection levels for congeners need to be lower than concentrations at which

important biological effects might occur. Workshop participants agreed that this can be achieved

with available methods. As with any analytical program where data will be used in risk

assessments, data quality objectives should be specified and care taken to insure that they are

met.

11. Because physical, chemical, and biological properties vary among the individual dioxin,

furan, and PCB compounds, exposure assessments that complement the TEF/TEQ methodology

may require more information and resources (i.e., effort) than exposure assessments for an

individual compound (e.g., TCDD) or a class of compounds (e.g., total PCBs). Fate and

transport models used to support the exposure assessment will need to account for individual

compounds through the various modeled components. In some cases, it may be possible to

model groups of compounds with similar fate and transport properties.

12. Information on the environmental behavior of individual chemical congeners is needed

to understand and use the congener-specific information in a modeling effort. With increasing

use of a TEF/TEQ approach, gaps in knowledge on chemical-specific environmental behavior

will become evident. Regulatory agencies will need to consider how best to acquire this

information and/or develop exposure assessment tools that can complement the use of TEF/TEQ

for specific regulatory applications.

13. Application of a TEF/TEQ method could be considered within the framework of a “lines

of evidence” approach as described within the EPA’s guidance for ecological risk assessment. 

As such, additional field and laboratory information could corroborate or improve the results of

an assessment that is based, in part, on the application of the TEF/TEQ method. analysis. Use

and integration of various lines of evidence in ecological risk assessment can often strengthen

the analysis and provide a greater degree of confidence in the results than can be achieved from

relying only on a single line of evidence. Each piece of information will have inherent strengths

and limitations, and the amount of confidence placed on the information will also reflect the

technical background of the individuals using the method and their experience with it.
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14. Several workshop participants stressed the value of applying population-level

assessment tools and obtaining population-level information in support of assessments (i.e., as a

line of evidence). These included methods by which risks to individuals could be described in

terms of potential risks to local populations. In addition, a few participants gave examples of

tools that could be helpful for assessing whether population-level effects were being manifested

(for retrospective assessments.) Examples included direct observations of hatching success, the

condition of fledgling birds, and the age structure of populations.

15. Participants also discussed the use of bioassay tools to support the assessment. These

methods could complement assessments that rely upon the TEF/TEQ approach. One participant

summarized the strengths and limitations of these tools as follows. In vitro TEQ bioassays have

the advantage of measuring the integrated effects of complex mixtures of Ah receptor agonists. 

In addition, such assays have the potential of identifying compounds that act via the Ah receptor

which would not be identified by a chemical residue approach that measures only dioxins,

furans and PCBs. In vitro bioassay-derived TEQ concentrations can be obtained at a lower cost

than TEQ concentrations obtained by analysis of chemical residues. One potential problem with 

in vitro bioassays is that they can overestimate the toxic potency of compounds which are

rapidly metabolized in vivo (e.g., PCB 77). However, recent research has shown that such

problems can likely be circumvented. Various in vitro bioassays have considerable potential for

predicting TEQs which are relevant to whole organisms.

16. Participants adopted the language given in the WHO document cautioning against the

potential misapplication of the TEF/TEQ method to environmental media (e.g., sediments or

soils). Specifically, the participants indicated that it is not appropriate to derive TEQs for these

media. TEQs are relevant only with respect to specific ecological receptors. The methodology

can be used to support decisions concerning the regulation of point source discharges and

environmental clean ups that involve chemicals in environmental media. However, in these

cases, the decision involves identifying concentrations of chemicals and/or the composition of

mixtures that would yield acceptable TEQ with respect to specified ecological receptors.
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Part Two: Conclusions Related to Charge Questions

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC

TEF VALUES

1. The WHO consensus TEF values are reported as point estimates and generally rounded

off to the nearest order of magnitude. For the risk assessment case studies, additional

background information used in the derivation of the TEF values is provided. Does this

additional information enhance the means of evaluating uncertainties in the assessments? If

so, how? If not, why?

 Conclusion: Participants found this information useful. However, they indicated that

additional information—beyond that provided—would be important for risk assessment

purposes. This additional information includes better documentation of the process used to

derive TEF values, references for the values employed for mammalian receptors, and access to

the database.

2. Some TEFs were determined from several studies, endpoints, and exposure routes, while

other TEFs were based on a single study and endpoint. Given the range of knowledge

associated with specific compounds, should all TEFs be considered to have similar

uncertainties? Why? Or why not?

Conclusion: All TEFs should not be considered to have similar uncertainties.

Participants discussed several derivation and extrapolation issues that affect the uncertainty

associated with using TEF values. They also provided an example of how these uncertainties

might be tracked.

3. The TEF values provided were based on endpoints that ranged from in vitro biochemical

responses (e.g., induction of cyp1A1) to in vivo early life stage mortality. To what extent

can these endpoints be extrapolated to the measures of effects that are relevant for the

assessment endpoint for each case study?

 Conclusion: Participants described several issues related to dealing with extrapolations. In

general, participants agreed that these extrapolations introduced uncertainties. A hierarchical

system for selecting values was recommended.

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

APPROACH

1. What are the implications, both quantitatively and conceptually, of assuming no doseadditivity or no interaction among the components of the mixtures described in the case 
C-66

studies? To what extent would the risk assessment conclusions differ if stressor response

analyses where based on total PCBs or 2,3,7,8-TCDD alone?

 Conclusion: Participants agreed that the TEF/TEQ method reduced some of the

uncertainties associated with assessments based on total PCBs or on TCDD alone. The

assumption of additivity was viewed as reasonable in the absence of information to the contrary.

2. Many TEFs are based on LC50 or EC50 values. To what extent should TEF values

derived at a median response level be used in risk assessments where a no adverse effect

level is being employed?

 Conclusion: Participants did not reach a specific conclusion. The use of median values

appears acceptable for determining the relative (as opposed to absolute) potencies of the

chemicals.

3. The TEFs values provided were typically based on a single or limited number of 

mammal, bird, or fish experiments. To what extent can class-specific TEFs be directly

extrapolated to the species identified within each case study?

 Conclusion: A hierarchical scheme for selecting REP or TEF values was proposed. Use

of this approach will require access to data on REP and TEF values.
C-67

III. EXPOSURE PROFILE

1. To what extent does the TEF approach present challenges, introduce new uncertainties,

or modify old uncertainties associated with modeling the exposure of AhR agonists? To

what extent does the availability and quality of congener-specific physico-chemical data

limit the means of employing fate and transport or food chain models?

 Conclusion: The approach will likely require additional resources to model exposure

because a larger number of chemicals will need to be taken into account. Because these

chemicals vary in their properties, information is needed on various physicochemical properties

in order to support modeling efforts.

2. The route of administered or absorbed dose used to derive TEFs may differ from those

needed to establish exposure profiles in a risk assessment. To what extent do exposure route

differences used in deriving the TEFs affect their application in the case studies?

Conclusion: This was not discussed at length.

3. To what extent does the TEF approach require a more rigorous analytical design in

quantifying sediments, soil, and biota AhR agonist concentrations than is apparent in other

methods which aggregate stressors (e.g., total PCBs)?

 Conclusion: Sampling design issues were judged to be comparable. However, as

discussed in the main conclusions, there will be additional analytical costs and care must be

taken to specify and meet data quality objectives.

IV. RISK CHARACTERIZATION

1. In evaluating the case studies, are the uncertainties associated with TEFs more

problematic than other uncertainties of the risk assessments? Do the uncertainties associated

with TEFs limit the means of performing the assessments, or do the other areas of the effect

and exposure characterization contribute similar or greater levels of uncertainty?

 Conclusion: These uncertainties are not more problematic than other uncertainties of the

risk assessment. They do not limit the means of performing assessments. However, use of the

method places demands on analytical methods and on modeling of exposure.
C-68

2. Biologically-based TEQ assays on environmental samples could be employed as an

alternative to the TEF-based approach. What would the strengths and weaknesses of such an

approach be? To what extent could these approaches be integrated?

 Conclusion: These assays should not be used as an alternative to the TEF/TEQ approach. 

However, they could be used to complement the analyses. They could also be used as a

screening tool. These assays were thought to be most useful in retrospective assessments. There

was not an agreement on how they would be used in a prospective (i.e., predictive) assessment.
Appendix C-A

WORKSHOP PARTICIPANTS
C-A-1

United States

Environmental Protection Agency

Risk Assessment Forum

Workshop on the Application of

2,3,7,8-TCDD Toxicity Equivalency

Factors to Fish and Wildlife

Chicago Hilton & Towers

Chicago, IL

January 20-22, 1998

Expert Reviewers

William Adams

Director, Environmental Science

Kennecott Utah Copper Corporation

8315 West 3595 South 

Magna, UT 84044-6001

801-252-3112 

Fax: 801-252-3083

E-mail: adamsw@kennecott.com

Björn Brunström

Associate Professor

Department of 

Environmental Toxicology

Uppsala University

Norbyvagen 18A 

S-752 36 Uppsala

Sweden

46-18-471-2626

E-mail: bjorn.brunstrom@etox.uu.se

Janet Burris

Senior Health Scientist

McLaren Hart/ChemRisk

109 Jefferson Avenue - Suite D 

Oak Ridge, TN 37830

423-483-5081 

Fax: 423-482-9473

E-mail: oakridge_office@

mclaren-hart.com

Steven Bursian

Professor

Department of Animal Science

Michigan State University

132 Anthony Hall 

East Lansing, MI 48824

517-355-8415 

Fax: 517-432-1518

E-mail: bursian@pilot.msu.edu

Peter deFur

Environmental 

Stewardship Concepts

11223 Fox Meadow Drive 

Richmond, VA 23233

804-360-4213 

Fax: 804-360-7935

E-mail: pldefur@igc.org

Joseph DePinto

Director

Great Lakes Program

State University of New York 

at Buffalo

202 Jarvis Hall 

Buffalo, NY 14260-4400

716-645-2088 

Fax: 716-645-3667

E-mail: depinto@eng.buffalo.edu

Lev Ginzburg

Professor

Department of 

Ecology and Evolution

State University of New York

11 Crane Neck Road 

Setauket, NY 11733

516-632-8569 

Fax: 516-751-3435

E-mail: lev@ramas.com

Jay William Gooch

Senior Scientist

Professional and

Regulatory Services

Paper Products Division

The Procter and 

Gamble Company

Winton Hill Technical Center 

6100 Center Hill Road

Cincinnati, OH 45224-1788

513-634-1053 

Fax: 513-634-7364

E-mail: gooch.jw@pg.com
C-A-2

Mark Hahn

Associate Scientist

Biology Department

Woods Hole 

Oceanographic Institution

45 Water Street (MS #32)

Redfield 338

Woods Hole, MA 02543-1049

508-289-3242 

Fax: 508-457-2169

E-mail: mhahn@whoi.edu

Sean Kennedy

Research Scientist

National Wildlife Research Centre

Wildlife Toxicology Division

Environment Canada

100 Gamelin Boulevard 

Hull, Quebec K1A 0H3 

Canada 

819-997-6077 

Fax: 819-953-6612

E-mail: sean.kennedy@ec.gc.ca

Wayne G. Landis

Director, Institute of Environmental

Toxicology and Chemistry

Huxley College of 

Environmental Studies

Western Washington University

516 High Street (MS-9180)

Bellingham, WA 98225-9180

360-650-6136 

Fax: 360-650-7284

E-mail: landis@henson.cc.wwu.edu

Lynn McCarty

Ecotoxicologist

L.S. McCarty Scientific 

Research & Consulting

280 Glen Oak Drive 

Oakville, Ontario L6K 2J2 

Canada

905-842-6526 

Fax: 905-842-6526

E-mail: lmccarty@interlog.com

Charles Menzie

Menzie-Cura & Associates, Inc.

2 Courthouse Lane - Suite 2 

Chelmsford, MA 01824

978-970-2620 

Fax: 978-970-2791

E-mail: charliemen@aol.com

Christopher Metcalfe

Chair, Environment and

Resource Studies

Trent University

Nassau Mills Road 

Peterborough, Ontario K9J 7B8 

Canada

705-748-1272 

Fax: 705-748-1569

E-mail: cmetcalfe@trentu.ca

Mike Meyer

Wildlife Toxicologist

Regional Headquarters

Wisconsin Department 

of Natural Resources

107 Sutliff Avenue 

Rhinelander, WI 54501

715-365-8858 

Fax: 715-365-8932

E-mail: meyerm@dnr.state.wi.us

Patrick O'Keefe

Research Scientist

Laboratory of Organic 

Analytical Chemistry

Wadsworth Center

New York Department of Health

Empire State Plaza 

P.O. Box 509

Albany, NY 12201

518-473-3378

Fax: 518-473-2895 

E-mail: okeefe@wadsworth.org

Richard Peterson

Professor

School of Pharmacy and

Environmental Toxicology Center

University of Wisconsin

425 North Charter Street 

Madison, WI 53706

608-263-5453 

Fax: 608-265-3316

E-mail: rep@pharmacy.wisc.edu

Mark Servos

Aquatic Ecosystem 

Protection Branch

National Water Research Institute

Canada Center for Inland Waters

867 Lakeshore Road 

P.O. Box 5050

Burlington, Ontario L7R 4A6 

Canada

905-336-4778 

Fax: 905-366-4420

E-mail: mark.servos@cciw.ca

Martin van den Berg

Associate Professor of

Environmental Toxicology

Research Institute of Toxicology

University of Utrecht

P.O. Box 80176 

Utrecht, 3508 TD 

The Netherlands

31-30-253-5265

Fax: 31-30-253-5077

E-mail: m.vandenberg@

ritox.dgk.ruu.nl

Bert van Hattum

Senior Lecturer

Head of Chemistry and

Ecotoxicology

Institute for Environmental Studies

Vrije Universiteit

De Boelelaan 1115 

1081 HV, Amsterdam 

The Netherlands

31-20-444-9555

Fax: 31-20-444-9553

E-mail: bvanhattum@ivm.vu.nl
C-A-3

United States

Environmental Protection Agency

Risk Assessment Forum

Workshop on the Application of

2,3,7,8-TCDD Toxicity Equivalency

Factors to Fish and Wildlife

Chicago Hilton & Towers

Chicago, IL

January 20-22, 1998

EPA/DOI Planning Group

Linda Birnbaum (not in attendance)

National Health and Environmental 

Effects Research Laboratory

Experimental Toxicology Division

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

919-541-2655 

Fax: 919-541-4284

E-mail: birnbaum.linda@epamail.epa.gov

Steve Bradbury

Regional Scientist

U.S. Environmental Protection Agency

999 18th Street - Suite 500 

Denver, CO 80202-2466

303-312-6016 

Fax: 303-312-6067

E-mail: bradbury.steven@epamail.epa.gov

Pat Cirone

Office of Environmental Assessment

U.S. Environmental Protection Agency

1200 Sixth Avenue 

Seattle, WA 98101

206-553-1597 

Fax: 206-553-0119

E-mail: cirone.patricia@epamail.epa.gov

Philip Cook

Acting Chief, Ecological Toxicology Branch

National Health and Environmental 

Effects Research Laboratory

Mid-continent Ecology Division

U.S. Environmental Protection Agency

6201 Congdon Boulevard 

Duluth, MN 55804

218-529-5202

Fax: 218-529-5003

E-mail: cook.philip@epamail.epa.gov

Mike Devito

National Health and Environmental 

Effects Research Laboratory

Experimental Toxicology Division

U.S. Environmental Protection Agency

86 TW Alexander Drive (MD-74)

Research Triangle Park, NC 27711

919-541-0061 

Fax: 919-541-4324

E-mail: devito.mike@epamail.epa.gov

Gerry Henningsen

Regional Senior Toxicologist

U.S. Environmental Protection Agency

999 18th Street - Suite 500 

Denver, CO 80202-2466

303-312-6673 

Fax: 303-312-6065

E-mail: henningsen.gerry@epamail.epa.gov
C-A-4

Tim Kubiak

Division of Environmental Contaminants

U.S. Fish and Wildlife Service

4401 North Fairfax Drive (ARLSQ 330)

Arlington, VA 22203

703-358-2148 

Fax: 703-358-1800

E-mail: tim_kubiak@mail.fws.gov

Cynthia Nolt

Office of Science Policy

U.S. Environmental Protection Agency

401 M Street, SW (8104R)

Washington, DC 20460

202-564-6763 

Fax: 202-565-2911

E-mail: nolt.cynthia@epamail.epa.gov

Robert Pepin

U.S. Environmental Protection Agency

77 West Jackson Boulevard (WT-16J)

Chicago, IL 60604-3590

312-886-1505 

Fax: 312-886-0168

E-mail: pepin.robert@epamail.epa.gov

Donald Tillitt

Biological Resource Division

U.S. Geological Survey

4200 New Haven Road 

Columbia, MO 65201

573-876-1886 

Fax: 573-876-1896

E-mail: donald_tillitt@nbs.gov

Steve Wharton

U.S. Environmental Protection Agency

726 Minnesota Avenue (SUPR/FFSE)

Kansas City, KS 66101

913-551-7819 

Fax: 913-551-7063

E-mail: wharton.steve@epamail.epa.gov

Lisa Williams

U.S. Fish and Wildlife Service

2651 Coolidge Road 

East Lansing, MI 48823

517-351-8324 

Fax: 517-351-1443

E-mail: lisa_williams@mail.fsw.gov

Bill Wood (not in attendance)

Risk Assessment Forum

National Center for Environmental Assessment

U.S. Environmental Protection Agency

401 M Street, SW (8601)

Washington, DC 20460

202-260-1095 

Fax: 202-260-3955

E-mail: wood.bill@epamail.epa.gov
C-A-5

United States

Environmental Protection Agency

Risk Assessment Forum

Workshop on the Application of

2,3,7,8-TCDD Toxicity Equivalency

Factors to Fish and Wildlife

Chicago Hilton & Towers

Chicago, IL

January 20-22, 1998

Observers

John Blankenship

Assistant Regional Director

U.S. Fish and Wildlife Service

Henry Whipple Federal Building

1 Federal Drive

Fort Snelling, MN 55111-4056

612-725-3536, Ext.: 201

Fax: 612-725-3526

Douglas Beltman

Manager

Hagler Bailly Services, Inc.

1881 Ninth Street - Suite 201 

Boulder, CO 80302

303-449-5515 

Fax: 303-443-5684

E-mail: dbeltman@habaco.com

John Bleiler

ENSR

35 Nagog Park 

Acton, MA 01720

978-635-9500, Ext.: 3050

Fax: 978-635-9180

Christine Boivin

Risk Assessment Forum

National Center for

Environmental Assessment

U.S. Environmental 

Protection Agency

401 M Street, SW (MC: 8601)

Washington, DC 20460

202-260-8248 

Fax: 202-260-3955

E-mail: boiven.chris@epamail.epa.gov

Eddie Buxton

Project Scientist

General Engineering

Laboratories

P.O. Box 30712 

Charleston, SC 29417

803-769-7378 

Fax: 803-769-7397

E-mail: jeb2@gel.com

Rhona Compton

Toxicologist

Cantox, Inc.

111 5th Avenue, SW Ste 1160 

Calgary, Alberta T2P 3Y6

Canada

403-237-0275 

Fax: 403-237-0291

E-mail: rcompton@cantox.com

Bruce Diel

Section Manager, Environmental &

Analytical Chemistry

Midwest Research Institute

425 Volker Boulevard 

Kansas City, KS 64110

816-753-7600, Ext.: 1631

Fax: 816-753-5359

E-mail: bdiel@mriresearch.org

Arunas Draugelis

Toxicologist

U.S. Environmental Protection

Agency

77 West Jackson Boulevard (SR6J)

Chicago, IL 60604

312-353-1420 

Fax: 312-353-5541

E-mail: draugelis.arunas@epamail.epa.gov

Steve Ellingson

Associate

Geraghty and Miller

105 5th Avenue, South - Suite 350

Minneapolis, MN 55401

612-339-9434 

Fax: 612-336-4538

E-mail: sellings@gmgw.com
C-A-6

William Enriquez

Ecologist

Office of Resource Conservation

and Recovery Act

U.S. Environmental 

Protection Agency

77 West Jackson Boulevard 

HRP-8J

Chicago, IL 60604-3590

312-886-1484 

Jay Field

National Oceanic & Atmospheric

Administration

HAZMAT

U.S. Department of Commerce

7600 Sand Point Way, NE 

Seattle, WA 98115

206-526-6404 

Fax: 206-526-6865

E-mail: jay.field@hazmat.noaa.gov

Brent Finley

Principal Health Scientist

ChemRisk

1135 Atlantic Avenue 

Alameda, CA 95404

707-526-1313 

Fax: 707-526-6705

Richard Fox

Sediment Quality Specialist

Hart Crowser

6250 River Road - Suite 3000 

Rosemont, IL 60018

847-292-4426 

Fax: 847-292-0507

Michael Gilbertson

Biologist

International Joint Commission

P.O. Box 32869 

Detroit, MI 48232

519-257-6719 

Fax: 519-257-6740

E-mail: gilbertsonm@ijc.wincom.net

Paul Goettlich

Board of Directors, Hoosier

Environmental Council

P.O. Box 6854 

South Bend, IN 46660-6854

209-273-0557 

E-mail: gottlich@sbt.infi.net

Steve Johnson

Geologist

Pesticides and Toxic

Substance Branch

U.S. Environmental 

Protection Agency

77 West Jackson Boulevard

(DRT 8J)

Chicago, IL 60604

312-886-1330 

Fax: 312-353-4342

E-mail:

johnson.steve@epamail.epa.gov

Russ Keenan

Vice President 

and Chief Health Scientist

ChemRisk

McLaren/Hart

1685 Congress Street 

Portland, ME 04102

207-774-0012 

Fax: 207-774-8263

E-mail: russell_keenan@mclarenhart.com

Edward Knapick

Director of Research

Marcal Paper Mills, Inc.

One Market Street 

Elmwood Park, NJ 07407-

1451

201-703-6472 

Fax: 201-703-6227

Carol-Ann Manen

Chief, Injury Assessment

Damage Assessment

National Oceanic and

Atmospheric Administration

U.S. Department of Commerce

1305 East-West Highway 

Silver Spring, MD 20910

301-713-3038, Ext.: 196

Fax: 301-713-4389

Afif Marouf

Toxicologist

U.S. Environmental 

Protection Agency

77 West Jackson Boulevard

(SR-6J)

Chicago, IL 60604

312-353-5550 

Fax: 312-353-5541

E-mail: marouf.afif@epamail.epa.gov

Stacy McAnulty

Project Manager

RMT, Inc.

744 Heartland Trail 

Madison, WI 53717

608-831-4444 

Fax: 608-831-3334

E-mail: stacy@rmtmsn.rmtinc.com

John McCarty

Program Manager

Site Assessment and Remediation

ENSR

740 Pasquinelli Drive 

Westmont, IL 60559

630-887-1700 

Fax: 630-850-5307

E-mail: jmccart@ensr.com

Margaret McDonough

Environmental Scientist

Superfund

U.S. Environmental Protection Agency

JFK Federal Building 

Boston, MA 02203

617-573-5714 

Fax: 617-573-9662

Michael Moore

Senior Toxicologist

PTI Environmental Services

15375 Southeast 30th Place - Suite 250 

Bellevue, WA 98007

206-643-9803 

Fax: 206-643-9827

E-mail: ptisvcs@halcyon.com

Terry Quill

Attorney

Beveridge and Diamond

1350 I Street, NW 

Washington, DC 20005

202-789-6061 

Fax: 202-789-6190

E-mail: tquill@ddlar.com

William Ruoff

Project Risk Assessor

Woodward-Clyde International

Stanford Place 3 - Suite 1000 

4582 South Ulster Street

Denver, CO 80237

303-694-2770 

Fax: 303-694-3946
Daniel Smith

Conestogo Rovers, Inc.

559 West Uwchlan Avenue

Suite 120 

Exton, PA 19341

610-280-0277 

Fax: 610-280-0278

E-mail: dsmith@phi.rovers.com

David Soong

Environmental Engineer

U.S. Environmental 

Protection Agency

77 West Jackson Boulevard 

(WN-16)

Chicago, IL 60604

312-886-0136 

Fax: 312-886-7804

Kirsti Sorsa

Project Scientist

RMT, Inc.

744 Heartland Trail 

Madison, WI 53717

608-831-1989, Ext.: 3338

Fax: 608-831-3334

E-mail: kirsti@rmtmsn.rmtinc.com

Thomas Starr

Principal

Environmental Corporation

7500 Rainwater Road 

Raleigh, NC 27615-3700

919-876-0203 

Fax: 919-876-0201

E-mail: tbstarr@mindspring.com

Ken Stromborg

Environmental Contaminant

Specialist

U.S. Fish & Wildlife Service

1015 Challenger Court 

Green Bay, WI 54311

920-465-7405 

Fax: 920-465-7410

E-mail: ken_stromborg@mail.fws.gov

Katherine Super

Risk Assessor

ICF Kaiser Engineers

1600 West Carson Street 

Pittsburgh, PA 15219

412-497-2328 

Fax: 412-497-2212

E-mail: ksuper@icfkaiser.com

Dan Thomas

Senior Editor

Great Lakes Basin Publications

P.O. Box 297 

Elmhurst, IL 60126

630-941-1351 

Fax: 630-941-1196

E-mail: dan@great-lakes.org

Angelique van Birgelen

National Institute of Environmental

Health Sciences

P.O. Box 12233 (MD-B307)

Research Triangle Park, NC 27709

919-541-2513 

Fax: 919-541-4255

E-mail: vanbirge@niehs.nih.gov

William van der Schalie

Office of Research and Development

National Center 

for Environmental Assessment

U.S. Environmental 

Protection Agency

401 M Street, SW (8623)

Washington, DC 20460

202-260-4191 

Fax: 202-260-6370

E-mail:

vanderschalie.william@epamail.epa.gov

Jack Walker

Project Manager

Regulatory/Permitting

Construction Operations

U.S. Army Corps of Engineers

334 Meeting Street (919)

Charleston, SC 29402-0919

803-727-4420 

Fax: 803-727-4445

E-mail: jwalker@sac.usace.army.mil

Jim Warchall

Partner

Sidley and Austin

First National Plaza - 53rd Floor 

Chicago, IL 60603

312-853-7692 

Fax: 312-853-7036
Appendix C-B

WORKSHOP AGENDA
C-B-1

United States

Environmental Protection Agency

Risk Assessment Forum

Workshop on the Application of

2,3,7,8-TCDD Toxicity Equivalency

Factors to Fish and Wildlife

Chicago Hilton & Towers

Chicago, IL

January 20-22, 1998

Agenda

TUESDAY, JANUARY 20, 1998

 3:00PM Registration

4:00PM Welcome .................................................... Ms. Christine Boivin

 Risk Assessment Forum ,

U.S. Environmental Protection Agency (U.S. EPA),

Washington, DC

Mr. John Blankenship, Assistant Regional Director

U.S. Fish and Wildlife Service (FWS),

Fort Snelling, MN

4:10PM Scope and Charge for the Workshop .............. Dr. Charles Menzie, Workshop Chair

Menzie-Cura & Associates, Inc.,

Chelmsford, MA

4:30PM Synopsis of the World Health Organization Workshop

Held in Stockholm ........................................ Dr. Martin van den Berg

University of Utrecht,

Utrecht, The Netherlands

5:00PM Presentation of Prospective Case Study and Discussion ........... Dr. Steve Bradbury

U.S. EPA,

Denver, CO

5:30PM Presentation of Retrospective Case Study and Discussion ............. Dr. Donald Tillitt

U.S. Geological Survey,

Columbia, MO

6:00PM B R E A K
C-B-2

TUESDAY, JANUARY 20, 1998

(continued)

6:15PM Review Structure of Workshop and 

Goals and Objectives of Breakout Groups ........................ Dr. Charles Menzie

 6:45PM Observer Comments

 8:00PM A D J O U R N

WEDNESDAY, JANUARY 21, 1998

8:30AM Expertise Group Sessions:

Toxicity Equivalency Factors (TEFs) Experts . . . . . . . . . . . . . . Dr. Richard Peterson, Facilitator

University of Wisconsin,

Madison, WI

Fate & Transport and Bioaccumulation Experts .............. Dr. William Adams, Facilitator

Kennecott Utah Copper Corporation,

Magna, UT

Risk Assessors and Population Modelers .................. Dr. Charles Menzie, Facilitator

10:30AM B R E A K

10:45AM Breakout Group Session I: Apply TEFs to Case Study 1

Group 1 ................................................... Dr. Peter deFur, Chair

Environmental Stewardship Concepts,

Richmond, VA

Group 2 .................................................. Ms. Janet Burris, Chair

McLaren Hart/ChemRisk,

Oak Ridge, TN

Group 3 ................................................ Dr. Charles Menzie, Chair

LUNCH (at discretion of individual groups)

3:45PM B R E A K

4:00PM Plenary Session

Breakout groups report on Case Study 1 and discuss commonalities and differences among their

groups

5:30PM D I N N E R B R E A K

8:00PM Plenary Session

Complete reports on Case Study 1 and continue plenary group discussion

9:00PM A D J O U R N
C-B-3

THURSDAY, JANUARY 22, 1998

8:30AM Breakout Group Session II: Apply TEFs to Case Study 2 

Same breakout groups as Wednesday

BREAK (at discretion of individual groups)

12:30PM L U N C H

1:30PM Plenary Session

Breakout groups report on Case Study 2 and discuss commonalities and differences among their

groups

3:00PM B R E A K

3:15PM Overall Meeting Conclusions and Wrap-Up ....................... Dr. Charles Menzie

5:00PM A D J O U R N

Note to Observers: We are aware that many of you did not have the opportunity to review the

materials prior to the workshop. We encourage you to submit written

comments to the workshop and discussion group chairs, so that your

comments can be considered during the writing of the workshop summary

report.
Appendix C-C

PREMEETING COMMENTS
C-C-1

Workshop on the Application of 2,3,7,8-TCDD 

Toxicity Equivalency Factors to Fish and Wildlife

Premeeting Comments

Chicago, Illinois

January 20-22, 1998

Prepared and compiled by:

Eastern Research Group, Inc.

110 Hartwell Avenue

Lexington, MA 02173
C-C-2
C-C-3

Table of Contents

Charge to Reviewers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-C-5

Peer Reviewer Comments

William Adams .................................................. C-C-10

Björn Brunström ................................................. C-C-17

Janet Burris .................................................... C-C-22

Steve Bursian ................................................... C-C-30

Peter deFur ..................................................... C-C-37

Joseph DePinto ................................................. C-C-43

Lev Ginzburg ................................................... C-C-51

Jay Gooch ..................................................... C-C-55

Mark Hahn ..................................................... C-C-65

Sean Kennedy .................................................. C-C-76

Wayne Landis ................................................... C-C-84

Lynn McCarty ................................................... C-C-93

Charles Menzie ................................................. C-C-105

Chris Metcalfe .................................................. C-C-111

Michael Meyer ................................................. C-C-117

Patrick O’Keefe ................................................. C-C-123

Richard Peterson ............................................... C-C-132

Mark Servos ................................................... C-C-142

Martin van den Berg ............................................. C-C-147

Bert van Hattum ................................................ C-C-152
C-C-4
C-C-5

CHARGE QUESTIONS AND PHYSICO-CHEMICAL PROPERTIES TABLE

It is reasonable to assume that the proposed WHO TEFs are appropriate for risk assessments

associated with permitting discharges, attributing causality to specific compounds, and establishing

remediation goals for AhR agonists. These risk assessment situations are the primary focus of the

workshop. The major issue to be addressed in the workshop is the extent to which a TEF/TEQ

approach can be used in risk assessments that have progressed beyond the screening stage.

The primary objective of the workshop is to identify, document, and compare uncertainties (lack of

knowledge and variability) in TEF development and their impact in ecological risk assessments. To

achieve this goal, two case studies that represent hypothetical situations for prospective and

retrospective risk assessments have been prepared. For each case study, a series of questions and

issues are raised that will help focus the panels’ deliberations. The majority of issues/questions

raised are directed towards effect characterization topics. However, it is recognized that assessing

the exposure of PCDD, PCDF, and PCB mixtures is also a significant challenge for implementation of

a TEF/TEQ approach in a risk assessment. Therefore, issues and questions concerning exposure

characterizations are also provided to highlight important concepts that can not be excluded from the

risk assessment process.

SPECIFIC QUESTIONS/ISSUES:

The major objective of the workshop is to address uncertainties associated with using a TEF/TEQ

approach in effects characterizations for ecological risk assessments. These uncertainties need to

be identified, documented, and to the extent possible, quantified. For example, there are gaps in the

TEF knowledge base for mammalian wildlife, avian wildlife, and aquatic life in terms of interspecies,

exposure route, and endpoint extrapolations. A challenge to the participants of this workshop is to

evaluate the relative contribution of TEF-related uncertainties in relation to other effect

characterization uncertainties found within an ecological risk assessment (e.g., uncertainties in

identifying 2,3,7,8-TCDD dose levels of concern; extrapolating effects from the individual to the

population). To place the effect characterization uncertainties associated with the use of TEFs in

perspective, TEF analyses in the case studies can, for example, be compared to analyses based on

total PCBs or 2,3,7,8-TCDD alone. Application of a TEF approach to an ecological risk assessment

also requires additional information for parameters in the exposure characterization for the mixture. A

critical need is the documentation of additional data requirements for use of a TEF approach (e.g.,

Kows, Kds, BAFs, BMFs, BSAFs, biotic and abiotic degradation rates, etc.). The extent to which these

exposure issues can contribute to risk assessment uncertainties needs to be estimated.

 

The following questions are generally organized around components of the draft U.S. EPA

Ecological Risk Assessment Guidelines (U.S. EPA, 1997). It is understood that not everyone will

answer every question. Please prepare responses to the questions appropriate to your area of

expertise.
C-C-6

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC TEF VALUES

1. The WHO consensus TEF values are reported as point estimates and generally rounded off to

the nearest order of magnitude. For the risk assessment case studies, additional background

information used in the derivation of the TEF values is provided. Does this additional

information enhance the means of evaluating uncertainties in the assessments? If so, how? If

not, why?

2. Some TEFs were determined from several studies, endpoints, and exposure routes, while

other TEFs were based on a single study and endpoint. Given the range of knowledge

associated with specific compounds, should all TEFs be considered to have similar

uncertainties? Why? Or why not?

3. The TEF values provided were based on endpoints that ranged from in vitro biochemical

responses (e.g., induction of cyp1A1) to in vivo early life stage mortality. To what extent can

these endpoints be extrapolated to the measures of effects that are relevant for the

assessment endpoint for each case study?

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ APPROACH

1. What are the implications, both quantitatively and conceptually, of assuming no dose-additivity

or no interaction among the components of the mixtures described in the case studies? To

what extent would the risk assessment conclusions differ if stressor response analyses where

based on total PCBs or 2,3,7,8-TCDD alone?

2. Many TEFs are based on LC50 or EC50 values. To what extent should TEF values derived at

a median response level be used in risk assessments where a no adverse effect level is being

employed?

3. The TEFs values provided were typically based on a single or limited number of mammal,

bird, or fish experiments. To what extent can class-specific TEFs be directly extrapolated to

the species identified within each case study?

III. EXPOSURE PROFILE

1. To what extent does the TEF approach present challenges, introduce new uncertainties, or

modify old uncertainties associated with modeling the exposure of AhR agonists? To what

extent does the availability and quality of congener-specific physico-chemical data limit the

means of employing fate and transport or food chain models? 

2. The route of administered or absorbed dose used to derive TEFs may differ from those

needed to establish exposure profiles in a risk assessment. To what extent do exposure route

differences used in deriving the TEFs affect their application in the case studies? 

3. To what extent does the TEF approach require a more rigorous analytical design in

quantifying sediments, soil, and biota AhR agonist concentrations than is apparent in other

methods which aggregate stressors (e.g., total PCBs)? 
C-C-7

IV. RISK CHARACTERIZATION

 

1. In evaluating the case studies, are the uncertainties associated with TEFs more problematic

than other uncertainties of the risk assessments? Do the uncertainties associated with TEFs

limit the means of performing the assessments, or do the other areas of the effect and

exposure characterization contribute similar or greater levels of uncertainty?

2. Biologically-based TEQ assays on environmental samples could be employed as an

alternative to the TEF-based approach. What would the strengths and weaknesses of such

an approach be? To what extent could these approaches be integrated?

3. Assume that site-specific data or additional research could be gathered or performed to

generate more information for the case study assessments. Provide a list of specific

investigations/studies and rank them from highest to lowest priority. What is your rationale for

the ranking? 

Additional Questions Specific to the Prospective Case Study:

RELATIVE TO THE EXPOSURE PROFILE:

1. The state adopted BAFw

f ds used by the GLWQG. What improvement in the accuracy of

maximum allowable concentrations for individual congeners in water, (MACw

t

)i,j, can be

expected through use of BAFw

f ds determined from Roundtail Lake data?

2. What errors are associated with the state’s application of the GLWQG TCDD water quality

standards for birds and mammals without consideration of congener-specific differences in

biomagnification factors from fish to tissues in wildlife relevant to the effects of concern?

RELATIVE TO THE RISK CHARACTERIZATION:

3. How should the uncertainties associated with the available fish, avian, and mammalian TEFs

be incorporated into decisions about which TCDD water quality standard should be chosen for

setting a TEqTMDL for regulating chemical discharges into Roundtail Lake?

Additional Questions Relative to the Retrospective Case Study:

RELATIVE TO THE RISK CHARACTERIZATION:

1. Would TEQ sediment cleanup goals be the same for each vertebrate group? If not, why

would there be a difference? If the vertebrate group with the most certainty is not the group

with the most restrictive sediment cleanup goal, how would you council the risk manager’s

concerns for the other vertebrate groups?

2. Would the TEF/TEQ-based sediment remediation goals be the same as those determined for

total PCBs for the identical vertebrate class? Assume that a simple ratio of total PCB

sediment concentration goal to TEQ sediment concentration goals was formulated to allow for

the use of total PCBs to monitor cleanup efforts based on TEQs. What exposure and effect

issues would need to be evaluated before using the less costly total PCB analysis to support

the TEQ-based sediment remediation goal?
C-C-8

 

7. BMFs be,I is the BMF from forage fish to bird eggs on a lipid basis calculated from the % lipid in the fish and bird eggs from Braune and Norstrom (1989).

 BMFs for PCB congeners 77, 126, and 169 are from the same samples, but reported in Hoffman et al. (1996)

 8. BMFdl,l is the BMF from diet to mink liver on a lipid basis from Tillitt et al. (1996). The BMFs were normalized to feed consumption which differed among treatment groups. The BMFs

 In this column are the means, among terrestrial groups, of the BMFs for which both values (diet and liver concentration) were above the limit of quantitation unless noted by an *.

NA = Not available EPA = Environmental Protection Agency OC = Organic carbon

BMF = Biomagnification factor Kow ti = Octanol water partition coefficient lip = lipid

BAF = Bioaccumulation factor GLI = Great Lakes Water Quality Initiative g = grams

Table 1

Parameters for PCBs, PCDDs, PCDFs

log Kow

Henry's 

Law Lake Trout BSAF BAFfd

l BMF

(EPA GLI)1 (Eisler & 

Belisle, 1996)2

(MacKay, Shiu & 

Ma, 1992)3

Constant4 (Oliver & Niimi)1 (EPA GLI)1 (EPA GLI)5

BMFbe,w6 BMF be, l7 BMFdl,l8

(g OC/g lip) (g OC/g lip) (L/kg) (g fish/g egg) (g lip/ g lip) (g lip/ g lip)

Total PCB 1.85 NA 1.17E+08 32 12 2.9

 PCB-1248 NA NA 5.8 - 6.3 NA NA NA NA NA NA NA

 PCB-1254 NA NA 6.1 - 6.8 NA NA NA NA NA NA NA

 PCB-1260 NA NA 6.3 - 7.5 NA NA NA NA NA NA NA

PCBs 

77 6.36 6.52 6.5 1.72 NA 0.29 9.68E+06 1.8 0.7 0.15

81 6.36 6.37 NA NA NA 0.67 2.24E+07 NA NA 1.0

105 6.65 6.66 6.0 NA 2.70 4.49 2.18E+08 20 7.3 5.0

114 NA 6.66 NA NA NA NA NA NA NA 4.0

118 6.74 7.12 NA NA 4.09 1.72 2.04E+08 31 11 4.3

123 NA 6.75 NA NA NA NA NA NA NA 0.8

126 6.89 6.90 NA NA NA 3.21 3.63E+08 29 11 12.6

153 6.92 7.75 6.9 42.9 4.22 1.91 3.31E+08 48 17 NA

156 7.18 7.19 NA NA 3.97 NA 8.12E+08 NA NA 8.6

157 NA 7.19 NA NA NA NA NA NA NA 11.0

167 7.27 7.28 NA NA NA 0.69 1.87E+08 NA NA 5.7

169 NA 7.43 NA NA NA NA NA 46 17 13.6

189 7.71 7.72 NA NA NA 0.71 5.30E+08 NA NA 9.1

PCDD

2378-TCDD 7.02 NA 6.8 3.331 NA 0.059 9.00E+06 20.75 7.5 11.0

12378-PCDD 7.50 NA NA NA NA 0.054 2.49E+07 9.7 3.5 6.3

12478-PCDD NA NA NA NA NA NA NA NA NA NA

123478-HxCDD 7.80 NA NA 1.084 NA 0.018 1.65E+07 NA NA 9.3*

123678-HxCDD 7.80 NA NA NA NA 0.0073 6.71E+05 16 5.8 33.5

123789-HxCDD 7.80 NA NA NA NA 0.0081 7.44E+05 NA NA 15.5*

1234678-HpCDD 8.20 NA 8.0 1.273 NA 0.0031 7.16E+06 NA NA 45.2

OCDD 8.60 NA 8.2 0.684 NA 0.00074 4.29E+06 NA NA 62.3

PCDF

2378-TCDF 6.50 NA 6.1 1.461 NA 0.047 2.16E+06 NA NA 0.4*

12378-PCDF 7.00 NA NA NA NA 0.013 1.89E+06 NA NA NA

23478-PCDF 7.00 NA 6.5 NA NA 0.095 1.38E+08 4.45 1.6 54.1

123478-HxCDF 7.50 NA 7.0 1.454 NA 0.0045 2.07E+06 NA NA 64.4

123678-HxCDF 7.50 NA NA 0.741 NA 0.011 5.07E+06 NA NA NA

123679-HxCDF NA NA NA NA NA NA NA NA NA 54.9

123789-HxCDF 7.50 NA NA NA NA 0.037 1.70E+07 NA NA NA

234678-HxCDF 7.50 NA NA NA NA 0.04 1.84E+07 NA NA 75.8

1234678-HpCDF 8.00 NA 7.4 1.425 NA 0.00065 9.47E+05 NA NA 27.5

1234789-HpCDF 8.00 NA NA NA NA 0.023 3.35E+07 NA NA NA

OCDF 8.8 NA 8.0 0.191 NA 0.00099 9.10E+06 NA NA 43.3*

References:

1. US EPA, 1995 (EPA-820-B-95-005).

2. Eisler, R., and A.A. Belisle. 1996. Planar PCB Hazards to Fish, Wildlife, and Invertebrates: A Synoptic Review. National Biological Service Biological Report 31. 75 pp.

3. Mackay, Shiu & Ma. 1992. Illustrated Handbook of Physical and Chemical Properties for Organic Chemicals. Boca Raton, FL: Lewis Publishers.

4 Values from Eisler & Belisle (1996) or MacKay, Shiu & Ma (1992).

5. Mean BAF fd

l for salmonids from Table 10 of US EPA (1995), with the exception of total PCBs which are from Appendix F. 

6. BMFbe,w is the BMF from forage fish to bird eggs on a wet weight basis from Braune, B.M., and R.J. Norstrom. 1989. Dynamics of Organochlorine Compounds in Herring

 Gulls: III. Tissue Distribution and Bioaccumulation in Lake Ontario Gulls. Environ. Toxicol. Chem. 8:957-968.

 BMFs for PCB congeners 77, 126, and 169 are from the same samples, but reported in Hoffman et al.(1996).
C-C-9

Peer Reviewer Comments
C-C-10

William J. Adams, Ph.D.

Director, Environmental Science

Kennecott Utah Copper Corporation

8315 West 3595 South

P.O. Box 6001

Magna, UT 84044-6001

801-252-3112

Fax: 801-252-3083

E-mail: adamsw@kennecott.com

An expert in bioaccumulation factors, Dr. Adams received both his Ph.D. in aquatic toxicology

and an M.S. degree in wildlife toxicology at Michigan State University, and his B.S. in

biological sciences at Lake Superior State University. In his current position as the director

of environmental science at Kennecott Utah Copper Corporation, Dr. Adams directs

environmental research in the areas of toxicology and environmental risk assessment. In

addition to aquatic toxicology, he has expertise in environmental fate and environmental risk

assessment. He has extensive experience with both metals and organics and has published

50 articles in his areas of study. Dr. Adams is also an editor/author of two books and two

book chapters. He is a member of the EPA Science Advisory Board, Committee on

Environmental Processes and Effects.
 William J. Adams

C-C-11

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC

 TEF VALUES

1. It was not clear to me that the additional information provided reduced the uncertainty

associated with the TEFs. Perhaps this will become clearer at the meeting. The rounding of

the TEFs to the nearest order of magnitude introduces uncertainty in the final calculation of

risk and it reflects the uncertainty associated with the individual values. Additional discussion

on how this uncertainty should be dealt with in a risk assessment context is needed at the

workshop.

2. Intuitively, I would say that all TEFs should not be considered to have similar uncertainties.

This is based on both weight of evidence and lines of evidence for those chemicals which

have been studied the most. However, it is fair to ask the question, can we quantify the

uncertainty through rigorous statistical assessment of the available data on TEFs? The

question posed is somewhat similar to asking the question, would a single acute toxicity test

with Daphnia magna have the same uncertainty in deriving a water quality criterion as a

genus mean acute value based on the average of several Daphnia magna studies as well as

several other daphnid species. The answer is, of course, that we would have less uncertainty

with a genus mean acute value than with a single acute toxicity test.

3. This question gets to the heart of the entire risk assessment approach for TCDD and other

HOHs and deserves in depth review at the workshop. The TEF approach is one that has

found favor because it provides a way forward for numerous chemicals with a similar mode

of action. The complexity of assessing all PCB, Furan and Dioxin isomers is monumental and

is somewhat simplified by this approach. However, care has to be taken in the use of the

“model” results as measurement endpoints for the purpose of evaluating key assessment

endpoints (i.e., the valued resource) in risk assessments. The data seem to indicate that the

use of in vitro measurements and QSARs introduce additional uncertainty into the measures

of effects that are ultimately used to estimate risk. A measurement of selenium in the egg of

a black-necked stilt, for example, provides a reasonable estimate of the potential for

reproductive effects at the individual level. A measurement of selenium in the diet of the birds

can be used to estimate egg concentrations and reproductive effects, but the uncertainty

becomes greater. Measuring the selenium in the sediments where the dietary species lives
 William J. Adams

C-C-12

as an indicator of potential for reproductive effects introduces even more error. The same

analogy applies here. As a general rule, the further away you get from the a direct measure

of the assessment endpoint the uncertainty becomes greater. I would add, that this does not

necessarily imply that as the uncertainty increase there is a need for use of additional safety

factors. The inappropriate use of safety factors has been shown to increase the conservatism

and decreases the accuracy of the risk estimate. Ultimately, what has to be answered is, can

TEFs be used to accurately predict population effects in aquatic ecosystems? This can only

be answered by the careful use of both laboratory and field data. 

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

1. When assessing chemicals with the same mode of action and the same receptor, the

literature strongly supports the use of an additive model. According to Konemann, when there

is no interaction between the chemicals they can be assumed to be additive. If you do not

consider dioxin isomer effects to be additive then one must assume they are either

antagonistic or synergistic or unpredictable. The consequence is that you have to assess

each isomer independently and determine its potential to cause effects (calculate separate

hazard quotients). Adding individual hazard quotients to assess the overall potential for risk

has serious limitations. The use of total PCBs, which has limitations unique to itself due to

the environmental degradation of the constituents, or the use of just 2,3,7,8-TCDD provides

a single point estimate of the potential for risk, but does not consider the cumulative potential

for risk from similar compounds co-located in the environment. The fundamental basis for

using an additive model exists, what hasn’t been determined accurately is when does it over

predict the potential for effects? The potential for antagonism appears to be somewhat

greater than for synergism.

2. The use of TEFs based on central tendency values such as LC50 or EC50 values can be

justified even though most in-depth assessment typically use chronic no-effect concentrations

or threshold values. The selection of a very sensitive EC50 value can in many cases be more

sensitive than some chronic threshold values. Risk assessments with atrazine, diazinon,

copper and cadmium have shown where there were lots of acute and chronic data that the

water concentrations selected as protective of aquatic species (95%) using sensitive acute

endpoints were nearly the same as the values selected using chronic no-effect levels.
 William J. Adams

C-C-13

3. My response to this question comes not from extensive experience with TEFs, but with having

performed risk assessments where laboratory to field relationships have been examined. As

a general rule, when one has to extrapolate within a class the best approach is to use the

same value for the species of interest as obtained from the toxicity test. The use of safety

factors in this situation provides protection, but sacrifices accuracy and predictability. 

III. EXPOSURE PROFILE

1. I don’t see the use of the TEF approach introducing significant new uncertainties into the

exposure assessment. However, in regards to the second part of this question, the lack of

congener-specific physico-chemical data provides considerable issues relative to accurately

modeling or predicting the fate and transport of these materials both within the physical

environment and the biota. Transport estimates within the food chain can be developed

without these data if one chooses to rely only upon sediment to biota and biota to biota

accumulation factors.

2. The route of administration is always important in an overall risk assessment. The key is to

match the route of “exposure” in the effects characterization with that which actually occurs

in natural systems. TEFs derived from in vitro biochemical measurements (will have greater

uncertainty associated with them because the potential for metabolism to occur in the body

is removed. This ultimately translates to an increase in the uncertainty associated with the

final risk estimate.

3. Interesting question. Is more error introduced via the analytical techniques used when

multiple chemicals are measured and quantified than when a class of chemical are measured

as a group. I would think so. I’ll leave this question to the chemists.

IV. RISK CHARACTERIZATION

1. Regarding uncertainty introduced into the risk assessment by the use of TEFs, I don’t think

the use of TEFs necessarily introduces additional uncertainty into the risk assessments that

other approaches would not. However, not all TEFs are equal (i.e., some are based on
 William J. Adams

C-C-14

QSARs, in vitro biochemical measures, in vivo effects measurements etc.), therefore,

depending on what is actually used as the set of TEFs for the risk assessment may have

more or less uncertainty. Clearly, extrapolating across species and perhaps classes

introduces uncertainty. Additionally, estimating exposure from sediment using BSAFs has

considerable uncertainty when one considers all the compounds of interest. So, does the use

of TEFs introduce more uncertainty than already exists? Who knows? The question which

should be asked is, can we measure and quantify the uncertainty in each part of the risk

assessment? If so this would be a useful research endeavor. 

2. The use of residue based approaches for deriving water quality criteria and performing risk

assessments is gaining favor for both organics and metals. I favor the approach and believe

the uncertainty associated with the risk estimate would be reduced. This approach can be

applied in several ways including evaluation of TEQs within a given tissue (liver) for a valued

species (otters) or alternatively by comparing the TEQs in the diet of a given species against

a known dietary effect concentration. I prefer the latter because it gets directly at the issue

of exposure for HOHs. 

The process of risk characterization typically looks at several lines of evidence to help

assess the uncertainty, therefore I don’t see the various approaches as mutually

exclusive. Why not perform the assessment using both TEFs and TEQs for comparative

purposes (cost aside)? Especially if you are locating a new industry as proposed in the

prospective case study.

3. A. Gather additional data on the species or resource to be protected. I would not assume,

for example, that Bull trout are as sensitive as lake trout and then divide by a factor of 10

to account for species to species extrapolation. Perform the necessary early life stage test

or egg exposure study to obtain the information. At each stage of the assessment I would

gather as much site-specific data as possible on the species of interest. This will reduce

uncertainty.

B. Collect additional field data at the population level at each of the sites used in the risk

assessment cases. Risk assessments performed at the species level and extrapolated to

the population or community level tend to be overly conservative. They typically assume
 William J. Adams

C-C-15

constant dietary and water exposure, for example, and this is rarely true. Individual level

assessments rarely consider the behavioral aspects of populations, e.g. migration, feeding

behavior, habitat selection, etc. all of which effect the exposure regime. Additional on-site

evaluation of the populations of interest (in the retrospective case) will provide additional

information on whether or not actual effects occurring at the site. 

Additional Questions Specific to the Prospective Case:

RELATIVE TO THE EXPOSURE PROFILE

1. No comment at this time.

2. No comment at this time.

RELATIVE TO THE RISK CHARACTERIZATION

3. Relative to the question as to how uncertainty should be handled in setting water quality

standards - this is an area where the state-of-the-science is improving. Probabilistic

techniques are emerging using Bayesian theory and Monte Carlo calculation to account for

uncertainty and to predict a range of values that might be protective. The advantage of this

approach is that it also provides an estimate of the confidence along the range of values

identified such that one can select a value with a given level of confidence (say 90%). The

approach can be used to include site-specific parameters and can be used at the population

level if sufficient data are available. We recently completed such an approach for selenium

to assess levels in water that are protective of bird egg concentrations to prevent teratogenic

effects.

Additional Questions Specific to the Retrospective Case:

RELATIVE TO THE RISK CHARACTERIZATION

1. I would think that the TEQ sediment cleanup goals would not be the same for each vertebrate

group. There are differences in sensitivity of different vertebrate species to TCDD and similar

compounds (consider the variability that exists just for trout species to TCDD) and this should
 William J. Adams

C-C-16

be evaluated and discussed as part of the effects characterization. 

Providing the risk manager with an assessment of the uncertainty associated with each of the

risk estimates in the overall risk assessment is the job of the risk assessor. Hence the

statements identifying the uncertainty with a given risk estimate become very important. A

decision, in fact, could be made by the risk manager to set a level of protection based on a

less sensitive species when the data are well characterized as opposed to using a more

sensitive species, but with an uncertainty level so large that the confidence in the estimate

is very low. Risk management is not a quantitative science and often involves personal

judgement and personal/societal values. The risk assessor must provide sufficient

information so the manager can make an informed decision. In short, if you do not have much

confidence in your risk estimate the selection of a level of protection is very difficult.

2. Relative to the second part of this question - what exposure and effects issues would have

to be evaluated before using the less costly PCB analysis as an alternative to TEQ-based

sediment remediation goal- I suggest the following might be important:

1. Exposure - the quantitation of the total PCBs has to be matched to the congeners used to

perform the TEF/TEQ assessment other wise there will not be a match between the

exposure and effects estimates. 

2. There needs to be in vivo laboratory evaluation between effects observed using the

TEF/TEQ approach and that obtained using the total PCB approach. This approach tests

the additivity model, reproducibility and provides the first level of field verification.
C-C-17

Björn Brunström, Ph.D.

Associate Professor

Department of Environmental Toxicology

Uppsala University

Norbyvägen 18A

S-75236 Uppsala, Sweden

011-46-18-471-2626

Fax: 011-46-18-518-843

E-mail: bjorn.brunstrom@etox.uu.se

Dr. Brunström received a Ph.D. from Uppsala University in zoophysiology/ecotoxicology

and continues to perform postdoctoral research at Uppsala. He is currently an associate

professor in ecotoxicology. He participated in the World Health Organization’s “Meeting

on the Derivation of Toxic Equivalency Factors for PCBs, PCDDs, PCDFs, and Other

Dioxin-like Compounds for Humans and Wildlife” held on June 15-18, 1997. Dr.

Brunström is also a member of the working group preparing the report, “Endocrine

Disrupting Substances: Impairment of Reproduction and Development” for the Swedish

EPA. Dr. Brunström’s research involves experimental studies on the reproductive toxicity

of persistent environmental contaminants in mammals and birds, and he has

approximately 65 publications related to that issue.
Björn Brunström

C-C-18

Workshop on the Application of 2,3,7,8-TCDD Toxicity Equivalency Factors (TEFs) to Fish and Wildlife

Premeeting comments

I.1. When carrying out a risk assessment based on a TEF/TEQ approach, it is important to be aware of the

limitations of such an approach and the uncertainties associated with the fish and wildlife TEFs for a

certain congener. The uncertainties in the TEF values add to the other uncertainties in the assessment and

the background information used in the derivation of the TEFs is important in the evaluation of these

uncertainties. The extent of the TEF value uncertainties in relation to the other assessment uncertainties

partly depends on which congeners that are of concern.

The TEF values provided are order of magnitude estimates based on the presently available

information and future research data will result in revaluation of these values. TEF values for certain

compounds have been estimated from a single study and relative potencies have generally been

determined only in a few species. However, it should be remembered that a conservative approach was

used when deriving the TEF values. The currently available data used as a basis for TEF development and

the major uncertainties in this development are discussed in the recent WHO report on proposed TEFs for

mammals, birds, and fish.

The very large interspecific differences in sensitivity to Ah receptor agonists that exist within

animal classes contribute significantly to the total uncertainty in a risk assessment. The WHO document

only deals with the relative potencies of various Ah receptor agonists and LOAEL and NOAEL values for

different species are not discussed. Other background documents give information about LOAEL and

NOAEL values and the uncertainties in these values for mammalian and avian wildlife.

I.2. Since a tiered approach was used when setting the TEFs, it is obvious that some TEFs should be

considered more uncertain than others. Some of the TEF values were estimated by using a QSAR model

based on enzyme induction data and these values of course are less reliable than those based on data from

a carefully conducted reproduction study. Uncertainties appear to be largest for the least potent

compounds since their TEFs are frequently based on biochemical effects observed in in vitro systems or

on estimates from QSAR studies. Values also tend to be more uncertain for easily metabolized

compounds, such as PCB 77, since these compounds show different relative potencies in acute and

subchronic studies.

Concerning the exposure routes, it should be kept in mind that mammalian TEFs are mainly
Björn Brunström

C-C-19

based on studies where the compounds were administered via the food and the effects were related to

concentrations in the diet. In contrast, the fish and bird TEFs are based on egg injection studies in which

the effects were related to egg concentrations.

I.3. Any Ah receptor-mediated response may, principally, be used when determining relative potency

values. The rationale for using a tiered approach when developing TEF values is nevertheless that certain

endpoints are considered more useful than others. It should also be kept in mind that metabolism is largely

overlooked in in vitro assays and in acute studies. Also, the shapes of dose-response curves in enzyme

assays may differ between congeners which leads to difficulties in the interpretation. 

The most relevant compounds in the case studies all were designated fish TEF values that are

based on early life stage mortality in rainbow trout. For protection of bull trout and lake trout these values

should be relevant. Several of the bird TEFs are based on EROD induction studies in chicken embryos.

For these values there are uncertainties associated with the interpretation of differently shaped

dose-response curves and also with the extrapolation from the chicken to the bald eagle and the Caspian

tern.

II.1. When 2,3,7,8-TCDD is not the major contributor to total TEQs, a response analysis based on TCDD

alone would significantly underestimate the impact of the chemical stressors present. In the prospective

study, TCDD is one of the major compounds of concern and an assessment based on a TEF/TEQ approach

would only decrease the permitted TCDD toxicity equivalent load from the effluent a few times. 

The impact of a PCB mixture depends on the relative concentrations of the congeners in the

mixture. Only if the relative concentrations of different congeners were determined in some samples, and

could be predicted to be similar across the lake, would total PCB determinations be sufficient. The relative

concentration of PCB 126 seems to be crucial in the retrospective case study.

II.2. The problem with using LC50 or EC50 values for determination of relative potencies is that the

shapes of the dose-response curves may differ for different congeners. This primarily seems to be a

problem involving the least active congeners.

II.3. Only few comparative studies addressing the relative potencies of various Ah receptor agonists across

species have been carried out. Whether class-specific TEFs are valid for different wildlife species is a

matter of concern. Most data suggest similarities but some studies indicate that there may be relative
Björn Brunström

C-C-20

potency differences across species within an animal class. For the species identified in the case studies, the

use of the new class-specific TEFs should give better estimations than the ”old” TEFs but extrapolations

between species, e.g., from the chicken to the bald eagle and the Caspian tern, are rather uncertain.

However, the contribution from interspecific differences in sensitivity to TCDD to total uncertainties may

be as large or larger as the contribution from differences in relative potencies across species. 

III.2. The mammalian TEFs are mainly based on food intake whereas fish and bird TEFs are based on egg

concentrations. When the models used predict levels in eggs of fish and birds there is no contradiction.

Uncertainties are introduced when models describe the relationship between the concentrations in

sediment and those in avian diet or mammalian tissue. For instance, the high metabolic transformation of 

PCB 77 is accounted for in the TEF value for mammals and this means that the contribution by this

compound to total TEQs will be underestimated if its TEF value is applied for a tissue concentration.

However, the concentration of PCB 77 appears to be low in the retrospective case study.

IV.1. I think that uncertainties other than those associated with the TEF values contribute to a similar or

even greater level to the total uncertainty. Major problems are uncertainties in the sensitivities of the

wildlife species to TCDD and uncertainties in exposure characterization. Also, it should be remembered

that the TEF values assigned are conservative estimates. 

IV.2. In biologically-based TEQ assays, the total effects of Ah receptor agonists and antagonists in a

sample are measured. Preparing extracts from fish eggs and bird eggs and injecting these extracts into eggs

of laboratory species gives an opportunity to study chemical interactions and relevant end-points. 

In certain in vitro systems, the relevant species may be studied. Disadvantages with using in

vitro systems include that they do not accurately model all the interactions that occur in vivo, and that the

biochemical end-points usually measured are more or less connected to adverse effects. 

By combining bioassays with chemical analysis and a TEF approach, the contribution from

the analyzed congeners and from non-analyzed compounds to total effects can be estimated. 

IV.3. The highest priority should be given to clarifying the extent of species differences in sensitivity to

TCDD for the relevant species and the basis for such differences. Are any piscivorous bird species as

sensitive as the gallinaceous birds? Sensitivities are difficult to determine for relevant species but the use

of in vitro assays and receptor studies may give some information about those species not available for in
C-C-21

vivo studies. Second, studies of the relative potencies of the congeners of concern in terms of various

end-points should be carried out in relevant species. For instance, the relative potency of

1,2,3,4,7,8-HxCDF in bull trout should be examined since the mill effluent was predicted to contain high

concentrations of this compound.

Both the relative potency value of 2,3,7,8-TCDF in bull trout and the BAF of this congener

in Roundtail lake would be important information for the prospective case study assessment.

Additional Questions Specific to the Prospective Case Study:

2. The tentative water quality standards (WQSs) for TCDD can not be used as WQSs for TCDD

equivalents. 2,3,7,8-TCDF would be the major contributor to the water TEQ concentration when using the

TEFs for mammals or birds without consideration of the low biomagnification factor for this congener.

3. An uncertainty factor including uncertainties in the BAFs, BMFs, and TEFs for the different congeners

should be considered.

Additional Questions Relative to the Retrospective Case Study:

1⁄2. The relatively low potency of PCB 126 in fish (TEF value of 0.005) means that the Caspian tern and

the otter are more likely to be affected than the lake trout. A PCB sediment concentration goal to protect

the Caspian tern and the otter should be related to PCB 126 as the major contributor to total TEQs. If the

concentration of PCB 126 in relation to total PCB concentrations would be similar in sediments across the

lake, then cleanup efforts may be monitored by total PCB analysis.
C-C-22

Janet A. Burris

Senior Health Scientist

McLaren Hart/ChemRisk

109 Jefferson Avenue - Suite D

Oak Ridge, TN 37830

423-483-5081

Fax: 423-482-9473

Ms. Burris received her B.A. in zoology from Depauw University and her M.S.P.H. in aquatic

toxicology from the University of North Carolina at Chapel Hill. Ms. Burris is a senior health

scientist for McLaren Hart/ChemRisk. Ms. Burris has over ten years of experience in the

successful design, management, implementation, and completion of risk assessments for

CERCLA, RCRA, and other types of hazardous waste sites. Ms. Burris specializes in

ecological risk assessments and has completed assessments for several federal facilities,

private clients, state programs, and the EPA. Ms. Burris has designed ecological risk

assessment methods and guidelines for the EPA Superfund Program and provided risk

assessment training for both clients and peers. She also has experience in the design and

implementation of biological sampling (fish, invertebrates, plants) bioassays (groundwater,

soil, and sediment), tissue analyses (fish, invertebrates, and plants), toxicity reduction

evaluations (TREs), toxicity identification evaluations (TIEs), macroinvertebrate studies, and

wetlands assessments. She is a member of the Society of Environmental Toxicology and

Chemistry and the American Society for Testing and Materials.
Janet A. Burris

C-C-23

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF

SPECIFIC TEF VALUES

Some TEFs were determined from several studies, endpoints, and exposure routes, while other TEFs

were based on a single study and endpoint. Given the range of knowledge associated with specific

compounds, should all TEFs be considered to have similar uncertainties? Why? Or why not?

Toxicity Equivalency Factors (TEFs) for mammals, fish and birds should not be considered

to have similar uncertainties. There is greater uncertainty in the derivation of some TEFs

versus others and these uncertainties should be understood in the application of the TEFs as

part of an ecological risk assessment (ERA). The uncertainties are expressed in part in the

tiered approach used to derive the World Health Organization (WHO) TEFs for fish and

birds. The tiered approach provides for preferential use of the more “certain” data, if

available. For example, several of the WHO TEFs for fish for furans and mono-ortho

polychlorinated biphenyls (PCBs) are based on no testing data and are estimated based on

structural similarity assumptions and/or Quantitative Structure activity relationships

(QSARs). There is obviously less certainty in these TEFs compared to TEFs derived from

LD50 data on overt toxicity in developing embryos (in vivo) studies.

Uncertainties in the TEF value directly results in associated uncertainty in the ERA. The

amount of uncertainty should be assessed qualitatively or quantitatively in order to

understand the influence of the uncertainty on risk assessment results. The stakeholders in

the ERA should have an accurate understanding of the confidence in the risk estimates. The

greater the confidence the greater the certainty that actions will result in actual reduction of

risks and attainment of the assessment goals.

Probabilistic techniques could be used to examine quantitatively the uncertainties associated

with the TEFs. Probability density functions could be used to represent TEF values (as well

as TEQs) in place of the existing point estimates. The stakeholder would then have a

quantitative understanding of the uncertainty. The current presentation of TEF values as

point estimates provides the illusion that all of derived values are “equal” in their predictive

ability of dioxin-like toxicity.
Janet A. Burris

C-C-24

The TEF values provided were based on endpoints that ranged from in vitro biochemical responses

(e.g., induction of cyp1A1) to in vivo early life stage mortality. To what extent can these endpoints

be extrapolated to the measures of effects that are relevant for the assessment endpoint for each

case study?

Certain endpoints used in the derivation of the WHO consensus TEFs may not be relevant to

the selected assessment endpoints for the case studies and ecological risk in general. For

example, maximum enzyme induction levels, tumor promotion, and increased organ weight

are used as endpoints in the derivation of TEFs. However, these toxic effects may not have

consequences on the survival, growth, development, and reproduction of individuals, and

the sustainability of populations and communities (typical assessment endpoints for an

ERA). Some of the toxic endpoints used to derive TEFs are not toxic responses but instead

represent biochemical effects (binding affinity or induction of cytochrome P4501A) that

may be in some way associated with subsequent toxic responses (WHO, 1997). Other toxic

effects used to derive TEFs (aryl hydrocarbon hydroxylase (AHH) or ethoxyresorufin odeethylase (EROD) activity) have been reported to not directly correlate with toxic injury

(Stegeman et al., 1992). Without a clear association between the toxicity endpoint used to

derive the TEF and the assessment endpoint for a specific ERA extrapolation may either

impossible or extremely uncertain.

One of the primary questions that should be addressed in reviewing the application of the TEF

values to the ERA process concerns endpoints. As with the case studies, each ERA will

have specific assessment endpoints that reflect site-specific risk management goals. The

WHO consensus TEF values, however, represent “fixed” toxicity endpoints. Are these

TEFs appropriate for use in effects characterization for all ERAs? Are the toxic effects used

to derive these TEFs reliable indicators of the toxic effects of concern (those relevant to the

assessment endpoint)? Should there be some site-specific flexibility in the selection of

TEFs for use in an ERA? Should TEFs be derived that are species-specific and/or endpointspecific?

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

APPROACH
Janet A. Burris

C-C-25

What are the implications, both quantitatively and conceptually, of assuming no doseadditivity or no interaction among the components of the mixtures described in the case studies? To

what extent would the risk assessment conclusions differ if stressor response analyses were based on

total PCBs or 2,3,7,8-TCDD alone?

The TEF approach inherently assumes dose additivity and this is considered in the case

studies. Possible interactions among mixtures of congeners, however are not addressed. 

The assumptions of additivity and no interaction could result in overestimation of risks.

Non-dioxin like PCBs and metabolites may be antagonistic to TCDD-like response (Zhao et

al., 1997; Biegel et al., 1989; Haake et al., 1987). PCB 153, a reported TCDD antagonist, is

the predominant congener in the tissue and eggs of a number of avian species (Focardi et al.,

1988; Elliott et al., 1989; Borlakogul et al., 1990; Ormerod and Tyler, 1994; Van den Berg

et al., 1994; and Mora, 1996). 

In other cases the assumptions of additivity may underestimate risks. Non-dioxin like PCBs

and metabolites may induce toxic effects not addressed in the TEF (Safe, 1990 and

McFarland and Clarke, 1989). Aryl hydrocarbon receptor (AhR) TEFs may be poor

predictors of PCB reproductive toxicity (Battershill, 1994).

Many TEFs are based on LC50 or EC50 values. To what extent should TEF values derived at a

median response level be used in risk assessments where a no adverse effect level is being

employed?

For screening level ERAs, a no adverse effect level is preferable to a median response value,

as the goal is to identify potential risks under conservative conditions. Application of a

toxicity equivalency approach, however, requires the use of response data to calculate

relative potencies. TEFs derived based on median responses can still be used in risk

assessments employing no-adverse effect levels, if the uncertainties are addressed

quantitatively or qualitatively. The use of probabilistic methods to derive distributions of

TEFs and/or TEQs in the ERA (in place of point estimates) could be used to address this

uncertainty in quantitative identification of margins of “safety”. 

The TEF values provided were typically based on a single or limited number of mammal,

bird or fish experiments. To what extent can class-specific TEFs be directly extrapolated to the

species identified within each case study?
Janet A. Burris

C-C-26

The TEF values provided represent the selection of the most sensitive test species and

endpoint. As such the TEFs may over represent risks for less sensitive ecological receptors. 

The WHO fish TEFs are based on testing of one fish species, the rainbow trout. Use of these

TEFs to characterize potential toxicity for the fish species of concern in the case studies

(cold water fisheries including lake trout and rainbow trout) is entirely appropriate due to

similarity in the specific species and sensitivity. However, in other applications outside of

the case studies for warm water fisheries, these fish TEFs may not be directly applicable.

Available data indicate that the relative risk of TCDD to early life stage survival for seven

freshwater fish species are from 16 to 180 fold less than that for lake trout (Spehar, 1998?). 

Existing information on relative toxicity could be used to derive interspecies extrapolation

factors to predict species-specific TEFs for non cold-water fish species. 

The possible problems in extrapolation between WHO TEFs for mammals and the specific

species of interest in the case studies is difficult to discern. More information on the

specific derivation of the WHO TEFs for mammalian species is required above that

provided in the distributed materials. 

III. EXPOSURE PROFILE

The route of administered or absorbed dose used to derive TEFs may differ from those needed to

establish exposure profiles in a risk assessment. To what extent do exposure route differences used

in deriving the TEFs affect their application in the case studies?

Exposures for risk assessment for mammals are typically expressed as oral exposures 

(dietary, water and/or sediment). These exposure routes are often not equal to the exposure

route used to establish potency of congeners (interperitoneal injections and in-vitro

exposures). As the exposure routes are not directly comparable between exposure estimate

(in the risk assessment) and the TEF, resulting TEQs are not accurate and introduce

uncertainties into the risk analyses. The potency of congeners can vary by exposure route

(intake orally with transfer and absorption through the gastrointestinal tract versus direct

injection into peritoneum. 

To what extent does the TEF approach require a more rigorous analytical design in quantifying
Janet A. Burris

C-C-27

sediments, soil and biota AhR agonist concentrations than is apparent in other methods which

aggregate stressors (e.g., total PCBs)?

The TEF approach requires a more rigorous and expensive analytical program compared to

the traditional analyses of aggregate stressors (total PCBs). In a practical sense this is one of

the more important questions in the general application of the TEF approach. The data that

exists for most contaminated sites is in the form of total PCB measurements. NPDES permit

and other regulatory monitoring requirements may not traditionally require congener

specific analyses? 

IV RISK CHARACTERIZATION

In evaluating the case studies, are the uncertainties associated with TEFs more problematic than

other uncertainties of the risk assessments? Do the uncertainties associated with TEFs limit the

means of performing the assessments or do the other areas of the effect and exposure

characterization contribute similar or greater levels of uncertainty?

The uncertainties associated with the TEFs are primarily related to the relevancy of the

toxicity endpoints used to establish potency [see previous endpoint discussion]. Without the

ability to complete an effect assessment specific to the unique assessment endpoints that is

directly comparable to the exposure assessment data, the TEFs are more problematic than

other uncertainties. Attaining the smallest difference in the laboratory (or field)

measurements and the assessment endpoints (species and exposure route) minimizes

uncertainties in the effect and exposure characterization (extrapolation error). Use of the

TEFs limit the means and scope of assessments in setting forth the measurement endpoint

(the toxic effect) and specifying the measurements of exposure that need to be performed

(egg tissue concentrations in birds and fish). 

Use of the TEFs also introduces uncertainty as it requires evaluation of risks for fish and

birds based on egg tissue exposures. Prediction of egg tissue concentrations based on

maternal exposures will often be necessary (due to analytical data constraints). This process

is probably less certain than other established procedures to estimate oral doses for avian

receptors.
Janet A. Burris

C-C-28

Additional Questions Specific to the Prospective Case Study

The state adopted BAFw

fds used by the GLWQG. What improvement in the accuracy of maximum

allowable concentrations for individual congeners in water, (MACt,w)ij, can be expected through

use of BAFfdws determined from Roundtail Lake data?

What errors are associated with the state’s application of the GLWQG TCDD water quality

standards for birds and mammals without consideration of congener-specific differences in

biomagnification factors from fish to tissues in wildlife relevant to the effects of concern?

How should the uncertainties associated with the available fish, avian, and mammalian TEFs be

incorporated into decisions about which TCDD water quality standard would be chosen for setting

a TEQTMDL for regulating chemical discharges into Roundtail Lake?

Additional Questions Relative to the Retrospective Case Study

Would TEQ sediment cleanup goals be the same for each vertebrate group? If not, why would there

be a difference? If the vertebrate group with the most certainty is not the group with the most

restrictive sediment cleanup goal, how would you council the risk manager’s concerns for the other

vertebrate groups?

The TEQ sediment cleanup goals would not be the same for each vertebrate group as the

TEFs represent different sensitivities across the general classes (mammals, birds and fish). 

The TEQs for each vertebrate are also based on different exposures (oral for mammals and

egg tissue of birds and fish) which would result in different cleanup goals.

My general advice to the risk manager’s concern would be somewhat practical. The

vertebrate group with the most certainty in the risk results represents the most certain clean

up option with the greatest chance of attaining the management goals. Specifically I would

substantiate recommendations with quantitative information on the uncertainties in the

assessment including the effect of the uncertainties on risk results and clean up
Janet A. Burris

C-C-29

concentrations. Clean up options for the protection of the different vertebrate classes would

be represented geographically. A cost-benefit analysis would also be completed to identify

for the various clean up concentration the amount of risk reduction per unit cost.

Uncertainties would be considered in the cost-benefit analyses. The primary goal of risk

assessment in most regulatory applications is to identify how to reduce the most risk for

least amount of cost. This type of quantitative analyses would be used to demonstrate the

most effective and protective options. 
C-C-30

Steven J. Bursian, Ph.D.

Professor

Department of Animal Science

132 Anthony Hall

Michigan State University

East Lansing, MI 48824

517-355-8415

Fax: 517-432-1518

E-mail: bursian@pilot.msu.edu

Dr. Bursian received a B.S. in experimental biology from the University of

Michigan - Dearborn, an M.S. degree in ecology and behavioral biology from

the University of Minnesota, and a Ph.D. in physiology from North Carolina

State University. He is currently a professor in the Animal Science Department

at Michigan State University. Dr. Bursian’s current research focus is

organophosphate-induced delayed neurotoxicity and assessment of animal

exposure to environmental contaminants. Professional memberships include

the Society of Toxicology, Sigma Xi, the Society of Experimental Biology and

Medicine, the Society for Environmental Toxicology and Chemistry, and

Gamma Sigma Delta. Dr. Bursian has authored or co-authored more than 85

publications on the assessment of animal exposure to environmental

contaminants. He is the chair of the All University Committee on Animal Use

and Care, a member of the Department of Animal Science Graduate

Committee, and the Institutional Biosafety Subcommittee on Animals and

Animal Pathogens.
Steven J. Bursian

C-C-31

I. STRESS RESPONSE PROFILE RELATIVE TO THE DERIVATION

OF SPECIFIC TEF VALUES

1. The additional background information which was provided enhances the

process of evaluating uncertainties. The supplementary material provides details

related to experimental design which can account for differences between studies

using the same species for determination of Lethal Dose 50 (LD50), lowest

observable adverse effect level (LOAEL), or no observable adverse effect level

(NOAEL). These differences may be due to the type of compound(s) administered

to a particular species [eg. commercial polychlorinated biphenyl (PCB) mixture as

compared to weathered PCBs/dioxins/furans provided through fish collected from a

contaminated site]; the method by which the compound is administered to the animal

(e.g. injection into air cell vs. yolk; injection on day 0 vs. day 4); the endpoint(s)

which are chosen to assess LOAELs or NOAELs; the time at which endpoints are

assessed (e.g. 18 days of incubation vs. hatch); whether the NOAEL is actually

determined from the dose-response curve or if it is estimated by dividing the LOAEL

by 10; and differences in doses used between studies which could result in

differences in LOAELs and NOAELs. The additional material is also helpful in terms

of assessing differences between species in terms of LD50 values for specific

chemicals (e.g. PCB 126) that could influence toxic equivalency factor (TEF) values.

2. In the process of deriving a TEF based on several studies, a number of

variables would be taken into consideration and the resulting TEF could be more

accurate than one derived from a single study. For example, if one considers the

data derived from studies in which PCB 126 and 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDD) have been injected into eggs of different species, a consensus TEF for PCB

126 could be established which would reflect considerations made for differences in

methodology and species. A TEF for a particular PCB congener that has been

derived using mortality data from a cormorant egg injection study (air cell on day 4)

and the chicken LD50 value for TCDD (yolk on day 0) could be very different from a

TEF in which the same species and methodology was used for both TCDD and the

PCB congener. It was apparent from the background material that depending on the

data chosen, TEFs for a specific congener could be different by an order of
Steven J. Bursian

C-C-32

magnitude, thus, a TEF derived from a single study introduces more uncertainty than

one which is based on a number of different studies.

3. TEF values based on egg injection studies utilizing embryo mortality as an

endpoint would be the most relevant in terms of the avian species to be protected,

particularly if the chicken was used as the animal model. In those situations in which

a TEF for a particular chemical has been developed using in vitro induction of

cyp1A1, for example, it could be applied to the present case studies with the

awareness that in vitro induction of cyp1A1 may occur at a different concentration

than an increase in embryo mortality in bald eagles. However, the variability in TEFs

derived in different studies for a particular chemical and species using similar

endpoints appears to be just as great in many cases as the variability in TEFs for the

same chemical based on different endpoints such as in vitro enzyme induction and

embryo mortality. Thus, it would be preferable to utilize a TEF based on the relevant

endpoint, if available. If not, a TEF based on another endpoint could be applied if it

was thought that the value was conservative and would protect the species in

question.

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF

THE TEQ APPROACH

1. If there is no dose-additivity or interaction among the components of the

mixtures described in the case studies, then each chemical would have to be

assessed individually. The risk assessment decision would have to be based on the

chemical judged to have the potential of causing the most harm to each targeted

species based on its potency and its environmental concentration. If stressor

response analyses were based on total PCBs or 2,3,7,8-TCDD alone in the

retrospective study, one could come to different conclusions concerning the risk.

The data presented in the table are from the retrospective case study and relate to

the concentration of total PCBs, TCDD, and toxic equivalents (TEQ) detected in

Caspian tern eggs (Table 2) and otter livers (Table 3) as well as the NOAEL 

thresholds for avian eggs and mink livers (Table 4).

Bird Egg Mammalian Liver
Steven J. Bursian

C-C-33

Concentration

Detected

NOAEL

Threshold

Concentration

Detected

NOAEL

Threshold

PCBs 5667 ng/gm 5000 ng/gm 1001 ng/gm 2000 ng/gm

TCDD 4.5 pg/gm 100 pg/gm 1.43 pg/gm 60 pg/gm

TEQs 445 pg/gm 100 ng/gm 144 pg/gm 60 pg/gm

In the case of the avian species, the concentration of total PCBs detected in the

egg is slightly higher than the NOAEL threshold, whereas the TCDD concentration in the

egg is 22 times lower than the NOAEL threshold. The TEQs present in the egg are 4.5

times greater than the NOAEL threshold. Thus, one could conclude that an analysis

based on TCDD only would suggest little risk, an analysis based on total PCBs would

suggest a risk, and an analysis based on TEQs would strongly support the notion that a

risk exists. In the case of the otter, an analysis based on total PCBs might indicate

concern, since the concentration detected in the liver is half of the NOAEL threshold

based on mink studies. The TCDD NOAEL threshold is 42 times higher than the

concentration of TCDD detected in the liver suggesting that there is little risk while the

TEQ concentration in the liver is twice the NOAEL threshold. For both species, an

analysis based on TEQs would suggest that the contaminants present in the

environment are posing a risk.

In the prospective study, the use of total PCBs would provide little protection

since the proposed paper mill is expected to generate dioxins and furans only. The use

of TCDD would certainly be an improvement but it would not provide the protection

afforded by the TEQ approach. If the relative mass concentration ratios of each of the 7

dioxins and furans expected to be in the mill effluent are multiplied by their respective

TEF values, then TCDD contributes 26% of the total TEQs while 2,3,7,8-TCDF

contributes 51%.

2. The NOAEL is dependent upon the doses employed in a particular study. 

For example, two studies are designed to assess the effect of PCB 126 on chick

bursa weights. One study employs doses of 0, 0.625, 1.25, 2.5, 5.0, and 10 Fg/kg

egg while a second study uses doses of 0, 1.0, and 10 Fg/kg egg. In both studies,

the bursa weight is reduced at 10 Fg/kg egg but not at the next lowest dose. Thus,
Steven J. Bursian

C-C-34

in the first study the NOAEL is determined to be 6.4 Fg/kg egg while in the second

study the NOAEL is 1.0 Fg/kg egg. While the difference between the two values is

relatively small, it would seem that considerations of the entire dose-response curve

in determining an LD50 or ED50 value is more accurate than designating the dose at

which no effect is observed in that particular study as the true no effect level.

3. The avian TEF for PCB 126 is 0.1 as indicated in the 1997 World Health

Organization (WHO) report. Egg injection studies in our laboratory which have

involved assessing the effects of PCB 126 and 2,3,7,8-TCDD in the chicken and

double-crested cormorant suggest that the PCB 126 TEF values for both species are

reasonably close to the 0.1 value. In the chicken, the LD50 value for PCB 126 was

2.3 Fg/kg egg and the LD50 value for TCDD was 0.15 Fg/kg egg. Thus, the TEF

value derived in this study was 0.07. In the cormorant, the PCB 126 LD50 value was

177 Fg/kg egg while the TCDD LD50 value was 4.2 Fg/kg egg. Based on these

data, the TEF for PCB 126 in the double-crested cormorant is 0.02. The consensus

avian TEF for PCB 126 and the two values established in our laboratory are within

the same order of magnitude despite the marked difference in sensitivity between

the chicken and cormorant to PCB 126 and TCDD. Thus, it would seem acceptable

to apply class-specific TEFs to the avian species identified.

III. EXPOSURE PROFILE

2. In egg injection studies, the site of injection (yolk vs. air cell) and the time of

injection (day 0 vs. day 4 of incubation) influence the concentration of the chemical

at which effects occur. Typically, yolk injections yield a lower LD50 value than air

cell injections and injection on day 4 of incubation precludes exposure of the embryo

during its first 96 hours of development. However, differences in LD50 values

because of injection site are relatively small and should not prevent the use of egg

injection-derived TEFs for environmental risk assessments. For avian species, the

use of egg injections is easier and probably more accurate than feeding the

contaminant in question to laying hens and then assessing the effects of the

compound on egg production and hatchability. Feeding contaminants to nondomesticated avian species such as cormorants or terns would be considerably
Steven J. Bursian

C-C-35

more difficult if not impossible, while the injection of eggs collected from relevant

species is feasible.

IV. RISK CHARACTERIZATION

2. Giesy et al. (1994) summarized the advantages and disadvantages of using

the H4IIE assay for determination of TCDD-EQ as compared to the chemical

analysis/TEF approach. The bioassay is rapid and considerably less expensive than

congener-specific analysis. Since the bioassay is a mechanistically-based

determination of an integrated biochemical response (induction of ethoxyresorufin Odeethylase activity), it is more biologically relevant. The bioassay accounts for

interactions between the polychlorinated hydrocarbons and other types of

compounds that may be present in the mixture. In a comparison study to determine

the TEQs by instrumental and H4IIE bioassay analysis, the bioassay determined a

higher number of TEQs in an environmental mixture when compared to the chemical

analysis/TEF approach. It was possible that components of the mixture were acting

synergistically or there were components in the mixture which were not quantified. If

feasible, both approaches could be used and in those cases where one method

offers greater protection than the other, the risk assessment would be based on the

most conservative approach.

3. Perform egg injection studies with the chicken as the experimental animal

(more sensitive than species of interest) to determine TEF values for each relevant

chemical. If such studies could be done utilizing consistent techniques throughout,

some of the uncertainty associated with the derivation of the TEFs would be

eliminated.

Conduct a mink reproduction trial in which animals would be exposed to

relevant concentrations of TCDD from 3 months prior to mating through weaning of

the young. Mink are extremely sensitive to PCBs and TCDD. While reproductive

trials utilizing commercial PCB mixtures, PCB congeners, and environmentally

derived PCBs have been run, no such trial has been conducted with TCDD.
Steven J. Bursian

C-C-36

Conduct a feeding study with otter in which they would be fed diets

containing TCDD, specific PCB congeners, or environmentally derived PCBs. While

otter are more difficult to work with than mink, it could be done.
C-C-37

Peter L. deFur, Ph.D.

Owner

Environmental Stewardship Concepts

11223 Fox Meadow Drive

Richmond, VA 23233

804-360-4213

Fax: 804-360-7935

E-mail: pldefur@igc.org

Dr. deFur received B.S. and M.A. degrees in biology from The College of William

and Mary, Williamsburg, Virginia. He holds a Ph.D. in biology from The

University of Calgary in Canada. Currently, Dr. deFur is affiliate associate

professor at the Center for Environmental Studies, Virginia Commonwealth

University, Richmond. He also owns and manages Environmental Stewardship

Concepts. Professional experience includes projects for the Environmental

Defense Fund in Washington, D.C. and an investigatorship at the Smithsonian

Environmental Research Center. He is active in SETAC workshops and is

currently a member of the Endocrine Disruptor Screening and Testing Advisory

Committee to the EPA. He participated in the National Research Council’s

Study Committee on Risk Characterization as well as being part of the drafting

team for a chapter of the EPA dioxin reassessment. Recent research includes

projects on the effects of medical waste disposal, the effects of hormone

disrupting chemicals on children’s health, and a number of risk assessment

programs in aquatic ecosystems. He has numerous published articles and

reports.
Peter L. deFur

C-C-38

Comments of Peter L. deFur on the Application of 2,3,7,8 TCDD TEF’s to fish and Wildlife

November 17, 1997

General comments on the use of TEF’s for wildlife and the two case studies

TEF’s: The concept of TEF’s and the application of TEF’s is neither new nor is it entirely

novel in biological sciences. Fundamentally, there is an abundant literature from the fields of

endocrinology, toxicology, pharmacology, neurobiology and other areas of physiology

supporting the concept of equivalencies in cellular/molecular biology. The specific

development of TEF’s for TCDD is also well established, although the literature may not be

as old. I strongly support the effort to extend the use of TEF’s from rodents and humans to

fish and wildlife. This should prove fruitful in applying basic information to environmental

control and clean-up and should provide important insight into the comparative aspects of

environmental biology.

That said, the one point that I see limiting TEF’s in this context is the metabolic differences

among animals, a point also made in the present papers. The present work notes that some

Ah active compounds are not metabolized in marine mammals as in other species (e.g.

rodents) with subsequent different accumulations. If we accept the fact that the molecular

events of Ah receptor binding are common to all Ah active compounds, then there are two

major steps where substantial interspecific differences are likely to occur. The first is in the

cellular events following Ah binding; the second is the process by which Ah active

compounds or products of Ah activity are metabolized (either the upstream or downstream

metabolic pathways). So far, most of the vertebrates examined for Ah activity, excepting

marine mammals, are temperate to northern boreal animals. Few, if any show extremes in

life history (lungfish), evolutionary development (platypus) or environmental adaptation (e.g.

desert reptiles). I would expect to see the most remarkable differences in metabolic

processing among these types of animals, as observed in marine mammals.

The use of TEF’s in risk assessment or any other regulatory program or plan should pose no

more or less problematic than any other analytical tool. TEF’s seem to apply to fish and

mammalian wildlife, but have not been attempted or well demonstrated in amphibians and

reptiles.

Both cases are based on well studied situations with rich databases and numerous examples. 

Data on Great Lakes fish and on pulp and paper mills discharging TCDD are abundant in the

EPA files and the literature. The advantage of using such familiar types of cases will be an

easier application of the method. After this exercise is completed and any modifications
Peter L. deFur

C-C-39

included, I urge EPA to consider a follow-up case that draws on a poorly studied type of

situation.

Specific Review Questions:

I. Stress-response

1. Point estimates of TEF’s still should include reference to the background

information from which the points were calculated. As the number and variety

of applications of TEF’s increases, so shall the need to consider additional

species less similar to the ones for which the original data were developed. The

background information should enable the users to determine the extent, if any,

to which new applications to other species require further modification.

2. It is clear from the literature that not all TEF’s have the same or even similar

levels of experimental data in the development. Yet, there seems to be no

apparent reason why one compound should behave fundamentally different from

those compounds for which there is a substantial database.

3. The TEF’s for biochemical and cellular effects should be usable for whole

organism effects. The mechanism whereby enzyme induction (or other

molecular event) is related to whole organism effects, e.g. reproductive

impairment, has not been elucidated in full. This information should be usable in

the future, but should not prevent the application of TEF’s now.a)

II. Stress- response and Application of TEQ’s

1. I will have to give this more consideration. Part of the answer to this is found in

the answer to V.1. 

2. The TEF’s derived at some dose to achieve median effect (response) are clearly

useful in the range in which they were developed, and for the effect or

mechanism for which they were developed. But the usefulness has not been

challenged or tested at low doses or perhaps not at high doses for wildlife and

aquatic life (presumably the high dose exposures in mammals and some rodents

may confirm the applicability in this end of the range). The low dose research

so far has focused on enzyme induction and similar biochemical events. Has 

anyone confirmed or refuted the applicability of TEF’s in very low doses in these

groups of animals?

3. Class-specific TEF’s should be more applicable than are more general TEF’s

(e.g. vertebrates). Thus far, the experimental evidence supports the class basis,
Peter L. deFur

C-C-40

even for the exceptions (marine mammals).

III. Exposure Profile

1. To what extent does the TEF approach limit exposure analysis? The challenges

associated with the TEF approach are those of increasing the complexity of

exposure modeling, including fate and transport. The congeners should not be

collapsed together in exposure models, but should be treated individually so that

congeners with dramatically different TEF’s can be accounted for in the

exposure, rather than assume that all congeners are similar. The physicochemical data suggest (or more) that congeners will act differently in the

physical environment. Because these congeners would likely (or certainly) have

quite different TEF’s and hence toxic effects, their exposures should be treated

separately. Models that do not now treat the congeners separately will not

suffice for use in TEF specific risk assessments.

2. Exposure route differences used in deriving TEF’s may alter the final outcome if

(and only if?) the route of exposure alters the absorbed and tissue dose, and if

this alteration is not accounted for in the final calculation. Efficiency of uptake

is high in digestive tracts of most, if not all animals; this is the primary route of

exposure and of administration in laboratory work. I cannot see that this would

be a problem in the derivation and use of TEF’s.

3. The TEF approach will prompt or require a more detailed analysis of TCDD (and

PCB) sources such as sediments, water and soils than is the case in which the

congeners are aggregated. The aggregation approach simply assumes that all

congeners in the total are the same, or that the aggregate can be treated in a

simple, single model approach. The different toxicity of each congener will

affect the final toxicity of the mixture; hence it will be much more accurate to

know the real mixture toxicity based on the sum of the TEF’s.

IV. Risk Characterization

1. Sources of uncertainty in an entire risk assessment are not so quantitatively

predictable as to make any conclusion as to the relative contribution a priori.

Uncertainties in TEF’s are largely from the variation in the results of

experimental outcomes, whereas the uncertainties in a “field” risk assessment

include fate and transport, exposures, endpoint sensitivities and population

dynamics. I doubt this can be determined on a generic basis.

2. Comparing the TEF approach with a site-specific TEQ analysis, there are a few

ways to approach this question. I think the first question is why would one do
Peter L. deFur

C-C-41

both? Or one or the other? I imagine that most scientists would want the

congener specific TEF analysis, based on a congener analysis of the source. 

Given the acceptance of TEF’s, and the confidence in them, as well as an

exposure analysis that incorporated individual congeners, a simple analysis of

the source of contamination (air, water, sediment, etc) would provide a

straightforward method for determining the dose to the target species. In the

cases where the TEF’s are most likely to apply tot he target (endpoint) species,

the TEF approach would likely provide the most accurate approach. But, in

cases where the TEF’s are not as likely applicable to the target species, then the

total sample TEQ approach would circumvent the lack of applicability.

The TEQ approach is likely to be more difficult.

 

V. Prospective Case

1. This question about improvement in BAF fd

w ‘s makes a comparison and it is not

clear to what the new BAF’s are compared or if it is to any alternative. The

improvement or increased accuracy is in targeting for lower MAL’s those

congeners that are more accumulative. I do not see the safety in permitting the

relaxation or raising of MAL’s for congeners with lower BAF’s in a single

species or in a class. In this latter case, the permitting of more discharge of any

congeners of TCDD assumes that the congeners will not ever pose a toxicity

problem in the lake. If at some later time the congener with the higher MAL

does pose a problem, then the loading over time will present future

contamination problems. The temporal lag between discharge and effect, control

and response is problematic for TCDD and congeners that have such a long halflife in the environment.

2. Without using congener specific data, the state may have to treat all congeners in

an approximately similar fashion, erring in exposure, dosimetry and in toxicity. 

Alternatively, the state may chose to ignore all congeners for which there are not

site-specific data, as has been done in the past when only TCDD was analyzed. 

Both approaches have the potential for underestimating toxicity; the latter

approach having been carried out by many states for years (and likely still

practiced). The former error could treat all lower toxicity congeners as more

toxic, thereby overestimating the effect. Unless, of course the state chooses to

“average” the toxicity along with lumping the dosimetry, thereby taking some

sort of average toxicity to use with a total dose of TCDD’s, TCDF’s and PCB’s. 

VI. Retrospective Case
C-C-42

1. I have not calculated the numeric clean-up goals for each vertebrate species. 

Seldom are the clean-up goals the same in such cases. Frequently one endpoint

drives the clean-up because of greater BAF, greater sensitivity (more toxic in one

species than the others), or because of a different target level in the clean-up

goals, as is the case with some endangered species, or one for which there is a

specific population recovery plan. 
C-C-43

Joseph V. DePinto, Ph.D.

Professor of Environmental Engineering

Great Lakes Program

State University of New York at Buffalo

202 Jarvis Hall

Buffalo, NY 14260-4400

716-645-2088

Fax: 716-645-3667

E-mail: depinto@eng.buffalo.edu

Dr. DePinto has a B.A. in physics from Miami University of Ohio, an M.S. in

physics and environmental engineering, and a Ph.D. in environmental

engineering all from the University of Notre Dame. Dr. DePinto is a professor

in the Department of Civil, Structural, and Environmental Engineering and the

director of the Great Lakes Program at the State University of New York at

Buffalo. One of Dr. DePinto’s major research interests is the mathematical

modeling of transport and fate of pollutants in aquatic systems. Other areas of

interest are aquatic chemistry, lake eutrophication, aquatic ecosystem modeling,

and the application of geographic information systems to water quality modeling.

He is the author of over 150 reports and scientific publications. Dr. DePinto is

a member of the American Chemical Society, the American Society of Limnology

and Oceanography, and the International Association of Great Lakes Research.

Dr. DePinto serves on several Great Lakes councils and advisory groups.
Joseph V. DePinto

C-C-44

Pre-meeting Comments in Response to Charge Questions for

Workshop on the Application of 2,3,7,8,-TCDD Toxicity Equivalency Factors to

Aquatic Life and Wildlife

Since my expertise is limited to modeling the transport, fate and bioaccumulation of

contaminants in aquatic systems, my comments in response to the pre-meeting questions will be

confined to those questions relating to exposure and risk characterization. I will leave comments

on toxicity questions to those with much more expertise in that area.

III. EXPOSURE PROFILE

1. To what extent does the TEF approach present challenges, introduce new uncertainties,

or modify old uncertainties associated with modeling the exposure of AhR agonoists? To

what extent does the availability and quality of congener-specific phyisco-chemical data

imit the means of employing fate and transport or food chain models?

Until the Green Bay Mass Balance Study and the modeling work conducted in that study,

fate and transport models of hydrophobic organic chemicals (HOCs) were not applied to specific

congeners. Having been one of the modeling team working on that project, I can state that one of

the significant outcomes of that study was that “once an accurate model for the dynamics of

sorbents (solids and non-settleable organic material) in a specific system has been developed, we

have enough knowledge of and appropriate formulations for the transport and fate of HOCs in

surface waters that we can accurately model the concentrations in water and sediments of

specific congeners of these compounds merely by having good congener-specific physicochemical data (e.g., Kow, He, biotic and abiotic degradation rates).” This result was demonstrated

for PCBs in the Green Bay study by successfully modeling PCB congeners spanning a wide

range of hydrophobicity and volatility using the same sorbent dynamics and only changing the

respective physico-chemical properties. This development has made it possible to use the TEQ

approach as proposed in the Prospective Case Study. However, as the statement above

suggests, we will be limited in this approach by the availability of accurate congener-specific

physico-chemical parameters. As indicated in Table 1 of the Charge Questions, there are gaps in

these data and potentially order of magnitude or more uncertainties in some of the properties

estimated for the more hydrophobic congeners. In my opinion, considerably more work needs to

be done in measuring or calculating (based on structure-activity models) these chemical

properties.
Joseph V. DePinto

C-C-45

The status of bioaccumulation model is slightly more problematic because of the

uncertainties of in model formulation and parameterization of food chain bioenergetics and

predator-prey dynamics and because of the large system-to-system variability of these ecosystem

dynamics. Measurements of BAFs and BSAFs on a congener-specific basis can obviate the need

for the more mechanistic food chain bioaccumulation models, but extrapolation of site-specific

measurements carries with it a significant uncertainty in terms of two things: 1) a different system

with a different food web will exhibit a different BAF or BSAF; and 2) the measurement in a

given system is representative of a specific point in time and there may be a lag between a change

in the concentration in the water column or sediments and the response of the concentration up

the food chain (i.e., the measurement may not have been at bioaccumulation equilibrium).

2. To what extent do exposure route differences used in deriving the TEFs affect their

application in the case studies?

This could be problematic because of the fact that we tend to see a decrease in BAF as a

function of log Kow for the super-lipophilic congeners (log Kow  6.5). It is not known whether

this is because the congener is so insoluble that it cannot transport as effectively across the gut

wall or whether the kinetics of the bioaccumulation process is so slow that the organism cannot

respond to a given exposure level in a reasonable length of time. In any event, there is likely to

be a big difference between the BAF for one of these compounds if the exposure is via food

intake versus direct injection.

3. To what extent does the TEF approach require a more rigorous analytical design in

quantifying sediments, soil, and biota AhR agonist concentrations than is apparent in

other methods which aggregate stressors?

If I understand the approach, projecting a AhR-based toxicity in fish or wildlife based on

will require a chemical measurement of the concentration of each congener in each stressor

source. In other words, if contaminated sediments are the source of toxicity, then the initial

concentration of each relevant congener will have to be quantified. Of course, with current

analytical methods, measurements like total PCBs are actually made by appropriately summing

individual congener concentrations. This may be somewhat problematic using EPA’s accepted

standard method because detection limits for individual congeners are often too high to give an

accurate TEQ for comparison against a effects standard. 
Joseph V. DePinto

C-C-46

IV. RISK CHARACTERIZATION

1. In evaluating the case studies, do uncertainties in the TEFs limit the assessment or are

other aspects of effect and exposure characterization contributing similar or greater

levels of uncertainty?

It is clear to me that uncertainties TEFs will increase the overall uncertainty of a risk

assessment, simply because we are propagating more error through the calculation as we increase

the number of parameters to specify. However, one must weigh uncertainty against the

information obtained — or utility — in a given calculation. In my opinion, there is a potential to

gain much more information using the TEF approach; therefore, it is worth using even though the

error might be somewhat higher. I feel confident that, over time, experience with the approach

and more empirical data will reduce the uncertainty.

2. Biologically-based TEQ assays on environmental samples could be employed as an

alternative to the TEF-based approach. What would the strengths and weaknesses of

such an approach be?

With regard to exposure modeling, if the biologically-based TEQ assays approach were

used, we would have to develop models for the fate and transport of whatever it was that the TEQ

assay was measuring. If we did this we would not only find it virtually impossible to

parameterize such a model, but we would have no way of guaranteeing that a TEQ assay level at

a source would be transported and transformed through the aquatic system in such a way that

made it directly comparable to the same assay conducted on the receptor (some fish, bird or

mammal). In other words, TEQ for multiple sources (including background sources) would not

necessarily be additive at the receptor. Put another way, modeling the fate and transport of

“toxicity” as a single constituent is fraught with problems and uncertainties that may indeed

exceed the errors introduced by making the analysis more complex by using the TEF approach.

3. Provide a list of specific research or site-specific data that would improve the analyses in

the case studies.

In my opinion, there are three primary areas of uncertainty associated with the type of

regulatory analysis described in both case studies: 

1. Quantifying the exposure distribution in water and sediments of the system of interest as a

function of the various sources.

2. Confirming that BAF or BSAF measurements in one system are applicable to another; in

other words, understanding the ecosystem factors that control bioaccumulation and hence
Joseph V. DePinto

C-C-47

these measurements.

3. Reducing uncertainty in TEF values by building an empirical database over time.

All three of these research/data acquisition areas are very important in my opinion. Suffice it to

say that application of the TEF-based for risk management requires continued research associated

with its application in order to build a better experience and knowledge base.

Additional Questions Specific to the Prospective Case Study

1. The state adopted BAFfd

w used by the GLWQG. What improvement in the accuracy of

maximum allowable concentrations for individual congeners in water can be expected

through use of BAFfd

w determined from Roundtail Lake data?

The GLWQG BAFs were determined largely using Lake Ontario measurements using

lake trout. Measurement error and time-variability aside, these BAFs could easily vary by 1-2

orders of magnitude between Lake Ontario and another system with a different food web (e.g.,

more benthic versus pelagic or having a different number of trophic levels). To the extent that

the food webs of Lake Ontario and Roundtail Lake are similar, I would not expect to gain much

accuracy in measuring site-specific BAFs in Roundtail Lake. However, if the food webs are

significantly different or if there had been a significant perturbation (e.g., one or more very bad

recruiting years) in one of the trophic levels (such the prey fish), then site-specific measurements

would certainly be advisable.

4. What errors are associated with application of TCDD water quality standards for birds and

mammals without consideration of congener-specific differences in biomagnification factors

from fish tissues?

If I understand this question correctly, there is potentially significant error involved in

not accounting for BMF from fish to wildlife. The BMF could be significant on a congenerspecific basis, which in turn might have a significant effect on the standard. Good data on the

fraction of the wildlife diet coming from fish and the age and size of fish in their diet is crucial

and perhaps an even greater source of error in many cases.

5. How should uncertainties associated with the available fish and wildlife TEFs be

incorporated into decisions for setting TeqTMDL?

We must attempt to quantify the uncertainty in TEFs for each target group and then 

propagate that error through the calculation of TeqTMDL for each. Then the actual TMDL
Joseph V. DePinto

C-C-48

allocated to the discharger should be based on the target group yielding the allowable loading that

is statistically lowest without making a Type I error.

Additional Questions Relative to the Retrospective Case Study

1. Would the TEQ sediment cleanup goals be the same for each vertebrate group? If not, why

would there be a difference? How would you handle a situation in which the group with the most

certainty is not the group with the most restrictive sediment cleanup goal?

I would think that sediment cleanup goals would vary from group to group; because,

even though the source may be the same and may have the same congener distribution for each

group, the pathway to each group is very likely to be different and each trophic level in those

pathways may be subject to different exposure distributions and may bioaccumulate and

metabolize that exposure differently. Therefore, I would not be surprised at all that computing a

cleanup goal based on a TEQ would yield different values. 

Refer to my response to the last question for my opinion on how to handle a situation

where unequal certainty exists among groups.

2. Would the TEF/TEQ-based sediment remediation goal be the same as those determined for

total PCBs for the identical vertebrate class? Assume that a simple ratio of total PCB

sediment concentration goal to TEQ sediment concentration goal was formulated to allow

for the use of total PCBs to monitor cleanup. What exposure and effect issues would need to

be evaluated before using the less costly total PCB analysis to support the TEQ-based

sediment remediation goal?

This is an excellent question, but it is difficult to answer without going through

significant calculation and modeling. But most importantly, the concentration of PCBs (and

other chemicals of interest) in the sediments of Yuckymuck River have not been measured (or at

least not specified); therefore, it is impossible to know if the two goals will differ. But given the

total PCBs in the lake surface sediments are 110 ppb, I would venture an educated guess that the

PCBs in the river sediments are still well over 1 ppm. Therefore, a goal based simply on getting

total PCBs down below 1 ppm would probably require removal of more sediment than the TEQbased remediation goal. 

Using a simple ratio of goals to permit measuring total PCBs as a means of monitoring

cleanup progress is fraught with error. I can think of no in-place or removal-treat-and-replace

sediment remediation process that would not be congener-specific in its removal efficiency. 

Even using a simple dredging and disposal approach would not necessarily work. This is because
Joseph V. DePinto

C-C-49

the congener distributions of PCBs and PCDFs would no doubt change with depth in the

sediments; therefore, a goal and ratio based on surface sediments would not necessarily be

constant through the full treatment depth of the sediments. If the spill occurred 30 years ago and

loss is by burial, the spill chemicals may have penetrated quite deep into the sediments.

Conclusion

I am strongly in favor of applying the TEF/TEQ approach for risk management of fish

and wildlife; however, we must move forward by maintaining a concurrent research and data

acquisition program that will allow us to continue to reduce uncertainty in decision-making.
Joseph V. DePinto

C-C-50

Bibliography

Gong, Yuyang, J.V. DePinto, G-Y. Rhee, X. Liu. 1997. Desorption rates of two PCB congeners from

suspended sediments: (I) Experimental results. In press: Water Research (October, 1997).

Gong, Yuyang and J.V. DePinto. 1997. Desorption rates of two PCB congeners from suspended

sediments: (II) Model simulation. In press: Water Research (October, 1997).

Velleux, M., S. Burns, J.V. DePinto, and J.P. Hassett. 1995. A screening-level mass balance analysis of

mirex transport and fate in the Oswego River. J.Great Lakes Res. 21(1):95-111.

Cheng, C-Y., J.F. Atkinson, and J.V. DePinto. 1995. Desorption during resuspension events: kinetic vs.

equilibrium model. Australian Journal of Marine and Freshwater Research.46:251-256.

DePinto, J.V., M. Morgante, J. Zaraszczak, T. Bajak, and J.F. Atkinson. "Application of Mass Balance

Modeling to Assess Remediation Options for the Buffalo River (ARCS/RAM Program)." Final

Technical Report for Cooperative Agreement CR-X995915 to U.S. EPA, Great Lakes National

Program Office, Chicago, IL. Report No. EPA 905-R95-007. (April, 1995).

Bierman, V.J. Jr., J.V. DePinto, T.C. Young, P.W. Rodgers, S.C. Martin, and R.Raghunathan. 1992.

Development and validation of an integrated exposure model for toxic chemicals in Green Bay, Lake

Michigan. Final Report for EPA Cooperative Agreement CR-814885, ERL-Duluth, Large Lakes and

Rivers Research Branch, Grosse Ile, MI, 350 pp. (September, 1992).

DePinto, J.V., T. Fiest, R. Raghunathan, D. Smith. 1997. Analysis of Toxaphene Behavior in the Great

Lakes. Organochlorine Compounds, 33:280-284 (Proceedings of 17th International Symposium on

chlorinated dioxins and related compounds).

DePinto, J.V., R. Raghunathan, P. Sierzenga, X. Zhang, V.J. Bierman, Jr., P.W. Rodgers, T.C. Young,

"Recalibration of GBTOX: An Integrated Exposure Model for Toxic Chemicals in Green Bay, Lake

Michigan." Final Technical Report to U.S. EPA, Large Lakes and Rivers Research Branch, Grosse Ile,

MI, March 1, 1994.

DePinto, J. V., R. Raghunathan, V.J. Bierman, Jr., P.W. Rodgers, T.C. Young, and S.C. Martin. 1993.

Analysis of organic carbon sediment-water exchange in Green Bay, Lake Michigan. Water Science

and Technology, 28(8-9):149-159.
C-C-51

Lev R. Ginzburg, Ph.D.

Professor

Department of Ecology and Evolution

State University of New York, Stony Brook

11 Crane Neck Road

Stony Brook, NY 11794

516-632-8569

Fax: 516-751-3435

E-mail: lev@ramas.com

Dr. Ginzburg is a professor of ecology at the State University of New York, Stony

Brook since 1977 and the president of Applied Biomathematics since 1982. His

expertise focuses on risk analysis and population modeling. He currently has the

following articles in press: Inertial growth: population dynamics based on maternal

effects and Assymmetry of population cycles: abundance - growth representation

of hidden causes of ecological dynamics. Dr. Ginzburg has had over 100 articles

and 5 books published during his career.
Lev R. Ginzburg

C-C-52

0. General and preliminary comments

The scope of the workshop’s discussion is confined by the organizers to considerations of direct

effects only. While perhaps useful in limiting the scope of the discourse for the sake of

manageability, this seems dangerously restrictive in the context of ecological risk assessments.

After all, indirect effects such as trophic cascades certainly do play a very important role in the

ecotoxicology of TCDD and its congeners. Perhaps this restriction should be relaxed somewhat.

Like many, I have serious and strong reservations about the use of the “hypothesis-testing”

approach in environmental risk assessment and management, including use of the hazard

quotient, no observed effects levels, and their ilk. The conceptual difficulties with EPA’s

approach are many and have been widely discussed (e.g., Barnthouse et al. 1986; Landis and Yu

1995; inter alia). Whether or not to regulate or remediate should be framed as a decision

problem, not a hypothesis testing problem. Much of the use of TEFs (toxicity equivalency

factors) has heretofore been embedded in hypothesis-testing approaches which I find barely

intelligible. It is heartening, however, that the TEFs should be of use beyond the rarefied context

of hazard quotients. I think it will be important for the workshop discussion to consider how

TEFs will continue to be useful when hazard quotients are replaced by probabilistic methods of

decision analysis.

II.3. Extrapolating class-specific information to particular species

Although one might hope that TEFs will provide a means of freely translating toxicity

information within the big matrix of chemical congeners and biological species, there appears to

be a considerable amount of interspecific variability in toxicity of TCDD itself (and presumably

this cannot be erased in the TEF method). It might be very interesting to explore the available

information about TEFs for the existence of allometries in which this residual variation might be

at least partially explained by a species’ body size, typical egg size, or other easily measured

species-level variable. If even crude allometric relationships exist, they may be very useful in

making the TEF method more accurate with little additional effort.

IV.1. Are uncertainties of TEFs more problematic?

I doubt that the uncertainty about TEFs is any more problematic than that of the other sundry

inputs to a quantitative risk characterization. The magnitudes of these uncertainties may be fairly

similar to those we’ve seen in other inputs, and even if they’re considerably bigger, it shouldn’t
Lev R. Ginzburg

C-C-53

necessarily lead to any fundamental incompatibility. It is important to understand, however, that

the uncertainty in TEFs will likely primarily be lack of knowledge (i.e., incertitude or ignorance),

rather than variability. We have argued that it may be necessary to use different uncertainty

propagation techniques to handle this kind of uncertainty (Ferson and Ginzburg 1996). In

particular, the indiscriminant application of Monte Carlo techniques in this case can lead to

erroneous conclusions that underestimate the risks involved. 

The task of identifying, and quantifying, the uncertainties associated with TEFs belongs

primarily to the empiricists who collect the original toxicity data and the synthesizers who collate

this information and compute the TEF values. The former must report their measurement errors

in full detail; the latter must propagate these uncertainties using appropriate techniques. 

Reviewers can help by checking that the results seem reasonable and by guessing at what

possible mistakes or omissions may occur, but they cannot be expected to develop

characterizations of uncertainty if the requisite underlying details are missing.

IV.3. Further empirical investigations for the case studies

Most of the documents focus on effects on juvenile survivorship. Are there known to be no

effects from common environmental concentrations of TCDD etc. on other demographically

important variables? Possibilities include time to reproductive maturity, onset of adult

senescence, growth rate, reproductive investment, among others. Since the toxicological effects

are believed to be additive, I would supposed they are likely to also be cumulative in time with

iterated exposures. Thus one might expect to see effects in later life stages. Unless it’s clear that

no effects on such variables are possible (via the Ah receptor mechanism or otherwise), I think it

would be very important that further specific empirical and synthetic studies be conducted to

extend the TEF method to such variables. It seems doubtful that a TEF value for one life stage is

really general for all life stages.

Often a biochemical response (e.g., induction of cyp1A1) is observed in lieu of measuring effects

on juvenile survivorship. It is harder and harder to justify regulations based merely on

measurable biochemical effects in non-human species. Unless this biochemical effect has an

obvious and direct consequence on some population-level vital rate (reproduction, mortality,

growth rates), or perhaps on some organismal-level variable related to individual health of

humans or a listed endangered species, we should expect to encounter “so what?” questions from
Lev R. Ginzburg

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the regulated communities and the public.

Extra Note:

The word ‘congener’, like ‘species’, has both a meaning in chemistry and another meaning in

biology. The documents are consequently rather confusing. 

References

Ferson, S. and L.R. Ginzburg. 1996. Different methods are needed to propagate ignorance and

variability. Reliability Engineering and Systems Safety 54:133-144.

Landis, W.G. and M.-H. Yu. 1995. Introduction to Environmental Toxicology. Lewis Publishers,

Boca Raton.

Barnthouse, L.W., G.W. Suter II, S.M. Bartell, J.J. Beauchamp, R.H. Gardner, E. Linder, R.V.

O’Neill and A.E. Rosen. 1986. Users’s Manual for Ecological Risk Assessment. Oak Ridge

National Laboratory, ORNL-6251. National Technical Information Service, Springfield,

Virginia.
C-C-55

Jay W. Gooch, Ph.D.

Senior Scientist

The Procter and Gamble Company

Professional & Regulatory Services

Paper Products Division

Winton Hill Technical Center

6100 Center Hill Avenue

Cincinnati, OH 45224-1788

513-634-1053

Fax: 513-634-7364

E-mail: gooch.jw@pg.com

Dr. Gooch received both his B.S. and M.S. degrees in fisheries and wildlife, and

a Ph.D. in environmental toxicology, all from Michigan State University. He is

currently a toxicologist at The Procter and Gamble Company, Cincinnati, as well

as adjunct assistant professor in the Zoology Department at Miami University in

Ohio. Within the past 11 years, he has held positions with the Chesapeake

Biological Laboratory in Maryland and at the Woods Hole Oceanographic

Institution in Massachusetts. His expertise for the task at hand, toxic equivalency

factors, is in toxicology, especially of fish in marine and estuarine settings. He

has received the SEA Grant and National Fisheries Institute Award for

Outstanding Applied Marine Research, and he is active in three professional

societies: the Society of Environmental Toxicology and Chemistry (SETAC), the

American Chemical Society, and the International Society for the Study of

Xenobiotics. Dr. Gooch is an ad hoc reviewer for 10 publications and has

organized and chaired professional meetings for SETAC. Pertinent publications

by Dr. Gooch are concerned with environmental fate and transport, PCBs, and

environmental risk assessment.
Jay W. Gooch

C-C-56

Responses to General Questions

Because I often had difficulty understanding exactly what was being asked in some of

these questions, my responses below contain my paraphrase of the question prior to the

answer. 

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC

TEF VALUES.

1. I was very perplexed by this question. Was there any information in the descriptions

of the case studies which was useful for reducing the uncertainties in the derivation of

the WHO TEFs and their application to each particular assessment? 

No. I believe the greatest uncertainties in the application of the WHO TEF values are in

the reliability of the extrapolations to other untested species, and extrapolations to

endpoints biologically or biochemically distant from the endpoints used to derive the

TEFs.

2. Should all TEFs be considered to have similar uncertainties? Obviously, no. The

uncertainties associated with each of the TEFs have multiple sources. As referred to

above, these are most importantly the cross species extrapolations, and the crossendpoint extrapolations. As pointed in several of the documents provided, the

uncertainties in the application of any given point estimate of a TEF increase the more

distant the endpoint on which the TEF is based is from the endpoint of interest in the

assessment. I believe this is nothing more than common sense and toxicology 101. 

There are several examples which exemplify these uncertainties; as eloquently pointed

out in document 6F by Cook, et al.. In Table 2 of that document TEFs are listed across

endpoints. By looking at the TEFs for a given chemical (across endpoints), it is

apparent that the relative difference in the TEFs is not the same across chemicals (see

below).

Table 1. 
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C-C-57

 TEF Ratio

 In vivo RBT liver EROD/ In vitro RBT liver EROD/

 RBT ELS Egg Mortality RBT/ELS Egg Mortality

Congener

2,3,7,8-TCDD 1.0 1.0

1,2,3,7,8-PeCDD 2.7 3.9

1,2,3,4,7,8-HxCDD 1.5 4.2

1,2,3,6,7,8-HxCDD 20 10

1,2,3,4,6,7,8-HpCdd 33 133

2,3,4,7,8-PeCDF 5.9 5.6

1,2,3,4,7,8-HxCDF 1.7 4.6

1,2,3,7,8-PeCDF 12.5 6.3

2,3,7,8-TCDF 16.7 6.7

PCB 126 0.4 41

PCB 81 6.5 4.8

PCB 77 28 17

Looking down the row of each ratio, it is notable that relative difference in the TEFs

derived from the biochemical endpoint of EROD induction to the more ecologically

relevant endpoint of ELS egg mortality is clearly non constant across the chemicals and

spans a range of nearly 200 fold. Also of interest is the lack of concordance in the

relative ratio for any given chemical (i.e. looking across the rows). As pointed out by

Cook et. al., the difference, hence the best example of uncertainty, is for PCB congener

126. 

Since uncertainty is also a general function of the information richness of the data set,

the trout data serves as the basis for another point. In general, the rainbow trout and

lake trout data sets are the most information rich data sets we have for formulating and

evaluating TEFs. And this data reveals the uncertainties above. It is very difficult to

say whether these same observations about uncertainty would hold if the data set were

on another species entirely, for example with a bird species. In other words, it is difficult

to say if the differences would be greater or less?

In addition to this source of uncertainty, there is also uncertainty derived from cross

species extrapolations. For example, the LC50/NOEC values derived from the data of

Spehar et al. are as follows.
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Species LC50/NOEC Ratio

Fathead Minnow 2.3

Channel Catfish 1.7

Lake Herring 5.2

Medaka 2.4

White Sucker 2.2

Northern Pike 2.1

Zebrafish 6.2

Avg ~ 3

Note that the NOEC values included non-lethality (non-acute) measures such as growth. 

Also the range (estimated) of egg LC50 values is 35 fold (lake trout to zebrafish).

Clearly, there are significant uncertainties in the risk benchmark values for any given

species. Would this same pattern hold true for the other PCDDs and PCDFs of

interest? Would the magnitude of the differences among species be more or less? 

3. To what extent can the TEFs be extrapolated to the measures of effect that are

relevant to the assessment endpoints? The best case, i.e. the one with the least

uncertainty, is the case where the extrapolation is biologically proximate—as is the case

when the risk endpoint is focused on early life stage mortality and the TEFs are based

on the same endpoint. So, in both cases, the fish assessments are on the firmest

footing. The bird assessments, because the TEFs are based primarily on biochemical

endpoints, are the least certain, and potentially the most conservative—if the lessons

from the fish data set are ultimately applicable.

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

APPROACH

1. What if we didn’t do a total TEQ approach? Quite simply, you would miss important

information in the estimation of the overall risk. It is clear from all of the information

provided, and the other literature in this area, that a total TEQ approach to the risk
Jay W. Gooch

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assessment is a rationale one. I agree that additivity at the cellular level is a reasonable

assumption for these chemicals.

2. Are we erring in our assessments by using TEFs based on median response levels? 

Probably not. The toxicology 101 answer to this question is “Not as long as the slopes

of the dose response curves and the magnitudes for the range of the responses are

similar across the chemicals of interest. From the data I’ve seen so far, it appears as

though, at least for TCDD, the slopes of the various dose-response curves are generally

similar (cf. The Spehar et. al. data). It also turns out that for this particular class of

chemicals the dose response curves are quite steep, with ratios of EC50 values to

NOEC values on the order of 2-3X. The downside to this type of dose response curve is

that in the effective range small changes in exposures can result in large changes in

levels of effect. The positive aspect is that below this narrow range, no measurable

effects are likely. 

3. To what extent can class-specific TEFs be extrapolated within each case study? As

per the data in my answer to question 2 of Section I above, there are indeed significant

uncertainties associated with applications across biological endpoints or species. For

the cases provided, these uncertainties are probably less problematic because it is

evident from the datasets that we are likely to have TEF values and toxicologic

benchmark values for species that are the most sensitive. For example, because the

TEFs for fish are derived from the lake trout and rainbow trout datasets, and because all

of the other data on toxicity of TCDD suggests that these are the most sensitive species,

one can have confidence that extrapolating across all other species of fish is not under

conservative (i.e. under protective). 

I would be willing to make similar conclusions for estimates of risk to wildlife which are

derived from TEFs based on mink. I think the extrapolation with the least certainty is

that with birds.

III. EXPOSURE PROFILE

1. Does the TEF/TEQ approach make it more difficult to assess “exposure” than if the
Jay W. Gooch

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assessments were focused on one chemical alone? Clearly yes, if only for the simple

reason that this approach deals with the aggregate uncertainty associated with dealing

with a larger number of chemicals. While we have built a fairly reasonable data set on

which to describe (or model) the likely fate profile of TCDD and some of the PCBs, it is

clear that we do not have the same level of understanding for all of the compounds

which are included in these TEQ calculations. We have what I would consider

“adequate” physico-chemical data on which to estimate fate and trophic level exposure if

we are willing to assume little or no biodegradation through metabolism at any (every)

trophic level. However, we clearly know this is not the case for many of these

chemicals. The conservative modeling approach is to assume no appreciable losses at

any given level of the food chain. The application of BSAFs and BAFs derived from the

literature are an improvement in realism, but are still subject to uncertainty depending on

the difference between the trophic structure of the system on which the BSAFs/BAFs

are derived versus the system in which these factors are being applied. As pointed out

in the GLI, the strongest application is where site specific BSAFs and BAFs are

available. 

In addition to the complexity of estimating general exposure to a large suite of

chemicals, there is also the uncertainty introduced by assuming that the internal kinetics

and dynamics of these chemicals are the same among species (and across the

chemicals). For example, there is data to suggest that the ratio of the whole fish or

muscle tissue concentration to the estimated gonadal tissue concentration of TCDD in

trout is approximately 3. Do we really know how valid this assumption is for all the

other AhR agonists?

2. How much uncertainty is introduced because TEFs are often derived from exposure

routes that do not simulate realistic exposure and tissue deposition? Given all the other

areas of uncertainty and other data gaps, I don’t believe this is an area of great concern. 

While I have often wondered about whether slow accumulation over long periods of time

(for example over the lifespan of a lake trout) leads to significant tissue pools (or

“compartments”) which are not bioavailable and effectively “sequestered”, I haven’t seen

any data which address this.
Jay W. Gooch

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3. Does the TEF approach require that the analytical data be more rigorous than with

aggregated measures like total PCBs? Clearly the TEF approach requires very specific

data, and the concentrations of many of the analytes are often close to the limits of

detection. In addition, in most cases relative amounts of the various analytes varies

over several decades of concentration. And finally, the TEFs themselves vary over

several orders of magnitude. The net effect is that the very low concentration, high

potency, analytes generally contribute most to the TEQ calculation. Because these low

concentration analytes are often the least certain from a general analytical standpoint, a

more rigorous analytical design is generally necessary. All of the standard QA/QC rigor

associated with stable isotope spiking, blanks, etc. etc. become paramount.

IV. RISK CHARACTERIZATION

1. Are the TEFs the dominant source of uncertainty in these assessments? I’m not

expert in uncertainty analyses, but would guess that the uncertainties in the TEFs are of

similar magnitude to the other uncertainties. 

2. Should cellular assays of TEQ content in extracts be used to make these

assessments? These assays can provide valuable data, particularly for screening

purposes. They have high throughput, are standardizable, and are relatively simple. 

However, because they are subject to potential interferences (depending on how

“refined” the extract is), they are best utilized as an exploratory tool. When a full and

specific assessment is required, the specific analytical data should be used. 

ADDITIONAL QUESTIONS RELATIVE TO THE EXPOSURE PROFILE OF THE

PROSPECTIVE CASE STUDY.

1. Will site specific BAFw,fds from Roundtail Lake improve the accuracy of the

allowable loadings over that of the BAFw,fds used from the GLWQG? Because they

take into account site specific differences in trophic structure (and hence trophic

transfer) and bioavailability, the allowable concentrations derived from site specific data

would certainly be more “accurate” for that system—accuracy relative to the models

being used. From a risk management perspective this implies that the calculations
Jay W. Gooch

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would be more certain in preventing adverse impacts. However, based on the

information provided about Roundtail Lake, and the nature of the Lake Ontario data on

which the GLWQG is derived, I would not anticipate that the differences between the

two would be large.

2. In estimating the WQS for TCDD based on estimated exposure and effects risks to

bald eagles and river otters, the state appears to have used only the BAFs/BSAFs etc.

relevant to TCDD (i.e. the calculation assumes that the practical bioaccumulation

potential for all the Ahr agonists of relevance in the assessment will be the same for all

compartments of the Roundtail Lake system). According to the data in the GLWCG

document, the Bioaccumulation Equivalency Factors for most of the other PCDDs and

PCDFs are significantly less than one—i.e. much less “practically bioaccumulative” than

TCDD. Therefore, assuming that all materials will have bioavailabilities and

bioaccumulative properties similar to TCDD appears to be significantly conservative. 

The assessment of the potential current risks from PCBs is an indication of the

potentially overly conservative nature of this calculation.

3. I believe the assessment makes a prudent choice in selecting TEqTMDL estimated

from the fish data. As stated in my response to an earlier question, the best data set we

have for relative TEFs is the fish data set. The bird data set is not sufficiently robust yet. 

Since we know very little about the River Otter, and it's clear that there is a very large

degree of difference among the mammals in sensitivity to dioxins, it is difficult to say

exactly just how conservative (or overly conservative) the numbers might be. While I do

not dispute the logic and the scientific underpinnings to the calculations that have been

used to derive the wildlife values of approximately 3 fg/l, I remain eager to see an

example where this calculation has been supported by field data. It is difficult to

imagine that it is not significantly overly conservative. 

One additional comment on the Prospective study. I believe it is appropriate to consider

the form in which the allowable TEqTMDL enters Roundtail Lake via the discharge. If

we assume that much of the measured mass loading that is contained in an effluent

(particularly a pulp mill effluent) enters in a form that is largely already associated with

organic material, have these assessments adequately attempted to account for the
Jay W. Gooch

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possibility that most of the mass of the material will never become freely dissolved or

otherwise bioavailable. The models that are used generally assume that whatever is

discharged is all discharged in a freely dissolved form, is instantaneously well mixed

throughout the system, and then partitions to equilibrium (again instantaneously) based

on affinities for organic carbon (living or dead). I contend that while this approach is in

many ways “necessary”, it is likely a very conservative one from an exposure standpoint. 

Since there are so many other uncertainties, should this source of conservatism (i.e.

uncertainty) also be articulated and dealt with? Is it reasonable to propose that a

significant amount of the mass flux of material that would be associated with a pulp mill

never partitions to a bioavailable form?

I think the 503 regulations for sludge application to land are a good example of an

attempt to deal with estimating allowable loadings taking into account the form and

availability of chemicals, in this case metals, as they enter the environment. The

analyses that did not take this into account, i.e. the ones that tried to establish

acceptable loadings based on total metal concentrations, ended up producing estimates

of acceptable loading that were low and impractical. My point here is that the form of

entry of the chemical, particularly ones that are poorly soluble and carbon reactive, can

potentially be an important factor to take into account when trying to estimate

“acceptable” loadings.

ADDITIONAL QUESTIONS ON THE RETROSPECTIVE CASE STUDY.

1. Pardon my sarcasm, but is this a rhetorical question? The answer has direct

parallels with the calculated WQS’ in the prospective study. In that example, the values

were different for the three vertebrates groups, the fish, the birds and the bald eagles. 

This is expected because of the input values that go into the derivation; the variable

TEFs and the variable BAFs, BSAFs and FCMs for each group. The common

convention for risk managers is to use the lowest, ostensibly most restrictive, value. 

However, this is generally judged against the amount and quality of the input data

relative to the estimated risk and protection goals. In the case of the WQS derived to

protect the otter in the prospective study, I assume from the information given that it was
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considered prudent, given the very low number, to get additional data to better

understand the exposure of otters in that system.

2. No, because the TEF/TEQ based sediment goals are based on chemicals which

have a potency/unit mass (or mole) which is much higher than for total PCBs. I interpret

the rest of this question to be “Can total PCBs, or any other co-occurring contaminant

with similar properties, be used as a proxy measure for TEQs for determining the

progress of a remediation attempt? Unless you believe that the PCDDs, PCDFs and

coplanar PCBs will behave differentially to the bulk PCBs during the clean-up process,

such that the ratio of these compounds to the bulk total will change, then I can’t think of

a reason why you couldn’t use totals to monitor. I would presume that a confirmatory

analysis would be done to confirm that the TEQ clean-up goal had been met.
C-C-65

Mark E. Hahn, Ph.D.

Associate Scientist

Biology Department, MS #32

Woods Hole Oceanographic Institution

Woods Hole, MA 02543-1049

508-289-3242

Fax: 508-457-2169

E-mail: mhahn@whoi.edu

Dr. Hahn received his B.S. in biological sciences from the State University of

New York at Binghamton and a Ph.D. in toxicology from the University of

Rochester, New York. His doctoral thesis was titled “Studies on the Role of the

Ah Receptor in Hexachlorobenzene-Induced Porphyria.” He received the New

Investigator Award from SETAC as well as the Individual National Research

Service Award from NIEHS. His research interests include chemical-biological

interactions, especially receptor-mediated mechanisms governing interactions of

halogenated aromatic hydrocarbons with fish and other aquatic wildlife, and his

major focus is on the comparative biochemistry and molecular biology of the Ah

receptor. Dr. Hahn is a reviewer for more than a dozen related publications, as

well as a frequent contributor to journals and other publications in the fields of

aquatic toxicology, marine environmental research, biochemistry, pharmacology,

and chemistry. Dr. Hahn is a member of the American Association for the

Advancement of Science, the Society of Toxicology, SETAC, the American

Society for Biochemistry and Molecular Biology, and the Society for Marine

Mammology.
C-C-66

Workshop on the Application of

2,3,7,8-TCDD Toxicity Equivalency Factors (TEFs)

to Fish and Wildlife

Pre-meeting comments

Mark E. Hahn, Ph.D.

Associate Scientist

Biology Department, MS-#32

Woods Hole Oceanographic Institution

Woods Hole, MA 02543-1049

email: mhahn@whoi.edu

WWW site: http://www.whoi.edu/biology/hahnm.html

Phone: 508-289-3242

Fax: 508-457-2169

I. Derivation of specific TEF values. 

1. Does the additional information provided enhance the evaluation of uncertainties in the

assessments?

Documents such as the draft WHO report identify some of the data gaps, sources of variability

and uncertainty, and possible shortcomings of the TEFs used and thus are helpful in evaluating

uncertainties in the assessments. Rather than the many papers and reports provided, a single

document summarizing all the TEF values in the literature might be more useful. Such a

document could also review the major sources of uncertainty and perhaps even provide estimates

of the magnitude of each.

2. Should all TEFs be considered to have similar uncertainties?

Theoretically, the degree of uncertainty associated with each "consensus TEF" should be

compound-specific. This is because certain compounds may be more strongly affected by the

variables that lead to uncertainty. For example, a compound that is broadly resistant to

metabolism may show less variability (uncertainty) in TEF values obtained in different systems

than a compound that exhibits differential metabolism among systems. Similarly, some

compounds (e.g. 2,3,7,8-PCDDs) will have high affinity for the AHR in most species, while for

other compounds (e.g. mono-ortho-PCBs) there may be substantial species-specific variation in

their ability to bind the AHR.

Whether this is true can be evaluated by looking at the range of TEF values for each

compound in a variety of systems. As an example, Figure 1 shows a comparison of all published

relative potency values for HAH in fish. This graph appears to illustrate different degrees of
Mark E. Hahn

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variability in the estimates, indicating different degrees of uncertainty depending on the

compound chosen.

Figure 1. A comparison of all published relative potency values for HAH in fish. Data were

obtained from in vivo studies (filled circles, references (1-8)) and in vitro studies (open

squares, references (9-14)). The values for mono-ortho-PCBs (105, 118, 156) include "upper

bound" estimates; in general responses with these congeners are minimal or absent Asterisks

indicate the "consensus Fish TEFs" used in the risk assessment scenarios.

3. To what extent can the endpoints used for TEF determination be extrapolated to endpoint(s) of

concern ("Measures of effect" or "Assessment endpoints")?

 Obviously, the goal should be to determine TEF values using the endpoints (and species) of

concern—for example larval mortality or reproductive success in lake trout or Caspian terns in

the retrospective scenario. If this is not possible, then one should choose the endpoints that are as

closely related mechanistically as possible to the endpoint of concern. One might then look at the

relationship between the chosen TEF endpoint and the "endpoint of concern" in other systems
Mark E. Hahn

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where it is known, and attempt to quantify or predict the degree of uncertainty introduced by

using the surrogate endpoint.

With regard to the use of CYP1A induction, there are often some misconceptions about the

relationship between this biochemical response and toxicity. Although the mechanism of toxicity

of dioxin-like compounds is not completely known, available evidence suggests that it involves

changes in the expression of genes involved in the regulation of cell growth and differentiation.

CYP1A induction is relevant as an endpoint for TEF determination for two reasons.

a) In a general sense, induction of CYP1A occurs in parallel with the changes in gene

expression that are responsible for dioxin toxicity. CYP1A induction signals activation of the Ah

receptor (AHR), which is the common initial step in toxicity. In this way, CYP1A induction is a

surrogate for toxicity.

b) In addition to acting as a surrogate for AHR-dependent toxicity, induction of CYP1A can

also be directly responsible for some forms of toxicity. This may occur through the generation of

reactive oxygen species, for example. Such a mechanism could be important for some endpoints

of concern, such as cardiovascular toxicity involved in early-life stage mortality in fish (15-17).

The correlation between potency to induce CYP1A and toxic potency is often strong (e.g. 18,

19), but is not perfect. CYP1A induction is usually measured as an acute effect, whereas effects

of concern may occur only after chronic or subchronic exposure. Thus, some compounds may

induce CYP1A acutely but-because of rapid metabolism, for example (e.g. PAH)-may not

produce the sustained activation of the AHR that appears to be important for toxicity (20, 21).

Another endpoint that is sometimes considered for TEF determination is the accumulation of

highly carboxylated porphyrins. This effect is AHR-dependent and also appears to be linked

mechanistically to induction of CYP1A. However, it also appears to involve two additional stepsinduction of aminolevulinic acid synthase and binding of HAH to the induced CYP1A (12, 22,

23)--that complicate the determination or interpretation of relative porphyrogenic potencies

II. Application of the TEQ Approach

1. What are the implications of assuming no dose-additivity (??) or no interaction among the

components of mixtures? How would risk assessment conclusions differ if analyses were based

on total PCBs or TCDD alone?

?
Mark E. Hahn

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2. Should TEF values derived using median response levels (LC50 or EC50) be used in risk

assessments where a "no adverse effect" level is being employed?

According to receptor theory, the relative potencies for full agonists should be independent of

the location on the dose-response curve where effects measurements are made. In the real world,

parallel dose-response curves are not always seen because of a) antagonism and partial agonism,

and, b) artifacts introduced by additional phenomena such as enzyme inhibition.

(a) Partial agonism occurs in situations where there are differences in the intrinsic efficacy of

compounds and where other factors (such as receptor number) are such that compounds with

lower intrinsic efficacies am incapable of producing the same maximal tissue response as

compounds with higher intrinsic efficacies (e.g. see reference 24). (Intrinsic efficacy refers to the

inherent property of a chemical that determines the activity of the chemical-receptor complex

(24, 25). Intrinsic efficacy is distinct from affinity, which is the probability of a chemical binding

to the receptor.) There is evidence for partial agonism of some PCB congeners in some systems

(26). Because of their lower intrinsic efficacy, partial agonists will antagonize full agonists under

certain conditions (25).

(b) Compounds may appear to be partial agonists or have non-parallel dose-response curves

as a result of secondary effects on the endpoint measured. For example, in some systems

compounds that induce CYP1A protein can also bind to and inhibit the activity of the enzyme

(27). This inhibition will result in reduced levels of maximally-induced CYP1A activity (EROD)

and an underestimate of the EC50 for CYP1A induction. This will lead to an overestimate of

relative potency (TEF) values (11, 13, 19).

For risk assessments in which a "no adverse effect level" is being employed, it may make

sense to use TEFs derived from lower level responses to avoid the potential problems discussed

above. Such lower response levels might include "threshold responses" (6), initial slopes ("slope

ratio" methods) (28), or EC values based on 25% (29) or 10% (19) of the maximal response

caused by TCDD.

3. To what extent can class-specific TEFs be used?

Whether "class-specific TEFs" exist is an important question. There seem to be some

differences that are characteristic of a vertebrate class (e.g. low activity of mono-ortho PCBs in

fish) but there has not yet been a systematic attempt to compare within-class and among-class

variability in relative potencies. It might be possible to address this question by determining

relative potencies of several HAHs in several species in each of several vertebrate classes, and

then using multivariate statistical techniques to evaluate the within-class and among-class

patterns.
Mark E. Hahn

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IV. Risk Characterization

1. Are the uncertainties associated with TEFs more problematic than other uncertainties of 

the risk assessments?

It would be useful to quantify the degree of uncertainty associated with each step of the risk

assessment process, and then to focus on the steps for which the uncertainties are greatest.

2. Strengths and weaknesses of biologically based TEQ assays? Integration?

The strengths of bioassays for determining TEQs include: a) relatively low cost, b) the

response integrates additive effects plus any non-additive interactions that may occur between

components of mixtures, c) the responses reflect the presence of all AHR agonists, including

compounds that may not have been identified by chemical analysis. For acute bioassays, and

depending on the source of the extract (e.g. sediment vs. tissue), a possible disadvantage is that

rapidly metabolized compounds such as PAH may contribute more significantly to the bioassay

response than they would likely contribute to toxicity in the target species. Because of the

advantages inherent in each approach-i.e. bioassay-derived TEQs and TEQs calculated from

chemical data and TEF values--a combined approach is desirable.

3. Additional data or research for use in the risk assessments?

Species-specific TEF values and relative sensitivities for the species of concern, i.e. bull trout,

river otter, bald eagle, Caspian terns would be helpful. It is important to characterize both the

relative potencies of HAHs (TEFs) as well as the absolute "dioxin sensitivity" of the target

species relative to species used to determine the levels of concern (e.g. no-effect thresholds). For

example, in the retrospective scenario, risk assessment for Caspian terns uses TEFs (and no effect

thresholds?) based on chicken data. Common terns are approximately 80-fold less sensitive to

TCDD than chickens (based on EROD induction in embryo hepatocyte cultures (30)) and exhibit

different TEF values (e.g. for PCB-126, which drives the TEQ in this assessment).

In both scenarios, it would be useful to have long-term data on population structure,

productivity, etc. for the species of concern so that possible population-level effects of current

chemical burdens (in the retrospective study) or future increases (in the prospective study) could

be evaluated. For example, in the retrospective scenario, TEQ levels in Caspian tern eggs are well

above the level of concern established using data from other species of birds. What is the

reproductive success of Caspian terns at this site now, in comparison to past success at this site

and current success at less contaminated sites?

Retrospective Case Study

2. Use of total PCB analyses to monitor cleanup efforts?

Total PCB levels could provide a useful surrogate for TEQs in monitoring cleanup. However,

to the extent that (a) PCB congener composition or (b) concentrations of PCBs relative to other
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HAH classes change with time or depth in the sediment, the ratio of total PCB to total TEQ could

change. Why not use bioassay-derived TEQs to monitor cleanup?

Miscellaneous comments on WHO (1997) Draft Report on Derivation of TEFs for humans

and wildlife

1. An important question is raised in this document (p. 9-10): To what extent do relative

potencies for lethality mirror relative potencies for sublethal effects? Direct comparisons of lethal

and sublethal endpoints are scarce. In mammals, the huge difference in TCDD LD50 values

(guinea pig 1 ug/kg to hamster 5000 ug/kg is not necessarily reflected to the same extent in

potencies for sublethal effects (e.g. see ref. 31). In birds, the correlation may be stronger (19).

2. With regard to the molecular basis for TEFs across species, it is noted that homologs of the

AHR and ARNT exist in the nematode C. elegans. These homologs have not yet been isolated,

but are predicted based on computer-predicted coding regions (exon structure) of genomic DNA

sequences (32). However, in the putative C. elegans AHR, the "PAS-B domain", which has been

associated with ligand-binding in the mammalian AHR, is not well conserved (32). Based on this,

it has been hypothesized (32) that the ligand-binding characteristics of the C. elegans AHR

homolog may be different than those of vertebrate AHRs.

An additional complication in understanding the molecular basis of dioxin action is the

identification of a second AHR in fish (32) and mammals (33). The functions of the second AHR,

including its ligand-binding properties, are not yet known.

In the discussion of the species differences in the AHR (p. 28), it should be noted that an AHR

gene has been identified in lamprey as well as in cartilaginous and bony fish (32). Interestingly,

however, adult lamprey appear to be non-responsive to AHR agonists, as CYP1A is not inducible

in lamprey treated with 3,3',4,4'-tetrachlorobiphenyl (34). The comparative biochemistry and

molecular biology of the AHR has been reviewed recently (35).

3. Antagonistic effects (pp. 36-37). According to receptor theory, antagonistic properties do

not result from differences in receptor-binding affinity, but rather from differences in intrinsic

efficacy (see discussion above). This is an important distinction because it means that low-affinity

compounds will not necessarily act as AHR antagonists. But compounds with lower intrinsic

efficacies may act as partial agonists, and partial agonists will antagonize full agonists under

certain conditions (25). In addition, because receptor number influences whether

compounds with lower intrinsic efficacy will act as fall or partial agonists, there will be tissueand species- differences in antagonistic properties of a given chemical.

4. Hexachlorobenzene (p. 41) is a low-affinity AHR agonist in rat (36). It has a relative

potency (in rat) of approximately 0.0001 based on receptor-binding affinity and approximately

0.0005 based on porphyrogenicity (36). HCB has a similar relative potency of approximately
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0.0001 for EROD induction and uroporphyrin accumulation in chicken embryo hepatocytes (23).

References cited

1. Walker, M.K. and Peterson, R.E. (1991) Potencies of polychlorinated dibenzo-p-dioxin,

dibenzofuran, and biphenyl congeners, relative to 2,3,7,8-tetrachlorodibenzo-p-dioxin, for

producing early life stage mortality in rainbow trout (Oncorhynchus mykiss), Aquat. Toxicol. 21:

219-238.

2. Zabel, E.W., Cook, P.M. and Peterson, RE. (1995) Toxic equivalency factors of

polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyl congeners based on early life

stage mortality in rainbow trout (Oncorhynchus mykiss), Aquat. Toxicol. 31: 315-328.

3. Zabel, E.W., Cook, P.M. and Peterson, R.E. (1995) Potency of 3,3',4,4',5-

pentachlorobiphenyl (PCB 126), alone and in combination with 2,3,7,8-tetrachlorodibenzo-pdioxin (TCDD), to produce lake trout early life stage mortality, Environ. Toxicol. Chem 14:

2175-2179.

4. Janz, D.M. and Metcalfe, C.D. (1991) Relative induction of aryl hydrocarbon hydroxylase

by 2,3,7,8-TCDD and two coplanar PCBs in rainbow trout (Oncorhynchus mykiss), Environ.

Toxicol. Chem. 10: 917-923.

5. Harris, G.E., Kiparissis, Y. and Metcalfe, C.D. (1994) Assessment of the toxic potential of

PCB congener 81 (3,4,4',5-tetrachlorobiphenyI) to fish in relation to other non-ortho- substituted

PCB congeners, Environ. Toxicol. Chem. 13: 1405-1413.

6. Parrott, J.L., Hodson, P.V., Servos, M.R., Huestis, S.L. and Dixon, D.G. (1995) Relative

potency of polychlorinated dibenzo-p-dioxins and dibenzofurans for inducing mixed function

oxygenase activity in rainbow trout, Environ. Toxicol. Chem 14: 1041-1050.

7. van der Weiden, M.E.J., de Vries, L.P., Fase, K., Celander, M., Seinen, W. and van den

Berg, M. (1994) Relative potencies of polychlorinated dibenzo-p-dioxins (PCDDs),

dibenzofurans (PCDFs) and biphenyls (PCBs), for cytochrome P450 1A induction in the mirror

carp (Cyprinus carpio), Aquat. Toxicol. 29: 163-182.

8. Newsted, J.L., Giesy, J.P., Ankley, G.T., Tillitt, D.E., Crawford, R.A., Gooch, J.W., Jones,

P.D. and Denison, M.S. (1995) Development of toxic equivalency factors for PCB congeners and

the assessment of TCDD and PCB mixtures in rainbow trout, Environ. Toxicol. Chem. 14: 861-

871.

9. Clemons, J.H., van den Heuvel, M.R., Stegeman, J.J., Dixon, D.G. and Bols, N.C. (1994)

Comparison of toxic equivalent factors for selected dioxin and furan congeners derived using fish

and mammalian liver cell lines, Can. J. Fish. Aquat. Sci. 51: 1577-1584.

10. Clemons, J.H., Lee, L.E.J., Myers, C.R., Dixon, D.G. and Bols, N.C. (1996) Cytochrome

P4501A1 induction by polychlorinated biphenyls (PCBs) in liver cell lines from rat and trout and

the derivation of toxic equivalency factors (TEFs)., Can. J. Fish. Aquat. Sci. 53: 1177-1185.

11. Hahn, M.E., Woodward, B.L., Stegeman, J.J. and Kennedy, S.W. (1996) Rapid assessment of

induced cytochrome P4501A (CYP1A) protein and catalytic activity in fish hepatoma cells
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grown in multi-well plates: Response to TCDD, TCDF, and two planar PCBs, Environ. Toxicol.

Chem. 15: 582-591.

12. Hahn, M.E. and Chandran, K. (1996) Uroporphyrin accumulation associated with

cytochrome P4501A induction in fish hepatoma cells exposed to Ah receptor agonists,

including 2,3,7,8-tetrachlorodibenzo-p-dioxin and planar chlorobiphenyls, Arch. Biochem.

Biophys. 329: 163-174.

13. Hahn, M.E. (1996) Overestimation of toxic equivalency factors (TEFs) resulting from

inhibition of EROD activity by cytochrome P450 1A inducers in cultured cells., Proceedings of

the 22nd Annual Aquatic Toxicity Workshop: Oct. 2-4,1995, St. Andrews, New Brunswick.

Canadian Technical Report of Fisheries and Aquatic Sciences No. 2093 132-134.

14. Zabel, E.W., Pollenz, R. and Peterson, R.E. (1996) Relative potencies of individual

polychlorinated dibenzo-p-dioxin, dibenzofuran, and biphenyl congeners and congener mixtures

based on induction of cytochrome P4501A mRNA in a rainbow trout gonadal cell line (RTG-2),

Environ. Toxicol. Chem. 15: 2310-2318.

15. Cantrell, S.M., Lutz, L.H.,Tillitt, D.E. and Hannink,M. (1996) Embryotoxicity of 2,3,7,8-

tetrachlorodibenzo-p-dioxin (TCDD): The embryonic vasculature is a physiological target for

TCDD-induced DNA damage and apoptotic cell death in Medaka (Orizias latipes), Toxicol. Appl.

Pharmacol. 141: 23-34.

16. Guiney, P.D., Smolowitz, R.M., Peterson, R.E. and Stegeman, J.J. (1997) Correlation of

2,3,7,8-tetrachlorodibenzo-p-dioxin induction of cytochrome P4501A in vascular endothelium

with toxicity in early life stages of lake trout, Toxicol Appl Pharmacol 143: 256-273.

17. Stegeman, J.J. Miller, M.R. and Hinton, D.E. (1989) Cytochrome P450IA1 induction and

localization in endothelium of vertebrate (teleost) heart, Mol. Pharmacol. 36: 723-729.

18. Safe, S. (1987) Determination of 2,3,7,8-TCDD toxic equivalent factors (TEFs): support for

the use of in the vitro AHH induction assay, Chemosphere 16: 791-802.

19. Kennedy, S.W., Lorenzen, A., Jones, S.P., Hahn, M.E. and Stegeman, J.J. (1996)

Cytochrome P4501A induction in avian hepatocyte cultures: a promising approach for predicting

the sensitivity of avian species to toxic effects of halogenated aromatic hydrocarbons, Toxicol.

Appl. Pharmacol. 141: 214-230.

20. Devito, MJ. and Birnbaum, L.S. (1995) The importance of pharmacokinetics in determining

the relative potency of 2,3,7,8-tetrachlorodibenzo-p-dioxin and 2,3,7,8-tetrachlorodibenzofuran,

Fund Appl Toxicol 24: 145-148.

21. Devito, MJ., Maier, W.E., Diliberto, J.J. and Birnbaum L.S. (1993) Comparative ability of

various PCBs, PCDFs, and TCDD to induce cytochrome P450 1A1 and 1A2 activity 

following 4 weeks of treatment, Fundam. Appl. Toxicol. 20: 125-130.

22. DeMatteis, F., Harvey, C., Reed, C. and Hempenius, R. (1988) Increased oxidation of

uroporphyrinogen by an inducible liver microsomal system. Possible relevance to drug-induced

uroporphyria, Biochem. J. 250: 161-169.

23. Sinclair, P.R., Walton, H.S., Gorman, N., Jacobs, JM. and Sinclair, J.F. (1997) Multiple
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roles of polyhalogenated biphenyls in causing increases in cytochrome P450 and uroporphyrin

accumulation in cultured hepatocytes, Toxicol. Appl. Pharmacol. 146: 000-000.

24. Kenakin, T. (1993) Pharmacologic Analysis of Drug-Receptor Interaction (Raven Press,

New York).

25. Goldstein, A., Aronow, L. and Kalman, SM. (1974) Principles of Drug Action: The Basis

of Pharmacology, 2nd Edition (Wiley, .

26. Richter, CA., Tieber, V.L., Denison, M.S. and Giesy, J.P. (1997) An in vitro rainbow trout

cell bioassay for aryl hydrocarbon receptor-mediated toxins, Environ. Toxicol. Chem. 16: 543-

550.

27. Gooch, J.W., Elskus, A.A., Kloepper-Sams, P.J., Hahn, M.E. and Stegeman, J.J. (1989)

Effects of ortho and non-ortho substituted polychlorinated biphenyl congeners on the hepatic

monooxygenase system in scup (Stenotomus chrysops), Toxicol. Appl. Pharmacol. 98: 422-433.

28. Ankley, G.T., Tillitt, D.E., Giesy, J.P., Jones, P.D. and Verbrugge, D.A. (1991) Bioassay

derived 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalents in PCB-containing extracts from the

flesh and eggs of Lake Michigan Chinook salmon (Oncorhynchus tshawytscha) and possible

implications for reproduction, Can. J. Fish. Aquat. Sci. 48: 1685-1690.

29. Engwall, M., Broman, D., Ishaq, R., Naf, C., Zebuhr, Y. and Brunstrom, B. (1996) Toxic

potencies of lipophilic extracts from sediments and settling particulate matter (SPM) collected in

a PCB-contaminated river system, Environ. Toxicol. Chem. 15: 213-222.

30. Lorenzen, A., Shutt, L. and Kennedy, S.W. (1997) Sensitivity of common tern (Sterna

hirundo) embryo hepatocyte cultures to CYP1A induction and porphyrin accumulation by

halogenated aromatic hydrocarbons and common tern egg extracts, Arch. Environ. Contam

Toxicol. 32: 126-134.

31. Pohjanvirta, R. and Tuomisto, J. (1994) Short-term toxicity of 2,3,7,8-tetrachlorodibenzo-pdioxin in laboratory animals: effects, mechanisms, and animal models, Pharmacol. Rev. 46: 483-

549.

32. Hahn, M.E., Karchner, SI., Shapiro, M.A. and Perera, S.A. (1997) Molecular evolution of

two vertebrate aryl hydrocarbon (dioxin) receptors (AHRI and AHR2) and the PAS family, Proc.

Natl. Acad Sci. U.S.A. 94: in press.

33. Fujii-Kuriyama, Y., Kobayashi, A., Ema, M., Mimura, J., Morita, M. and Sogawa, K. (1997)

Transcription regulation by Ah receptor, ARNT, and their related transcription factors, FASEB J.

11: A780 (Abstract P56).

34. Hahn, M.E., Woodin, B.R., Stegeman, J.J. and Tillitt, D.E. (1997) Aryl hydrocarbon receptor

function in early vertebrates: Inducibility of cytochrome P4501A in agnathan and elasmobranch

fish, Comp. Biochem. Physiol. : in revision.

35. Hahn, M.E. (1998) The Aryl Hydrocarbon Receptor. A Comparative Perspective, Comp.

Biochem. Physiol. : submitted.

36. Hahn, M.E., Goldstein, J.A., Linko, P. and Gasiewicz, T.A. (1989) Interaction of

hexachlorobenzene with the receptor for 2,3,7,8-tetrachlorodibenzo-p-dioxin in vitro and in vivo.
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Evidence that hexachlorobenzene is a weak Ah receptor agonist., Arch. Biochem. Biophys. 270:

344-355.
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Sean W. Kennedy, Ph.D.

Research Scientist

Wildlife Toxicology Division

National Wildlife Research Centre

Environment Canada

100 Gamelin Boulevard

Hull, Quebec, Canada K1A 0H3

819-997-6077

Fax: 819-953-6612

E-mail: sean.kennedy@ec.gc.ca

In addition to his work as a research scientist with Environment Canada, Dr.

Kennedy is currently adjunct professor of biology at the University of Ottawa in

Ottawa. He received his Ph.D. and B.Sc. degrees in chemistry and biochemistry,

respectively, from Carleton University. His research focus is to gain knowledge

of the effects of environmental contaminants on wildlife and human health, with a

current emphasis on the development and application of novel biochemical

techniques to measure the effects of dioxins, PCBs, and structurally-related

chemicals whose affects are meditated by the Ah receptor. He has worked in

programs internationally and was an invited participant at the World Health

Organization’s (WHO) meeting in Stockholm in 1997 on the derivation of TEFs

for PCBs, PCDDs, PCDFs, and other dioxin-like compounds. Dr. Kennedy is a

member of the Canada-Germany Agreement on Environmental Health as well as

being part of assessment panels in Canada, the United States, the Netherlands,

and other locations in Europe. His toxicology studies are often related to avian

subjects. He is currently a primary author of 40 publications. 
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I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC TEF

VALUES

1. Does additional information on TEFs enhance the means of evaluating uncertainties in the

assessments?

To answer this question, I examined Tables 1 and 2 in the Retrospective Scenario. While there

are some changes (e.g. fish TEQ1 for PCB 77 is 0.031 and fish TEQ2 is 0.62), such differences

have very little effect on the relative contributions of total PCBs, total PCDDs or total PCDFs to

total TEQ concentrations. Therefore, I do not think that the additional information is valuable,

particularly when one considers all of the other uncertainties which go into a risk assessment.

2. Should all TEFs be considered to have similar uncertainties?

I do not think that all TEFs should be considered to have similar uncertainties. The WHO

meeting in Stockholm established the use of a tiered approach for ranking studies from which

TEFs could be derived. I think this approach is reasonable, and TEFs obtained from in vivo 

studies should be (and were, at the WHO meeting) ranked higher than other types of studies. For

example, in fish, TEF values that are based on mortality following egg injections are more likely

to be “accurate” (I use the word “accurate” to mean that they are more likely to be predictive of

the relative potency in vivo than are values obtained from in vitro studies or from methods that

use QSAR. This statement is not meant to indicate that in vitro and/or QSAR derived TEFs are

of no value - they certainly are. They can be particularly useful for helping one decide what in

vivo studies are required. For example, several studies with avian hepatocytes have shown TCDF

to be either equipotent or more potent than TCDD at inducing EROD activity. For the timebeing, a TEF for TCDF in birds of 1.0 seems reasonable, but in vivo studies are warranted to test

this prediction. 

TEFs that were derived from several studies (rare for fish and birds, common for mammals)

should be considered to have less uncertainty than TEF values obtained from single studies.
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3. To what extent can different types of endpoints that were used to derive TEFs be extrapolated

to effects that are relevant for the assessment endpoint for each case study?

Retrospective Scenario:

In general, one should be cautious when using TEFs that have only been derived from in

vitro biochemical responses. However, it should be noted that compounds which contribute the

most to total TEQ concentrations (see below) have been tested for overt toxicity in vivo both in

fish and birds (albeit in a limited number of species). 

Fish Approximately 93% of the total TEQ concentrations in both shiners and lake trout was

obtained from the following compounds: PCB 126, 1278-TCDD, 12378-PCDD, 2378-TCDF,

1,2,3,7,8-PCDF and 23478-PCDF. The TEFs for all of the these compounds were obtained from

studies which determined mortality in rainbow trout following injection of compound into the

egg (i.e. a Tier 1 study). In my opinion, the total TEQ is highly relevant to the assessment

endpoint of interest, despite the fact that lethality-based TEFs have, to date, only been reported in

one species of fish.

Birds Approximately 85% of the total TEQ concentration in Caspian tern eggs was obtained

from the following compounds: PCB 77, PCB 126 and PCB 105. TEFs for all of these

compounds were derived from egg injection studies which measured lethality. In my opinion, the

total TEQ is highly relevant to the assessment endpoints of interest for birds. Despite the fact

that TEFs for many other compounds were only obtained from studies that measured either

EROD induction in vivo or in cultured hepatocytes ,or were from QSAR estimates, these

compounds contribute, in total, only approximately 15% to the total TEQ.

Mammals For mammals, the TEFs for most of the compounds were derived from several

studies, and TEQ estimates are likely to be relevant.

It should also be noted, that there are generally quite good correlations between relative potencies

of compounds as EROD inducers and their respective toxic potencies (as long as one considers

some of the “problems” with in vitro assays - such as differences in efficacy and metabolism - see

Bastien and Kennedy, Organohalogen Compounds (1997) 34, 215 - 220. In vitro derived REPs

can be very useful for predicting in vivo TEFs.

Prospective Scenario:
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Above comments for the Retrospective Scenario are of relevance for this case.

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

APPROACH

1. What are the implications of assuming no dose-additivity or no interaction among the

compounds? To what extent would the risk assessment conclusions differ if the stressor

response analyses were based on total PCBs or 2,3,7,8-TCDD alone?

In general, is dose-additivity is not assumed, then the risk assessments need to be based solely on

TCDD and total PCBs. In the following, differences between using TCDD and total PCBs vs.

the TEQ approach is examined. No-effect thresholds indicated in Table 5 of the Retrospective

Scenario were used in all cases.

Retrospective Scenario:

Shiners and Lake Trout

The concentration of TCDD in shiners and lake trout is much lower (230-fold and 55-fold,

respectively) than the no-effect threshold for fish (30 pg/g). PCB concentrations in these species

of fish are 14-fold and 5-fold lower than the no effect level (5 ug/g), shiners and lake trout,

respectively. However total TEQ concentrations of 1.3 pg/g and 4.2 pg/g in shiners and lake

trout, respectively are at a level which approaches levels which might be expected to have some

effect in sensitive species (e.g. lake trout). I say this because the lowest value for no-effect

threshold in fish indicated in table 5 is 3 pg/g. PCB 126 and two PCDFs are major contributors

to total TEQ concentration. It should be noted, however, that 4.2 pg TEQ/egg is much lower than

the reported LD50 for TCDD in lake trout (74 pg/g).

Caspian Tern

The concentration of total PCBs in Caspian tern eggs is 5.7 ug/g, which is higher than the noeffect threshold for birds indicated in table 5 (1-20 ug/g). Thus, sensitive species might be

expected to have some effects using a risk assessment that is based on total PCBs alone. A risk

assessment that used TCDD alone would conclude that levels of TCDD were much below the noeffect level (concentration of TCDD is 4.5 pg/g and the no-effect threshold is 100 pg/g). In

contrast, if one were to use the TEQ approach, the total TEQ concentration (426 pg/g) exceed
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the no-effect threshold of 100 pg/g by approximately 4-fold. Thus, the TEQ approach certainly

indicates more reason for concern than does a risk assessment that is based on TCDD alone. In

addition, the TEQ approach might indicate more reason for concern than would an assessment

that is based on total PCBs.

Otter

The concentration of TCDD in otter liver of 1.4 pg/g is much lower than the no-effect level

indicated in table 5 for mammals (60 pg/g). The concentration of total PCBs of 1 ug/g is

approximately 1⁄2 of the no-effect level (2 ug/g). However when the TEQ approach is used, a

much different conclusion is reached. The total TEQ concentration is 144 pg/g, which is higher

than the no-effect threshold of 60 pg/g. PCB 126 contributes the most to the total TEQ.

Prospective Scenario:

I did not to make the type of detailed analysis for this that I did above for the Retrospective

Scenario because residue levels in eggs were not given - my reasoning would be the same,

however. 

2. To what extent should TEF values derived at median response levels be used in risk

assessments where no adverse effect level is being employed?

I not see a serious problem at all with using LC50 or EC50 values (assuming one carefully

considers, and accounts for situations where compounds are not full agonists at eliciting a

particular effect; e.g. EROD induction, in some cases). 

3. To what extent can class-specific TEFs be directly extrapolated to the species identified in

each case study?

Given that only a limited number of species have been tested, one cannot be absolutely certain. 

However, in my opinion, large errors are not likely to be made. For example, PCB 126 has been

assigned a TEF of 0.1 in birds, based on Tier 1 studies with chickens. REPs for PCB 126 as an

EROD inducer have been determined to be very close to this TEF in hepatocyte cultures prepared

from a large number of avian species. My conclusion, is that the TEF of 0.1 for PCB 126 is

likely to be reasonable across avian species, including those of interest in the present scenarios. 

Further studies are required to determine relative potencies of compounds in different species of

fish, but the values derived from egg injection studies in rainbow trout are likely to be relevant to

fish in general (based on studies we and others are seeing in hepatocyte cultures) and, almost
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certainly, to be relevant to bull trout (Prospective Scenario).

III. EXPOSURE PROFILE

1. To what extent does the TEF approach present challenges, introduce new uncertainties, or

modify old uncertainties with modeling exposure to AhR agonists?

The modeling of exposure to contaminants, including AhR agonists is beyond my area of

expertise. However, based on the data provided in Table 1 in the document entitled, “Charge

Questions and Physico-Chemical Properties Table”, it would seem that BAFs have come from a

very limited number of studies, and I would question how reliable these are across species. In

addition, one needs to have a lot of information on feeding patterns of the species being studied.

2. To what extent do exposure route differences used in deriving the TEFs affect their

application in the case studies?

For fish and birds, the exposure route used to derive TEFs for the most important contributors to

total TEQs (see above) was from egg injections. It is my opinion that these values of definitely

of relevance to the species of interest in the case studies. Values for mammals are also relevant.

3. To what extent does the TEF approach require a more rigorous analytical design?

The TEF approach requires the measurement of dibenzo-p-dioxins, dibenzo furans and non-ortho

PCBs by GC-MS. This increases the analytical costs over the costs of total PCBs.

IV. RISK CHARACTERIZATION

1. Are the uncertainties associated with TEFs more problematic than other uncertainties of the

risk assessments?

In my opinion, the uncertainties associated with TEFs are no more problematic than other

uncertainties which are associated with the risk assessment for the tow scenarios.

2. What are the strengths and weakness of using biologically-based TEQ assays, and to what

extent could these approaches be integrated?

Biologically-based TEQ assays (I refer here to in vitro assays) have the advantage of measuring

the integrated effects of complex mixtures of Ah receptor agonists. In addition, such assays have
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the potential of identifying compounds that act via the Ah receptor, which would not be 

identified by a chemical residue approach measuring only dioxins, furans and PCBs. Some of

these assays are considerably less expensive than chemical residue analysis (particularly where

measurement of dioxins, furans and no-ortho substituted PCBs is required).

 One potential problem with such in vitro assays is that they can over estimate the toxic potency

of compounds which are rapidly metabolized in vivo (e.g. PCB 77) but recent research has shown

that such problems can likely be circumvented. For example, Bastien and Kennedy

(Organohalogen Compounds (1997) 34, 215-220) and others have reported that the REPs of

rapidly metabolized compounds are dependent on the length of time between the addition of the

compounds to the cells and analysis. Thus, various bioassays under development have

considerable potential for predicting TEQs which are relevant to whole organisms. For the two

case scenarios, I would recommend the incorporation of in vitro bioassays.

I would recommend incorporation of at least one in vitro bioassay into the risk assessments for

both scenarios. This might either be the H4IIE bioassay or an assay which uses a reporter gene. 

In addition, I would consider using primary hepatocyte cultures for species of interest (e.g.

Caspian tern and bull trout). Such methods can be very useful in predicting the sensitivity of

species of concern to complex mixtures of compounds that elicit effects which are mediated by

the Ah receptor.

3. What additional research do you recommend?

I would recommend incorporation of a study that would include the addition of extracts from soil

and tissues to hepatocytes cultures prepared from species of concern. For example, this could be

done for the bull trout in the Prospective Scenario and for Caspian terns for the Retrospective

Scenario. Such methods are now routine in my laboratory and others, and show considerable

promise for risk assessment purposes. If bull trout cannot be obtained from any location (due to

their endangered status) , then rainbow and/or Lake trout could be used. A small number

(approximately 10 eggs) of Caspian tern eggs could be obtained from another location, incubated,

and primary hepatocyte cultures could be prepared. The advantage of doing such studies is that

one can obtain important information regarding species sensitivity to complex mixtures of

compounds that elicit effects which are mediated by the Ah receptor which would not be

identified by the chemical-based TEQ approach.

I would also recommend inclusion of other biologically-based TEQ (e.g. H4IIE, CALUX) assays

into the assessments.
Sean W. Kennedy

C-C-83

Additional Questions

Prospective Case Study:

The questions asked here go beyond my area of expertise.

Retrospective Case Study

Sediment cleanup goals would be the same for birds and mammals since PCBs are, by far, the

most important contributors to total TEQ concentrations in these taxa. However, in fish, PCDFs

are major contributors. Total PCBs could be used to monitor the results of clean-up efforts

providing a good correlations were found between major PCB congeners and the following:

PCDFs, TCDD and PCB 126.
C-C-84

Wayne G. Landis, Ph.D.

Director

Institute of Environmental Toxicology and Chemistry

Huxley College of Environmental Studies

Western Washington University

Bellingham, WA 98225

360-650-6136

Fax: 360-650-7284

E-mail: landis@henson.cc.wwu.edu

Dr. Landis’s expertise is in environmental toxicology, ecological risk assessment,

and population biology. He holds a bachelor’s degree in biology from Wake

Forest University, a master’s in biology from Indiana University, and a doctorate

in zoology from Indiana University. He is currently director and professor at the

Institute of Environmental Toxicology and Chemistry at Huxley College, Western

Washington University. He belongs to six professional societies, including

SETAC, ASTM, Sigma Xi, and the Genetics Society of America. Dr. Landis is

currently the principal investigator in a project entitled “Novel Models for the

Evaluation and Interpretation of Ecological Datasets Applied to the Ecological

Risk Assessment of Biotechnological Products” funded by EPA. His current

research includes regional risk assessment, the application of metapopulation

dynamics in estimating the impacts of toxicants, and microcosm/mesocosm

research. Dr. Landis also is co-developer of the Community Conditioning

hypothesis, a non-equilibrium description of the impacts of toxicants to

populations and communities. He serves on a variety of advisory committees.
Wayne G. Landis

C-C-85

Workshop on the Application of 2,3,7,8 -TCDD toxicity Equivalency Factors

(TEFs) to Fish and Wildlife

Answers to Charge Questions

Wayne G. Landis, Director

Institute of Environmental Toxicology and Chemistry

MS 9180, Western Washington University

Bellingham, WA 98225

360-650-6136

landis@cc.wwu.edu

General comments:

 The assessment of compounds that have modes of action similar to that of TCDD and

yet also have estrogenic type interactions is challenging. Many of my comments that

are specific to the charge questions are based on two factors. First, the risk assessment

process stated here is based on the derivations of LC50s or NOELs (no observed

effects levels), methods that misrepresent the toxicity of the compounds. Second, in

both case examples, the risk assessments are purely toxicological not ecological.

 In my reading of the material supplied to us only one paper, the draft by Elonen et al,

used the dose-response curves in order to judge the relative toxicity of TCDD to a group

of organisms. The other papers used a median lethal dose, a no-effects level or a

lowest observed level to compare toxicity. The failings of the NOEC and LOEC (lowest

observed effect level) approaches have been discussed (Stephan and Rodgers 1985,

Chapman et al. 1996, Chapman and Chapman 1997) although debate continues

(Dhaliwal et al 1997). A summary of the problems of NOECs and LOECs can be also

found in Landis and Yu (1995). 

 Basically, NOECs and LOECs are artifacts of the hypothesis testing process and the

concentrations selected by the researcher. While they may be of some interest within a

set of experiments conducted under identical conditions with similar experimental

variance, replication and statistical power, they can not be compared in a strict sense

between laboratories or species because the statistical power of the experiments

change. As the statistical power changes so does the results of the NOEC and LOEC. 

As the statistical power decreases, the NOEC and LOEC will increase even without a

real change in the concentration-response curve.
Wayne G. Landis

C-C-86

 An alternative approach using regression techniques and curve fitting have been

proposed (Stephan and Rodgers 1985, Moore and Caux 1997). Specific points along

this curve can then be compared (an ECx) in order to determine relative potencies at

concentrations that correspond with acceptable effects. In this instance we can

compare numbers with similar units. The uncertainty in the comparisons can also be

quantified since the error in the estimates will also be available. This is a much better

situation than comparing statistical artifacts.

 The second failing of the ecological risk assessments provided to us as examples is

that they are still toxicological assessments. Only direct toxicity is considered, as is

appropriate for determining effects upon a particular receptor. However, the goals of

these example assessments is to attain the same fish populations as before. 

Oneofakind lake has depressed fish and tern populations. It is claimed to have healthy

pelagic and benthic invertebrate communities, but since health is undefinable

ecologically I have no idea what this means. Roundtail lake has seen the introduction of

mysids that have drastically altered the food web and the bull trout populations. The

introduction of paper mill effluent would constitute another stressor, with the impacts

partially controlled by the other introductions. Observed alterations in the fish dynamics

could be due to historical impacts, the rates of migration due to landscape structure, or

the toxicity of the effluent.

Reference:

Caux, P-Y and D. R. J. Moore. 1997. A spreadsheet program for estimating low toxic

effects. Environ. Toxicol. Chem. 16:802-806.

Chapman, P. M., R. S. Caldwell and P. F. Chapman. 1996. A warning: NOECs are

inappropriate for regulatory use. Environ. Toxicol. Chem. 15:77-79.

Chapman, P. F. and P. M. Chapman. 1997. Author's reply: Environ. Toxicol. Chem.

16:125-126.

Dhaliwal, B. S., R. J. Dolan, C. W. Batts, J. M. Kelly, R. W. Smith, S. Johnson. 1997. 

Warning: Replacing NOECs with point estimates may not solve regulatory

contradictions. Environ. Toxicol. Chem. 16:124-125.

Landis, W. G. and M.- H. Yu. 1995. An Introduction to Environmental Toxicology:

Impacts of Chemicals on Ecological Systems. Lewis Publishing, Boca Raton, FL.

Moore, D. R. J. and P-Y Caux. 1997. Estimating low toxic effects. Environ. Toxicol.

Chem. 16:764-801.

Stephan, C. E. and J. R. Rodgers. 1985. Advantages of using regression analysis to

calculate results of chronic toxicity tests. In Aquatic Toxicology and Hazard

Assessment: Eighth Symposium. R.C. Bahner and D.J.H. Hansen, eds., American

Society for Testing and Materials, Philadelphia, pp. 328-339.
Wayne G. Landis

C-C-87

I. Stress-Response profile relative to the derivation of specific TEF values.

3, The TEF values provided were based on endpoints that ranged from in vitro

biochemical responses to in vivo early life stage mortality. To what extent can these

endpoints be extrapolated to the measures of the effects that are relevant for the

assessment endpoint for each case study?

 The more the test is run under conditions similar to the exposure in the field, the easier

and more confident the extrapolation. Biochemical responses observed from in vitro

tests are more like bioassays for exposure to specific concentrations than indications of

toxicity. Early life stage mortality tests are more useful, but rarely does the dosing

correspond to situations typical of the field. Each test allows more confidence in the

prediction, and the greater the number of endpoints measured the better the

characterization of concentration-effects. However, laboratory tests can not take the

place of properly designed field studies or taking advantage of natural experiments

(spills, prior contamination etc.).

II. Stress-Response profile relative to the application of the TEQ approach.

2. Many TEFs are based on LC50 or EC50 values. To what extent should TEF values

derived at a median response level be used in risk assessments where no adverse

effect level is being employed?

 In keeping with my introductory comments, the use of LC50 and EC50 values is

inappropriate, but no more than the use of a no adverse effect level for the risk

assessment. The use of the LC50 and EC50 for TEFs uses a part of the concentrationresponse curve that is of relatively little interest for the protection of ecological

endpoints. No adverse effect level is a statistical artifact at best, at worst it is trying to

prove a negative and that can not be accomplished scientifically. A more appropriate

alternative would be to settle on an acceptable effect, even one as small as an EC10. 

Then use the EC10 values from the LC50 or EC50 data to calculate the TEFs. Once the

risk assessment goal is quantified, then the appropriate endpoints for the computation of

the TEFs is trivial.
Wayne G. Landis

C-C-88

 The concentration-response curves illustrated in the Elonen et al. manuscript

demonstrate the variability of the slopes. Given the same EC50, the compound with the

shallowest slope will have greater effects at lower concentrations.

3. The TEFs values provided were typically based on a single or limited number of

mammal, bird, or fish experiments. To what extent can class-specific TEFs be directly

extrapolated to the species identified within each case study?

 In the Elonen et al. manuscript (Table 5), the range of LCegg10 and LCegg50 both have

a five-fold range in toxicity for seven teleost fish. The data presented in Figure 3 show a

twenty-five fold range from lowest to highest LCegg50 values among the fish. Without

comparable data for other Ah receptor compounds It is not possible to tell if the ratios

between TCDD and other compounds shows a comparable interspecific variability. Do

comparable data exist for the ratios and can that be used to examine the range of

TEFs? Getting more data would answer that specific question, otherwise it simply is

speculation.

III. Exposure profile

IV. Risk Characterization

1. In evaluating the case studies, are the uncertainties associated with the TEFs more

problematic than other uncertainties of the risk assessments? Do the uncertainties

associated with TEFs limit the means of performing the assessments, or do the other

areas of the effect and exposure characterization contribute similar or greater levels of

uncertainty?

 Given the current methods of estimating the TEFs, reliance on NOECs and LC50

values, the uncertainty in the estimates of these values at realistic levels of impacts is

high. Without the basic biological effects data, the basic yardstick by which to judge

impact is uneven and bent. It is like measuring a centimeter with only a meter stick

marked in meters. It does not matter than there is uncertainty is the other factors as

much because they are not the yardstick by which impacts are measured.
Wayne G. Landis

C-C-89

2. Biologically-based TEQ assays on environmental samples could be employed as an

alternative to the TEF-based approach. What would the strengths and weaknesses of

such an approach be? To what extent could these approaches be integrated?

 Data from well designed experiments from environmental samples is always a

preferred approach for several reasons. 1) It provides data for sediments and water

conditions that will be found at the site of interest. 2) Field work can provide a measure

of the temporal and spatial heterogeneity of the environment and the fate and

bioavailability of the contaminants. 3) Data from field samples can provide a measure of

uncertainty provided by the laboratory studies and the TEF approach. 4) Site specific

data forces the investigators to pay close attention to the site and reality instead of

laboratory tests and models.

3. Assume that site-specific data or additional research could be gathered or performed

to generated more information for the case study assessments. Provide a list of specific

investigations/studies and rank them from highest to lowest priority. What is your

rationale for the ranking?

 Highest to lowest ranking; assuming that this is a prospective risk assessment.

1) Obtain as much data as possible on the spatial and temporal distributions of the

species of interest, their supporting food web, and the organisms that alter the physical

structure of the habitat. This information will eliminate a lot of the guesswork about

exposure and population effects. Particularly important are data about other stressors, 

patch distribution and landscape form that may confound predicted impacts.

2) Simulate the dosing of the system using a model multispecies system that includes

fish as a receptor. Have specific questions and predictions in mind to guide the

experimental design. If the models and toxicity data can not effectively predict the risk

to a model system there is little hope that it can predict risk to the ecological system of

interest. It should also be possible to obtain correlations between biomarkers, 

reproductive success and population and community alterations that should allow the

answering of so what type questions.

3) Get reliable concentration-response data that actually includes accurate estimates of
Wayne G. Landis

C-C-90

effective levels of concern, not NOELs (not real) and LC50s ( too high). For

bioavailablity studies use sediments and water from the site of interest in order to gain

site-specific data. These studies should allow the elimination of a great deal of the

uncertainty in the toxicological and exposure aspects of the risk assessment.

Additional questions specific to the prospective case study:

1. The sate adopted BAFs used by the GLWOG. What improvement I the accuracy of

the maximum allowable concentrations for individual congeners in water (MAC) can be

expected through the use of BAFs determined from Roundtail lake data?

 This is a crystal ball, not a scientific question. The accuracy in indeterminable without

doing the experiment. The important fact is that it is the BAFs from the Roundtail lake

data that should be the most relevant to a risk estimation since they can provide a range

of values assisting in the quantification of the variance, and data on spatial and temporal

variability. This type of data will not be available using model results. After all, models

produce output, not data.

3. How should the uncertainties associated with the available fish, avian and

mammalian TEFs be incorporated into decisions about which TCDD water quality

standards should be chosen for setting a TEqTMDL for regulating chemical discharges

into Roundtail Lake?

 Tell me how much uncertainty the decision maker can live with. The uncertainties

need to reported fairly and as accurately as possible. How the decision is made is more

a political issue when such unspecified and indeterminable criteria such as no adverse

effect are used.
Wayne G. Landis

C-C-91

Additional questions specific retrospective Case Study:

1. Would TEQ sediment cleanup goals be the same for each vertebrate group? If not,

why would there be a difference? I the vertebrate group with the most certainty is not

the group with the most restrictive sediment cleanup goal, how would you council the

risk manager’s concerns for the other vertebrate groups?

 Of course the clean up goals will be different for each vertebrate group depending

upon the route of exposure. Terrestrial mammals will be exposed in a very different

fashion compared to sediment dwelling fish. Seed eating birds are likely to have little

concern about sediment concentrations compared to fish eating birds. Reptiles and

amphibians that burrow in the mud during parts of the year will have a direct exposure to

the sediment for prolonged periods. Amphibians have to breed in the water, mammals

and birds do not and so have different exposure routes and sensitive stages.

 The second part of the question is amusing. For the most vertebrate groups are not

represented by any toxicity data and when they are for only a few species. Given the

lack of representation of the different vertebrates the level of uncertainty is going to

relatively high no matter what. Considering the problems with estimates of exposure,

lack of tissue data for most species, and the lack of truly comparative toxicology, I do not

hold out much hope for reducing uncertainty for vertebrate groups, only the few well

studied species.

 How about uncertainty factors for extrapolation across vertebrate types? Considering

the reported 25 fold difference in TCDD toxicity in teleost fish, how much more

uncertainty is there between vertebrate groups. I suspect the answer is species specific

given the precise mode of action of the TCDD and similar compounds. Very subtle

alterations in biochemistry may give rise to big differences in realized toxicity in a largely

stochastic fashion.

2. Would the TEF/TEQ-based sediment remediation goals be the same as those

determined for total PCBs for the identical vertebrate class? Assume that a simple ratio

of total PCB sediment concentration goal to TEQ sediment concentration goals was

formulated to allow for the use of total PCBs to monitor cleanup efforts based on TEQs. 

What exposure and effect issues would need to be evaluated before using the less

costly total PCB analysis to support the TEQ-based sediment remediation goal?

 No, total PCBs are comprised of many compounds that work with very different modes
Wayne G. Landis

C-C-92

of action compared to the TCDD like PCBs. The proportion of the various PCB types

will be important in estimating the likely toxicity resulting from the mixture. Why not a

TEF for estradiol mimics as well as TCDD mimics?

 I am generally against clean up goals set on chemical concentration alone. Chemistry

does not estimate toxicity very well, and when have been so caught up in numerical

analytical goals that toxicity prevention can get lost. 
C-C-93

Lynn S. McCarty, Ph.D.

Ecotoxicologist

L.S. McCarty Scientific Research & Consulting

280 Glen Oak Drive

Oakville, Ontario, Canada L6K 2J2

905-842-6526

Fax: 905-842-6526

E-mail: lmccarty@interlog.com

Dr. McCarty received B.Sc. and M.Sc. degrees from Brock University and a

Ph.D. from the University of Waterloo. He has spent over 19 years as an

environmental scientist in both business and government positions and currently

operates a consulting business. In these positions he has been involved in a

wide variety of projects examining environmental impacts and/or human health

effects. This included the production or critical review of a number of air and

water quality guidelines, as well as work on risk assessments in Canada and the

USA. He has been involved in the preparation of over several dozen scientific

papers, many presentations/posters at scientific meetings, and numerous

proprietary reports for a variety of clients. As well he is a coauthor of two

chapters in the second edition of the "Fundamentals of Aquatic Toxicology"

(Rand, 1995). A particular interest is the theory and practice of toxicity test

design and interpretation and application to risk management and assessment.

Dr. McCarty has been an invited expert at a number of workshops dealing with

human and environmental health issues sponsored by the Canadian Forestry

Service, Environment Canada, Canadian Network of Toxicology Centres,

SETAC, US EPA, and US Army Corps of Engineers. He currently serves on the

editorial boards of Human and Ecological Risk Assessment and Journal of

Aquatic Ecosystem Stress and Recovery. 

 
C-C-94

L.S. McCarty Scientific Research & Consulting 1156868 Ontario Inc.

280 Glen Oak Drive, Oakville

Ontario, Canada L6K 2J2 905/842-6526 (phone & fax) lmccarty@interlog.com

MEMORANDUM

TO: U.S. EPA TCDD TEF Workshop

FROM: L.S. McCarty

DATE: November 3, 1997

TOPIC: Answers to Questions/Issues for the Workshop on the Application of 2,3,7,8-TCDD

Toxicity Equivalency Factors (TEFs) to Fish and Wildlife

CHARGE QUESTIONS AND PHYSICO-CHEMICAL PROPERTIES TABLE

I have an objection with a statement in the opening paragraph: "It is reasonable to assume that the proposed

WHO TEFs are appropriate for risk assessments ... " I agree that this is the basis for the subsequent questions

on refinements of the TEF approach to assessments beyond the screening stage, but do not believe that it is a

universally agreed upon assumption for either the initial application or the refinements being considered by

the workshop. In fact, it should be made clear that such an assumption clearly establishes this workshop as a

policy-based exercise. The workshop is a means of obtaining the best professional judgement of scientific

experts on how, in their opinion, to most suitably apply available but incomplete scientific facts to serve

policy objectives. Without explicit clarification there is a danger that such deliberations may be perceived by

many as being a purely scientific discussion when it is not. I both understand and support the general need

for some degree of precautionary activity, but strongly object to dressing it up as science. Good, reasonable

policy incorporates input from a variety of sources and does not need a scientific aura for respectability.

Rather than stating that it is reasonable, I believe that a list of the assumptions required to enable the TEF

process to be used in risk assessment be presented, both for the screening and advanced cases. Any reader

can then judge the degree of reasonableness for themselves. This is consistent with the concerns which

prompted the Levin-Thompson bill currently under debate in the U.S. Senate. This bill illuminates the need

for identification and clarification of both the scientific and policy basis of assumptions used in risk

assessment. Such a separation of science and policy in should make the risk assessment process more

transparent and understandable. The recent Presidential/Congressional Commission on Risk Assessment and

Risk Management (1997) has made a call for improved risk communication and a clear identification of

science and policy aspects of the TEF approach would also contribute to achieving such a goal. I have also

commented on the confusion of policy for science in risk assessment (Power and McCarty, 1997).
Lynn S. McCarty

C-C-95

It is my opinion that the TEF approach as currently constituted is not sufficiently rigorous or comprehensive

to be employed in other than screening level risk assessments for aquatic, avian, and mammalian wildlife.

The approach represents a reasonably founded policy for screening that also serves as a useful guide for

directing additional scientific research. However, the limitations and restrictions specified in the meeting

description and charge questions represent little more than a detailed list summarizing why, at this time, it

should not be used beyond an initial screening risk assessment. 

The method addresses only Ah-receptor mediated effects. This provides only an illusion of full protection

since non-Ah-receptor-mediated effects associated with the dioxin-like chemicals may still cause adverse

effects by other modes of action. Thus, the overall goal of environmental protection may not be achieved

using a TEF risk analysis alone. The method assumes strict additivity and, although a reasonable assumption,

cases of over- and under-protection are possible for a variety of reasons. The TEF approach is strictly a

toxicological approach dealing only with direct effects and ignoring indirect and nondirect (induced) effects. 

Nondirect or induced effects are the result of changes in physical/ecological conditions which are not either a

direct or indirect biological response of an organism to a chemical stressor, but may be a sequela. Examples

of this would be changes in benthic communities associated with changes in sediment texture or quality

resulting from biological or physical/chemical events associated with the contaminant of concern, or loss of

habitat associated with ecological or anthropogenic events related to the chemical contamination of concern. 

In the traditional toxicological sense, no pharmacological dose of a chemical can be described to model the

situation, but such effects may combine with or dominate the direct toxicological effect (Munkittrick and

McCarty, 1995). 

Ecological dynamics in the field are not considered. Population (both intra-species and interspecies) and

community level compensating factors can have substantial influences on the nature and degree of response

in natural field populations are ignored. This issue is particularly problematic as empirical information

questions the validity, or at least the accuracy, of the extrapolation method: "However harmful effects (e.g.

effects on survival, growth and reproduction) of dioxin-like chemicals are often difficult to detect at the

population level. Therefore, methods to assess and predict effects on individuals are required" (WHO, 1997). 

Also, the method does not address interactions, both positive and negative, with other stressors and factors in

the real environment that is being assessed. 

Despite the problems noted above and my views of them, I will attempt to include answers to the supplied
Lynn S. McCarty

C-C-96

questions which are in the context of the question asked. 

SPECIFIC QUESTIONS/ISSUES

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC TEF VALUES

1. The additional background information available for some TEFs provides an attractive, but illusionary

means of evaluating uncertainties. I am not aware of a comprehensive list of possible sources of

uncertainties, with a quantitative ranking of the possible contribution of each. Thus, it is not possible to

quantitatively evaluate the data that is available and assign valid, comparable uncertainty rankings. Although

some qualitative assessment may be carried out, it too is prone to being misleading since it is a evaluation of

only the uncertainty information known. It is be quite possible that influencing factors for which there is

currently no information could dramatically alter any uncertainty evaluation made with incomplete

knowledge. As well, since the overall uncertainty level is not quantified, the relative magnitude and

significance of any uncertainty reduction cannot be determined.

By my estimate about 25% of the proposed WHO TEFs (Table 5) are rounded to the nearest 1/2 order of

magnitude (significant digit is 5 rather than 1). I think that the statement in this question that TEFs are

generally rounded to the nearest order of magnitude is overstating the case. With 25% rounded to the nearest

1/2 order of magnitude I think that is more representative statement of the actual rounding practice. I note

that this is stated correctly in Tables 1-3 in the retrospective case study.

2. All TEFs should not be considered to have similar uncertainties. As noted, a variety of studies, endpoints,

and exposure routes have been employed. Until such time as either there is a common experimental basis for

the TEF scheme or there is quantitative knowledge of the toxicokinetic and toxicodynamic relationships

between various tests, endpoints, and exposure routes, the uncertainty associated with derivation remains

problematic. Although there are greater amounts of background information for some congeners, the

information base for all is insufficient or incomplete. Therefore, all uncertainties associated with each TEF

are not quantifiable and the similarities in the uncertainties associated with each TEF are unknown. Although

TEF estimate uncertainties may lie in a similar range, or be of modest influence compared to other

uncertainties, partial quantification at this time would impart a false sense of accuracy.

3. There is a question as to whether any of the TEFs derived from in vitro and in vivo laboratory testing can

be reliably extrapolated to the effects relevant to the chosen assessment endpoints. Assessment endpoints are
Lynn S. McCarty

C-C-97

usually clear goals related to the maintenance of populations of certain valued or threatened/endangered

species or, more specifically, the maintenance of reproduction and protection of sensitive life stages in these

species. Protection of the community is assumed to be accomplished when the sensitive or sentinel species

are protected. This is a very broad, unfounded assumption.

Success in protecting a community/ecosystem is closely related to population modellers knowledge of the

system being examined and their ability to employ the toxicological data in their models to address the

assessment endpoints selected. Currently toxicological data that are or can be quantitatively related to

growth, reproduction, and survival (mortality) are most likely to be of use, since these are the effects that

current models have been developed for. Any other endpoints are likely to be of little use for extrapolation

modelling and of little use for risk assessment purposes.

The following has been noted on page 3 of the WHO Draft Report (WHO, 1997) "However harmful effects

(e.g. effects on survival, growth and reproduction) of dioxin-like chemicals are often difficult to detect at the

population level. Therefore, methods to assess and predict effects on individuals are required." It appears

that most TEFs based on laboratory tests are likely to be unreliable or at least unvalidated for prediction of

populations/communities of organisms in field situations at the current state of the knowledge. If relatively

dramatic effects of dioxin-like chemicals, such as survival, growth, and reproduction, are difficult to detect at

the population level it suggests that compensating mechanisms are active. Without a good knowledge of the

number, types, and effectiveness of such compensating mechanisms it will not be possible to reliably

extrapolate laboratory data. Since a variety of effects observed in laboratory testing are more subtle or less

clearly linked to survival, growth, and reproduction, they will be of even lesser utility in predicting effects in

the field.

In summary, since many TEFs are based on effects that are poorly linked to survival, growth, and

reproduction, and since it appears that compensating mechanisms in field populations/communities are poorly

understood for the effects of dioxin-like chemicals, accurate extrapolation using current TEFs to protect

selected populations in the field is unlikely. 

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ APPROACH

1. Conceptually, no additivity or mixture interaction would result in a lower estimation of risk. Risk would

be based on the extrapolated effect of only the most toxic congener, that is to say the chemical present with
Lynn S. McCarty

C-C-98

the an expected or observed ambient concentration closest to or most in excess of an estimated or regulated

adverse effect level. This is the opposite situation to that where simple, non-potency adjusted mixture

additivity is employed and a higher estimation of the risk would result. The degree of underprotection or

overprotection of these different approaches to mixture toxicity compared to the TEF approach cannot

currently be assessed quantitatively since considerably more toxicology and ecology knowledge and data

would be required. Furthermore, there are insufficient data to perform a qualitative evaluation. The TEF

approach, although clearly based on current scientific understanding and principles, is best viewed as a policy

based on good judgement, and should not be presented as having strong empirical support for risk assessment

extrapolation.

The risk assessment conclusions for the retrospective case study would not be completely different if based

on total PCBs or 2,3,7,8-TCDD alone. For this the TEF approach is assumed to be used to adjust potency but

only the single most potent congener is used to assess risk relative to the proposed guideline. Based on Table

1 it can be seen that neither the total TEQs nor the PCB TEQs exceed the provisional fish guidelines of 30

μg/g. Similarly, the TEQs from PCDDs and PCDFs as groups or any individual congener alone does not

exceed the guideline. Also, the total PCB concentrations do not exceed the provisional guideline of

5,000,000 μg/g. 

The original TEQ analysis for birds (Table 2) finds exceedences of the provisional guidelines for total TEQ

(100 μg/g) by PCBs, but not for the TEQs from PCDD or PCDF. Using a non-mixture approach the total

TEQ guideline is exceeded by PCB-126 . No other individual congener exceeds the it. The total PCB

concentration in Caspian tern eggs exceeds the 5,000,000 μg/g limit by a relatively small amount. Thus, the

conclusion of a modest adverse effect on birds can be obtained from either the detailed TEQ analysis or the

total PCB analysis. The analysis for mammals (Table 3) is different. The original TEQ analysis finds

exceedences of the provisional guidelines for total TEQ (60μg/g) by both the TEQ total and the PCBs, but not

for the TEQs from PCDD or PCDF. Using a non-mixture approach the total TEQ guideline is exceeded only

by PCB-126. The total PCB concentrations do not exceed the provisional guideline of 2,000,000 μg/g. In this

case the exceedence estimated by the TEQ analysis is not confirmed by the total PCB analysis.

Although the results of the risk assessment do change somewhat, the general conclusion drawn from them

would not change substantially with an alteration from mixture additivity to consideration of only the most

significant single congener or to consideration of total PCB concentration alone. The conclusion is that, in
Lynn S. McCarty

C-C-99

this watershed, there are levels of certain organochlorine chemicals present in organisms above the proposed

effect levels and the dominant source is PCBs, in particular PCB-126. Of course, the conclusion depends on

the nature of the residue levels present in the study and the above conclusion would not be universal for all

cases. However, the multiple receptor approach with foodchain considerations does appear to be robust and

appears to provide more certainty than a less diverse examination would provide.

2. Given the uncertainties and variability in the data on which TEFs are based, any differences caused by the

use of median response level versus no adverse effect level data is likely within the considerable noise

associated with the TEF estimation process. However, an estimate of the contribution can be made. There are

empirical data to suggest that differences between acute and chronic responses in conventional aquatic

toxicity data is usually of the order of a factor of 10 or less (see Rand et al., 1995). Also, some fish TCDD

TEFs calculated at the threshold of EROD induction were about four to five times larger than international

TEFs (I-TEFs), while being similar to I-TEFs when conventional ED50 data were employed (Parrott et al.

1995). This suggests that at low concentrations typical of environmental exposures, fish TEFs may be

different from mammalian-based TEFs and/or there may be a difference between TEFs calculated at median

response levels versus those calculated from information closer to no effect levels. If the latter is the primary

source of the difference, then it supports the contention that TCDD TEF toxicity estimates are affected by

differences in endpoint response proportion, that such differences may be as great as the order of a factor of 5,

and that such differences represent nonconservative errors in the risk assessment process using TEFs since

congeners appear to be more toxic compared to TCDD than when compared a median response levels. It

should be noted that the opposite appears to be true for TEFs for PCBs since they are often smaller that ITEFs when estimated away from median response levels. 

3. Extrapolation of class-specific TEFs (e.g., primarily based on single or limited mammal, bird, or fish data)

to species identified in the case studies is currently a matter of policy rather than science. It is not

uncertainty, but rather ignorance, that is the main controlling factor. In addition to the general laboratory-tofield extrapolation problems discussed in the response to question I.3, there is now the differences between

the species used in class TEF development and the species selected in a given risk assessment. There are

exposure and toxicokinetic differences. These include differences composition and timing in exposure routes

(e.g., water, diet (sediment, foodchain)), lifestage and other seasonal factors, and metabolic handling

differences. Toxicodynamic factors such as differences in Ah-receptor density in target tissues, as well as

possible differences in receptor character, also complicate extrapolation. 
Lynn S. McCarty

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In addition, the choice of assessment-specific species is not based on a rigorous scientifically-based method,

and it is clearly not optimized for toxicological extrapolation. For example, in the prospective study bull

trout "... as a potentially very sensitive species (probably as sensitive as or more sensitive than lake trout),

was chosen because of its status as a threatened species." while bald eagle and the river otter were chosen as

"representative bird and mammal species" without any detailed technical justification being supplied. 

Knowledge concerning TEF extrapolation is largely qualitative, semi-quantitative at best, and if TEFs are to

be used it should be clear that such use is based on professional judgement and is a policy-based assumption

rather than a scientific fact. At the moment TEF extrapolation should be considered as good policy but

inadequate, incomplete science.

III. EXPOSURE PROFILE

1. The exposure modelling uncertainties associated with TEFs are those common to modelling the fate of any

chemical contaminant or contaminant mixture. The TEF approach has an advantage that, unlike the case

where a mixture of chemicals may contain a diverse group of chemicals with differences in mode of toxic

action, dose additivity is an integral part of the approach. The ranking of the potency of various congeners

does provide an advantage since the degree of accuracy on the ambient level estimation can be adjusted

relative to potency. For congeners not on the TEF list, chemical analysis can be avoided. For low potency

congeners, analysis can be less rigorous as their contribution is likely modest anyway. Analytical efforts can

then focus on for high potency congeners, since these have the greatest contribution and should be

determined most accurately. A similar logic applies to fate/transport and foodchain models, since the level of

effort and degree of accuracy can be tailored to the potency of the congener. 

Although there are some differences in the availability and quality of congener-specific physico-chemical

data I believe that any deficiencies here are less significant than in the knowledge of physical, chemical,

biological, and ecological processes and relationships used in fate/transport and foodchain models. 

2. Exposure route differences between the data used to derive the TEFs and the exposure profile(s) in a

particular case study can be of great importance and effort are required to address this issue. The closer or

more representative the dose surrogate is to the dose at the site of toxic action, the more useful and more

readily interpretable it is likely to be from a toxicological point of view. Parrott et al. (1995) provide a useful

example. Liver concentrations of PCDD/F congeners were better predictors of EROD activity than oral

doses. There were some differences in the ranking of potencies of the PCDD/Fs between fish and mammalian
Lynn S. McCarty

C-C-100

data As well, fish TEFs calculated at the threshold of EROD induction were about four to five times larger

than international TEFs, suggesting that at low concentrations typical of environmental exposures, TEFs may

be different from mammalian-based TEFs which are often based on median response levels. This suggests

another twist related to different exposure routes. Since an estimate of the received dose is not usually

obtained in exposure-based dosing, some of the differences in TEF estimates reported in different species or

endpoint testing may be simply related to differences in the amount of the received dose.

In summary, estimates of received doses are more readily interpreted from a toxicological point of view.

However, if only received dose data are available information on bioavailability, partitioning, and metabolic

breakdown differences may be missing. This is the very data needed to facilitate risk assessment which is

commonly focused on concentrations of dioxin-like chemicals in environmental media. Thus, unless

bioavailability, partitioning, and metabolic breakdown differences between organisms, congeners, and test

endpoints are available, along with either an exposure or received dose estimate, application of TEFs in risk

assessments will be difficult and potentially misleading. 

3. In all regulatory approaches based on comparison with a critical effect or no-effect level it is important to

minimize measurement and manipulation errors and uncertainties to the extent reasonably possible. The

simple total PCB approach relies on summing PCB data and comparing the result to a guideline level. In the

TEQ approach congener-specific measurements are manipulated by equations containing several parameters

and the errors/variability increases as a result. The greater the uncertainty in the parameters the greater the

uncertainty in the product which is the basis of the comparison. Thus, in the interests of keeping uncertainty

down, and perhaps comparable to the simple total PCB approach, chemical analysis of AhR agonists should

be more rigorous and thereby produce less uncertain estimates that will allow the TEQ product to exhibit a

similar uncertainty.

The above comments are based largely on mathematical considerations. On the other hand both methods have

substantial, but unquantified errors and uncertainties associated with toxicological and ecological aspects.

Thus, the overall extent to which any additional analytical efforts would substantially reduce TEF

methodological uncertainty is unknown.

IV. RISK CHARACTERIZATION

1. The uncertainties associated with TEFs are not more problematic than other uncertainties associated with
Lynn S. McCarty

C-C-101

case study risk assessments. In fact, given their relatively narrow focus and comparatively detailed

examination, they are likely less uncertain than some of the other aspects of the risk assessment process. 

With the TEF approach at least some attempt has been made to quantify the differences in toxic potency. On

the other hand, as noted elsewhere, assumptions required to project populations, communities, and ecosystem

effects from controlled toxicity testing results are rather broad and, for the moment, little quantification of the

influence of current practice has been attempted. Also, bioavailability directly from the environment, as well

as at various stages in the foodchain (direct bioavailability from dissolved water phase, dietary absorption

efficiency from ingested sediment and prey organisms), is a major source of variability. Although addressed

in some degree in the current BSAF, BAF, BMF, and FCM approaches, detailed consideration would allow

for better understanding and quantification of this likely important source of variability. I expect that it

would be at least a significant a source of variability as the TEF toxicity scheme.

2. At this time I do not believe that biologically-based TEQ assays with environmental samples represent a

useful or viable extension to the current TEQ screening approach to regulation. Certainly such activities

would be useful in the examination of the validity and accuracy of TEQ screening, and should provide useful

insights helpful to further refinement of the scheme. However, it is premature and unwise to use research

tools in a regulatory process.

3. For regulatory purposes I would not desire any further site-specific data. As I noted earlier I do not

believe that the TEF approach should be used for anything other than a screening risk assessment. Although

there can be debate about what constitutes a screening risk assessment and a detailed site specific risk

assessment, the case studies provided certainly tend more towards the latter. I do not believe that there is

enough understanding of the toxicology and, especially, ecology to further refine such regulatory approaches

at this time. Even the current status is providing a false sense of scientific validity and I would not wish to

have it go any further. Additional work in basic research is needed to better understand the toxicology and

ecology in a field situation to aid in better understanding extrapolation. Only then would additional sitespecific data be of substantially greater utility.

Additional Questions Specific to the Prospective Case Study

RELATIVE TO THE EXPOSURE PROFILE

1. I trust the question refers to BAFfd

l

 rather than BAFfd

w since the latter does not appear in the GLWQG,

Table 1 in the Charge Questions, or Figure 5 of the prospective study. Improvements in the accuracy of
Lynn S. McCarty

C-C-102

congener-specific MACs using site-specific data for BAFfd

l

 determination will be a function of how different

the site-specific values would be compared to those values used in the GLWQG determination process. It

will also depend on whether the first or second most preferred method of deriving baseline BAFs is followed

(see GLWQG, 1995, page 2). Since the values used in the GLWQG consider all routes of exposure and all

aspects of environmental fate, including metabolism, a very thorough extensive sampling and analysis

program on Roundtail Lake would be required to improve the estimates. However, even given that, the low

to nondetectable levels of Ah-receptor stressors currently in the system make it unlikely that improvements

could be made in a prospective study since non-detect data points would confound the analysis, especially for

PCDD/F.

2. Not answered.

RELATIVE TO THE RISK CHARACTERIZATION

3. As presented in the prospective case study the water quality standard estimates of 0.032, 0.028, and 0.021

pg TCDD/L have too many significant digits. The equations used (e.g., 1 or 2) employ parameters with

various significant digits. However, the TEF estimates which are used in the equations are declared to be a

single significant digit which is rounded to the nearest order or 1/2 order of magnitude, depending on the

source of the statement. Thus, values with 2 significant digits, such as are presented, represent a serious

distortion of the actual precision of the output of the formulas. Conventionally, the output of such an

equation is presented with a level of significance no greater than that of the least precise parameter. In this

case it is the TEF. Thus, rather than a choice of 3 values the choice should be between either 0.01 or 0.05 pg

TCDD/L if 1/2 order of magnitude precision is used. If the precision is at the order of magnitude level there

is only one estimate: 0.01 pg TCDD/L. Given the uncertainty and lack of precision in the other input

parameters of these equations I am inclined to go with the order of magnitude estimate. This represents a

more realistic consideration of the uncertainties in the estimation process. 

Additional Questions Relative to the Retrospective Case Study

RELATIVE TO THE RISK CHARACTERIZATION

1. It is very unlikely that the sediment cleanup goals would be the same for each vertebrate group, although I

cannot confirm this without doing the detailed calculations. The reason for the expected difference is that the

three formulas used to estimate fish, bird egg, and mink TEQ relationships to sediment use differing

BASF/BMF and TEF values, as can be seen from the information in the included tables. These differences are
Lynn S. McCarty

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appropriate and expected since the target organisms occupy different locations in the food chain. Given the

variety of data sources and limitations, and numerous assumptions required I feel it will be difficult to

quantify meaningful differences in certainty of clean-up goals. If there are substantial differences in the

sediment cleanup goals from the various methods, the scientists should offer a best professional judgement

ranking the values and the manager should consider additional non-scientific (i.e., economic, technological

etc.) factors in the choice of a final project cleanup value. 

2. Not answered.
Lynn S. McCarty

C-C-104

References Cited

Munkittrick, K.R. and L.S. McCarty, 1995. An integrated approach to ecosystem health management:

top-down, bottom-up or middle-out? J. Aquat. Ecosys. Health 4:77-90. 

Parrott, J.L., P.V. Hodson, M.R. Servos, S.L. Huestis, and D.G. Dixon, 1995. Relative potency of

polychlorinated dibenzo-p-dioxins and dibenzofurans for inducing mixed-function oxygenase activity in

rainbow trout. Environ. Toxicol. Chem. 14:1041-1050.

Power, M and LS McCarty, 1997. Fallacies in Ecological Risk Assessment Practices. Environ. Sci. Technol.

31(8):370A-375A.

Presidential/Congressional Commission on Risk Assessment and Risk Management, 1997. Volume 1:

Framework for Environmental Health Risk Management. Volume 2: Risk Assessment and Risk Management

in Regulatory Decision-Making. Commission on Risk Assessment and Risk Management, Washington DC.

Rand G.M, P.G. Wells, and L.S. McCarty, 1995. Chapter 1: Introduction to Aquatic Toxicology. In: G.M.

Rand (ed.), Fundamentals of Aquatic Toxicology II: Effects, Environmental Fate, and Risk Assessment.

Taylor and Francis, Bristol PA. pp. 3-67.
C-C-105

Charles Menzie, Ph.D.

Menzie-Cura & Associates, Inc.

2 Courthouse Lane - Suite 2

Chelmsford, MA 01824

978-970-2620

Fax: 978-970-2791

E-mail: charliemen@aol.com

Dr. Menzie specializes in assessing environmental risks of toxics in aquatic and marine

systems. He has many years of experience working with EPA on projects related to the

development of the ecological risk assessments, case studies, and guidelines. Dr. Menzie

has a good working knowledge of EPA's risk assessment guidance and has used this in

risk assessments for several aquatic and marine environments. He has also used a risk or

hazard assessment framework to identify research needs related to the fate and effects of

toxics in estuaries. He has performed cross-media risk assessments for ocean-dumped

wastes. He investigates marine and estuarine environmental problems on all coastal areas

of the United States, including Alaska and Hawaii, and provides multi-media assessments

of various remedial action alternatives.

Dr. Menzie has chaired multiple workshops and colloquia related to the development of the

EPA ecological risk assessment guidelines. Additionally, he has prepared case studies

and process diagrams, performed peer reviews, and other tasks for EPA related to

ecological risk assessment. Dr. Menzie will serve as the chair of the EPA workshop on

shrimp virus issues related to ecological risk assessment. Dr. Menzie acted as the

facilitator for a series of public stakeholder meetings on shrimp virus issues in July of 1997.

He was chosen as the chair based on his extensive experience with ecological risk

assessment, environmental risk in marine and estuarine systems, and his skill as a

facilitator and chair.
Charles Menzie

C-C-106

To: Eastern Research Group

From: Charles Menzie

Topic: Pre-meeting comments on TEF Charge Questions

I. STRESS-RESPONSE PROFILE RELATIVE TO DERIVATION OF SPECIFIC TEF

VALUES

1. Does the additional information enhance the means of evaluating uncertainties in the assessments? If

so, how? If not, why?

The additional background information is useful for evaluating the uncertainties in the assessments

primarily because these give insight into the methodology used to derive the estimates. The

“uncertainties” probably have more to do with the methodology than to rounding issues.

2. Should all TEFs be considered to have similar uncertainties?

No. Because TEFs are “models” based on empirical data, the amount and quality of data affects the

level of confidence that can be given to each value. The derivation of TEFs is commonly based on a

weight-of-evidence approach. Therefore, as the weight of evidence increases, there is greater

certainty about the TEFs as well as the variability of these values. 

3. To what extent can endpoints be extrapolated to the measurers of effects that are relevant for the

assessment endpoint for each case study?

The different measured endpoints are related to the endpoint of interest. As long as the same type of

related endpoint is used to develop relative measures of effects, extrapolation is possible. There is

greater uncertainty associated with using endpoints that are surrogate measures of the effects of

interest than endpoints that are more directly related. This source of uncertainty is difficult to

quantify. However, where data sets exist for several endpoints, it may be possible to quantify the

extent to which relative measures diverge from one another.

I. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

APPROACH
Charles Menzie

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1. What are the implications of assuming no dose additivity or no interactions among the components of

the mixtures?

Most environmental exposures of consequence occur at relatively low doses. Most available

information suggests that an additivity (i.e., non-synergistic and non-antagonistic) model is

appropriate under such circumstances. The use of such a model is consistent with our knowledge of

effects under low dose exposures. Alternatively, it is unlikely that sufficient information would be

obtained in the near future to support an alternative model. Assuming additivity is probably the most

appropriate approach and is more likely to overestimate than to underestimate effects.

2. To what extent should TEFs derived at a median response level be used in risk assessments where a

no adverse effect level is being employed?

The question suggests that there is a potential “apples and oranges” problem associated with mixing

these different types of information. This is not the case. Median response data are selected because

they provide useful – and more stable values – of relative measures than do data at the tails of doseresponse curves (e.g., NOAEL values). However, these relative measures can still be combined with

absolute toxicity data at the tails of a distribution for the purpose of estimating risks. In such cases,

there would be uncertainty associated with the selected toxicity data but the relative measures would

still be appropriate.

3. To what extent can class-specific TEFs be directly extrapolated to the species identified within each

case study?

It would be useful to have measures of variability among species within a class for both toxicity and

relative measures of toxicity. Without such information, it is difficult to comment on the uncertainties

associated with extrapolation.

I. EXPOSURE PROFILE

1a. To what extent does the TEF approach present challenges ....?

The approach reduces uncertainties associated with estimating risks associated with mixtures because

it makes greater use of the information available on the relative toxicities of the compounds within

the mixture. Because the mixture is variable in composition, a method that accounts for such
Charles Menzie

C-C-108

variability is likely to provide a better estimate of effects than a method that assumes a specific

composition.

1b. How does the approach affect fate and transport modeling considerations?

The approach does require more detail to be included in fate and transport models. For simple

models, the impact will be small. However, for large models with extensive computations, the additional

effort (models runs and times) can become demanding. Modeling these complex mixtures will require the

same types of considerations that have been given to models of petroleum hydrocarbons. The recent work of

the Total Petroleum Hydrocarbon Workgroup (TPHCWG) is a good example. This group has divided the

complex mixture of petroleum hydrocarbons into manageable fractions for the purpose of modeling and for

risk assessment.

2. To what extent do exposure route differences used in deriving the TEFs affect their application in the

case studies?

TEFs are relative measures of effects. However, it is possible that the relationships between

administered, absorbed, and effective doses could vary depending on route of exposure and that these

do not vary consistently among compounds. Thus, there is greater uncertainty with using TEFs that

are based on routes of exposure different from those being evaluated in the risk assessment. 

To what extent does the TEF approach require a more rigorous design...?

The TEF approach will require greater analytical costs. Based on experience, the analytical cost may

be higher by a factor of two to ten as compared to total PCB measurements. The TEF approach will

also require greater efforts to perform QA/QC, data validation, and data management. 

I. RISK CHARACTERIZATION

1. Are the uncertainties associated with TEFs more problematic than other uncertainties?

No. Use of TEFs does not introduce greater uncertainties into the analysis in cases where toxicity

data are based on literature values (as compared to direct measures of toxicity.) These uncertainties
Charles Menzie

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do not limit the analysis.

2. What would be the strengths and limitations of a biologically-based TEQ approach?

The major strength is that such an approach provides a better measure of the effects of the mixture

and avoids having to rely upon a reconstruction of the effects from an estimated “sum of the parts.”

The major disadvantage has to do with having an acceptable approach and the analytical costs

associated with implementing that approach.

3. Provide a list of investigations and rank them.

I would rely upon a weight-of-evidence approach. This would consist of three components: a) field

observations of effects using an ecoepidemiological approach, b) laboratory exposures using extracts of

sediment, water, or fish, and c) an assessment of effects based on chemical measurements. All of these

contribute to an overall understanding of effects. I place greater reliance on field observations for retrospective

studies and on laboratory toxicity tests for prospective analyses.
Charles Menzie

C-C-110

Additional Questions for Prospective Case Study

RELATIVE TO EXPOSURE PROFILE

1. The Roundtail Lake data are more relevant for site-specific evaluation. Therefore, MAC based on

these data should be more appropriate than GLWQG.

2. The approach should be internally consistent. If a TEF approach is being applied to assess toxicity,

then it should also be used to evaluate exposure. Otherwise, the improvements gained on effects may

be offset by uncertainties and errors associated with modeling exposure.

3. I suggest that a Monte Carlo approach be used. The approach should adhere to recent EPA policy

concerning the use of probabilistic methods. A policy decision will need to be made concerning level

of protection. Typically, this is selected as a value at the tail of the distribution (e.g., 95th percentile.)

In lieu of Monte Carlo analyses, other probabilistic methods may be helpful.
C-C-111

Christopher D. Metcalfe, Ph.D.

Chair, Environmental & Resource Studies

Trent University 

Peterborough, Ontario, Canada K9J 7B8

705-748-1272

Fax: 705-748-1569

E-mail: cmetcalfe@trentu.ca

Dr. Metcalfe is currently chair of Environmental and Resource Studies at Trent

University in Peterborough, Canada. He received a B.Sc. degree at University of

Manitoba in zoology/chemistry, a M.Sc. in biology from the University of New

Brunswick, and a Ph.D. in biochemistry from McMaster University. He is a recent

winner of the “Excellence in Research and Technology” award from the Ontario

Ministry of the Environment. Dr. Metcalfe has a range of experience in international

projects concerning aquatic contaminants. Particular research interests are in

determining fate and toxic effects of halogenated aromatic hydrocarbons,

polynuclear aromatic hydrocarbons, and alkylphenol ethoxylate surfactants in the

aquatic environment. Dr. Metcalfe has been involved in international projects in

Belize, Mexico, Ecuador, Argentina, and Indonesia, as well as conducting several

research projects on toxicity in the Great Lakes. He has published more than 70

journal articles and other refereed publications.
Christopher D. Metcalfe

C-C-112

Response to Charge Questions

from Chris Metcalfe

Trent University, Peterborough, Ontario, Canada

November 14,1997

I. Stress-response profile related to the derivation of TEF values:

Question 1. The additional information for derivation of TEFs provided in the case studies

informs the reviewers that the toxic endpoints of interest in these case studies are reproductive

success and recruitment within the populations of exposed organisms. This informed me to

place greater emphasis on TEFs that have been derived using toxic endpoints that affect

recruitment, such as early life stage mortalities.

Questions 2 and 3. TEFs will vary in level of certainty. There is a good toxicity data base with in

vivo and in vitro mammalian models from which TEFs for wild mammals can be derived. For

fish, there is a comprehensive data base for TEFs that are based upon early life stage

mortalities in salmonids, but data for other in vivo endpoints are incomplete. I am particularly

concerned about TEFs derived for birds, which are mainly based on in vitro assays using

endpoints that are only peripherally related to effects that are relevant to the assessment

endpoints in the case studies (i.e. recruitment).

II. Stress-response profile relative to the application of the TEQ approach:

Question 1. The implications of assuming no dose-additivity or no interactions in the case

studies are a major leap of faith for the risk assessment process. The 

limited information on this subject indicates that other non-toxic halogenated aromatic

hydrocarbons (HAHs) exert a modulating effect upon the toxicity of planar HAHs; hence a TEQbased risk assessment based upon an assumption of no interactions will over-estimate the

toxic risk to fish and wildlife. However, having said this, risk assessments based upon

concentrations of TCDD or total PCBs would offer no major advantages over the TEQ

approach. Basing toxicity assessments upon TCDD concentrations would be problematic in the

retrospective case study where planar PCBs contribute to a large percentage of the total TEQ,

and in the prospective study where chlorinated dibenzofurans are major contributors to the total
Christopher D. Metcalfe

C-C-113

TEQ. Estimates based upon total PCBs do not take into account the changes in congener

proportions that take place through a process of partitioning in the environment and

biomagnification through food-webs.

Question 2. For me, estimates based upon EC50 or LC50 values are not a problem for

calculating NOAELs. As stated in the documentation for this exercise, the dose-response

curves for planar HAHs tend to be so steep that there are not likely to be large differences in

EC50s and NOAELs. The use of median response levels for risk assessment based on

NOAELs will add a safety factor that partially compensates for the uncertainties that are

inherent in the TEF estimates.

Question 3. There are some problems in extrapolating TEFs based upon tests with a limited

number of test species to an entire taxonomic group. In the case of fish, TEFs based upon early

life stage mortalities with salmonids are particularly appropriate for assessing risk to salmonid

species of esthetic or economic value; a situation that is common for assessing risk in

temperate lakes. However, these TEFs may be of limited value for risk assessment in warmwater environments with species such as bass and channel catfish. The mustelids appear to be

particularly sensitive to the toxic effects of planar HAHs, so risk may be underestimated for

these mammals when using TEFs based upon rodent models. The limited amount of data

available on TEFs for birds indicates that interspecies differences in sensitivity are large, so

applications of TEFs to the avian species identified in the case studies may be inaccurate;

either under- or overestimating the toxic risk.

Ill. Exposure Profile:

Question 1: The TEF approach presents challenges for modeling the environmental distribution

and exposure dynamics of planar HAHs. I am not particularly concerned with the quality of the

physico-chemical data for these compounds. In most cases, there are adequate data for Kow,

Koc, H, etc. for each of the toxic compounds, and where there is not, estimates can be made

from empirical relationships or structure-activity relationships. However, I am concerned that

there are few data on the relative rates of biodegradation of these compounds. There may be a

tendency to model bioaccumulation and biomagnification of planar HAHs solely on the basis of

ability to partition into lipids. (or fugacity); forgetting the effect of biotransformations and

excretion on this process. We particularly do not understand the relative biotransformation

capabilities of various taxa, since it appears that different groups of organisms (e.g. fish-eating
Christopher D. Metcalfe

C-C-114

birds; marine mammals) may how different metabolic capabilities for PCB congeners, PCDDs

and PCDFs.

Question 2: It is difficult to assess the effect of differences in exposure routes on estimates of

TEFs. For instance, injections of eggs in studies with fish and birds may not reflect the normal

toxicokinetics and partitioning of contaminants that occur in eggs as a result of parental transfer

of contaminants. More work is needed to assess this problem.

Question 3: The methods required for analysis of specific congeners of PCDDs, PCDFs and

PCBs, (in particular, coplanar PCBs) are definitely more rigorous, time consuming and

expensive than methods for aggregate stressors. This means that only a small number of

analytical labs with appropriate technical expertise and analytical instrumentation (e.g. high

resolution GC-MS) will be able to provide the data that is appropriate for risk and fight research

budgets will limit the number of samples that can be analyzed. In addition, some of the

analytes identified in these risk assessment scenarios are often not routinely analyzed (e.g.

PCB congener 81).

IV. Risk Characterization:

Question 1: In my opinion, uncertainties in modeling the bioaccumulation and biomagnification

of planar HAHs are a limitation of the risk assessment process that may exceed the

uncertainties associated with calculating the TEFs. 

Question 2: In vitro or in vivo biological assays to determine TEQs may be a useful approach.

However, protocols must be developed to define the degree of sample fractionation prior to the

assays. For instance, typical in vitro assays for EROD induction with H411E cell lines have

been conducted with environmental samples that have undergone considerable fractionation to

isolate planar HAHs. Use of these samples may result in overestimates of biological responses.

Use of a more crude fraction also containing non-toxic PCBs, for instance, may significantly

modulate the degree of EROD induction.

Question 3: Studies are needed to address: 

i) The appropriateness of an additive approach for estimating total TEQs.

ii) The relative rates of transformation and elimination of planar HAHs in
Christopher D. Metcalfe

C-C-115

different taxa, and the effects upon bioaccumulation and biomagnification. iii) The

influence of exposure route on estimates of TEFs.

Additional Questions:

Questions Specific to Prospective Case Study:

Questions 1 and 2: No comments until I can further examine the basis of the BAFs used by the

GLWQG.

Questions Specific to Retrospective Case Study:

Question 1: 1 would council a risk manager to develop TEQ sediment cleanup goals that

ensure protection of the vertebrate group with the most certainty in TEQ estimates. In my

opinion, the lack of certainty in TEQ estimates (e.g. 10-20 fold?) would probably exceed the

differences in sediment cleanup goals calculated for the various vertebrate groups.

Question 2: 1 do not consider a ratio of total TEQs to total PCBs to be an effective method for

setting TEQ-based sediment remediation goals. The reason for this opinion is illustrated in the

attached figure (from Metcalfe and Metcalfe, 1997, Sci. Total Environment) that shows

variations in total TEQs for coplanar PCBs relative to total PCBs in different components of the

Lake Ontario food web. The ratio vanes among environmental compartments and groups of

biota; probably as a result of differences in metabolism and bioaccumulation of coplanar PCBs

relative to other PCB congeners. This is especially noticeable when comparing TEQ/PCB

ratios in biotic and abiotic compartments.
C-C-116
C-C-117

Michael W. Meyer, Ph.D.

Wildlife Toxicologist

Bureau of Integrated Science Services

Wisconsin Department of Natural Resources

107 Sutliff Road, Box 818

Rhinelander, WI 54501

715-365-8858

Fax: 715-365-8932

E-mail: meyerm@dnr.state.wi.us

Dr. Michael Meyer received his B.S. in biology from the University of Wisconsin -

Stevens Point, his M.S. in animal science from Texas A&M University System, formerly

known as Texas A&I University, and his Ph.D. in wildlife ecology from the University of

Wisconsin - Madison. Dr. Meyer is a wildlife toxicologist at the Bureau of Research for

the Wisconsin Department of Natural Resources. Dr. Meyer has studied and written

many articles on bald eagles and loons in Northern Wisconsin, including Patterns of

common loon (Gavia immer) mercury exposure, reproduction, and survival in Wisconsin;

and A geographic trend in mercury exposure measured in common loon feathers and

blood.
Michael W. Meyer

C-C-118

Response to Charge Questions for Workshop on the Application of TEFs to Fish and Wildlife

Section 1.

1. Because of the variety of species and endpoints used in developing TEFs, additional

information describing TEF derivation is required for uncertainty analysis.

Ideally, standard protocols would be established for congener specific TEF derivation (same

species, same endpoint) and a TEF profile be established for each class (mammal, fish, bird)

Unless or until this is established, additional information should be provided for all TEFs which

comprise > 10% of a calculated TEQ, including endpoint, species, and study citation. The effect

of TEF rounding on the risk assessment process should be investigated via model sensitivity

analysis.

2. If the TEF is derived from an enzyme induction endpoint, from QSAR studies, or if

multiple TEFs have been calculated for the same congener in different studies, an uncertainty

value should be assigned to the TEF. Perhaps a "sliding scale' of uncertainty could be assigned

to all TEFs comprising > 10% of a calculated TEQ (i.e. zero uncertainty assigned to TEFs that

are derived from embryo toxicity studies using the "target" species, with more uncertainty added

incrementally as quantitative rigor diminishes).

3. TEF values developed using in vivo early life stage endpoints for relevant species can

be directly used to predict a stress response in a risk assessment. However I am skeptical of

using biochemical responses unless they have been closely correlated to a toxic endpoint in a

relevant species.

Section II.

1 . The implications are that no antagonistic or synergistic effects are occurring between a

complex mix of congeners as they compete to bind with the AhR receptor - if synergistic effects

do occur the risk assessment would be too permissive, if antagonistic effects occur it would be

too conservative. If one where to use the total PCB and 2,3,7,8-TCDD no-effect thresholds

presented in the Retrospective Case Study (i.e. 5 ug PCB, 100 ppt TCDD/g bird egg), the

Caspian tern eggs collected from Oneofakind Lake would be close to the no-effect threshold.

However, the calculated egg total TEQ for the same Caspian tern eggs exceeds a reported total
Michael W. Meyer

C-C-119

TEQ avian egg no-effect level (calculated by Giesy et al. 1995 Arch. Env. Cont. 29:309-321) by

a factor of nearly 60. This reflects the author's establishment of a 2,3,7,8-TCDD no effect level

of 7 ppt vs. 100 ppt in the Retrospective Study.

2. Calculating a NOAEL from the slope of the LC50 or EC50 dose-response curve may not

protect the most sensitive individuals in a population. This could be permissive if the risk

assessment targets an endangered or declining species.

3. The existing data for birds and mammals indicates that use of TEFs derived from

chicken or mink studies will provide highly protective, conservative calculated TEQs. Chicken

and mink are nearly an order of magnitude more sensitive to TCDD TEQs than other species

within their respective classes, and wild mink may consume a limited amount of contaminated

fish in their natural diet. Establishment of conservative TEQ standards is desirable from the

perspective of the risk assessor and the resource, but will predictably result in controversy

amongst the regulated community. If a permitting process uses the most conservative

calculated TEQ to establish effluent discharge, and achieving that new discharge goal requires

substantial capital investment by the regulated parties, you can expect litigation and delay in

implementation of the new rule. The cost-benefit of this trade off should be addressed from a

policy perspective.

Section Ill.

2. One will need to assume that assimilation efficiency and detoxification/metabolism

routes are similar when one pools TEFs derived from various dosing (injection, oral gavage,

dietary) experiments. One also then needs to assume that wildlife contaminant exposure in the

natural environment will result in similar assimilation, metabolism, and effects patterns. These

assumptions should be kept in mind when establishing a TEF.

3. Sediment, soil, and biota will likely have differing congener patterns within the same

environmental system due to differential metabolism/degradation of the various PCB/TCDD

congeners present in the parent contaminant. An understanding of these differences may be

necessary to predict risk within the various biotic and abiotic compartments of an ecosystem,
Michael W. Meyer

C-C-120

requiring additional sampling and analysis costs. A biologically based TEQ assay may be the

preferred route to travel (see Section IV #2).

Section IV.

1. To be quite frank, it is difficult to answer this question without simulating the risk

assessment for the various contaminants and species of concern. In most risk assessments

there is a great deal of uncertainty in describing exposure (limited diet studies for few species,

few prey items characterized to congener content, etc.) and effect (species sensitivity, endpoint

characterization, etc.). How that uncertainty compares to that generated by extrapolation of

TEFs between species and endpoints is beyond the capability of my hand calculator.

2. Biologically-based TEQ assays are by far the best conceptual approach and most

economical means of using TCDD TEQs in the regulatory process. The cost associated with

collecting the data required to conduct a calculated TEQ risk assessment may prohibit a

meaningful characterization of exposure in most scenarios. Therefore a bioassay would be cost

effective. Furthermore, the bio based TEQ would theoretically account for

antagonism/synergism between congeners in complex mixtures. Unfortunately, none of the

existing bioassays appears ready to go on line for routine screening in risk assessment

exercises. For instance, though bioassay TEQ values (using rat liver hepatoma cell line H4IIE)

and congener specific calculated TEQs were very similar in an experiment where mink were fed

diets containing Saginaw Bay carp, suggesting the additive assumption to be correct (Fillet at

al. 1996. Env. Science Tech. 30: 283-291), large discrepancies exist between bioassay TEQ

values and congener specific calculated TEQs values in birds (Tillet at al. 1991. Arch. Env. Tox.

Chem. 21:91-101). An understanding of these inter-class differences and receptor binding

mechanisms is necessary before a bioassay can be implemented.

3. Suggested research/site specific data

a. Establish a standard protocol for deriving TEFs (early stage mortality

endpoint) and determine the TEFs for the most relevant congeners for all 3

classes.

b. Establish standard bio-based TEQ assay and conduct research to
Michael W. Meyer

C-C-121

understand mechanisms responsible for different results between classes.

c. Establish protocol for quantifying site-specific

biomagnification/bioaccumulation factors to quantify TEQ exposure including

dietary habit studies, prey base contaminant characterization, magnitude of

trophic level biomagnification, etc.

d. Conduct additional research to provide scientifically defensible TEQ

effect levels for mammals, birds, and fish if current data is insufficient.

Investigations of potential TCDD TEQ interactions with residual DDE in Great

Lakes systems is also desirable.

Prospective Case Study

 2. It has been shown that non-ortho PCB congeners are more readily bioaccumulated and

are more resistant to metabolism when compared to ortho substituted PCB congeners. It

follows that wildlife tissues may contain a larger proportion of dioxin-like PCB congeners/g total

PCB, enriching the toxic potency of the total PCBs measured in their tissues. While not firmly

established, it is also likely that species differ in their ability to assimilate/metabolize the various

PCB and TCDD congeners.

 3. A risk assessment model should be developed which simulates exposure and effect

thresholds under a ranges of values which reflect the uncertainty inherent in the model and its

parameters. This model output should then produce a range of possible TeqTMDL with

associated risk attached (zero risk for the lowest value, "x"risk for the greatest value). Once this

range has been established, a final rule can be developed which is most protective of the

ecological concerns while utilizing the best available technology.

Retrospective Case Study

1. It seems obvious that the variability in BSAFs, as well as thresholds of effect between

vertebrate groups, will result in different sediment clean up goals. In addition, it does not seem

possible to provide a scientifically defensible TCDD TEQ threshold of effect for Caspian terns

and river otters as their sensitivity to these compounds has not been experimentally

established. Indeed, it seems that Caspian terns are insensitive to the embryo toxic effects of
C-C-122

PCBs (hatching success was not depressed despite eggs PCB levels of 19-40 ug total PCB/g

wet weight; Struger and Weseloh, 1985, Colonial Waterbirds 8:142-149). No data is currently

available on the relative sensitivity of otters to TCDD TEQs as compared to mink though rumor

has it such work is underway. I would therefore council the risk manager to go with the

sediment TCDD TEQ value that protects lake trout, a species whose TCDD TEQ for early life

stage mortality is well characterized. I would then request that the responsible party support a

dose-response study for river otters/TCDD-TEQs, or, at a minimum, a study which compares

river otter TCDD sensitivity to that of the mink. I'd exclude the Caspian tern from the risk

assessment because of their insensitivity to the toxic effects of PCBs.
C-C-123

Patrick W. O’Keefe, Ph.D.

Research Scientist

Laboratory of Organic Analytical Chemistry

Wadsworth Center

New York Department of Health

Empire State Plaza

P.O. Box 509

Albany, NY 12201

518-473-3378

Fax: 518-473-2895

E-mail: okeefe@wadsworth.org

Dr. Patrick O’Keefe received his B.S. in chemistry from University College in Dublin, his

M.S. in food science from Cornell University, and a Ph.D. in food science from Oregon

State University. Dr. O’Keefe is an assistant professor at the school of public health and

a research scientist for the Wadsworth Center for the State University of New York in

Albany. Prior to his current work, Dr. O’Keefe was a research fellow for Harvard

University, a food scientist for ITT Continental Baking, and a research scientist for

Battelle Northwest Laboratories. He is a member of the American Chemical Society and

is a consultant to the Science Advisory Board. Dr. O’Keefe has published numerous

articles on PCDDs, PCDFs, and related compounds in contaminated sediments and

biota. 
Patrick W. O’Keefe

C-C-124

(TEqC t

w)pecdd' (TEqC t

w)tcdd(BAF dl )tcdd(fd)tcdd

(BAF d

l )pecdd(fd)pecdd(TEFpecdd)

' 0.021x9.0E06x0.2

2.49E07x0.08x1 '0.019pg/L

USEPA Workshop on the Application of TEFs to Aquatic Life and Wildlife

 January, 20-22, 1998, Chicago, Illinois

Answers to Premeeting Questions

 Patrick W. O’Keefe, Ph.D.

 Wadsworth Center

 New York State Department of Health

 PO Box 509, Albany, NY 12201

III. EXPOSURE PROFILE

1. In addition to uncertainties in the TEFs themselves, there are numerous challenges and uncertainties

associated with the application of TEFs to environmental risk situations. In the prospective study the

consultant choose to calculate permitted concentrations in water (TEqCt

w values) based on the initial

premise that each compound contributed alone to the TCDD toxic equivalence. In the final step it appears

that the mass loadings of each compound were then distributed on the basis of their relative mass

distribution in the effluent. This is a complicated process and since I did not have access to the modeling

program I was only able to carry out the initial calculation for allowable water concentrations for fish as

shown: 

The fd values were determined using DOC , POC and Kow values as described in EPA-820-B-95-005. The

complete set of TEqCt

w values are shown below (pg/L):

Fish Avian Wildlife

1,2,3,7,8 PeCDD 0.019 0.021 0.0029

1,2,3,4,7,8 HxCDD 0.115 0.106 0.018

2,3,7,8 TCDF 1.2 1.29 0..183
Patrick W. O’Keefe

C-C-125

1,2,3,7,8 PeCDF 1.9 2.05 0.289

2,3,4,7,8 HxCDF 0.026 0.028 0.004

1,2,3,4,7,8 HxCDF 2.28 2.45 0.348

It is apparent that with the DOC and POC values quoted in the prospective study there would be

considerable errors if the BAFd

l

 values from the background literature were applied without converting

them to BAFt

l

 values using fd values. For instance the TEqCt

w values for PeCDD would be increased by

nearly a factor of 3.

When the WASP4 model is applied to the calculation of maximum allowable loads (MALi,j) for

each congener, several other parameters are required in addition to maximum allowable water

concentrations (MACt

w). These are sediment related parameters (settling flux, respusension flux, log KOC

etc.) and two important physico-chemical parameters, the Henry’s Law constant for vapor/water

partitioning and the photolysis rate constant. In the Lake Ontario TCDD study it was determined under

steady-state conditions, that for a given annual load to the lake from the Niagra River, 6% would be

transported out of the lake via the St. Lawrence River, 25% would be incorporated into the bottom

sediments, 31% would volatilize and 38% would undergo photolysis. The Henry’s Law constants have

been determined by a number of investigators and are known with reasonable accuracy for several PCBs

and PCDDs/PCDFs, although only a very small number of PCB values are shown in the physico-chemical

parameters table (Table 1). Since PCBs have absorption maxima at wavelengths below the lowest sunlight

wavelength (less than 300 nm ) photolysis may not be as important a removal process for these compounds

as it is for PCDDs/PCDFs which absorb light in the UV-B region (300-340 nm). Based on studies carried

out in the laboratory using 50/50 acetonitrile:water solutions a value of 0.002 was selected for the quantum

yield (φ) for TCDD in the Lake Ontario study. Our studies confirmed this value for both pure water and

acetonitrile/water solutions photolyzed at 300 nm in the laboratory. However for reasons that are not

completely clear quantum yields for PCDDs and PCDFs are an order of magnitude higher in sunlight than

at 300 nm, a finding corroborated by work carried out in the laboratories of Derek Muir and Barrie

Webster. Furthermore Dung and O’Keefe (Environ. Sci. Technol. 28: 549-554, 1994) and Friesen et al

(Environ. Sci. Technol. 24: 1739-1744, 1990) have shown that dissolved organics potentiate the

photodegradation of PCDDs/PCDFs. Taken together these studies show that photodegradation should be

given serious consideration in any studies modeling the transport and fate of PCDDs/PCDFs in the aquatic
Patrick W. O’Keefe

C-C-126

environment. A major uncertainty at the present time is the lack of knowledge on the extent to which

PCDDs/PCDFs photodegrade when they are bound to suspended sediment particles. 

2. In mammalian and avian species the extent of absorption and the tissue distribution of toxic compounds

do not appear to differ significantly between i.p. and oral routes of administration. However if toxicity to

fish eggs is used as an endpoint for risk characterization and the water quality standard is based on tissue

residue measurements in whole fish, then it must be noted that concentrations in fish eggs are 2 to 3 times

lower than maternal tissue concentrations.

3. Since measurements of individual congeners are required for the TEF approach analytical methods must

be much more rigorous than those used in the determination of total compound type concentrations. In the

case of PCBs, the non-ortho (coplanar) congeners are the most toxic but they may only constitute 1% of the

total PCBs. However PCBs are generally analyzed using GC/EC instrumentation, a relatively nonspecific

analytical technique. Under these circumstances the trace signals from the coplanar congeners may be

obscured by coeluting diortho congeners. Therefore relatively complex cleanup methodologies based on

carbon chromatography must be used to separate the coplanars from the noncoplanars prior to GC/EC

analysis. In PCDD/PCDF analysis individual congener identity is accomplished more readily since analyses

are carried out by GC/MS and isomer identity within a given chlorine group is simplified by the fact that

most biota accumulate predominantly 2,3,7,8-substituted isomers. Rigorous cleanup methodologies are still

required since PCBs are generally present in environmental samples at higher concentrations than

PCDDs/PCDFs (ng/g - μg/g vs. pg/g) and certain PCB congeners can interfere with the analysis of

PCDDs/PCDFs by low resolution GC/MS.

IV. RISK CHARACTERIZATION

1. As pointed out above in answer to Question 1 on Exposure Profiles, there are some major uncertainties

associated with some of the physico-chemical parameters used for modeling the fate and transport of PCBs

and PCDDs/PCDFs. On the other hand there are uncertainties associated with the TEFs determined for

avian and mammalian species. In the case of both avian and mammalian species an uncertainty factor of 10

was used to adjust NOAEL levels from subchronic exposure studies conducted in the laboratory to chronic

exposures which biota would experience in the field. Is this appropriate ? Another uncertainty relates to the

nature of the diets consumed by predatory avian and mammalian species. If a diet contains a high

proportion of fish-eating biota then food chain biomagnification can be significant and the Great Lakes
Patrick W. O’Keefe

C-C-127

Water Quality Criteria may severely overestimate MACt

w values. On the other hand MACtw values can be

underestimated if plant-eating terrestrial biota are consumed.

2. If the TEFs are indeed additive then biologically-based TEQ assays may not provide any additional

information from a risk assessment viewpoint. However there are several literature citations in the Interim

1993 Report on TCDD Risks in Aquatic Life and Wildlife (p 4-4) suggesting that an additive model may

not always be appropriate. Recent studies conducted at the Wadsworth Center, NYSDOH have shown that

PCBs 126 and 169 inhibit TCDD-induced estradiol (E2) metabolism by hydroxylation at both the 2

(CYP1A1 activity) and 4 (CYP1B1 activity) positions in certain human cancer cell lines (Shaokun Pang,

Ph.D. Thesis, 1997). In the absence of TCDD the two PCB congeners induced E2 metabolism. Under these

circumstances a biologically-based assay would provide more definitive information on the risks associated

with a defined mixture of compounds. On the other hand a nonadditive model would make it extremely

difficult to regulate the compounds on an individual basis. Perhaps the most useful approach would be use a

bioassay to adjust TEF values. As pointed out in the Interim Report the bioassay would also need to be

calibrated against a biological endpoint of environmental significance. 

3. Since the total PCB concentrations in gull eggs from Roundtail Lake approach 3 μg/g a major concern

would relate to the coplanar and mono-ortho PCB concentrations in the gull eggs and also in sensitive

mammals such as mink and otter. In the retrospective study a total PCB concentration of 5.7 μg/g in

Caspian tern eggs results in a TEQ value of 400 pg/g, which is x10 higher than the no-effect threshold. In

conjunction with these monitoring studies the state should determine the status of the river otter population

in the area. Results from the population survey may indicate that the more stringent WQS for river otters

should be adopted in the risk assessment. The next research priority would be the determination of BAFt

l

values for those Ah active PCBs which are present in high enough concentration to be measured in water

samples. Since the fish currently in Roundtail Lake have no detectable concentrations of PCDDs or PCDFs

additional monitoring of biota or sediments for these compounds is probably not warranted. However it

would be appropriate to determine PCDD/PCDF residue levels in biota from other lakes where there are

discharges of known magnitude from pulp and paper mills.

 In the retrospective study, the fact that the TEQ concentrations in the Caspian tern eggs and the

otter livers exceed the NOAELs for these species is a major concern. Three research questions arise from

this concern: (1) Could the low success rate in Caspian terns be explained by interactions between the DDE

residues in the tern eggs and the PCBs/dioxins ? Field observations coupled with residue measurements

might useful information, (2) The manager of the area should determine if the anecdotal accounts of low
Patrick W. O’Keefe

C-C-128

(BAF d

l )i

(BAF d

l )r

' (BSAF)i

(KOW)i

(BSAF)r(KOW)r

numbers of mink and otter are valid, and (3) the proportions of fish and fish-eating biota in the diet of mink

and otter should be assessed. Consumption of fish-eating biota could have a considerable impact on the

biomagnification of PCBs and dioxins by these mammals. 

Additional Questions on the Prospective Study

1.Since there is no information on either dissolved or total aqueous concentrations of the chemicals, field

derived BAFs cannot be derived. However field-derived BSAFs can be determined for PCB 77 and PCB

126 lake trout and sediment data and it is possible to use this information to obtain a ratio of the BAFd

l

using the following equation:

This value can then be compared to the ratio determined from the GLWQG document. With i = PCB 126

and r = PCB 77 the ratio was determined to be 5.7 compared to a ratio of 37.5 determined from Table 1.

Therefore we might expect some errors if the GLWQG values are used.

2. The bald eagle data are most suitable for this type of comparison since the data can also be analyzed by

the BMF approach described on pages 33 through 44 of the USFWS Critique of the GLWQG Document.

Basically the established water standard for bald eagles needs to be divided by 21, the BMF for forage fish

to bird eggs. This will adjust all the dietary components for biomagnification from forage fish, using the

equation on page 3-13 of the GLWQI Criteria Document for TCDD (the BMFs for forage fish to piscivorus

fish and piscivorus fish to bird eggs have already been taken into consideration by using two BAFs and the

factor of 30, respectively). The BMF-adjusted WQS then becomes 0.0013 pg/L instead of 0.028 pg/L.

Using the chicken embryo NOAEL of 100 pg/g for bald eagles together with a safety factor of 10 the same

WQS can be determined by the USFWS approach as shown below:
Patrick W. O’Keefe

C-C-129

NOAEL

totalBMF' 100x103

0.736x210.184x100.056x659'TargetDietaryConcentration

TargetDietaryConcentration

BAF ' 1840pg/kgforagefish172,100kgforagefish/L

'0.0011pg/L

 

It is more difficult to determine a BMF-adjusted WQS for mink since the diet-to-mink BMF is

presented on a lipid basis in Table 1. If we assume that the lipid concentration in the mink is 4% compared

to a lipid forage fish concentration of 8% then the wet weight BMF for TCDD would be 5.5 and the WQS

value should be divided by this number to give a BMF-adjusted WQS of 0.0005 pg/L rather than the value

of 0.00292 pg/L. However if the USFWS approach is used assuming a NOAEL of 60 pg/g, as per the

retrospective study, and a diet composed exclusively of forage fish then the WQS would be 0.0063 using

the x10 safety factor. The discrepancy between the two approaches is partly related to the fact that the EPA

method uses a daily toxic dose (TD) whereas the USFWS uses the NOAEL body burden. The NOAEL/TD

ratio is 7 for the bald eagle compared to 60 for the mink.

 

Additional Questions Relating to the Retrospective Study:

1. The cleanup goals would not be the same for each vertebrate group since the order of sensitivity of the

groups is mammals>birds>fish. As shown in the retrospective study document TEQs can be directly linked

to sediment concentrations of the chemicals via BSAFs. Therefore the most restrictive sediment cleanup

standard would be based on the otter TEQs and the extent of cleanup required would depend on the extent

to which the TEQs exceed the NOAELs.

2. This question can be addressed by considering the equation for calculating TEQs in birds and mammals

on page 8 of the retrospective study. In addition to the organic carbon-normalized congener concentration

in the sediment (Coc) and the appropriate TEF, this equation involves the use of two partition coefficients, a

BSAF and a BMF. When the shiner BSAFs were determined from the field data and the BMFs from Table

1 were used TEQs were obtained which can be compared with the TEQs listed in Tables 2 and 3:
Patrick W. O’Keefe

C-C-130

Caspian Tern Eggs

TEQ Calc. TEQ Table 2

PCB 77 29 54

PCB 126 232 275

2,3,7,8-TCDD 2.3 4.5

2,3,4,7,8-TCDF 4.1 9.58

Otters

TEQ Calc. TEQ Table 3 

PCB 105 2.5 2.6

PCB 118 4.8 4.8

PCB 126 98 99.8

2,3,4,7,8-PeCDF 25.96 25.92

It is apparent that there is considerable agreement between the calculated TEQs and the TEQs

derived from tissue concentrations. However these data were obtained using field-derived spottail shiner

BSAFs. As shown in the table below the agreement would have been much lower if the EPA lake trout

BSAFs had been used:

BSAF shiners BSAF lake trout BSAF EPA

Oneofakind Lake Oneofakind Lake Table 1 Questions

PCB 77 0.91 1.82 0.29

PCB 105 10 14 4.49

PCB 118 7.3 23.5 1.72

PCB 126 5.76 5.1 3.21

2,3,7,8-TCDF 0.006 0.069 0.047

2,3,7,8-TCDD 0.14 0.35 0.059

2,3,4,7,8-PeCDF 0.017 0.035 0.095

Consequently if generic BSAFs cannot be used, field-derived BSAFs must be determined for

individual congeners using state-of-the art analytical methods. If this were the case there would be no
Patrick W. O’Keefe

C-C-131

savings in analytical costs by analyzing sediments for total PCBs and then determining individual COC

values by a ratio calculation.
C-C-132

Richard E. Peterson, Ph.D.

Professor

425 North Charter Street

School of Pharmacy

University of Wisconsin

Madison, WI 53706

608-263-5453

Fax: 608-265-3316

Dr. Peterson received his B.S. degree from the University of Wisconsin, and his

Ph.D. in pharmacology from Marquette University Medical School (now The

Medical College of Wisconsin). He is currently a professor of toxicology and

pharmacology at the University of Wisconsin School of Pharmacy, as well as being

a toxicology consultant on the Science Advisory Board of EPA. He was a recipient

of a Research Career Development Award from the National Institutes of Health

and sits on the editorial board of Toxicology and Applied Pharmacology. His fields

of specialization include toxicology of halogenated aromatic and halogenated

aliphatic hydrocarbons in fish, avian, and mammalian species; the neurotoxicology

of halogenated aromatic hydrocarbons; and bioaccumulation, tissue distribution,

biotransformation, and elimination of halogenated aromatic hydrocarbons. He is

frequently invited to participate on review committees and projects concerned with

halogenated aromatic hydrocarbon toxicology issues by NIH, EPA, NOAA, and

other major research organizations. Dr. Peterson was the chair of the wildlife

workgroup at the World Health Organization’s “Meeting on the Derivation of Toxic

Equivalency Factors for PCBs, PCDDs, PCDFs, and Other Dioxin-like Compounds

for Humans and Wildlife” held on June 15-18, 1997. Dr. Peterson has more than

150 publications in his fields of study.
Richard E. Peterson

C-C-133

Specific Questions/Issues

I. STRESS-RESPONSE PROFILE RELATIVE TO THE DERIVATION OF SPECIFIC TEF

VALUES

1. Inclusion of the WHO draft report (July 30, 1997) on derivation of toxic equivalency factors

(TEFs) for polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), and dibenzofurans

(PCDFs) for humans and wildlife is useful in evaluating uncertainties in the fish-, bird-, and

mammal-specific TEFs as they relate to the retrospective and prospective case studies. 

In the case studies the fish receptors of concern are lake trout and bull trout. By consulting

Table 3 of the WHO report one finds that fish-specific TEFs for PCBs, PCDDs, and PCDFs were

based on early life stage mortality in rainbow trout. This is useful information because the fish

receptors of concern are species of trout. So there is a relatively low level of uncertainty in

extrapolating the fish-specific TEFs to lake trout and bull trout because they are a closely related

fish species to rainbow trout. In addition, the endpoint upon which the fish-specific TEFs were

based, early life stage mortality, is relevant to recruitment which is the assessment endpoint

proposed for fish in the case studies. Thus, in assessing the risk to recruitment of lake trout and

bull trout caused by exposure to PCBs, PCDDs, and PCDFs there is a relatively low level of

uncertainty in using the internationally agreed upon TEFs for fish. 

In the retrospective and prospective risk assessments the wild bird receptors of concern are

the Caspian tern and bald eagle, respectively, and the assessment endpoint is also recruitment as it

was for fish. In consulting Table 4 of the WHO report for bird-specific TEFs one observes that the

TEFs for PCDDs and PCDFs are based on induction of ethoxyresorufin-O-deethylase (EROD)

activity in the chicken embryo. This is important, because there is greater uncertainty in these

EROD induction-based TEFs for PCDDs and PCDFs, with respect to the assessment endpoint of

recruitment, than there would be if they had been based on embryo mortality. In this context, it is

useful to find in the WHO report that the TEFs for essentially all of the environmentally relevant

PCBs were based on the LD50 for embryo mortality in the chicken. Thus, the bird-specific TEFs

for PCDDs and PCDFs are more uncertain than those for the PCBs when assessing the risk to

recruitment of Caspian terns and bald eagles caused by the presence of complex mixtures of

PCDDs, PCDFs, and PCBs in the eggs.. In addition, it is important to note that all of these TEFs

whether they were based on EROD induction or embryo mortality tended to be based on results

obtained in chicken which is the most sensitive of all bird species to aryl hydrocarbon receptor

(AhR) agonist toxicity. Yet the wild bird receptors of concern in the case studies, the Caspian tern
Richard E. Peterson

C-C-134

and bald eagle, are not closely related to the chicken. This may be significant because it is

uncertain to what extent TEFs determined in a highly sensitive species like the chicken can be

extrapolated to more TCDD insensitive and distantly related bird species like the Caspian tern and

bald eagle. It is concluded, in assessing the risk to recruitment in lake trout and bull trout versus

Caspian terns and bald eagles, due to exposure of the fish or bird embryo to TCDD and related

compounds, that there is more uncertainty in using the internationally agreed upon bird-specific

TEFs than the fish-specific TEFs. Another point is that of the two case studies, there is more

uncertainty in estimating TCDD equivalents (TEQs) from bird-specific TEFs in the prospective

study. This is because the mill effluent of concern in this case study is predicted to contain only

PCDDs and PCDFs and bird-specific TEFs for these classes of AhR agonists are the most uncertain

for birds.

In the two case studies the mammal receptor of concern is the river otter and the assessment

endpoint, recruitment, is the same for both case studies. In consulting Table 2 of the WHO report

no information is provided on which species were used in the derivation of the mammal-specific

TEFs, however, from the text of the report it is clearly stated that the majority of these TEFs were

based on studies in laboratory rodent species. Furthermore, it is stated in the report that relative

potencies of the PCBs, PCDDs, and PCDFs toward mink reproductive toxicity are not different

from those of the rodent models from which most of the data to derive the TEFs were obtained. 

This interpretation is useful, because in the case studies the mammal-specific TEFs will be used to

determine TEQs in river otter liver and there is uncertainty in the extent to which TEFs can be

extrapolated across species as well as across endpoints. Another way in which the WHO report

was useful is that it demonstrates that the most rigorously determined TEFs among the three

vertebrate classes are those for mammals. In fish and birds a TEF might be based on one study

whereas in mammals the results of several studies using different routes and durations of exposure

are available for consideration in the derivation of TEFs. This leads to the conclusion that the

mammal-specific TEFs probably have less uncertainty than those for fish and birds. 

2. Within and between the three vertebrate classes (fish, birds, and mammals) there is a range of

uncertainty in the TEFs determined for individual PCDD, PCDF, and PCB congeners. However, if

TEFs are acknowledged to be order of magnitude estimates of actual relative potencies for AhRmediated responses in a particular species it might not be necessary to apply an additional

uncertainty factor to the TEQs that are generated by the TEF approach in order to acknowledge the

uncertainty that exists in these estimates. In general the mammal-specific TEFs would appear to
Richard E. Peterson

C-C-135

have the least uncertainty because they are derived from a larger number of studies than is the case

for fish and birds. In the latter two vertebrate classes, it would seem that the degree of uncertainty

associated with the TEFs for PCBs is probably similar because the studies that were relied upon to

derive the TEFs for PCBs used an egg injection route of exposure and an LD50 for embryo

mortality as the basis for deriving the TEFs. However, for PCDDs and PCDFs there is a significant

difference between the endpoint used to derive the TEFs for fish and birds. In the case of fish the

TEFs were based on embryo mortality whereas for birds they were based on EROD induction

which is more uncertain because it is an adaptive rather than a toxic response.. Thus, the degree of

uncertainty in the TEFs varies across vertebrate classes and would appear to be less in mammals

than fish and birds. The uncertainty in the TEF for a particular PCDD, PCDF, or PCB congener is

influenced by a number of factors including whether it was based on an in vivo or in vitro study,

species, route and duration of exposure, endpoint assessed, and reproducibility of the results in

similarly designed studies.

3. The measures of effect in the case studies pertain to reproductive success as measured by effects

on egg production and viability and/or larval and offspring survival. Until evidence is presented to

the contrary for each vertebrate class (fish, birds, and mammals) it would seem to be more

uncertain to extrapolate TEFs based on cytochrome P4501A1 induction (determined in vivo or in

vitro) to these measures of effect, than to extrapolate from TEFs based on clearly adverse

developmental and reproductive toxicity endpoints such as early life stage mortality in fish, embryo

mortality in birds, or a reduction in litter size in mammals. 

If one is going to rely on EROD induction based TEFs for PCDDs and PCDFs in birds,

which is currently the situation for bird-specific TEFs, then it would be prudent to show that

relative potencies (REPs) for a few of the most environmentally relevant PCDD and PCDF

congeners following injection of graded concentrations into bird eggs give rise to REPs for EROD

induction and embryo mortality that are within an order of magnitude of one another. Also to the

extent such information is available for PCBs in bird embryos it would be helpful to include the

findings in the WHO report. If REPs for these two endpoints are similar for PCBs in birds it

would suggest this will probably also be the case for the PCDDs and PCDFs.

II. STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ

APPROACH
Richard E. Peterson

C-C-136

1. Assuming no additivity of the PCDD, PCDF, and PCB congeners that are AhR agonists would

underestimate the risk which exposure to this mixture of chemicals poses to recruitment of the fish

and wildlife receptors of concern in the two case studies. Furthermore, if an alternative method of

ecological risk assessment, based on total PCBs relative to an Aroclor standard or TCDD alone,

were applied to the prospective and retrospective scenarios they would both probably

underestimate the risk to the fish, bird and mammal receptors of concern. 

In the prospective scenario, mill effluent will contain, in addition to TCDD, three PCDDs

and four PCDFs which are AhR agonists, but no PCBs. Since the mill is not a source of PCB

contamination, the measurement of total PCBs would be inappropriate. Relying on the

concentration of TCDD alone in fish and wildlife tissues as the exposure metric has the problem of

neglecting the potential contribution to AhR-mediated toxicity of the other PCDD and PCDF cocontaminants in the effluent which have the potential to bioaccumulate in the fish and wildlife

receptors of concern. For example, if TCDF which is present in the mill effluent at a 20 times

higher concentration than TCDD was found to bioaccumulate in bald eagle eggs to a higher

concentration than TCDD, then TCDF would contribute more TEQs to the eggs than TCDD (birdspecific TEF for TCDF = 1.0). However, this potentially greater contribution to egg TEQs by

TCDF would be missed if the exposure analysis were based solely on TCDD. Thus, the risk

assessment conclusions reached from relying on total PCBs or TCDD alone would underestimate

the actual ecological risk posed by the discharge from this particular mill and would result in higher

concentrations of PCDDs and PCDFs being permitted in the effluent than would be justified from

an ecological risk perspective.

For the retrospective study, involving the PCB spill it would not be appropriate to assess

exposure of fish and wildlife to these halogenated aromatic hydrocarbons by measuring TCDD

alone because it was not a contaminant of the used hydraulic fluid. The major source of PCBs in

the used fluid was Aroclor 1248. However, it would also be inappropriate to monitor the impact of

this spill on fish and wildlife by measuring total PCBs in the tissues of such animals. This is

because Aroclor 1248 is contaminated with PCDFs which are not detected by measuring total

PCBs. Also the used hydraulic fluid might be actually enriched in PCDFs when compared to

Aroclor 1248 if it was used at high temperatures that could result in PCDF formation. Thus, the

main point is that certain PCDFs that would be expected to be present in significant concentrations

in the used hydraulic fluid that was spilled into the Yuckymuck River would not be detected by

measuring total PCBs in fish and wildlife inhabiting the river. 
Richard E. Peterson

C-C-137

Another point is that weathering of the PCBs that were spilled into the river would result in

PCB concentration profiles in the fish and wildlife receptors of concern (lake trout, Caspian terns,

and otter) that are different from both that of the PCBs spilled and an Aroclor 1248 standard that

might be used to quantify total PCBs. In this regard it is possible that lake trout and Caspian tern

eggs and otter liver will have greater concentrations of PCB 126 than are present in Aroclor 1248. 

This enrichment of these tissues in PCB 126, which is a major contributor to TEQs in this

particular case study, might be missed if exposure to AhR agonists is based on total PCBs. Thus,

if TCDD alone or total PCBs were used to assess exposure in the retrospective case study the

results obtained would underestimate, retrospectively, the risk to recruitment of fish and wildlife

caused by the spill.

2. There is less variability in the LC50 and EC50 on a dose response curve than there is in the LC1

or EC1 which are closer to the NOAEL. Therefore, REPs based on the LC50 and EC50 should be

more accurate than those based on a certain percent response at the lower end of the dose response

curve near the NOAEL in deriving TEFs. In my judgement TEFs derived in this manner can be

used in risk assessments where a NOAEL is being employed. The only exception to this

generalization is if TEFs are based on REPs for EROD induction in cell culture systems where full

dose response curves are unable to be generated for certain congeners. In those cases it has been

recommended that REPs for this particular response be based on ED10 values. 

3. The assumption in deriving vertebrate class-specific TEFs (WHO, 1997) was that they could be

used to determine TEQs in fish, bird, and mammalian species, respectively, with less uncertainty

than if a single set of TEFs was used. However, significant uncertainty still remains in

extrapolating these new TEFs across species. This is reflected in the new TEFs still being referred

to as “order of magnitude estimates”. This certainly applies in directly extrapolating the fishspecific TEFs (determined in rainbow trout) to lake trout and bull trout, the bird-specific TEFs

(based on studies in chickens) to Caspian terns and bald eagles, and the mammal-specific TEFs

(determined in laboratory rodents) to the river otter. 

Those TEFs that are the most uncertain are the ones derived solely from either QSAR or

AhR binding affinity studies followed by TEFs that are based on CYP1A1 induction in vitro and in

vivo. Bird-specific TEFs are the most problematic in this latter regard because all PCDD and

PCDF TEFs for birds are based solely on EROD induction in the chicken embryo. Thus, for the
Richard E. Peterson

C-C-138

Caspian tern and bald eagle there is more uncertainty in directly extrapolating the bird-specific

TEFs for PCDDs and PCDFs. 

Nevertheless there is such a paucity of studies, particularly in fish and birds, on the

magnitude of species differences in REPs for the same endpoint that it seems prudent to assume for

each vertebrate class that the TEFs can be extrapolated across species with a one order of

magnitude uncertainty until there is evidence to the contrary. In support of this assumption sets of

TEFs for PCDDs, PCDFs, and PCBs, determined by various authors/agencies, were recently used

to determine TEQ concentrations in lake trout eggs from the Great Lakes. It was found that the

TEQs so determined varied by less than one order of magnitude in spite of the different sets of

TEFs that were used (Cook et al., 1997). In light of these findings, and recognizing that TEFs are

one order of magnitude estimates, it would appear that class-specific TEFs can be directly

extrapolated to the fish and wildlife receptors of concern in the case studies.

The REP determined for PCB126 in rainbow trout eggs, based on the endpoint of early life

stage mortality, the Peterson laboratory has shown to be accurate in predicting the egg dose of PCB

126 that caused early life stage mortality in lake trout eggs. Thus, between these two closely

related fish species, rainbow trout and lake trout, the REPs for PCB 126 were quite similar. 

Whether this will be the case for fish species that are not as closely related is not known, but is an

important area for future research. 

III. EXPOSURE PROFILE

1. The TEF approach, in and of itself, does not present new uncertainties or modify old

uncertainties associated with modeling the exposure of AhR agonists, because it is not applied until

after the concentration of an AhR agonist has been estimated in a particular tissue for the receptors

of concern (i.e., fish egg, bird egg, or mammal liver).

2. The TEF of a particular congener is based on its potency for producing a particular response,

relative to that of TCDD, when both compounds are administered by the same route. 

For fish and birds, the majority of TEFs were based on the egg injection route of exposure. This is

significant because the concentrations of PCDDs, PCDFs, and PCBs in eggs when multiplied by

such TEFs should give rise to a TEqC that has a greater level of certainty associated with it than if

a different route of exposure had been used. Also in lake trout it has been shown that the potency

of TCDD in causing early life stage mortality is essentially identical irrespective of whether TCDD
Richard E. Peterson

C-C-139

is transferred naturally from the female to the oocytes prior to spawning, is directly injected into the

egg, or is taken up by the egg following waterborne exposure to TCDD. 

3. No comment.

IV. RISK CHARACTERIZATION

1. Uncertainties associated with the TEFs are not more problematic than the other sources of

uncertainty in the ecologic risk assessment nor do they limit the means of performing the

assessment. In my judgement uncertainties associated with estimating exposure to the various 

PCDD, PCDF, and PCB congeners, retrospectively and prospectively, are greater than those

associated with the TEFs.

2. The H4IIE bioassay, has the advantage over the TEF-based approach of assessing interactive

effects of AhR agonists. The endpoint of such a bioassay is that a “net” AhR-mediated response in

cell culture, such as induction of cytochrome P4501A1 activity, is determined. TEQ concentrations

are then estimated by comparison to a TCDD standard curve after appropriate corrections for

dilution of the tissue extract are made. Another strength of the TEQ bioassay is that it is relatively

inexpensive when compared to congener-specific GC/MS, and can be used, therefore, to screen a

large number of samples for high concentrations of TEQs in a more cost effective manner. The

weakness of the method is that it will detect other AhR agonists that are not PCDDs, PCDFs, and

PCBs, such as polyaromatic hydrocarbons and several other classes of compounds, and can lead to

false positives. 

The two approaches could be integrated if the H4IIE bioassay, the recently developed

CALUX bioassay, or an equivalent, validated, TEQ bioassay were used to screen large numbers of

environmental samples for TEQs. Congener-specific GC/MS which is more cost prohibitive could

then be reserved for confirming PCDD, PCDF, and PCB congener related AhR agonist activity in

only the most highly contaminated samples and for confirming reduced AhR agonist activity in

designated “cleaned up” media such as lake or river sediments in the case studies.

3. List of specific studies that would reduce uncertainty in the case study assessments (ranked from

highest to lowest priority)
Richard E. Peterson

C-C-140

1. There is a need for determining a NOAEL and LOAEL for effects of TCDD in reptiles

(snakes and turtles) and amphibians as well as TEFs that can be used to determine TEQs

for these species. 

2. There is a need for a laboratory-conducted, dose response, developmental and

reproductive toxicity study in mink exposed in utero and via lactation to TCDD or PCB

126 alone. Such a study does not exist causing uncertainty in the NOAEL and LOAEL

used for TCDD in piscivorous mammals. 

3. There is a need to determine the NOAEL and LOAEL for TCDD and PCB126 in bull

trout. The bull trout is related to the lake trout, the most sensitive fish species to TCDDinduced early life stage mortality. Given its threatened status, it might be significantly

more sensitive than lake trout to TCDD-induced early life stage mortality. If so,

determination of the NOAEL and LOAEL for bull trout might change the conclusion of an

ecological risk assessment which otherwise would have relied on the higher NOAEL and

LOAEL for TCDD in lake trout eggs.

4. For fish and birds there is a need to conduct cross-species comparisons of REPs based on a

population relevant endpoint such as embryo mortality. This should be done for those

PCDD, PCDF, and PCB congeners that are generally considered to be the major

contributors to the TEQ concentrations in fish and bird eggs in North America. The

question to be addressed is: for each individual congener tested, in fish and bird species of

widely differing sensitivity to TCDD-induced embryo mortality, will the REPs vary by

more than one order of magnitude? 

5. There is a need to determine, for a wide variety of environmentally relevant egg or body

burden mixtures of AhR agonists in fish and wildlife, if in vivo exposure (fish and birds)

and in utero and lactational exposure (mammals) causes population relevant signs of

toxicity (i.e., developmental and/or reproductive) by an additive interaction.

6. TCDD embryotoxicity studies need to be conducted in long-lived aquatic species that live

in close contact with contaminated lake and river sediments such as snapping turtles and

sturgeon. 

Additional Questions Specific to the Prospective Case Study

RELATIVE TO THE EXPOSURE PROFILE:
Richard E. Peterson

C-C-141

1. No comment.

2. No comment.

RELATIVE TO THE RISK CHARACTERIZATION:

3. A source of uncertainty in applying TEFs across species of the same vertebrate class is not

knowing to what extent TEFs vary between species. Until the scientific literature clearly

demonstrates (within the same vertebrate class for those PCDD, PCDF, and PCB congeners that are

generally considered to be the major contributors to TEQs) that the TEFs determined for one

species are consistently more than one order of magnitude different from TEFs determined for the

same endpoint in a different species - it is recommended that an uncertainty factor not be applied

to the TEFs. 

Additional Questions Relative to the Retrospective Case Study

RELATIVE TO THE RISK CHARACTERIZATION:

1. The TCDD equivalents sediment clean up goal would be determined by which wildlife receptor

of concern would have its recruitment adversely affected at the lowest concentration of TCDD

equivalents in eggs (lake trout or Caspian tern) or liver (otter). This sediment clean up goal,

because it is the most restrictive, would also be protective of recruitment in the other two species. 

If the vertebrate group with the most certainty is not the group with the most restrictive

sediment clean up goal it might still play a useful role in directing the sediment clean up. That is,

the sediment clean up goal for the “less sensitive but more certain group” could represent an “upper

bound” for clean up whereas the sediment clean up goal for the “more sensitive but less certain

group” would represent the “lower bound” for clean up. By bracketing the sediment clean up goal

in this way would set limits on what is acceptable. 

2. No comment. 
C-C-142

Mark R. Servos, Ph.D.

Aquatic Ecosystem Protection Branch

National Water Research Institute

Canada Center for Inland Waters (CCIW)

867 Lakeshore Road

P.O. Box 5050

Burlington, Ontario, Canada L7R 4A6

905-336-4778

Fax: 905-336-4420

E-mail: mark.servos@cciw.ca

Dr. Mark Servos received a B.S. in fisheries biology, an M.S. in aquatic science

from the University of Guelph, and a Ph.D. in philosophy from the University of

Manitoba. Dr. Servos is the project chief of the aquatic ecosystems protection

branch of the National Water Research Institute of Environment Canada. He leads

a team of scientists in investigations on the fate, exposure, bioavailability, and

effects of priority contaminants in effluents and aquatic environments. Previously,

Dr. Servos was a research scientist for the Great Lakes Laboratory for Fisheries

and Aquatic Sciences where his focus of research was on the environmental

chemistry and biological responses of fish to organic contaminants in effluents; the

isolation and identification of toxic components of effluents, pesticides, and

environmental samples. Dr. Servos is currently on the board of directors of the

Society of Environmental Toxicology and Chemistry. He has published many

relevant articles such as Confirmation of chloro-nitro-trifluoromethyl-substituted

dibenzo-p-dioxins in lampricide formations of 3-trifluoromethyl-4-nitrophenol (TFM):

Assessment to induce P450A1 activity; Evaluation of temporal and age related

trends of chemically biologically generated 2,3,7,8-tetrachlorodibenzo-p-dioxin

equivalents in Lake Ontario lake trout; and Evidence for a reduction of 2,3,7,8-

TCDD toxic equivalent concentration in white sucker (Catastomus commersoni)

exposed to bleached kraft mill effluent, following process and treatment

involvements. 
Mark R. Servos

C-C-143

TEF Workshop: Responses to questions

Stress response profile 

1. There is very little gained by using the exact values for TEFs. The values are derived from

information for which there is a lot of variability with the experimental data, approaches, species,

etc. Using the actual values would in many ways be misleading because it gives more credibility

to the number than is justified.

2. No. There is certainly more data available for some chemicals. There should be more credibility

give to studies that use whole organism responses as an endpoint or that have been validated in

field studies. Only a few congeners generally contribute to the REQ and these are the ones for

which the uncertainty is most critical. Emphasis should be on estimating the uncertainty of these.

3. The farther you move from a whole organism response the less faith we can have on the

predictive ability of the measure. The whole organism responses integrate the many complex

responses in the fish. Many factors can alter, inhibit or modify biochemical responses at the cellular

level dramatically alter the interpretation of the relative toxicity. Early life stage mortality is an

endpoint which we can apply with some certainty. However, there are numerous mechanism by

which these chemicals can interact with organisms and cause adverse effects. The early life stage

mortality is a very well studied and sensitive endpoint for fish. In birds most of the studies are at

lower levels of organization. Of particular interest in birds would be the validation of the TCDF

TEF of <1. 

Stress-response profile relative to the application of the TEQ Approach

1. A single chemical approach would seriously underestimate the potential Ah-receptor mediated

toxicity. The TCDD alone would not be considered a major problem at any of the trophic levels in

the northern lake scenario. Looking at t-PCBs alone would also be very misleading as the t-PCBs

are less than would be expected to cause any responses in the trout eggs or otter liver. In contrast

the t-PCBs are in the range that would raise concern in the terns; 5.657 compared to 5 (1-20) ug/g

no effect threshold. If we assume additivity, the conclusions are very different. The total-TEQs in

trout eggs is below the threshold for effects (30) but approaching the range of reported values. The
Mark R. Servos

C-C-144

total-TEQs in the terns is 4 times higher than the threshold values. This driven by the high levels of

PCB 126, 77 and 81 with a relatively small amount of 2,3,4,7,8-PCDF. Mink liver also have high

values (although less than terns) but the total-TEQs are driven by PCB 126 and 2,3,4,7,8-PCDF. By

assuming additivity we have ignored potential antagonistic and synergistic effects which could alter

the expression of toxicity.

2. The threshold values may differ from the EC50s leading to a misinterpretation of the relative

toxicity of congeners. The assumption that the dose-response slope are parallel is often valid and is

testable although it may differ according to species and response measured. Caution should be

used when making conclusion based on this type of data. 

3. In the spill case study, it would seem reasonable to apply the TEFs to all of the species within

each class of biota with some caution. There will be considerable variability within each class but

if caution is used and the limitations recognized this approach will be very useful. The closer the

phylogenetic relationship the higher the level of confidence in extrapolating the results. The lake

trout TEF can be applied with considerable confidence to the lake trout and salmon but less so to

the carp and sturgeon. Many fish species such as fathead minnows show a much reduced response

(EROD) to exposure to various chemicals which may affect the application of the TEF developed

from trout, etc. 

III. Exposure profile

1. The need to model numerous chemical presents a challenge. The weakness of the

physical/chemical and bioaccumulation data for specific congeners introduces considerable

uncertainty. This leads to many assumptions or simplified approaches being employed. The

particularly important weakness is knowledge as to the extent of bioaccumulation and the changes

in the relative composition of congeners at different trophic levels resulting from differential

metabolism and/or biomagnification. This can lead to very different relative importance of each

congener in different organisms. The chemical focused for remediation may differ depending on

the trophic level that is at risk. For example, in the spill scenario, the PCDFs are the most important

(more than half) the lake trout while the terns are driven by the PCBs especially PCB 126. PCB 126

is the dominant congener of concern for otter but PCB 77 and 81 are important for terns.
Mark R. Servos

C-C-145

2. For the persistent slowly metabolized or excreted compounds this would not be a significant

problem. For other compounds it could be if the actual dose is not considered. There is some

concerns about continuous exposure to congeners that do not bioaccumulate (this could be

happening in a pulp mill discharge). A tri-substituted dioxin found in the pesticide TFM caused

induction of MFO enzymes in fish even though it was relatively water soluble and easily degraded.

A constant exposure to low levels may result in responses which would not be seen in experiments

where a single dose is administered and the chemical is quickly metabolized.

3. Congener specific analysis is difficult and expensive. It require additional steps in the clean-up

and high resolution GC-MS detection. 

IV. Risk characterization

2. The biologically based TEQs could be employed as an alternative to address some specific

questions. Biologically derived TEQs could be used as a surrogate for more expensive chemical

analysis to monitor the success of remediation or to detail the distribution of the contamination.

However, the concentrations would have to be validated and it would have to be demonstrated that

the chemicals of concern were causing the biological response in the environmental samples being

monitored. The biologically derived TEQs would differ based on the type of cell line used (fish vs

mammals) so the appropriated procedure would be important. Biologically derived TEQs have

been used to demonstrate that chemicals other than PCDD/Fs at pulp and paper mills were present

and contributing to the MFO induction response. In a case such as this the biologically derived

TEQ would respond to the other chemicals and fail to demonstrate a reduction in the chemicals of

concern (e.g. PCB 126, PCDF). 

3. site specific studies, etc. 

• Relative contribution of items in the diet of the birds and otter. Are the PCBs associated with

bioaccumulation through the aquatic system in the lake?

• The role of other sources to the diets. To determine the relative role of the lake and determine if

remediation will reduce levels. The calculation of the concentration of chemicals in the birds

and mink did not consider that only part of the diet comes from the lake. 
Mark R. Servos

C-C-146

• The seasonal contribution of the lake to loadings and reproductive success in terns.

• Reproductive success of the terns and hatchability of lake trout. To determine if there is a

predicted effect.

Risk Characterization

The clean-up goals would not be the same for each vertebrate group. The fish are just approaching

the threshold values so there would be no apparent need for remediation. The birds have the highest

TEQs but also a higher thresholds than the otters. The birds and the mammals have about the same

BMF for PC 126 but the mammals have a BMF of 54 compared to only 1.6 for 2,3,4,7,8-PCDF in

birds. The otter metabolized some of the PCBs, most notably the PCB 77 and 81 which changes the

relative composition of the congeners at this trophic level. PCDF contributed the most to the

PCDD/PCDF TEQ value in terns and otters. However, when considered alone or with the

PCDD/PCDF totals they are predicted to not be high enough to cause the effects. There is a huge

difference between the TEFs of PCB 77 and 81 in birds and mammals but the value for PCB 126

which is the major contributor in both is the same. The focus should be on the PCBs in the higher

trophic levels but these goals would have to be translated into sediment concentrations goals. The

sediment cleanup goals should be set for the group with the most restrictive values. However, if the

uncertainty is high the values may be too low and unnecessary and expensive cleanup goals may be

set. If the terns are getting only a small dose from the lake when we have assumed it all comes from

the lake then remediation may not result in the desired goal. On the other hand the poor prediction

of the BMFs or other factors may set a value to low which is not adequate to protect the most

sensitive species. If possible the various scenarios should be presented with some level of

confidence (or lack of ) for the risk manager to use as needed. To use the t-PCBs to guide

remediation we would need to ensure that the key toxic congeners are changing in proportion to the

total-PCBs and the relative bioavailability of the congeners is not changing with the remediation.
C-C-147

Martin van den Berg, Ph.D.

Associate Professor of Environmental Toxicology

Research Institute of Toxicology

University of Utrecht

P.O. Box 80176

Utrecht, 3508 TD, The Netherlands

011-31-30-253-5400

Fax: 011-31-30-253-5077

E-mail: m.vandenberg@ritox.dgk.ruu.nl

Dr. Martin van den Berg received his MS. and his Ph.D. in environmental and

toxicological chemistry from the University of Amsterdam. Currently, Dr. van den

Berg is an associate professor of environmental toxicology at the Research

Institute of Toxicology (RITOX) of the University of Utrecht in the Netherlands. He

is the leader of the research group studying toxicokinetics and dynamics of

persistent environmental contaminants. Dr. van den Berg’s areas of research

interest include: toxicokinetics and reproductive effects of halogenated

polyaromatics, interactions of xenobiotics on steroid hormone metabolism, and

interactions between dioxin-like compounds and PAHs with respect to genotoxicity.

He conducts these studies on fish, birds, and mammals. Dr. van den Berg has

published over 120 scientific articles and papers on this area of research. Dr. van

den Berg was the chairman of the World Health Organization’s “Meeting on the

Derivation of Toxic Equivalency Factors for PCBs, PCDDs, PCDFs, and Other

Dioxin-like Compounds for Humans and Wildlife” held on June 15-18, 1997.
Martin van den Berg

C-C-148

Comments on the questions related to the Workshop on the Application of TEFs to Fish

and Wildlife.

I.1

With respect to additional information provided, I do not think that it will provide the

workshop with more information regarding the use of TEF values for wildlife, as it mostly

concerns guidelines for TCDD only. During the WHO Stockholm meeting all available

material has been evaluated for “Eco-TEFs”. Therefore it does not enhance the means of

evaluating the uncertainties more.

I.2

The uncertainties in “Eco-TEFs” are much larger than those obtained from mammalian

studies due to the limited information available. Nevertheless, by using a tiered approach

(see WHO document) and the rounded off procedure the most protective way was chosen

which was possible. 

I.3

At the TEF Stockholm meeting a tiered approach was followed for “Eco-TEFs” in which

priority was given to more classical toxic parameters, e.g. ELS mortality, above

biochemical effects or QSARs.

II.1.

If it is assumed that no additivity or no interactions exist, each compound involved should

be evaluated separately and the basic information for this process is than lacking,

because it was simply not available. Actually, because we acknowledge the Ah-receptor

mechanism and derived TEF concept we are worrying about these compounds. In

addition, there are sufficient in vivo and in vitro studies which support this TEQ/TEF

approach. In general non-additive interactions which have been reported are general

within one of a magnitude or even much lower. For ecotoxicology the largest uncertainties

do not seem to be these non additive interactions but large differences in species

sensitivity.
Martin van den Berg

C-C-149

II.2.

From a practical point of view there is not much choice as at best usually the EC50 or

LC50 values were reported. As the efficacy of a dose response curve varies, especially for

PCBs, another value maybe better. An EC10 or even lower might be better. However I

feel that the amount of information now available in literature does not permit such an

approach. With respect to differences in slope of the dose response curve I am not

convinced that the statements which have been made by the critics that this phenomenon

makes the TEF concept impossible to work with are that valid. Opponents using this

“difference in slope” argument have to my knowledge this argument not solidly supported

by statistical analysis.

II.3.

I think that the differences observed in species specific TEFs are less of a problem than

the value which is actually used as a LOAEL or NOAEL for the species of concern. In the

environment we have a huge amount of species variation and we know from lab studies

with different taxa that sensitivities towards these compounds, usually TCDD, can vary

more than three orders of a magnitude. Therefore the right choice of LOAEL/NOAEL

seems to much more essential to the process.

III.1.

One of the challenges within the present approach of the TEF concept comes from the

fact that the efficacy (Ymax) of the response varies a lot, especially with PCBs. This

difference in efficacy strongly influences the TEF values we work with. I would like to know

how this problem should be approached in future and what the fundamental reasons are

behind this phenomenon. At my lab we have developed some ideas, but so far have not

come up with a good solution or suggestion (Maybe this topic belongs to another

question). 

I think food chain models will work pretty nicely as long as the compounds are highly

resistant against biotransformation. As soon as compounds are more effectively

metabolized the modeling becomes more difficult due to species differences in metabolic

capacities. Luckily for most of the PCDDs, PCDFs and PCBs which accumulate in the

food chain this seems not to play a dramatic role. I think other fate and transport models

will also be a good approach for these compounds as long as you stay with the more
Martin van den Berg

C-C-150

hydrophobic compounds. In addition, I think that physico-chemical data can be much

easier obtained for these compounds than biological data, while further more physicochemical data could be easier estimated using e.g. log Kow.

III.2

The TEF concept could certainly be strongly improved if values are derived in the future

from e.g. tissue levels instead of administered dose. Using PB-PK modeling in

combination with the right toxicodynamic models would improve the risk assessment for

these compounds significantly and even bridge the differences between species. The

present TEF concept has always been presented as an “interim” method for risk

assessment during the last 10-15 years, but in fact nobody ever came up with a better

method. In addition, governmental agencies have not put much effort in improving the

TEF-concept either. So for the time being we are just stuck with this interim method if we

want to do (eco)risk assessment for these compounds.

III.3

I do not think that the present TEF concept requires a more rigid design of analysis as

most labs which are involved already analyze the non and mono ortho PCBS in addition to

the 2378-PCDDs and PCDFs already measured. Measuring total PCBs is rather risky for

sediments etc. as you might miss geographical and temporal changes in the most toxic

PCBs in the matrix easily. As these are the congeners you are interested in, this

information should not be ignored. However, it should be noted that TEQ values from e.g.

sediments have more a comparative meaning than an actual toxicological one. As is

illustrated nicely with both risk assessment exercises much more information is necessary

before e.g. a risk assessment can be done for a top predator. In other words when

ecological risk assessment is done on these compounds models should work as long as

possible with the congener specific approach. The combination between TEQs and

toxicity should be done in the final step of final food. In general the use of TEFs and TEQs

should not exceed that of a single trophic level.
Martin van den Berg

C-C-151

IV.1.

I think that the problems with the TEFs and associated TEQs are in general greater than

those for a number of other groups of environmental contaminants e.g. OP-esters,

because:

a) It involves an exceptionally high number of compounds with a specific mode of action.

b) These compounds show a large variability in physico-chemical properties.

However, if one would develop an appropriate TEF model for the polyaromatic

hydrocarbons and genotoxicity, I am sure similar problems as with the dioxin TEF model

would be encountered.

See also earlier comments I made regarding the uncertainties of the present TEF concept.

IV.2.

I think that biological based assays, e.g. Caluc Ah-receptor, could serve very well as a

prescreening method for selecting abiotic environmental samples for further chemical

analysis. In fact they could also serve as a way of measuring TEQ tissue levels from

target species. However, these bioassays could never be used in biological samples as

long as the species specific sensitivity is unknown. Alternatively, a large general safety

factor for eco risk assessment in combination with these bioassays might do it also. I do

believe that these bioassays can save us a lot of money on expensive chemical analysis

as long there limitations are acknowledged. 

IV.3.

I think that the present amount of information is adequate for the two case studies

presented. From my expertise (TEFs, toxicity and pharmacokinetics) the recent WHO

evaluation tried to incorporate as much as possible all available scientific information.

More information was simply not available. It might be desirable to have the WHO TEF

database available at the meeting for consultation, If necessary I can bring it with me.
C-C-152

Bert van Hattum, Ph.D.

Program Manager, Environmental Chemistry and Ecotoxicology

Institute for Environmental Studies

Free University, De Boelelaan 1115

1081 HV Amsterdam

The Netherlands

011-31-020-444-9555

Fax: 011-31-020-444-9553

E-mail: bvanhattum@sara.nl

Dr. van Hattum received a Ph.D. in biology from Vrije University in Amsterdam and

a master’s degree in chemistry at the State University of Utrecht. At Utrecht his

major areas of study were analytical chemistry and chemical oceanography. Most

recently he has researched food chain transfer and effects of planar PCBs in top

predators, analysis and bioaccumulation of organic compounds, risk assessment of

water discharges from offshore installations, design of biomonitoring programs, and

sustainability and environmental quality in river basins. Dr. van Hattum has

participated in several advisory committees for the Dutch government on

secondary poisoning in mammals and birds and on the development of

environmental quality criteria for PCBs. He is currently a member of the board of

the Environmental Toxicology Section of the Dutch Society of Toxicology, a

member of IAWQ, SETAC, and the editorial board of Environmental Pollution

journal. He has published more than 40 articles and reports, 4 books or chapters,

and he has made presentations at a number of conferences in Europe and

Canada.
Bert van Hattum 

C-C-153

Premeeting Comments

With much pleasure I have read the extensive set of documentation. I was impressed by the

profound and critical approach laid down in the different documents. Below I have tried to answer

some of the charge questions in my field of expertise. Some of the citations include recent technical

reports on Dutch and Danish otter studies, which can be made available on request. 

I. Stress-response profile relative to the derivations of specific TEF-values. 

Q1,2,3 The material provided for the workshop and the references cited in the case studies,

provide further experimental corroboration of the validity of the approach laid down in the WHOdocument for the evaluation of the hazards of AhR agonists, such as PCBs, diobenzodioxins and

dibenzofurans. Although many questions remain unanswered (WHO-1997), it helps to identify

critical compounds, pathways, species at risk, and to focus emission reduction programmes.

Especially the material from recent experimental or review studies on mink (Tillitt et al. , 1996;

Leonards et al. , 1995) provides substantial evidence for the extreme sensitivity of this species, the

cause-effect linkage between contaminants in the diet and reproductional effects, and the soundness

of the TEF-approach as a framework to account for the joint toxicity of mixtures of contaminants.

The uncertainty of the proposed TEQ-based no-effect concentrations (NOEC) for mink probably is

much lower than for other chemicals, for which NOECs usually are being extrapolated using

safety-factors from experimental studies with 'surrogate' laboratory species (Luttik et al. ,1993). 

II. Stress-response profile relative to the application of the TEQ-approach

Q1. Rejecting the additive dose-interaction model of the TEQ-approach, would imply separate

risk-assessments for all potentially active individual congeners. In that case the focus should be

directed towards the compounds that are most likely to induce effects. Depending on the target

organism, a significant proportion of the toxic potency may be left out of the evaluation. Based on

the TEQ-values calculated for the Oneofakind Lake case study, it can be hypothesised that

individual congeners may contribute represent at most up to 30-40% (23478-PCDF) of the total

dioxin-equivalent concentration for fish, and 60-70% (PCB-126) for birds and mammals. 
Bert van Hattum 

C-C-154

Reasoning further along this line, an evaluation based solely on total PCBs would probably

underestimate fish early life stage mortality, and an evaluation based solely on 2378-TCDD would

underestimate potential reproductional effects in mammals or birds. 

Q2. I am not familiar with the discussions held during the preparation of the WHO-TEF

document. Based on analogy of risk assessments conducted for other compounds, chronic NOEC

(no observed effect concentration) or NOAEL-based values (no observed adverse effect level) if

available, should be preferred. Relationships between NOECs and other endpoints values have been

studied systematically for several compounds and test-species (Slooff et al. 1986), and have

resulted in specific extrapolation factors, which are used in the Dutch risk-assessment protocols

(Luttik et al. , 1993). I have no knowledge if similar surveys have been made for AhR-agonists. 

Q3. If available, data from eco-epidemiological studies for potentially affected species should

be used to evaluate the feasibility of the lethal body-burden concept and within-class extrapolations

of toxicity endpoints for AhR-agonists. In Leonards (1997) a comparison was made of no-effect

and critical levels for otter, mink and seal, expressed as concentrations in the liver of the targetspecies. Hepatic TEQ-based NOECs for mink (0.4 -9 ng/g lipid wt.) and otter (1 - 2 ng/g lipid wt.)

were comparable. A lower NOEC was found for seals (0.1 ng/kg lipid wt.). Due to the large

differences in toxicokinetics and biotransformation between the species, fish diet-based NOECs for

TEQs (ng/kg wet wt.) exhibited a much different ranking, ranging from 0.7 ng/kg wet wt for otters,

and 1-50 ng/kg for mink and 8 ng/kg wet wt for seals. This demonstrates that within-class

extrapolations of toxicity endpoints should be dealt with only with great care. 

III. Exposure Profile

Q1. The proposed risk-modelling framework requires the input of high-quality data for

calibration of currently used parameter estimates (BAFs, BSAFs, BMFs, rate constants) in chemical

fate and bioaccumulation modelling. Special attention should be given to habitat- or target speciesspecificity of parameter estimates, in order to judge the validity of the application of generic

parameter-estimates. In the studies conducted in food chains of European river otters (Lutra lutra)

in Danish and Dutch habitats (Smit et al. , 1996; Leonards et al. , 1997), we observed extremely

high otter-fish BMF values (indicated in Table 1) especially for some non-ortho substituted PCBs

(126 and 169), which are much higher than the mink-diet based BMFs reported by Tillitt et al. ,

(1996) and applied in the case-studies. As concentrations in otters and fish are influenced by factors

such as age, sex, reproductional activity, and other species-specific factors, BMF estimates may be
Bert van Hattum 

C-C-155

highly sensitive to sampling design and experimental methodological choices made in the various

studies. 

Especially with respect to the role of non- and mono-ortho substituted PCBs, the availability of

high-quality analytical exposure data is a limiting factor. The analytical procedure (pre-separation

followed by HRGC-MS) is costly, and a rigorous analytical quality control is required in order to

produce accurate and precise exposure data. Most of the currently involved laboratories in OECD

countries have participated in round robin exercises or proficiency testing-programmes. There is a

need for development of low-cost analytical techniques for AhR agonists, which e.g. can be used in

combination with AhR-responsive bioassays. 

Most of the old exposure assessment data for PCBs are expressed as equivalent technical-mixture

concentrations, as concentrations of selected dominant di-ortho substituted congeners, e.g. PCB153, or as total concentrations of individual congeners. In most European monitoring programmes

and regulatory practices attention is focused mainly on di-ortho PCBs. Therefore, there also is a

need to develop and evaluate the feasibility of generic or habitat-specific extrapolation algorithms

to derive TEQ-exposure profiles from e.g. PCB-153 concentration data. 

Q2. Exposure route differences may have a profound influence on the actual dosage at receptor

sites, due to variations in e.g. bioavailability, feeding-preferences, toxicokinetics and

biotransformation of contaminants in the target-species and in species from lower trophic levels.

The differences in susceptibility to dietary PCBs between mink and otter, as discussed previous

section, seem to be related to variation in toxicokinetics and lipid-metabolism between both

species. 

IV. Risk Characterization

Q1. Many methodological, habitat-related and biological factors contribute to the uncertainty of

parameter estimates used in risk assessment models. Complementing the angle taken in the case

studies, with evaluations based on trials with probabilistic models would provide insight in the

effect of the uncertainties on the extent to which target species are protected. 

Q2. The added value of some of the recently developed bioassays and biomarkers, is that they

provide insight in the total quantities of AhR-responsive compounds. In studies conducted by Murk

et al. (1996) and (1997) good correlations were found between CALUX-based TEQs and values

derived with TEF-values from measured concentrations. Care should be taken to account for

confounding response of other potentially active compounds, such as e.g. PAHs in sediments. In

the study of Smit et al. (1996) a combined approach was applied, in which the low-cost and
Bert van Hattum 

C-C-156

sensitive CALUX-assay was used for screening purposes and selection of samples for further

extensive chemical analysis.

Q3. With respect to gathering of site-specific data my recommendation would be:

• exposure concentrations in sediments, fish and tissues of predatory species to examine if

generic BSAF and BMF values can be used, or that site-specific values should be applied. For

the prognostic case-study the predicted water column partitioning (dissolved, DOC-bound and

POC-bound) should be corroborated with experimental data. 

• ecological assessment of status of targeted species, assessment of influence of other natural or

anthropogenic stress-factors

• confirmation of predicted risks with relevant laboratory-bioassays 

• uncertainty analysis with probabilistic modelling 

V Prospective case-study

Q1. Due to variable hydrodynamic conditions, large variations may be expected in the

transport, distribution and bioavailability of contaminant in the water column. The applicability of

generic BAFfd values needs to be investigated. Additional studies could contribute to the accuracy

and precision of the proposed water quality objectives.

Q2. Based on the high biomagnification of some non-ortho substituted PCBs (PCB-126, PCB

169) in the food chain of the otter, an approach which ignores congener-specific biomagnification, 

may result in an underestimation of risks of these congeners to sensitive predators. 

VI Retrospective case-study

Q2. In the study of Smit et al. (1996) TEQ-based NOECs and critical levels (for Vitamin A

reduction) in otter-liver, were extrapolated with congener-specific BMFs and BSAFs to equivalent

critical levels and quality objectives in fish-diet and sediments. Significant double-logarithmic

correlations were observed - for sediments and biota - between concentrations of Σ 7PCBs

(summation of 7 selected congeners, which usually account for 50% of total PCBs : 28, 52, 101,

118, 138, 153, 180) or indicator congeners (PCB-153) and TEQ levels. Although the relative

contribution of individual congeners to the total TEQ-based concentration appeared to be speciesspecific, extrapolation factors could be derived to express the proposed critical levels on the basis

of Σ 7PCBs and of PCB-153. As a rule of thumb, a one order of magnitude range of uncertainty

may be introduced due to this extrapolation. Nonetheless, as most of the European fish and

sediment-monitoring data are based on these standard congeners, this provided a framework to
Bert van Hattum 

C-C-157

evaluate the quality of potential otter habitats in the Netherlands. Similar relationships also have

been observed in recent (unpublished) studies planar PCBs in sediments and cormorant food chains

in the Rhine-Meuse estuary.

As correlations between total PCBs and TEQ-based concentrations may be site-and speciesspecific, my recommendation would be to validate such extrapolations with measurement data.

Some of the low-cost biomarker techniques also could have potential for screening purposes in this

context. 

References

Ahlborg U.G. Becking, G.C. Birnbaum, L.S. Brouwer, A. Derks, H.J.G.M. Feeley, M. Golor, G.

Hanberg, A., Larsen, J.C. Liem, A.K.D. Safe, S.H. Schlatter, C. Waern, F. Younes, M. Yrjanheikki,

E. 1994. Toxic Equivalency Factors for Dioxin-Like PCBs - Report on a WHO-ECEH and IPCS

Consultation, December 1993. Chemosphere. 28:1049-1067.

Hoffman, D.J., C.P.Rice, and T.J. Kubiak (1996): PCBs and Dioxins in Birds. In: Environmental

Contaminants in Wildlife - Interpreting Tissue Concentrations, edited by W.N. Beyer, et al, pp. 165-

20CRC Press, Boca Raton, FL, USA.

Leonards P.E.G., T.H. de Vries, W. Minnaard, S. Stuijfzand, P. de Voogt, W.P. Cofino, N.M. van

Straalen and B. van Hattum (1995). Assessment of experimental data on PCB-induced reproduction

inhibition in mink, based on an isomer- and congener-specific approach using 2,3,7,8-

tetrachlorodibenzo-p-dioxin toxic equivalency. Environ. Toxicol. Chem. 14, 639-652.

Leonards P.E.G., Y. Zierikzee, U.A.Th. Brinkman, W.P.C. Cofino, N.M. Van Straalen and B. Van

Hattum (1997). The selective dietary accumulation of planar polychlorinated biphenyls in the otter

(Lutra lutra). Environ. Toxicol. Chem. 16):1807-1815. 

Leonards, P.E.G. (1997). PCBs in mustelids - analysis, food chain transfer and critical levels. Thesis.

Vrije Universiteit, Amsterdam.

Luttik, R., Romijn, C.A.F.M., and Canton, J.H. (1993). Presentation of a general algorithm to include

secondary poisoning in effect assessment. Sci. Total Environ. (Supplement, Part-2), 1491-500.

Murk AJ., Legler J., Denison MS., Giesy JP., Vandeguchte C., Brouwer A. (1996). Chemical-activated

luciferase gene expression (CALUX) - a novel in vitro bioassay for ah receptor active compounds in

sediments and pore water. Fund. Appl. Toxicol. 33(1):149-160.

Murk AJ., Leonards PEG., Bulder AS., Jonas AS., Rozemeijer MJC., Denison MS., Koeman JH.,

Brouwer A. (1997). The CALUX (chemical-activated luciferase expression) assay adapted and

validated for measuring tcdd equivalents in blood plasma. Environ. Toxicol. Chem. 16(8):1583-

1589.

Slooff, W., J.A.M. Oers, and D. de Zwart (1986). Margins of uncertainty in ecotoxicological hazard

assessment. Environ. Toxicol. Chem. 5, 841-852.

Smit, M., P.E.G. Leonards, A.J. Murk, A.W.J.J. de Jongh, B. van Hattum, (1996). Development of Otterbased Quality Objectives for PCBs (DOQOP). ISBN-90-5383-528-8. IVM-R96/11, Institute for

Environmental Studies, Vrije Universiteit. Amsterdam, 170 p
Bert van Hattum 

C-C-158

Tillitt, D.E., Gale, R.W., Meadows, J.C., Zajicek, J.L., Peterman, P.H., Heaton, S.N., Jones, P.D.,

Bursian, S.J., Kubiak, T.J., Giesy, J.P., and Aulerich, R.J. (1996): Dietary exposure of mink to carp

from saginaw bay. Environmental Science & Technology, 30:283-291.

WHO (1997). Draft report on the meeting on the derivation of toxic equivalency factors (TEFs) for

PCBs, PCDDs, PCDFs and other dioxin-like compounds for humans and wildlife. June 15-18, 1977,

Stockholm, Sweden.
Bert van Hattum 

C-C-159

Table 1. Average fish diet-based biomagnification factors (lipid weight basis) of PCBs for otters in

Danish (Lymfjord) and Dutch (Lakes Oude Venen) habitats.

PCB No. BMF*

Oude Venen

(NL)

BMF**

Lymfjord

(Denmark)

28 0.044 0.47 (0.002 -

8.4)

31 0.049

44 0.014

49 0.022

52 0.016 0.54 (0.2 - 2.3)

101 0.066 2.1 (0.03 - 36)

105 12 7.9 (0.7 - 50)

118 15 35 (2 - 251)

128 9

138 26 51 (4 - 297)

149 0.16

153 15 28 (2 - 172)

156 30 37 (3 - 505)

157 19 84 (2 - 2086)

158 4

166 2

167 6 13 (2 - 83)

170 15

180 123 63 (5 - 442)

187 23

189 50 144 (12 - 1073)

194 21

77 1.4 2.5 (3 - 7.9

126 70 130 (4.2 - 900) 

169 348 108 (3 - 1700)

Σ PCBs 14 36 (2.9 - 209)

Σ TEQs*** 41 95 (3.5 - 640)

* Geometric mean BMFs for 5 otters from Leonards et al. (1997); ** From Smit et al. (1996), geometric

mean values ( di-ortho PCBs n=20; non/mono-ortho PCBs n=9) and minimum to maximum ranges of BMFs

between brackets; *** calculated with TEFs from Ahlborg et al. (1994). BMFs are expressed on a lipid

normalized basis and calculated for an average diet composition.
Appendix C-D

DETAILED SUMMARIES

OF EXPERTISE GROUP DISCUSSIONS
C-D-1

FATE AND TRANSPORT EXPERTISE GROUP

Facilitator: William Adams

Group Members: Joseph DePinto, Lynn McCarty, Christopher Metcalfe, Patrick O'Keefe, Mark

Servos, Phil Cook, Cynthia Nolt, and Lisa Williams (notetaker)

Discussion started with the questions distributed.

Question 1: How well do we know the uncertainties associated with accuracy and precision of

analytical chemistry data, including measurement of BAFs, BSAFs, and BMFs?

BAFs may vary from one aquatic system to another. Modeling of these aquatic systems may

allow estimates of BAFs to be made. Modeling BAFs requires parameters like Kows and Henry's

Law constants for individual congeners. Kows are known reasonably well, but Henry's Law

constants are usually calculated themselves and may be uncertain to within a couple of orders of

magnitude.

BAFs can be determined empirically by measuring concentrations of congeners in biological

tissues and in water. Concentrations of some compounds, including TCDD itself, may be near

the detection limit in water, especially in the dissolved phase. Water concentrations may be

calculated from other partitioning coefficients.

Precision and accuracy of the compound-specific measurements needed to determine BAFs and

BSAFs vary among matrices. The sources of uncertainty in these measurements include

analytical variability, the extent to which sampling protocols represent the real heterogeneity in

the system, and magnitude of the real heterogeneity. Analytical variability increases as

concentrations approach the limits of quantification (LOQ) and detection (LOD). In sediments

and tissues, concentrations of individual PCBs, PCDDs, and PCDFs can currently be determined

to within ± 30% for most samples. As concentrations approach the LOD, concentrations can be

determined to within a factor of 5 to 10. Concentrations in ambient water samples are near LOQ

and LODs in most samples, so determinations are generally accurate to within a factor of 10. 

Sampling protocols need to be designed using power analyses. The real heterogeneity of

concentrations of these compounds may be huge for sediments within a given aquatic system

because of the heterogeneity of sediment types. Concentrations vary spatially and with organic

carbon type and amount, particle size distribution, and other sediment characteristics. 

Concentrations of these compounds in individual fish within a population may vary by an order

of magnitude. Heterogeneity in water samples within a system is not well studied, but varies

with solids dynamics in the system.

Analytical techniques are available for all of these compounds. Many commercial laboratories

are currently analyzing 2,3,7,8-substituted PCDDs and PCDFs. Fewer are regularly analyzing

non-ortho-substituted PCBs, but more would likely add these to their available analysis if

regulations began requiring quantification of these compounds. No capital expenditures would

be required as the methods currently being used for analysis of PCDDs/PCDFs are very similar

to those used for the non-ortho-substituted PCBs. The most likely method to be used for these

analyses is a carbon column separation step followed by gas chromatography/mass spectrometry
C-D-2

with isotope dilution internal standards.

The uncertainty in sampling and analysis of the individual congeners is similar to that introduced

by sampling and analyzing total PCBs or 2,3,7,8-TCDD alone. The samples required would be

the same for any of these analyses. Measurements of total PCB concentrations may have greater

uncertainty than those for individual congeners. Analysis of total PCBs is generally done by

analyzing individual congeners and then summing their concentrations. Laboratories may use a

different number of congeners to quantify total PCBs and may use Aroclor mixtures or

individual congeners as standards. Analysis of total PCBs is less expensive than for congenerspecific analysis which includes the non-ortho-substituted PCB congeners. The difference in the

cost of analyzing 2,3,7,8-TCDD and analyzing all of the 2,3,7,8-substituted PCDDs and PCDFs

is negligible.

Question 2: Are chemical fate and transport properties (hydrophobicity, volatility, photolysis,

biodegradability, etc.) well characterized for all chemicals with TEFs? If not, what uncertainties

are introduced in exposure predictions?

Hydrophobicity (Kow) and volatility are better known and generally more important in

determining fate and exposure than photolysis and biodegradability. Kows are known reasonably

well. Using Kows to predict Kocs introduces an uncertainty of about an order of magnitude (based

on a 95% confidence interval) because of inherent differences in the nature of organic matter. 

Henry's Law constants for some congeners may only be accurate to within a couple of orders of

magnitude, so in systems in which volatilization is a significant fate pathway, this uncertainty

could have a significant impact on predicting water concentrations. Biodegradation is system-,

congener-, and concentration-specific, so generalizability of this process is very poor. The time

scale for this process is very long, so this is a less significant process in determining overall fate

than partitioning, sorption, volatilization, and many other processes. Photolysis is also generally

of minor importance to an overall mass balance for these compounds, but sensitization could

result in this process being important for some compounds in some systems. In mass balance

exercises in major rivers and bays, approximately 80% of the accuracy of the model was

determined by the accuracy of the modeling for the solids dynamics of the system.

Question 3: What degree of uncertainty is associated with biotransformation/ metabolism in the

food chain?

Biotransformation, metabolism, and differential absorption patterns alter congener patterns more

significantly in birds and mammals than they do in fish and other biota. Congener patterns

among fish species and fish tissues are relatively homogeneous. This is not true for birds and

mammals that eat those fish. Relative to the pattern in fish, concentrations of PCB 77 decrease

and those of PCB 126 increase as a percentage of total PCBs (see Figure D-1). 
C-D-3

Figure D-1. Mean ratios of CB 77/CB 153 and CB 126/CB

153 in trophic levels of Lake Zandemeer and Lake Ontario 
C-D-4

These shifts, especially for PCB 77, may be quite species-specific. Changes in patterns among

tissues within a bird or mammal are not well characterized. Most environmental data on birds is

for eggs and most mammalian data is for livers.

Biomagnification factors (BMFs; ratio of concentration of a given compound in the tissue of the

predator to the concentration in the tissue of its prey) are needed for the species of concern,

especially for fish-eating birds, for a given assessment. BMFs for a given species in one system

could be applied in another system if the dietary composition is known. The basic ecology (food

web structure and dietary composition) of a system is usually less certain than the BMFs

themselves.

The TEF approach is critical in estimating risk to birds and mammals in particular because of the

change in congener patterns from source Aroclors and through the food chain. For birds and

mammals at the top of the food chain, these changes in pattern appear to be even more speciesspecific or class-specific than they are source-specific (in chronic exposures). In lakes with only

atmospheric sources of PCBs and compared to those with local sources with varying patterns of

PCBs, the patterns of congeners in the top of the food chain are similar although significant

differences in patterns are observed low in the food chain. In the Great Lakes, analysis of

archived samples of fish and bird eggs have shown little or no change in congener patterns

within species over a time period, while absolute concentrations dropped by an order of

magnitude.

Question 4: For these classes of chemicals (PCDDs, PCDFs, and PCBs), what are the greatest

sources of inter-ecosystem variability in bioavailability and bioaccumulation? Are there any

unique considerations for exposures in marine ecosystems?

The greatest sources of inter-system variability in bioavailability and bioaccumulation are solids

dynamics and food web structure. This variability is most important in predicting absolute

concentrations of PCBs, PCDDs, and PCDFs rather than in predicting the relative proportions

among them. The solids dynamics is perhaps the most difficult to determine, especially in a

marine system. Systems can be compared by knowing the distribution of contaminants between

sediment organic carbon and the freely dissolved phase (psocw; Cook and Burkhard, National

Sediment Bioaccumulation Conference, September 1996). This relationship can be measured for

one congener and generalized to the others. Differences in benthos structure may have

significant influences on absolute concentrations at the top of the food chain for compounds with

Kow greater than 6. At the top of the food chain, the source of the contaminants to critical tissues

is important because birds and mammals are mobile. For example, a migratory bird may arrive

on a breeding ground and begin feeding locally and then transfer lipid from the bloodstream to

the developing egg. In that case, the contaminants in the eggs would reflect local sources and

diet. If the eggs are developing during migration or if the bird arrives on the breeding ground

and must rely on stored lipid to produce eggs, then the contaminants in the eggs would reflect

other sources of contamination.

Question 5: From the standpoint of TEF applications, what are alternatives to, or improvements

for, the waste load allocation process model described in Figure 5 of the prospective scenario?

The waste load allocation process model needs to be validated, but it is expected that this process
C-D-5

is more likely to change the values of the parameters than the tool itself. Overall, this model

appears to be a good approach for dealing with this complex mixture of compounds with additive

toxicity and which are bioaccumulated and exhibit chronic toxicity. The model would need to be

more complex for an acutely toxic substance or condition. The assumption in this model of the

existence of an assimilative capacity for these persistent compounds can be questioned on

philosophical grounds. Loss processes from a given system are dominated by physical

movement within or from the specific system rather than by chemical destruction.

A critical component of this waste load allocation process model is the system level mass

balance model. A mass balance model can be very complex, so a hierarchy of mass balance

models may need to be developed. Different mass balance models could be developed for

different types of systems and with varying degrees of complexity and number of input

parameters required. There is nothing unique about mass balance modeling for this application. 

If one can model TCDD for a given system, then one can model other PCBs, PCDFs, and

PCDDs. The modeling will become more accurate as more parameters are measured in more

systems.

The overall uncertainty in the waste load allocation model is unknown, but is largely related to

knowledge of the system, not knowledge of congener-specific information. For example, ratios

of BAFs among congeners across various systems are fairly constant (varying by less than a

factor of 2 or 3), whereas absolute BAFs among systems are less certain. The uncertainty can be

reduced with more measurements and can be explored using Monte Carlo simulations. 

Regulators may be given guidance on an amount of the waste load to be allocated to the

uncertainty. Overall uncertainty in calculated final MALs may currently be a couple of orders of

magnitude.
C-D-6

TEF EXPERTISE GROUP

Facilitator: Richard Peterson

Group Members: Bjorn Brunstrom, Steve Bursian, Jay Gooch, Mark Hahn, Bert van Hattum,

Sean Kennedy, Martin van den Berg, Steve Bradbury, Mike Devito, Don Tillitt, and Tim Kubiak

(notetaker)

Dr. Richard Peterson opened the session by describing basic terminology for the discussion. 

Two terms, Toxicity Equivalency Factors (TEFs) and Relative Potency (REP), were defined to

provide clarity. In the group discussion, Toxicity Equivalency Factors (TEFs) were defined as

consensus values derived from multiple REPs. A REP pertains to the relative potency of a

dioxin-like congener to TCDD in a single study. Additionally, it was discussed that a receptor

could be defined as both a species under assessment and for pharmacological use of Ah receptor

interactions. It was recommended that "target species" be used for the species and "receptor" be

reserved for pharmacological use. 

Dr. Peterson reviewed and handed out copies of the nine questions to be considered by the

workgroup. 

An issue was raised pertaining to the specific purpose of the World Health Organization TEFs. 

Group members questioned how broadly they are to be applied, and whether these values are for

screening only. These questions were subsequently addressed in the case studies and are

addressed elsewhere.

The following discussions pertain to the numbered questions prepared for the expertise group. It

should be noted that six of the twelve individuals in this group also attended the WHO TEF

meeting in Stockholm, where the WHO TEFs were developed for non-human mammals, birds,

and fish.

Question 1. Are taxa-specific TEFs a reasonable approach given current scientific understanding

of relative differences between fish, birds, and non-human mammals?

Discussion centered on the use of TEFs for the various classes and their use for site-specific

analysis to be protective. There was considerable discussion about the meaning of the term

"protective." It was generally agreed that the term was misused in the sense that there was no

numerical uncertainty factor included to provide a any margin of safety due to uncertainty. 

Derivation was by a tiered approach (WHO 1998). Site-specific use of the TEFs was thought to

provide predictive value in the interpretation of dioxin-like exposure, risk, and effects. While

the WHO TEFs were rounded up or down to the nearest half or whole order of magnitude, the

differences were not large. Fish and bird TEFs were considered predictive because the

documentation was available for specific REP to TEF conversion and these values represented a

toxicological endpoint for the most important congeners. There was a clear indication that a

statistical validity assessment of the different TEFs was not performed. 

Question 2. What are the sensitivity analysis arguments for using the WHO TEFs that have been

rounded to provide maximally harmonized TEFs across taxa versus the unrounded TEFs that are
C-D-7

more taxa-specific?

The discussion resulted in a finding that, despite rounding, the TEFs are not less but more

predictive. The relative contribution of PCDFs, PCDDs, and PCBs to total TEQs does not

change. Differences in species sensitivity is a larger source of uncertainty.

Question 3. How would you distribute uncertainty in the methodology among the following

categories:

A. High dose to low dose extrapolation

Data indicate that using REPs from EC50 versus EC10 does not result in much

difference. In vitro CYP1A protein induction is less consistent and there are problems

associated with in vitro tests. The greatest confidence in the data occurs at the EC50 level of

relative potency. Therefore, greater uncertainty involves the extreme ends of the dose-response

curves, such as in the EC1-10 range. Inflection points vary in dose-response.

B. Species differences in relative potency

There was general agreement that there is a large degree of uncertainty associated

with the comparative data across species when looking at REP data sets. This includes all tiers

of the WHO database. The uncertainty is not associated with major variation but with a lack of

interspecies comparative data for the same endpoint. For example, the chicken response to

embryo toxicity is 70X more than double-crested cormorant. For PCB 126 the difference is 40X. 

REPs between the two are 0.07 and 0.02 for PCB 126. The question is what are minor

differences for REPs? There should be some evolutionary differences. CYP1A1 has little

difference in REPs and EC50s are not statistically different. As long as dose-response curves are

parallel, it works. There was some concern that there may be an overestimation of relative

potency. While there is a lack of toxicity data for many REPs, those based on mortality data

show some coherence.

C. Species differences in the sensitivity to TCDD

For birds, the data are limited. There is a need to be able to explain the mechanism of

action at the receptor level relative to different species' sensitivity. It was mentioned that

receptor occupancy determines toxicity, but receptor populations in tissues and organs are not

routinely measured. There are some limited data on four species that supposedly can explain this

but they were not identified. There is still high uncertainty in this area due to lack of

information, that is equal to B above and greater than A.

D. Experimental versus environmental exposures

There is not a high degree of uncertainty. Target tissue doses are important in the

environment. Routes of exposure in laboratory are different from environmental measurements

in some cases, but for bird eggs there appears to be consistency between lab egg injected doses

and maternally deposited doses. Egg injection can underestimate responses based on

methodology.
C-D-8

E. Differences in the relative potency across endpoints

There was general agreement that there is some uncertainty, as with items B and C, above. For

fish and birds, REP/TEF data rely on ecologically relevant population assessment endpoints

through a reproductive endpoint (LD50) for many congeners. Some other TEFs in mammals may

have greater uncertainty.

F. Use of the assumption of additivity

TCDD & 153 are a limited data set showing antagonism at certain relative

concentrations. Generally, data for dioxin-like congeners are consistent with additivity. Others

present problems, because there is not much information. There appears to be low uncertainty

relative to other sources. Mink data clearly support the additive model. Also low uncertainty

based on chickens, lake trout, and brook trout. Environmental mixtures have a factor of 2 or 3

for variation. For fish, the departure factor from additivity is small (2-5). Methods to verify

additivity have not yet been established.

Question 4. Are there different uncertainties due to chemical classification of the chemicals (i.e.,

dioxins versus dibenzofurans versus biphenyls)? If so, is there a biological explanation for this

difference?

Mono-ortho PCBs produce mixed effects and induce more than CYP1A1. This is problematic in

fish, since fish are not responsive to mono-ortho-substituted PCBs. Among vertebrates, there are

class-specific differences. For humans, PCBs 118 & 105 give high tissue TEQs. The respective

class differences are four orders of magnitude. PCB 126 drives TEQ calculation across classes

and results in reduced uncertainty. Uncertainty is generally lower than for items B and C,

above.

Question 5. If we decide that a given species is the most sensitive species in the ecosystem of

study and relative potency values are available for that species for all of the congeners present,

should we use the relative potencies specific for that species or should we use the WHO TEF

values for all chemicals?

The purpose of the TEF is for risk assessment. WHO TEFs were rounded to half/whole order of

magnitude. While there are great data gaps across species, there is also uncertainty that the

assessed species is the most sensitive. Species-specific data could be used.

Question 6. If we chose to use relative potencies specific for the species of interest instead of

the WHO TEF values, will we decrease the uncertainty in the TEF methodology?

Use of species-specific values will reduce uncertainty.

Question 7. How would a surrogate species be identified given that relative sensitivities among

species cannot be easily predicted?

It is common sense to use similar species. There may not be a need for surrogates, if a species of

interest can be used. Uncertainty associated with extrapolations across species is greater than
C-D-9

that associated with extrapolations across endpoints.

Question 8. Given the toxic endpoints used to establish each set of TEFs, how far-ranging

should the assessment endpoint diverge before the TEF predictive uncertainty is high? How

would uncertainty be valued to account for these other endpoints when communicating risk?

The answer to this involves seeing data on species-related differences in REPs. Having not seen

this sort of data makes the question difficult to address. At a minimum, tier consistency for a

congener reduces uncertainty. 

Question 9. What are the advantages and disadvantages of using the TEF methodology over that

for total PCBs? Based on the type of AhR agonists that are present at a contaminated site is one

approach preferable to the other?

Pharmacodynamic differences between species result in a need for better exposure assessment,

which is important for ecosystems assessment. TEFs should be applied only to biotic matrices,

not to abiotic. Different commercial mixtures vary in congener composition, resulting in further

uncertainty associated with total PCB assessment. It may be possible to use indicator congeners

to track and extrapolate from known-composition mixtures.

An observer addressed the need for feedback on the global background of contamination. For

fish and wildlife, other Ah-active compounds, such as azobenzenes, hexachlorobenzene, some

chloronaphthalenes, should be receiving attention.
C-D-10

RISK ASSESSMENT/POPULATION MODELING

 EXPERTISE GROUP

Facilitator: Charles Menzie

Group Members: Janet Burris, Peter deFur, Lev Ginzburg, Wayne Landis, Mike Meyer, Pat

Cirone, Robert Pepin, and Steve Wharton (notetaker)

CM: provided an overview of goals of the workshop and reviewed key issues:

• we should ask ourselves how we as a group will help the other groups understand

the risk assessment process

• risk assessment terminology is important and should be clarified (three handouts

provided)

• screening level vs. baseline ERA; we should have a common working definition

WL: For screening, the CCME has defined a Tier 1 assessment (not probabilistic, uses an array

of risk quotients, done quickly, some lab/field data).

JB: In Superfund, screening level ecological risk assessments (SLERAs) are used to identify

whether there is a problem, and the results are then used to design or scope a more in-depth

ecological risk assessment (ERA), which is likely probabilistic.

CM: Typical characteristics might include identification of possible receptors, use of literature

values, limited additional investigation, benchmark comparisons (“quick and easy” or “off-theshelf” comparisons).

MM: They are often used as a measure of exposure to identify the need for additional studies.

SW: The number of versions of SLERAs is increasing, with states and non-governmental

professional organizations generating their own approaches (e.g., ASTM, Soil Screening

Levels).

LG: It is important that we not restrict our definition to only non-probabilistic risk assessments;

this is not necessarily the case for all SLERAs.

CM: How do we characterize SLERAs in the context of TEFs?

WL: All TEFs are already a screening approach; they are an expression of a relative value (i.e.,

high-med-low); this is Tier I.

PdF: TEFs go beyond screening by virtue of the congener-specific nature of their analysis; TEFs

shift the boundary between screening and full-blown risk assessment (toward more detailed).

CM: We have the potential to move to more complicated evaluations using the TEF approach. 

What are the next couple of steps in this process, and do the TEFs lend themselves to taking

these steps? In taking these steps, do we move toward more or less uncertainty?

JB: ERAs will typically address specific receptors, pathways, and endpoints beyond the limits

used to derive the TEFs. In some ways TEFs may restrict your approach, because some

assumptions are inherent in the TEF approach. The decisions are not transparent in the WHO

document and these may affect their applicability. One should track the toxicity, assumptions,

and uncertainties used in the TEF approach.

CM: Is there a way to expose these? Would we need to collect additional data to complete the

SLERA and move beyond it to the ERA? Yes; examples include:
C-D-11

• site-specific data

• more complex data

• field data

• relate TEFs to field conditions (chicken versus eagle)

• try to reduce uncertainty of lab-derived TEFs

MM: Screening is setting up plausibility of effects, baseline allows conversion of TEF to actual

pg/g values.

Uncertainty Terminology

LG: There is another choice between deterministic and probabilistic. There is a problem with

the probabilistic description of uncertainty. The mode associated with each distribution assumes

independence (e.g., triangular distribution results from algebraic extension of a normal

distribution), but not all environmental data are actually independent; rather, they are sometimes

dependent. A “tight” mode may underestimate extremes owing to too much central tendency. If

variables A and B are dependent or correlated, we often don’t know their actual relationships due

to lack of adequate data.

WL: Correlation matrices developed in the field lead us to focus on dependent variables.

LG: Probability bonds allow expression of uncertainties through generalized application of full

algebra. Alternatively, “fuzzy arithmetic,” where you do not assign a specific uncertainty to

distributions, allows description relationships. Ignorance versus variability should be identified

so that we apply the correct tools to reduce uncertainty when possible. Fuzzy arithmetic does

not consider dependent/independent terms. We are more in the ignorance mode rather than the

variability mode when extrapolating between species.

CM: Variability is measurable, but not reducible; lack of knowledge (ignorance) is reducible.

PdF: It is important that everyone agrees with these concepts.

CM: Which of these are we tracking? There are two different techniques to propagate these

forms of uncertainty. How do the risk assessments document elements of uncertainties? Should

the details of each uncertainty be carried forward throughout the risk assessment all the way to

the decisionmaker (risk manager)? 

[At this point, the group discussion turned to the issues raised in the charge questions raised by

the Planning Group; the comments/conclusions are listed under specific questions where

possible, otherwise, they are grouped at the end of this section.]

Question 1. How does one characterize a risk assessment with total PCBs and congener-specific

PCBs?

PC: Do you lump the endpoints, or do you carry through the individual endpoints? Congener

endpoint may be behavior whereas the endpoint for total PCBs may be lethality. Is this a piece

of information that should be provided to the risk manager?

WL: It depends on the nature of the site.

SW: It also depends on the nature of the risk management decision to be made and the level of

uncertainty deemed acceptable in the decisionmaking process.

Question 2. How does one treat uncertainties associated with variable detection limits for

individual PCB, dioxin, and furan congeners?

PdF: Do we agree that where mixtures of compounds are at fairly high levels, those ERAs are
C-D-12

more certain than those where the levels are near the detection limits? Generally, yes.

WL: It may depend on the nature of the risk management decisions that need to be made.

PdF: This also raises the multiple stressor issue.

Question 3. How does one characterize a risk assessment where the risks are primarily due to

the PCB or dioxin congener that has the lowest toxicity limit but the highest concentration, or the

highest toxicity but the lowest concentration?

Question 4. How does one distinguish the risk characterization of a “screening assessment”

from a “final risk assessment” where both rely on TEFs?

CM: The differences in uncertainties between screening and final risk assessments may be based

on policy decisions related to screening level assessments. For a SLERA (minimum case), what

information do you carry on through the assessment—a simple narrative providing a qualitative

uncertainty analysis?

LG: If you use any numbers, you should fully disclose the associated uncertainties. One should

have some idea of the spread.

WL: Be honest about your uncertainties, and if you need to, go out and take some samples.

MM: The site-specific ERA can build on the SLERA.

Question 5. Describe the lines of evidence that should be included in all TEF risk assessments.

Question 6. Is a quantitative uncertainty analysis appropriate for the TEF toxicities?

(Toxicological uncertainty)

WL: There is some quantification available, although exposure models (fate and transport) are

problematic in that they are not necessarily empirically derived (e.g., Kow) and may create the

perception of “falsely precise” values..

PdF: They also may be based on lab as opposed to field data.

LG: TEFs are not the only component of uncertainty in the risk analysis.

PdF: We are willing to use available data to do screening, including TEFs.

MM: We should ask ourselves does your risk estimate bound the realistic value?

GROUP CONSENSUS: There was a general desire to provide the basis for a numeric estimate

even in the SLERA.

Question 7. Do you need to present all toxicity data when you do the risk assessment or is a

reference to an EPA or WHO summary document on toxicity sufficient?

Question 8. How do you describe the severity of effect? By individual congener or by TEQ?

Miscellaneous Issues

LG: We should carry forward some form of uncertainty analysis.

PC: Do congener-specific ERAs leave out information that total PCBs ERAs capture or presents

more realistically?

CM: This issue should be dealt with in Problem Formulation - up front endpoints.

Exercise in Cataloging Uncertainties

CM: Derivation of TEFs by WHO included multiple uncertainties: lab studies, rounding up,
C-D-13

limited information, lack of knowledge (including technical limitations in measurement).

LG: Uncertainty is associated with the level of aggregation (i.e., only three classes of animals

are used in the ERAs and many chemicals versus using many species and only a few chemicals).

CM: Only some information is available; confidence is given qualitatively. There may be a

need to access the original data to quantify uncertainties.

PdF: Each TEF should come with a set of information regarding the uncertainties associated

with the derivation of these values.

CM: For the compounds that drive the risk, they should be based on higher quality data/less

uncertainty.

MM: For the regulators to adopt the risk assessment, they should be given the information

regarding uncertainty so that they may use it with the responsible parties to reduce the risk.

WL: EROD versus toxicity relationship is not expressed in the TEFs. Are there data for a

higher-tiered TEF, or does the toxicity data have the lowest uncertainty?

CM: If this is important, we don’t have the information at hand.

WL: There has not been a plot of the correlation of the tiered approach and the associated

reduction in uncertainties. This would be helpful.

RECOMMENDATION: Justify the tiered approach qualitatively. There may be knowledge not

presently available in the WHO report.

Application of TEFs

CM: What thoughts do you have on other uncertainties associated with application of TEFs?

PdF: There are uncertainties related to sources: Is the list of chemicals correct? What form are

they in? Are there similarities or differences in the chemicals in the environment brought about

through biological, chemical, and physical transfers?

WL and PdF: Biotic transformations prior to entering aquatic systems are important.

CM: Compare the uncertainties of this approach with modeling total PCBs.

WL: We should ask ourselves several questions: How well do these models work? Are they

predictive? Are they applicable to all media/endpoints? Are model uncertainties constant across

all compounds?

WL: TEFs have been described as order of magnitude estimates. When modeling populations,

order of magnitude changes are very important.

LG: It is foreseeable that we may likely have to apply standard food-chain models in the future

(to reduce modeling uncertainties).

WL: Detection limits are a source of uncertainty

PdF: Whether the risk assessment is based on a few congeners as opposed to many congeners is

important in determining heterogeneity in exposure.

WL: There are spatial and temporal issues associated with sampling; aggregation of samples

destroys heterogeneity of environmental data.

CM: Would you expect greater variability in tissue with congeners than an aggregate (total

PCBs)?

WL: At lower concentrations, variability would likely be greater with congeners.

RECOMMENDATION: Variability in tissue concentrations for congeners vs. aggregate Ah

receptor agonists may present significant uncertainty. Data addressing this issue should be

reviewed/compiled.
C-D-14

Effects-Related Issues

CM: What are the sources of uncertainty?

PdF: The greater the phylogenetic extrapolation, the greater the uncertainty. Also, uncertainty

associated with life stage sensitivities may be as great (due to metabolic changes related to

age/life stage).

LG: Simplistic description of populations using extreme endpoints (e.g., death) is meaningless. 

We should use more meaningful endpoints. Resilience should also be considered.

PdF: There may be genetic determinants of population effects (e.g., Fundulus).

WL: Patch dynamics present uncertainties (breeding occurring in one area versus adults living

in another).

LG: Endpoints derived for one life stage applied to another life stage (e.g., survival of adults

versus larva)

MM: When you get down to dietary habits, you need precise data for TEF development. 

Preferential exposure by congener may be different than total PCBs.

WL: Compounds are distributed preferentially into food items, and BAF for exposure may be

determined by log P.

MM: You can differentiate dietary habits by age.

PdF: Gender, life stage, and overall condition will contribute uncertainties.

WL: The shape and slope of the dose-response curve (TCDD) may add to uncertainty with

respect to measured values in environmental media.

CM: Consider other dose-response interspecies differences (Spehar’s paper). There are

significant interspecies differences and there may be a broad range within a species by

compound.

WL: Source(s) of population regulation are important.

MM: When you look at the overall population versus the impacted (or studied) population, the

level of risk may be different.

CM: Social considerations (values) come into play when determining level of protectiveness

(extinction of protected species versus prevention of population declines).

WL: The shape of the dose-response curve will impact what happens on a meta-population

level.

CONSENSUS: Congener-specific information is important for exposure and effect

characterization, predicting severity of adverse effects, magnitude (number of individuals

affected), and subtle effects. The details of uncertainties associated with congener-specific

ERAs should be retained throughout the risk assessment and interpreted for the decision maker. 

Congener-specific risk assessments are actually a form of multiple stressor risk assessment. 

These points should be discussed during problem formulation and in the selection of appropriate

assessment endpoints.

Charge Questions Related to Prospective Case Study

CM: Are the TEF uncertainties greater than or less than other approaches?

CONSENSUS: They are no worse than other approaches (ignorance exists in both areas), they

are not the only source of uncertainties, and TEFs may present advantages.

CM: Biologically based TEF assays, what would be their strengths?

SW: Generally, assays are cheap, and they provide an aggregate response.
C-D-15

WL: They allow direct testing (validation) of risk hypotheses (risk estimates).

WL: If TEF data contain bioassay (toxicity) results, then you may make direct extrapolations to

risk management decisions.

CM: What would be their disadvantages?

LG: The metabolic processes are undefined.

MM: We are extrapolating from assays on a limited number of organisms to populations.

SW: There is an inability to know which compound to regulate.

CONSENSUS: Bioassays provide another line of evidence. Biocriteria versus chemical criteria

present difficulties in implementation. Testing methods need improvement.

Responses to Charge Question IV-3 . (discussion also relevant to Question 5, above)

LG: Additional demographics on species are necessary, both historical and present conditions.

MM: Precise dietary habits are needed, including ecological implications.

WL: Abiotic characterization of ecosystems is needed (e.g., physical, chemical, meteorological)

LG: Additional information on patch dynamics is needed for interpreting population effects,

especially for statutorily protected species.

PC: We should exercise caution discounting risk when laboratory assays or modeling fails to

demonstrate predicted risks. They represent one line of evidence.


Appendix C-E

DETAILED SUMMARIES

OF CASE STUDY DISCUSSIONS
C-E-1

Breakout Group Assignments

Wednesday, January 21, 1998––10:45AM-3:45PM

Thursday, January 22, 1998––8:30AM-12:30PM

Group 1 Group 2 Group 3

Chair: Peter deFur Chair: Janet Burris Chair: Charles Menzie

TEFs Experts

Jay Gooch (fish) Mark Hahn (fish) Richard Peterson (fish)

Martin van den Berg

(mammals)

*(See EPA/DOI Planning

Group)

Bert van Hattum (mammals)

Sean Kennedy (birds) Björn Brunström (birds) Steve Bursian (birds)

Fate & Transport/BAF Experts

William Adams Patrick O’Keefe Mark Servos

Joseph DePinto Christopher Metcalfe Lynn McCarty

Population Modeler

Lev Ginzburg Mike Meyer Wayne Landis

Risk Assessor

Peter deFur Janet Burris Charles Menzie

EPA/DOI Planning Group

Gary Henningsen *Mike Devito Pat Cirone

Lisa Williams Tim Kubiak Cynthia Nolt

Robert Pepin Steve Wharton Don Tillitt

Steve Bradbury

Phil Cook
C-E-2

WORKGROUP #1

Facilitator: Peter deFur

Prospective Case Study

The discussion focused on the prospective case study, which involved permitting a new

pulp/paper mill on a generic lake in the Midwestern United States. The point of the discussion

was to review the uncertainties and issues raised by using TEFs to evaluate permit application

and discharge conditions for the hypothetical facility. The facilitator briefly reviewed the case,

going over the basic facts and issues. The group discussed the elements of the case: the complex

effluent, the multiple chemicals with dioxin-like activity, the ambient atmospheric input of

dioxin-like materials, and the level of scientific knowledge and uncertainty surrounding the case. 

The group agreed that several issues were critical: bioaccumulation, ambient inputs, and species

variability.

The group opened with a discussion of the bioaccumulation factors used in this case and

the issues associated with application of BAFs to TEFs. The charge questions included several

for this case that the group felt were a good starting point, specifically: “What errors are

associated with the use of BAFs, given the uncertainties?”

The group discussed the scientific nature and derivation of the BAFs , and compared

these with the TEFs for wildlife. The BAF issues are largely ones of application, and do not so

much directly affect the TEFs as they influence the final outcome of the technical analysis in

which the toxicity of a “mixture”—in this case, a complex effluent—is assessed. The TEF

approach requires the use of BAFs because of the nature of dioxin-like compounds.

The group agreed that there is significant uncertainty in “generic” BAFs determined for

one site with the intent of using the BAFs elsewhere. However, the species used in deriving the

BAFs and the basic similarity of the systems (both northern freshwater systems) provides greater

usability in the application to the system in the case study. Such is not always the case for other

applications. The group agreed that the greatest certainty in BAFs exists in systems where the
C-E-3

species and the aquatic conditions are the same. The group recommended that research efforts to

improve the understanding and use of TEFs focus on applicability to the same system under

different conditions rather than on expanding to new systems. The group felt that there was less

variability in the system than in the variation among systems, and that the need for site-specific

factors was greatest when moving across widely different systems. The interspecies variation

largely came up when addressing the species that had no counterparts in other systems. One

great source of variability among ecosystems derived from the differences in trophic structure,

especially the number of trophic levels and the nature of the top level.

The use of a Monte Carlo or other probabilistic approach generated some discussion, but

most agreed that the Monte Carlo approach could give greater insight into the variations in the

system, providing that the Monte Carlo was not misapplied. Additional information on this issue

is provided at the end of this summary of the workgroup discussion and in the summary of

discussions in the Risk Assessment Expertise Group (Appendix D).

At least one member felt that the use of probabilistic approaches was largely over-rated. 

He noted that everyone had a propensity for using the same independent variable approach with

the same distributions, mostly normal (in statistics). This member noted that the normal

functions used to predict the distributions of these things do not follow the way in which the

effects actually occur in the natural world. In fact, he noted, real-world variables are often

dependent and not normal in distribution, but either non-normal or stochastic. Thus, some of the

conditions cannot be predicted in the Monte Carlo approach because the wrong formulas are

being used. Other statistical approaches will offer a different analysis of those dependent and

non-predictable events. Two important points are that the variables are often linked, that is they

are dependent, and that the distributions are not normal, in a statistical sense. Additional

discussion of this topic is provided later in this summary.

In discussing this case, the group decided that it would be better to understand the

behavior of a single congener (i.e., TCDD) across many different systems than to understand the

behavior of all congeners in a single system and then expand to the next system. The reason for

this is that the behavior of congeners in relation to one another is more constant than other
C-E-4

variables. Thus, by knowing how one congener performs, the others can be extrapolated with

greater certainty than other extrapolations. The group agreed that acquiring BAF data for a suite

of chemicals would be ideal, but the utility of such data is directly related to its specificity.

The data should be coherent among data types and forms for calculated versus measured

values and for field measurements versus estimates. Different approaches (e.g., TEFs for

individual congeners versus TEQs for a whole effluent) should reveal concordance or lack

thereof. These approaches then turn out to be data checks.

The present case offers a scenario with multiple sources and multiple chemicals and the

requirement to conduct a TMDL for the water body. In this scenario, the TEF makes it possible

to examine options and compare data from widely disparate sources. This is a positive feature of

the approach.

Retrospective Case Study

This case, which involves contaminated sediments from a spill, was handled somewhat

differently in the group discussion than the case study from the previous day. For this case, after

discussion with the steering committee, the facilitator urged the group to reach a decision

regarding the use of the TEF approach in applying the available data to a decision. The group

was asked to determine if a decision could be reached, and specifically if the TEF approach

would or would not affect the outcome of the decision.

The facilitator summarized the case as follows. A chemical spill took place previously,

resulting in contaminated sediments in an upstream segment of a river that flowed into a lake. 

Despite some time passing, populations of several species of wildlife seemed to remain at some

level of risk or impairment. Some of the data on the populations may be more qualitative and

observational than quantitative. Three species have already been identified as species of

concern: lake trout, otter, and Caspian tern. Data were provided to assess the toxic load from the

PCBs using two alternative approaches, either based on total PCB levels in the tissues (and

sediments), or based on the TEF approach for all dioxin-like compounds summed across
C-E-5

individual congeners. The case provided previously determined decision reference values for

action for each of the three species, using either approach. These reference values were given as

single values and as ranges, indicating that if the predicted exposures exceeded these reference

values, some sort of action would be recommended.

The case description included the notes that atmospheric deposition is a source of TCDD,

that there are indications of population effects on wildlife, that the analysis already includes a

conceptual model and selection of endpoints, and that the exposure pathway has some

predictions, but uncertainties as well. There is knowledge of prior eutrophication, with unknown

consequences. The group agreed, reluctantly, to treat the source(s) as constant, without

degradation or recalculation or loss. This assumption makes the “no-action” option less viable.

The group could not make a recommendation for the site to be cleaned up, but attempted

to determine whether the data warranted some type of conclusion about the risks that might lead

to an action, which might be to leave the system alone, study it more, or identify management

options to reduce/control toxicity). A brief analysis and summary of the data on exposure and

reference dose revealed that the two approaches gave somewhat different conclusions. The TEF

approach yielded exposure concentrations higher than the reference values, for the most part,

while the total PCB-estimated exposures yielded values that were somewhat lower, and less

clearly exceeding the reference values. This observation was in a general form, recognizing that

the magnitude of the ranges made it difficult to determine whether or not the predicted values

overlapped the ranges in Table 5 of the case study.

The group discussed how to interpret the data on exposures from the two estimation

methods. The resulting discussion revealed that individual members of the group did not view

the results the same, in that some members did not see such a great difference in the

interpretation based on the two approaches. The group members who thought that results did not

differ between the two approaches saw both as giving borderline “positive” (exceeding the

reference dose) results. Everyone in the group considered the data to be at least in the borderline

category for making a decision, but everyone agreed that there was room for interpretation of the

meaning and significance of the impacts of the exposures. One concern expressed by several
C-E-6

members of the group was the lack of data on ranges, or on variability of the data. The ranges of

values given for the NOAEL in Table 5 was not sufficient. The group wanted data on TEF

variability, BAF variability, toxicity, and so on. There was a general discussion of the need for

species-specific dose-response functions for the biological processes in this ecosystem, and

several members wanted to see the measures or other expressions of variability in the data,

including the TEFs. 

The group had a range of opinions regarding the quality and sufficiency of the data in

supporting a decision on the case using the TEF approach. Basically, all thought more data

would make the decision easier, but only part of the group were satisfied enough with the

available data to make a decision. Others wanted to see site-specific data before concluding that

a decision could be made, and still others thought that population data were needed for the three

species before decisions could begin.

All in the group were satisfied that the TEF approach provided a useful way to

understand the data, the case, and the situation. Also, the available data were sufficient to

support use of TEF approach at least for screening level analyses.

Discussions Relevant to Both Case Studies

A number of points were raised in the course of the two days that the group agreed were

important to include in the summary report of the workshop. The following paragraphs were

drafted by individual members of the workgroup to reflect the nature of the discussion and the

agreement or lack thereof among the members of the group.

Need for Ground-Truthing of the TEF Approach. Field verification of TEFs and the

resulting TEQs is highly desirable, in the least, and was considered essential by some. The TEQ

approach has been examined in the field for birds (Tillitt et al., 1992; Murk et al.; Kubiak et al.,

1989; Harris et al.), fish (Cook and Peterson's retrospective work on lake trout in the Great

Lakes), and mammals (otter in Europe). Effects observed in the field are generally consistent

with what would be predicted from exposures expressed on a TEQ basis. Differences in TEQs
C-E-7

among colonies of double-crested cormorants in the North American Great Lakes explained

more of the differences in hatching success among colonies than did differences in total PCBs

(Tillitt et al.). Among-species variability in absolute sensitivity to dioxin-like compounds is

greater than among-species within-class variability in TEFs.

In the ecological risk assessment process, exposure and effects data are integrated and the

potential for risk is characterized. As a general rule, when exposure levels exceed the effects

level (threshold), expressed as a risk quotient greater than 1.0, excess risk is expressed. When

excess risk is calculated (e.g., when the summation of TEQs exceeds a threshold effects value),

it is important that the potential for effects to occur in natural environments (i.e., at the

population or community level) be assessed. There is a need to ground-truth the TEF/TEQ

approach such that when this approach is used to demonstrate risk that measured effects at those

exposure levels have been observed in field populations.

Status of the TEF Approach as a Screening-Level versus Decisionmaking Tool. As a

general rule, screening level assessment/ranking/scoring tools should have an accuracy in the

range of a factor of 5-10. For more definitive/quantitative risk assessment, the accuracy of the

assessment tool should be less than a factor of 5, preferably between 2 and 3. In general, the

uncertainty associated with the derivation of a TEQ based on class-specific TEFs (i.e., TEFs for

fish, mammals, or birds) is on the order of a factor of 5-10. In light of this, we conclude that the

risk ratios derived using the application of a TEQ value based on TEFs are best viewed as a

screening tool, to provide direction in determining additional data needs. We recognize that in

some cases, for example where the species and endpoints are in fact the species and endpoints on

which the TEF values are derived, the TEQ portion of the risk quotient may be sufficiently

accurate to justify more confidence in the estimation of risk inferred from the hazard quotient. 

In these cases, obtaining a better estimate of the TEQ would be less of a priority than obtaining a

better estimate of exposures. 

This level of certainty in the TEF-derived estimates was apparent in the interpretation of

data from the two case studies. One of the reasons the group members held somewhat different

interpretations was the understanding of the data that support the TEFs used in these cases. The
C-E-8

members with greater familiarity with this data were more satisfied with the final outcomes.

Uncertainty. Throughout the two case study discussions, group members urged the

incorporation of variability and/or uncertainty in the numerical expressions and elsewhere. This

expression of uncertainty may take different forms, only one of which is a Monte Carlo or

probabilistic analysis. The group acknowledged the dual nature of uncertainty, which includes

both statistical variability and the unknown. The group was confident that the former type of

uncertainty was adequately, if not always, expressed in statistical ways, often as standard error or

mean, as confidence limits, or other such numerics. However, the latter type of uncertainty,

unknowns (also called ignorance), is less well expressed and is not conveyed through traditional

statistics or through probabilistic approaches.

Both no-effect levels (NOAELs) and computed values, whether of risk or of variables

such as BAFs, have to explicitly incorporate error. No meaningful comparison of values (e.g.,

benchmark versus NOAEL) is possible otherwise. In the retrospective case study, the

benchmark values did come with a range, but the computations did not include explicitly

propagated errors in the input values, which are significant. 

Uncertainty does not have to be described in a probabilistic framework. Lack of

knowledge, or ignorance, does not easily lend itself to a frequentist's interpretation. We have to

clearly distinguish natural variability (heterogeneity) from ignorance-based uncertainty. The

first can be described probabilistically; the second, by either range or in terms of fuzzy

arithmetic (i.e., using formulas that do not recognize frequentist views but attempt to reflect

subjective uncertainty). Reference to such techniques (Ferson and Ginzburg, 1996) can be found

in Dr. Ginzburg's pre-meeting comments (Appendix C). 

Grounding of the TEF Approach in Ecology. The idea of a so-called "no-effect"

concentration certainly provides a useful first cut, but it effectively disconnects toxicologicallybased decisions from ecological considerations. In practice, and in our case study, the "noeffect" concentration is exceeded, and another level of analysis (i.e., population or ecosystem

dynamics) is need to make ecologically-based judgments. Specifically, we need information
C-E-9

about:

• the slope or, preferably, the shape of the dose-response curve above the "no-effect"

level; and

• demographic characteristics of the target species, including an idea of the strength of

population regulation (density-dependence).

In the retrospective case study, even though a no-effect level is exceeded, population

level consequences are uncertain and may not warrant immediate action but rather a closer look

at population dynamics. The group was confident that many, if not most, cases would exhibit

some ambiguity in the interpretation of effects of dioxin-like chemicals on animal populations. 

Thus, data on and evaluation of population dynamics is the next step.

Measurement Issues. The group agreed that current knowledge of dioxin, Ah receptors,

the dioxin-like compounds, and the mechanism of action of the dioxin-like compounds

necessitates measuring all of the relevant congeners when taking environmental samples. It

makes no sense scientifically to measure only the one congener, TCDD.
C-E-10

WORKGROUP #2

Facilitator: Janet Burris

Prospective Case Study

The following sections summarize the discussions completed by Group 2 during its

review of the prospective case study. The summary is organized according to the primary issues

discussed, and indicates which members contributed to the related text.

TEF Derivation and Application in Ecological Risk Assessment (Janet Burris and Mark

Hahn). The group reached an agreement that the TEFs used in ecological risk assessment should

be selected using a hierarchical approach. Species-specific values should be used if available or

a value for a closely (phylogenetically) related species. The WHO consensus TEFs would be

used as a default. The proposed system also considered in vivo results preferable to in vitro in

selection of the most appropriate endpoint . See the section entitled "Selection of TEF values for

use in a TEQ-based Ecological Risk Assessment)," in the Group 2 summary of the retrospective

case study, below, for a more complete description of the proposed hierarchy.

The group expressed the need to have more information on the derivation procedures

used to identify the WHO TEFs, as well as the underlying data. This information is necessary to

understand the uncertainties in the values and to carry those uncertainties through the risk

assessment.

The group felt that the rounding procedures used in the WHO TEFs do introduce some

uncertainty in the ecological risk assessment. This uncertainty is, however, quantifiable and can

be evaluated in a sensitivity analysis where results are calculated using both rounded and

unrounded values. A sensitivity analysis can also be performed to evaluate uncertainties

associated with the use of species-specific TEFs versus the WHO TEFs and endpoint-specific

TEFs versus the WHO TEFs. Further quantification of the uncertainties in the derivation of the

WHO TEFs may be limited as the TEFs were not derived in a systematic manner. The exact

process used to derive the WHO TEFs is not specified in the current draft report.

Discussion of Appropriate TEF Values for Species of Interest in the Case Study (Janet
C-E-11

Burris). The group's selection of TEFs for the species of interest was:

• Bald Eagle: The group recommended using the WHO avian TEF values. The

rounded and unrounded values should both be used to perform a sensitivity analysis. 

The group was comfortable with extrapolating from the endpoints used to derive the

TEF to the reproductive assessment endpoint, but this needs re-evaluation

 • Bull Trout: The group recommended using the rainbow trout values and the endpoint

of early life stage mortality. 

• Otter: The group recommended using the WHO TEFs as a default, since values for

the otter or more closely related species are not known to be available. Uncertainties

in the extrapolation from the TEF endpoint to the assessment endpoint are not large, as

these values (the TEFs) represent relative potency values.

TEF Approach Compared with TCDD or Total PCBs (Bjorn Brunstrom). The group

agreed that the TEQ approach provides significantly more information compared with an

assessment based on TCDD alone, total PCDDs, or total PCBs. However, use of the TEQ

approach should not replace or exclude risk assessment based on total PCBs, since non dioxinlike effects may also be important. In the prospective case study, the TEF/TEQ approach

provides information on the potential toxicity of the mixture of congeners in the effluent and

identifies the specific congeners that may make the largest contribution to toxicity.

Using a TEQ approach in the modeling process is principally similar to modeling a single

compound. The challenge is to get valid fate and transport-related parameters for a number of

compounds. 

The Use of Median Values to Derive TEFs (Mike Devito and Janet Burris). The group

discussed the issue of the use of median values (EC50 and LC50 data) to derive the TEFs and the

implications of this on their application in risk assessments where no effect levels are used to

identify risks. The group decided that median values for the derivation of TEFs is appropriate

and does not effect their application in risk assessment. 

Derivation of REP values typically use the ratio of ED50s, EC50s, LOELs or NOELs of

the test chemical compared to TCDD. A question often asked about the use of the TEF
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methodology is whether the TEF should be based on the ratio of the lower end of the doseresponse curve as opposed to the ED50s. The relative potency for full agonists should be

independent of the level of response where the measurements are determined. One advantage of

using ED50s is that the ED50 can be determined with greater accuracy and precision than the

NOEL, LOEL, or either the ED01 or ED10. The increased precision and accuracy are related to

the greater ease of measuring a 50% response above background. In comparison, determination

of LOELs, NOELs, or ED10s and ED01s are fraught with uncertainty. The ability to accurately

detect NOELs and LOELs, ED10s, or ED01s is dependent upon the magnitude of the maximal

response compared to the controls and the variability in the measurement of the control and

lower-dose groups. Estimates of these low-dose parameters are also highly dependent upon

study design and dose selection. The uncertainty of the ED01 or ED10 estimate is much greater

than the uncertainty of the estimated ED50 (Devito et al., 1997). The increased uncertainty of the

estimate of the low-dose parameters would increase the uncertainty of the REP. While REPs

may be dependent upon where on the dose-response curve they were derived, the greater

accuracy and precision of the ED50 determination provides a significant advantage for its use in

estimating the REP.

While the relative potencies of full agonists are independent of the measured response,

the same is not true for partial agonists. Partial agonists do not have the same intrinsic efficacy

of full agonists and may antagonize the effects of full agonists under certain conditions. 

Assigning REPs or TEFs for these chemicals is problematic. Under certain conditions

(predominately low-dose exposures), the interactions of full agonists and partial agonists may be

additive. Under high-dose conditions, the interactions of full and partial agonists maybe

antagonistic. The use of the TEF methodology for partial agonists should be viewed with

caution. The interactions may be additive in the low-dose region but non-additive in the high

dose region of the dose-response curves. 

Use of Bioassay-derived TEQs (Christopher Metcalfe). The group discussed the

potential for using bioassay-derived TEQs in the prospective risk assessment scenario. 

Reservations were expressed over the value of this technique for monitoring total TEQs

discharged in wastewater. The potential for generating “false positive” responses in in vitro

assays was considered high. For instance, in pulp mill effluents, there are high concentrations of
C-E-13

potent EROD-inducing compounds (e.g. retene) which are not AhR-agonists. PAHs will also

give a response in some in vitro assays if they are not removed from complex environmental

mixtures by fractionation. The biologically-based TEQ assays are not chemical-specific and

therefore do not show causality.

Discussions with Sean Kennedy indicated that bioassay-derived TEQs could be applied

to estimating burdens of planar HAHs in fish and wildlife in the lake system, both before

construction of the mill and for monitoring purposes after the mill is operational. This was

discussed by the group and was considered a valid application for these techniques.

 Challenges and New Uncertainties Associated with Modeling the Exposure of AhR

Agonists (Christopher Metcalfe). The group concluded that the use of the TEF/TEQ approach in

this case study introduces no uncertainties in exposure estimation that are not also common to

other chemical-specific assessments. The challenge of the TEF/TEQ method is that it requires

the modeling of individual congeners, as well as modeling of the fate and transport of the many

congeners, in comparison to traditional modeling for individual or lesser numbers of chemicals. 

Measurements of specific congeners may be problematic, considering congener-specific

detection limits; and the problem will be greater for analyses of water, due to the higher volumes

required than for sediment or tissue samples. When the detection limits are high, uncertainty is

high, but the uncertainties are offset by the advantages of using congener-specific as opposed to

aggregate (i.e., total chemical) values. 

The group further discussed the fate and transport issues in two parts: physical/chemical

and metabolic parameters. Regarding the former, there were concerns expressed about the

quality of chemical and physical data that could drive the mass balance models involved in the

risk assessment process for the prospective scenario. The quality of log Kow data was considered

relatively high, but there was concern over the lack of empirically-derived Koc data, and over the

quality of Koc values derived from Kows. There was also concern over the lack of credible

Henry’s Law constants (H) and photolysis data for planar HAH congeners, although it was

acknowledged that this may not be a problem for hydrophobic compounds that bind readily with
C-E-14

the particulate phase. Finally, there was discussion of and general agreement with a point made

by Joe DePinto (in the Wednesday morning meeting) and by Phil Cook, that the partitioning of

the planar HAHs between sediment and water and the particle dynamics in the aquatic system

are the most important processes that will drive the mass balance model. There was concern

expressed that “getting these processes right” will require an extended and expensive research

effort, a luxury that may not be feasible for the risk manager. For further discussion of this

topic, see the section of this summary dealing with generic mass balance modeling, below.

Regarding metabolic parameters, concern was expressed over the lack of information on

the metabolic capacity of target organisms for planar HAHs. This may have implications for

estimating BAFs and BSAFs. It was pointed out that many fish-eating birds and mammals

appear to “enrich” PCB congener 126 and appear to metabolize PCDFs relative to the levels in

the fish they are eating. Therefore, a knowledge of the metabolic capacities of fish-eating target

vertebrates for the various planar HAHs is essential for generating BAFs in the prospective

scenario.

Requirements and Considerations in Analytical Design Associated with TEF-Based as

Compared to Aggregate Analyses (Patrick O’Keefe). The TEF approach requires quantitative

analytical data on a large number of PCB and PCDD/PCDF congeners. Consequently, more

complex and costly analytical methods must be selected compared to those used for the

measurement of total PCBs. In general terms, sample extracts will need to be analyzed by gaschromatography/mass spectrometry (GC/MS) using isotope-labeled internal standards rather

than by GC with electron capture (EC) detection, the current method-of-choice for total PCBs.

While many laboratories engaged in PCB analysis do not have the equipment or expertise to

carry out these procedures, those laboratories currently involved in PCDD/PCDF analysis do

have the appropriate equipment and expertise.

Since compound identification is more specific using GC/MS methods, the possibility of

false positives is considerably reduced compared to using GC/EC. Quantitation is also improved

when isotope-labeled internal standards are included rather than the surrogate compounds used

in GC/EC. However, it should also be understood that, in the TEF approach, the PCB and

PCDD/PCDF congeners with the largest contributions to the TEQs are usually present at very
C-E-15

low concentrations relative to the total PCB concentration. Consequently, rigorous quality

control procedures will be required to ensure accuracy and precision in the analytical data. In

addition to the generally accepted internal quality control samples (blanks, duplicates and

spikes), standard reference materials should be used for calibration purposes. Currently, fish

tissue and soil sample standard reference materials are available for 2,3,7,8-substituted

PCDDs/PCDFs and for coplanar (non-ortho) PCBs. Similar materials are not currently available

for mono-ortho PCBs. Round-robin studies using selected samples from different matrices

represent an alternative method for comparing the results from different laboratories.

In the prospective case study, there are five matrices of concern: avian eggs (bald eagles),

fish tissue (bull trout and lake trout), mammalian tissue (otter), sediment, and water. In this

scenario, a fish tissue TEQ residue level of 9 pg/g was judged to be the level of concern. Since

the proposed pulp mill would release PCDDs/PCDFs but not PCBs, the discussion can be limited

to the two former compound classes. Laboratories with proficiency in PCDD/PCDF analysis can

achieve detection limits of 1 pg/g for individual PCDD/PCDF congeners. With this detection

limit, it should be possible to obtain reliable data near the level of concern for fish, especially

since the major contributor to the TEQ value (2,3,7,8-TCDF) has a TEF value of 0.05. 

Accuracy of ± 30% should be achievable with a signal-to-noise value of 10, but would be

reduced for data near a detection limit of 3:1, the minimum detection limit used in many

laboratories.

It is difficult to determine the residue levels of concern in the bald eagles and otters,

since the data are discussed in the scenario using water quality considerations which are in turn

related to ingestion levels leading to toxic responses. However, if it is assumed that the no-effect

threshold levels in the retrospective study (100 pg/g for bird eggs and 60 pg/g for mink liver) are

also appropriate for the prospective study, then laboratories that are capable of analyzing the fish

tissues should have no problem meeting the higher detection limits of the avian and mammalian

species. The same considerations apply to sediment samples, since the biota-sediment

accumulation factors are all less than 1 and therefore the sediment concentrations will exceed

the fish tissue concentrations.
C-E-16

When the tissue levels of concern are translated into water quality guidelines, using biota

accumulation factors, the maximum allowable total water concentrations (MACt

w) will all be less

than 2 pg/L, and in many cases they are below 0.1 pg/L. Currently, there are no routine

laboratory procedures available that are capable of meeting these detection limits.

 

This discussion of the prospective scenario assumes that all of the permissible discharge

will be allocated to the pulp mill. However, in the description of the risk assessment scenario it

was proposed that only 25% of the maximum allowable load (MAL) would be allocated to the

pulp mill. If the mill is required to measure this increment at the levels of concern for the three

species described above, the data will be very close to the 1 pg/g detection limit, and accuracy

will probably not be better than a factor of 2 and possibly even a factor of 10. As a final note,

the group realized that, in this scenario, use of the TEF approach will require the plant to assume

responsibility for producing state-of-the art analytical data. However, by doing this the plant

management will have considerable flexibility in controlling the mix of PCDDs/PCDFs in the

plant effluent.

Challenges in the Modeling the Food Chain Transfer of AhR Agonists (Janet Burris and

Mike Devito). The group concluded that sound modeling could be completed if the transfer of

congeners from the sediment/water interface and sediment transport within the lake are both well

understood. However, the group observed several challenges in the modeling of food chain

transfer. 

First, there is concern that the poor understanding of biodegradation and metabolism of

specific congeners may limit modeling. Biodegradation and metabolism rates are absent or

incomplete. Second, composition of the diet and metabolism affect the transfer of congeners.

There are large mixture composition changes of congeners from plankton to fish and from fish

to fish-eating birds. Third, the food chain transfer of congeners is species-specific. To address

this factor, knowledge of the composition of the diet of species within the food chain needs to be

clearly understood and considered in the modeling exercise. Fourth, the data available for

estimates and projection of food chain transfer are good for fish but are not adequate for

wildlife. Measured biomagnification factors (BMFs) are better for fish, but are much less

certain than those for fish-to-wildlife transfers. 
C-E-17

The group observed that BMFs should be consistent with the dosimetric basis of the

TEF. In ecological risk assessments of dioxin-like chemicals, BMFs and TEFs are important

parameters in exposure and toxicity assessment, respectively. The BMF is a function of the

physical/chemical and pharmacokinetic parameters of the individual chemical. The TEF of a

chemical is related to its binding affinity to the Ah receptor and its pharmacokinetic parameters

compared to TCDD. Because BMFs and TEFs are both dependent upon pharmacokinetics,

differences in pharmacokinetics of a chemical between species may alter the BMF and the TEF

in the same direction. For example, if the BMF increases between two different species for a

test chemical, while the BMF remains constant for TCDD, the TEF may increase across these

species as well, since retention or accumulation of the chemical has increased relative to TCDD

across the two species. A note of caution is that the relationship between BMF and TEF is not

direct. Chemicals that have high BMFs may have low or no TEFs. However, if BMFs for a

chemical change dramatically between species, the TEF may also change dramatically between

species. Hence large changes in BMF between species warrant further examination of the TEF

for that congener.

Bioaccumulation Factors (BAF), Biomagnification Factors (BMF), and Toxic

Equivalency Factors (TEF) (Mike Meyer). Site-specific BAFs for PCB and dioxin congeners

will provide greater accuracy in prediction of wildlife and fish tissue congener concentrations

than will extrapolation of the BAF derived for the GLWQG. The GLWQG BAF was derived

from Lake Ontario data that predicted tissue concentrations of total PCBs and 2,3,7,8-TCDD in

lake trout. The trophic structure of water bodies can differ greatly, as a function of the

complexity and structure of the food web. Stressors other than chemical contamination (e.g.,

climate, nutrient loading, introduction of exotic species, and so on) can create perturbations in

the recruitment at various trophic levels (such as prey fish). Elimination, reduction, or additions

of organisms to the food chain can affect the trophic transfer of chemical contaminants, thus

altering BAFs from site to site. When using the TEF approach for risk assessment, calculations

of BAFs become more complex, as the individual congeners will have unique BAFs, and those

BAFs can vary between sites. If this level of precision and accuracy is desired, sampling,

modeling, and database management efforts will become more complex and costs will increase. 

However, the additional information will allow risk managers to provide congener-specific

discharge allowances or remediation goals.
C-E-18

It has been shown that non-ortho PCB congeners are more readily bioaccumulated and

are more resistant to metabolism when compared to ortho-substituted PCB congeners. It follows

that wildlife tissues may contain a larger proportion of dioxin-like PCB congeners per gram of

total PCBs than do fish, reflecting the increased toxic potency of the total PCBs measured in

their tissues. Therefore, direct extrapolation of the TCDD BMF from the GLI will provide

erroneous risk estimates. A recent study in the Netherlands demonstrated the effect of this error. 

In that study, a diet-specific BMF of 14 was calculated from fish to otter on a total PCB basis,

however the BMF for total TEQs was 41 (Leonards et al., 1997, Env. Tox. Chem 16: 1807-

1815). This was mainly due to the high BMF of PCB 126. When incorporating BMFs into the

risk model, it is essential that congener-specific BMFs be used, and, when possible, speciesspecific BMFs should be measured at the risk assessment site.

Possible Errors in the Application of the TCDD Water Standard (Janet Burris). The

group identified two possible errors in the application of the TCDD water standard to the

prospective scenario. The standard does not consider the enrichment of PCB 126 from fish to

wildlife or the loss of chlorinated dibenzofurans in some species of birds. Some members of the

group observed that underestimation of effects may result.

Other Possible Approaches: Generic Mass-Balance Modeling (Christopher Metcalfe). 

The group explored the idea of alternate or improved approaches to the solution of the problem

presented in the prospective case study. How could we do it better? Several group members

acknowledged that a model for the system could be constructed but it was not clear whether such

a model would be predictive given the uncertainties. Other existing food chain models could be

used if they could handle the metabolism issues. Another idea that was put forth involved the

use of a generic mass-balance model.

A potential problem with the prospective scenario for TEQ-based risk assessment is the

complexity of the mass-balance modeling exercise. Discussions at the workshop indicated that

knowledge of sediment-water partitioning and particle dynamics within an aquatic system are

essential for accurate prediction of assimilative capacity. Our experience with mass-balance

models indicates that it may take several years of research effort to obtain the necessary

information to develop an accurate model. In a prospective scenario, this level of research
C-E-19

would not be possible. Therefore, several members of Group 2 felt that another approach to this

situation would be to develop “generic” mass balance models for different types of ecosystems

that could be used by risk managers to make decisions. Generic models could be developed for:

• Small eutrophic and small oligotrophic lakes;

• Small embayments that connect to larger lake systems;

• Large embayments that connect to large lake systems;

• High flow and low flow rivers; and

• Marine or estuarine systems with high or low tidal flushing.

Risk managers could use these generic models to make initial decisions on the siting of industrial

facilities, and so on. More complex site-specific mass-balance models could be used later, at the

discretion of the risk manager or siting applicant affected by the decision. In the latter case, it

may be beneficial to adopt a “polluter pays” policy, in which the applicant is responsible for

paying for the more complex modeling exercise.

Overall Conclusions Concerning the Use of the TEF/TEQ Approach in the Prospective

Case Study and Associated Uncertainties (Janet Burris). The group observed that uncertainty is

less manageable in the prospective case study (risk assessment) than in the retrospective case. In

a retrospective case, actual measurements of congeners in the environment or within the food

chain could be used to decrease uncertainties. In a prospective application, such measurements

are not possible.

Uncertainties in the exposure profile appear to be equal to or greater than those

associated with stressor-response (effects) assessment. A sensitivity analysis would be beneficial

to evaluate the various uncertainties in the risk estimates. Suggested parameters for sensitivity

analyses include: TEFs, Kow, Koc, Psocw , and BMFs (location- and species-specific)

The group observed that risk managers are attempting to “titrate the system” to permit

the release of the last increment of chemical into the system, based on its full assimilative

capacity. Use of the TEF/TEQ approach reduces uncertainty in the assessment, as there is not a
C-E-20

better approach to be applied, and use of a TEQ is more appropriate than the current TCDD

standard. However, the uncertainty in the exposure profile may be result in a high enough

uncertainty that the risks of loading the system beyond capacity are much greater than the

manager is willing to face. 

Measuring Uncertainty at the Population Effect Level (Mike Meyer). At present, little

effort has been made to assess the impact of “threshold levels of effect” on target wildlife

populations. In most cases, the assumption is that early life stage mortality measured in

laboratory studies (with species such as rats and chickens) translates into population level effects

in wildlife (such as otters and bald eagles). This extrapolation is not supported by correlational

data from the field nor with laboratory studies using relevant wildlife species (the exception

being feeding studies in mink). This lack of knowledge produces a level of uncertainty that

dwarfs any presented by the TEF/TEQ approach. The current ecological risk assessment process

is seriously compromised by the inability of the “best available science” to accurately predict

effects (see Meyer, 1998, Env. Tox. Chem. 17: 137-138). Until this uncertainty is addressed, the

effectiveness of the TEF approach to establish water quality guidance is suspect.

Retrospective Case Study

The following sections summarize the discussions completed by Group 2 during its

review of the retrospective case study. The summary is organized according to the primary

issues discussed.

Selection of TEF Values for Use in a TEQ-Based Ecological Risk Assessment (Mark

Hahn). As part of a TEQ-based ecological risk assessment (ERA) for PCBs, PCDDs, PCDFs,

and other compounds that act via the Ah receptor (AhR), toxic equivalency factors (TEFs) or

relative potencies (RPs or REPs) are used to convert congener-specific chemical residue data

into 2,3,7,8-TCDD toxic equivalents (TEQs). Although the TEQ approach is based on the broad

similarities in relative potencies that exist across different endpoints and species (Safe, 1990),

specific REP values can vary between species and across endpoints within a species. In an ideal
C-E-21

ERA situation, congener-specific relative potencies would be known for the species of concern

(e.g., lake trout) and the endpoint of concern (e.g., early-life-stage mortality). Often, however,

such data are not available. The use of REP values determined in a different species or for a

different endpoint, or use of a "consensus TEF," represents an important source of uncertainty in

a TEQ-based ecological risk assessment. This uncertainty is separate from the uncertainty

occurring as a result of species differences in sensitivity to TCDD, which affects the choice of

the "threshold" or action level to which the calculated TEQ is compared.

In the absence of species-specific REP values for the endpoint of concern, a decision

must be made as to which REP or TEF values provide the most accurate measure of relative

potency for use in calculating TEQs from congener-specific residue data. In essence, the

decision involves choosing between the uncertainty introduced by species differences in relative

potencies (for the same endpoint) and endpoint-dependent differences in relative potencies (in

the same species). In some cases, both types of uncertainty may be present. Common sense

suggests that one should select the REP or TEF value that represents the best (i.e., most

accurate) information available. However, since the uncertainty or "potential error" inherent in a

given REP/TEF choice is not always known (i.e., quantifiable), the choice is often not clear. 

 The approach described in Figure E-1 provides a framework for thinking about the

different kinds of REP or TEF values that may be available, and the types of uncertainty

inherent to each. Using this matrix, selection of a REP or TEF value is based on a hierarchical

approach involving use of the best available information, relative to the ideal choice—a speciesspecific REP for the endpoint of concern.
C-E-22

Framework for choosing relative potency values for use in

Ecological Risk Assessment for fish, birds, and mammalian wildlife.

Same Species Related Species

(e.g., same genus

or family)

WHO TEFs

(“Class-specific”) 

Tier 1

1a (endpoint of

concern)

Best

1b (other in vivo

toxic endpoint)

Tier 2

(in vivo CYP1A)

Tier 3

(in vitro CYP1A)

Tier 4

(QSAR)

Worst

Figure E-1.

 In the first column, four tiers reflecting and prioritizing the various in vivo and in vitro

endpoints used to determine REP values are listed. These categories are based on the tiered

approach used by WHO in deriving TEFs for fish and birds (van den Berg et al., 1997). The

first tier has been subdivided to differentiate in vivo data for the endpoint of concern (Tier 1a)

from other in vivo toxic endpoints (Tier 1b). As with the WHO TEF approach, the highest

priority is given to REPs determined for the in vivo endpoint of greatest concern. Lower

priorities are assigned to REP values determined using endpoints more distantly related to the

assessment endpoint.

 The top row in the matrix indicates the phylogenetic relatedness of the species of

concern to the species in which REPs were determined. It is divided into three levels, reflecting
C-E-23

different degrees of uncertainty. If REPs are available for the species of concern, there is

interspecies extrapolation and so no uncertainty associated with species extrapolation (although

there could be differences between populations within a species). If REP data are available for a

closely related species—a species within the same genus or family, for example—uncertainty is

higher due to potential species differences, but not as high as when REP data are from a more

distantly related species within the same class or when "consensus" TEF values (such as the

WHO TEFs) are used. 

 The matrix might be used to consider and select among the types of REP data

available, by comparing the relative position of each set of REP data to the ideal. An example is

provided later in this discussion.

 The rationale behind this hierarchical approach is a mechanistic understanding of

AhR-mediated toxicity as well as empirical data that support such the extrapolation of relative

potency data across endpoints and/or species. There is abundant evidence that most effects

(endpoints) of dioxin-like compounds, whether biochemical effects such as induction of

CYP1A1 or toxic effects such as ELS mortality, occur through the same initial step—binding to

the AhR. The structure-activity relationships are similar across various endpoints, including

receptor binding, CYP1A induction, and various forms of toxicity (Safe, 1987; Safe, 1990). The

basic AhR-dependent mechanism of toxicity is the same in most vertebrate species. Most

vertebrate taxa express an AhR (Poland and Glover, 1987; Hahn et al., 1994; Hahn et al., 1997)

and are sensitive to dioxin toxicity (Poland and Knutson, 1982; Cook et al., 1991), although

there may be exceptions (Jung and Walker, 1997). Despite the commonality of the basic

mechanism, however, there may be species- or endpoint-dependent variation in specific details

of the mechanism that result in different REPs. 

 The basis of the four-tiered approach used to derive the "class specific" WHO TEFs for fish

and birds has been described (van den Berg et al., 1997). This approach involves weighting

REP values based on the endpoint for which they were derived, with preference to REPs

determined for in vivo toxicity in developing embryos.
C-E-24

 The basis for the phylogenetic approach reflected in the top row of the matrix in

Figure E-1 is both theoretical and empirical. It assumes that two species that are more closely

related phylogenetically will have REP values (determined for the same endpoint) that are

similar or identical. This approach is supported by data such as that showing that the REPs for

CB-126 to produce ELS mortality in lake trout and rainbow trout are similar (Zabel et al., 1995). 

However, it is clear that a more systematic effort to test this assumption will be needed. 

Moreover, although it is expected that closely related species will in general exhibit similar

REPs, exceptions to this assumption for certain species and/or congeners may be revealed as

additional data are collected. 

 As stated earlier, it is important to keep in mind that the issue of species- or endpointspecific differences in REP values is separate from that of species differences in sensitivity to

TCDD. In fact, there may be little or no relationship between the two issues. Two species that

differ widely in their sensitivity to TCDD can have similar REP values for most congeners. For

example, chickens are 119-fold more sensitive than Pekin ducks to in vitro effects of TCDD, yet

for TCDF and PCB congeners 126 and 81 the REPs differ less than 5-fold between these species

(Kennedy et al., 1996). 

 The matrix in Figure E-1 is intended to provide a framework for thought and

discussion concerning the selection of REPs for ecological risk assessments. There are a number

of practical questions that arise when considering this approach: 

• Often, REP data sets are incomplete. Is it appropriate to draw REPs from multiple

data sets to calculate TEQs for a given species? For example, in performing a risk

assessment for lake trout, the only "Tier 1a" REP value that exists for lake trout is for

CB-126. For other congeners, REPs exist only for rainbow trout or other fish species. 

A "best available information" approach would lead one to choose the lake trout REP

for CB-126, and rainbow trout REPs for the other congeners.

• The phylogenetic approach assumes that closely related species will exhibit similar

REP values. But how close is "close"? Can we expect species within the same family

to show greater similarities in REPs than occur between families, or must the species

be within the same genus before such similarities are evident? Again, more data are

needed to resolve this question. 

• One of the most difficult questions concerns choosing between uncertainties based on
C-E-25

species differences versus endpoint differences, in the absence of data that would

allow one to quantify the uncertainty in each. For example, suppose a risk assessor is

performing an assessment for Caspian terns, using measured, congener-specific

concentrations of PCBs, PCDDs, and PCDFs in tern eggs. There are no data on REPs

for ELS mortality in Caspian terns, but let us suppose that there are REP values (A)

for in vitro CYP1A induction in Caspian terns, and (B) for in vivo ELS mortality in

domestic chickens (the latter used to establish the WHO "consensus TEFs"). Perhaps

there are also data for in vivo CYP1A induction in embryos of common terns, a

closely related species (C). Figure E-2 illustrates the positions these three types of

data would have in the matrix. Which of these three sets of REP data would provide

the most accurate estimate of TEQs in Caspian terns? One option when confronted

with such a decision might be to perform the TEQ calculations with each set of REPs;

a comparison of the resulting TEQ values might provide a measure of the uncertainty

in selecting any one of the REP sets.
C-E-26

Matrix showing position of three choices of REP values

for the scenarios described above

Same Species Related Species

(e.g., same genus

or family)

WHO TEFs

(“Class-specific”) 

Tier 1

1a (endpoint of

concern)

? B

1b (other in vivo

toxic endpoint)

Tier 2

(in vivo CYP1A)

C

Tier 3

(in vitro CYP1A)

A

Tier 4

(QSAR)

Figure E-2.

An example related to the last of these practical questions can be found in the

retrospective scenario discussed at the workshop. In this case, the avian species of concern is the

Caspian tern. Figure E-3 shows TEQ values and STEQ determined using two different sets of

REPs. The first set of values are based on the WHO TEFs, which are derived largely from

chicken embryo data (van den Berg et al., 1997). The second set of values are based on REPs

from in vitro CYP1A induction in embryo hepatocytes from common terns, Sterna hirundo

(Lorenzen et al., 1997), a species closely related to the Caspian tern (Sterna caspia). There is a

substantial difference in the STEQ calculated using each set of values; the difference is due

largely to the 3-and 17-fold lower relative potencies for CB-126 and CB-77 in common terns as

compared to the WHO TEFs.
C-E-27

TEQ values and TEQ, determined using two different sets of REPs,

for Caspian terns in the retrospective scenario.

Bird TEF1 Caspian Tern

Egg TEQ

Common Tern

REP2

Caspian Tern

Egg TEQ

PCB-77

PCB-126

PCB-169

TCDF

0.05

0.1

0.001

1

54.17

275

0.32

2.79

0.003

0.03

0.02

0.4

0.32

82.5

6.4

1.1

 TEQ for all

congeners (including

those not shown)

426 184

1

WHO TEFs (van den Berg et al., 1997).

2

REP values for in vitro CYP1A induction determined for common tern (Lorenzen et

al.., 1997).

Figure E-3.

The WHO TEFs, based largely on chicken embryo mortality, are thought to be preferable

because the endpoint used is more relevant to the effect of concern. However, the differences

between WHO TEFs and common tern REPs could indicate some fundamental difference

between terns and chickens in the relative potencies of these congeners. The comparison is

useful in providing an indication of both the magnitude and source of the uncertainty (in this

case, two PCB congeners). Thus, this type of analysis contributes to the risk assessment itself as

well as identifying additional data that might help to reduce the uncertainty.

When confronted with a lack of REP data for the species and endpoint of concern,

alternative REP values must be chosen. This choice involves the introduction of uncertainty

based on species differences and/or endpoint differences in relative potencies. There is currently

insufficient data to determine which type of uncertainty is greater, and thus to guide the selection

of particular values. A best available information approach is recommended; this may involve

use of multiple REP values and sensitivity analysis of the resulting TEQs. 
C-E-28

Selection of Appropriate TEF Values for Species of Interest in the Case Study (Janet

Burris). The group recommended construction of a hierarchy for selection of TEFs for use in

the retrospective risk assessment. The group could not complete the hierarchy in consideration of

the time and the absence of data and derivation procedures for the TEFs in the WHO report. The

group recommends expressing consensus TEF values as a range instead of point estimates, since

risk management decisions are often not point estimates. Use of a range could provide the

manager with and understanding of the uncertainty and confidence in the results.

In its review of the retrospective case study, the group selected TEFs for the species of

concern as follows:

• Lake trout: The group recommended using the rainbow trout REPs for all congeners

except PCB 126, for which the lake trout REP was selected. Lake trout is the species

of concern, and it was decided that a species-specific value would result in less

uncertainty than a REPs for another species. Both REPs (lake trout = 0.003 and

rainbow trout = 0.005) are for early life stage mortality. The group agreed that

uncertainty is introduced into the assessment by extrapolation from rainbow trout to

other non-salmonid species (e.g., largemouth bass). This uncertainty could not be

quantified, since it represented a lack of knowledge about relative REPs between fish

species.

• Caspian tern: The group recommended using the WHO TEF of 0.1 (embryo mortality)

and a common tern in vitro EROD REP of 0.03. The common tern value represents

data for a more closely related species (compared to the chicken data used for the

WHO TEFs). However, the common tern REP is based on an in vitro endpoint, which

is less useful. In this case, the group decided to run the analysis with both REPs to

provide a sense of uncertainty by giving a range of reasonable risk estimates. This

would be one way of avoiding the use of a single point estimate in the risk assessment.

• Otter: The group recommended using the TEFs in the WHO report, as values could

not be identified for the otter or another closely related species. The group could not

fully evaluate the uncertainties in the mammalian wildlife TEFs, since the derivation

of these values is not fully described in the WHO report. The group observed that the

values seem consistent with mink exposure studies of AhR agonist mixtures.

The group would have liked to use the same TEF selection hierarchy for mammals as

was recommended for birds and fish. However, no description of the derivation procedures for

the mammalian TEFs or the underlying data is included in the present WHO report. Questions

raised by the group included:

 • What endpoints were used? Were in vivo endpoints used?

• What species-specific data was available?
C-E-29

• Can cellular effects, tumor promotion, and other endpoints be extrapolated to

reproductive effects?

• Were the endpoints used in the derivation applicable to reproductive and population

level effects?

• How were consensus values selected? Were these the most conservative values?

• What rounding procedures were used?

The group agreed that the risk assessor would need this information to document the values and

assumptions for the risk assessment, to examine the uncertainties, and for “transparency”

requirements.

Exposure Issues (Janet Burris). The group concluded that the exposure assessment is

driven by the analytical measurements used to determine concentrations of congeners in

sediment and tissues. Several members of the group observed that if sediment remedial goals

were needed, these could be easily identified based on the linear relationship between sediment

concentration and receptor tissue concentrations, as depicted in the equations in the retrospective

case study. In other words, to reduce tissue concentrations in the organisms of concern,

sediment concentrations would need to be decreased proportionately.

The group concluded that use of the TEF/TEQ approach in the fate and transport

modeling is not different from traditional chemical-specific methods. Use of a congener-specific

approach in the retrospective case study does not create a new problem for the exposure

assessment. It could, however, change the approaches engineers would use in developing

remediation plans.

Analytical Considerations (Patrick O’Keefe). In the retrospective scenario, the analytical

problems are not as difficult as those associated with the prospective study. In the first place,

data on concentrations of chemicals in the water are not required to carry out the risk

assessment. Second, the TEQ values for the avian and mammalian species of concern—the

Caspian tern and river otter, respectively—are determined by relatively high concentrations of

certain PCB congeners, primarily PCB 126. Using the detection limit of 1 pg/g discussed above,

regulatory agencies should be able to assess the effects of an order of magnitude reduction in
C-E-30

sediment concentrations on tissue residues in the species of concern.

Sampling programs need to be carefully designed in order to answer the questions posed

in a risk assessment. For instance, fish samples should be collected in the same vicinity as

sediment samples for calculation of BSAFs, and sediment samples should be collected along an

appropriate grid to monitor chemical input from an effluent, as in the case of the prospective

scenario. In addition to analytical precision, sample heterogeneity will play a major role in

determining the number of samples required to obtain data with an acceptable variance. For

statistical purposes, it may be necessary to carry out a preliminary sampling program to obtain

information on the variability of the chemical concentrations with respect to sampling location.

This is especially true in the case of sediments where organic carbon concentrations can have a

major influence on residue concentrations. For tissue samples, other variables such as sex and

age are important factors in determining contaminant concentrations.

 

Food Chain Modeling (Janet Burris). The group concluded that a full food chain model

was not necessary. Such a model would be difficult to construct due to lack of equilibrium in

system, heterogeneity, and detection limit issues. It would be especially difficult to build a

model from sediment to the water interface to fish. A partial model would, however, be useful. 

Under assumption of steady-state could use the linear relationship between sediment and biota

levels to assess various sediment remediation options without having to deal with lower trophic

levels. The partial model would use site-specific BSAF/BMFs. Such a partial model would

allow the risk manager to examine reductions of chemicals in target species under given

remediation scenarios, but would be difficult to use to predict chemical movement over time.

If not all of the exposure data were available as provided in the case study, then the

group recommended obtaining sediment samples in a transect of depositional zones to get a

sense of gradient, obtaining measurements of congeners in prey species, and relating these to the

dietary composition and forage habits of the predators.

Risk Characterization (Janet Burris). The prospective assessment is set up to provide a

point estimate of exposures and risks that can be identified as a hazard quotient for an individual
C-E-31

organism. The real question is how or whether the adverse effect to individuals is reflected in

the population.

Risks are not identified for the lake trout. The risks for the Caspian tern are summarized

below and were identified by the group as being “on the edge.”

 Individual

Threshold Exposure Hazard Quotient

(WHO TEF) 100 pg/g 426 pg/g 4

Common tern 100 pg/g 185 pg/g 2

 Total PCBs 5 g/g 4.5 g/g <1

The group discussed the likelihood of population-level effects. Based on the hazard

quotients for embryo mortality, the group did not expect population effects, but acknowledged

that not all possible endpoints were assessed. The potential effect of species-specific TEFs is

noted here. The common tern exposure is 2 times the threshold. Exposure based on the WHO

TEF is, however, 4 times higher than the threshold. Basing the assessment on TCDD or any

other single compound would in most cases underestimate the potential risk in comparison to the

TEF/TEQ approach.

The group also noted that Caspian terns are not year-round residents and could be

getting exposures elsewhere. The group advised that further data would be required to evaluate

the origin of exposure including possible reference samples and a weight-of-evidence evaluation.

Risks for the otter were also identified as being “on the edge:”

 Individual

Threshold Exposure Hazard Quotient

TEF 60 pg/g 144 pg/g 2

TCDD 60 pg/g 1.4 pg/g <1

 Total PCBs 2.0 g/g 1.0 g/g <1

Action Decision (Janet Burris). When asked what they would do next, the group

concurred they would leave the site alone. They recommended monitoring trends in TEQs with

time to illustrate declining risk probabilities to the population. No further loading of the system

should be permitted. The monitoring should include further diagnostic studies to better

characterize risks, including expression of risks in the context of population-level effects. Group
C-E-32

members felt that the risk characterization should also include a description of what the system

may look like in 10 years, an estimate of effects of a 100-year flood and redistribution of

contaminated sediments, and an estimate of habitat destruction that could result in more risks. In

order to understand if there is a population level effect, we would need to understand the level of

decreased reproduction associated with a meaningful reduction in the population. 

When the group voted on action versus no action in the retrospective case study, two

members voted for action and eight for no action.

If the risk manager chose to proceed with remediation, the otter would be the primary

species of concern. The otter is related to the mink, but is known to have greater sensitivity to

AhR agonists. The group would provide scenarios to the risk manager and discuss population

level effects in the context of adverse effects associated with the remedial alternatives.

References

Cook, P.M., Kuehl, D.W., Walker, M.K., and Peterson, R.E. (1991) Bioaccumulation and toxicity

of TCDD and related compounds in aquatic ecosystems, in Banbury Report 35: Biological Basis

for Risk Assessment of Dioxins and Related Compounds, Gallo, M.A., Scheuplein, R.J., and Heijden,

K.A.V.d., Editor., Cold Spring Harbor Press: p. 143-167.

Devito, MJ, Diliberto, JJ, Ross, DG, Menache, MG, Birnbaum, LS (1997). Dose-response

relationships for polyhalogenated dioxins and dibenzofurans following subchronic treatment in

mice. I. CYP1A1 and CYP1A2 enzyme activity in liver, lung and skin. Toxicol. Appl.

Pharmacol. 147: 267-280.

Hahn, M.E., Karchner, S.I., Shapiro, M.A., and Perera, S.A. (1997) Molecular evolution of two

vertebrate aryl hydrocarbon (dioxin) receptors (AHR1 and AHR2) and the PAS family. Proc. Natl.

Acad. Sci. U.S.A. 94: 13743-13748.

Hahn, M.E., Poland, A., Glover, E., and Stegeman, J.J. (1994) Photoaffinity labeling of the Ah

receptor: Phylogenetic survey of diverse vertebrate and invertebrate species. Arch. Biochem.

Biophys. 310: 218-228.

Jung, R.E. and Walker, M.K. (1997) Effects of 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) on

development of anuran amphibians. Environ. Toxicol. Chem. 16: 230-240.

Kennedy, S.W., Lorenzen, A., Jones, S.P., Hahn, M.E., and Stegeman, J.J. (1996) Cytochrome

P4501A induction in avian hepatocyte cultures: a promising approach for predicting the sensitivity

of avian species to toxic effects of halogenated aromatic hydrocarbons. Toxicol. Appl. Pharmacol.

141: 214-230.
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Lorenzen, A., Shutt, L., and Kennedy, S.W. (1997) Sensitivity of common tern (Sterna hirundo)

embryo hepatocyte cultures to CYP1A induction and porphyrin accumulation by halogenated

aromatic hydrocarbons and common tern egg extracts. Arch. Environ. Contam. Toxicol. 32: 126-

134.

Poland, A. and Glover, E. (1987) Variation in the molecular mass of the Ah receptor among

vertebrate species and strains of rats. Biochem. Biophys. Res. Commun. 146: 1439-1449.

Poland, A. and Knutson, J.C. (1982) 2,3,7,8-Tetrachlorodibenzo-p-dioxin and related halogenated

aromatic hydrocarbons: examination of the mechanism of toxicity. Annu. Rev. Pharmacol. Toxicol.

22: 517-554.

Safe, S. (1987) Determination of 2,3,7,8-TCDD toxic equivalent factors (TEFs): support for the

use of in the vitro AHH induction assay. Chemosphere 16: 791-802.

Safe, S. (1990) Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans

(PCDFs), and related compounds: environmental and mechanistic considerations which support the

development of toxic equivalency factors (TEFs). CRC Crit. Rev. Toxicol. 21: 51-88.

van den Berg, M. and et al. (1997) Draft Report of Meeting on the derivation of Toxic Equivalency

Factors (TEFs) for PCBs, PCDDs, PCDFs and other dioxin-like compounds for humans and

wildlife. World Health Organization (WHO), June 15-18, Stockholm, Sweden (manuscript).

Zabel, E.W., Cook, P.M., and Peterson, R.E. (1995) Potency of 3,3',4,4',5-pentachlorobiphenyl

(PCB 126), alone and in combination with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), to produce

lake trout early life stage mortality. Environ. Toxicol. Chem. 14: 2175-2179.
C-E-34

WORKGROUP #3

Facilitator: Charles Menzie

Prospective Case Study

The group initiated their discussions by outlining and reviewing the features of the case

study. This provided a basis for understanding the approach that would be taken. Key aspects

of the analysis included identifying the organisms of concern, the pathways of exposure

(conceptual model), and the target concentrations in water that are judged “acceptable” for the

various organisms.

Our subsequent discussions are organized around two categories. The first involves

points that were made relative to the case study. The second involves a discussion of how to

identify and track uncertainties in an ecological risk assessment process that includes application

of the TEF/TEQ method.

Part 1: Points Drawn from the TMDL Case Study

With respect to the case study, the group discussed various issues. Each of these issues

is described further in the paragraphs that follow.

Rounding and Significant Digits. Some participants noted that care should be taken to

not overstate (via calculation) the number of significant digits. Some numbers presented in the

case study appear to be presented at a level of precision that is unlikely to have been achieved. 

By presenting the numbers to two or more significant numbers, a false sense of precision is

given.

Application of REP versus TEF Values. The participants discussed how values should

be selected for use in the TEF/TEQ methodology. The group concluded that, where available,

REP values should be selected over generic TEF values. The group believed that uncertainties

were reduced if taxa-specific unrounded values were utilized in lieu of the “order of magnitude”
C-E-35

values presented in the WHO TEF report. A hierarchical approach was suggested, within which

the best and most appropriate values were selected first. The REP values are not rounded and

have their own level of significance. When asked if the group would usually elect to use REP

values even when they needed to extrapolate to another species (e.g., trout to largemouth bass),

the group favored use of REP over TEF values. In part, this view reflected concerns over the

rounding done when the WHO TEF values were developed. A few members of the group did

not agree with this, because of the variability that might exist around REP values within a class

of animals. When asked if they felt that the results would be compromised if they were required

to use TEFs, the participants said that they would not, but that there would be additional

uncertainty in the estimate.

Uncertainty in TCDD Toxicity Values and Target Concentrations. The group

acknowledged that there was uncertainty in the target numbers used in the analysis. Underlying

this uncertainty was the toxicity data as well as the models used to derive the concentration of

TCDD in water.

Selection of Chemicals and Percentage Allocation. The group noted that for this

exercise, we did not have to pick the chemicals. However, we should recognize that atmospheric

PCBs also add to the Waste Load Allocation. For this exercise, it had already been decided that

the site would be given 25% of total allocated load. This allows us to focus on this smaller set

of chemicals.

Uncertainty Analysis. There was considerable discussion concerning uncertainty

analysis. A more detailed account of this discussion is provided later in this workgroup report. 

However, workgroup participants discussed the need to track and document uncertainties in the

ecological risk assessment process. At a minimum, this includes a narrative discussion. It is

also possible to talk about the sources and potential magnitudes of uncertainties. One member of

the workgroup noted that we could make a semi-quantitative attempt to estimate the magnitude;

for example, we can say REPs have uncertainty of about 2 or 5.

Part 2: Identifying and Tracking Uncertainties
C-E-36

The workgroup discussed procedures that might be used to identify and to take into

account the various uncertainties in the assessment. We worked through a process that is based

on the assessment of multiple stressors. A key aspect of this approach is writing down the

criteria used to judge the uncertainties associated with each aspect of the analysis. This

information is also useful for helping clarify the issues of concern in discussions with managers

and with stakeholders.

After much discussion, the group agreed upon an ordinal ranking system for uncertainty

that reflected our level of confidence regarding the relative uncertainty in the information. We

chose values of 1 (most confidence) to 4 (least confidence). We selected a range of 1 to 4 in part

because this is consistent with the number of categories in the WHO TEF document.

A quantitative aspect of uncertainty which the group did not include but acknowledged as

important was the magnitude of the error around the values. Such information would be

important for sensitivity analyses. Our group did not address this because of time and data

constraints. However, we believe that this is important to consider.

The group then identified the areas of uncertainty within the ecological risk assessment

process. These do not represent all the possible areas, and some areas could have been broken 

into smaller components. Our purpose here was to illustrate the concept rather than arrive at a

definitive approach. The areas that were evaluated include:

• uncertainty criteria for TEF values;

• uncertainty in comparing TEFs to target water levels;

• uncertainty factors for BAF values;

• uncertainty factors associated with species extrapolations; and

• uncertainty factors associated with the exposure model.

Each of these areas is described in more detail below.

Uncertainty Criteria for TEF Values. Our group had two separate discussions
C-E-37

concerning these criteria. The first was on our first day; on the second day we revisited the issue

and made some modifications. The criteria we came up with on our first attempt were:

• Level 1: egg injection with mortality endpoint;

• Level 2: whole organism with other endpoints;

• Level 3: in vitro studies (e.g., enzyme induction); and

• Level 4: QSARs (from in vitro data).

Based on this set of criteria, we initially assigned the bull trout REP an uncertainty level of 1, the

value for eagle an uncertainty level of 2, and the value for river otter an uncertainty level of 1. 

These assignments reflect the levels of confidence given in the WHO report. When we revisited

these assignments, it was noted that some participants had more certainty in the fish values than

in those for mammals and birds and that the initial assignments did not capture this. The

following ranking procedure was subsequently proposed:

• Level 1: REP with population-relevant endpoint;

 • Level 2: TEF from in vivo study with toxicological endpoint;

• Level 3: TEF based on biochemical response; and

• Level 4: TEF based on QSAR or enzyme induction.

Individuals familiar with the derivation of the TEF values noted that there was more

uncertainty associated with the bird values than with the mammal values (which are based on a

rich body of data). These individuals had greatest confidence in the fish values. Based on our

reassessment of uncertainty levels, the uncertainty for the bull trout remained at 1, but

uncertainty for the bald eagle increased from 2 to 3 or 4, and for the river otter from 1 to 2 or 3.

Uncertainty in Comparing TEFs to Target Water Levels. The group assigned uncertainty

factors based on taxonomic extrapolation from the available toxicity databases. It was noted that

there were other sources of uncertainty in these values, including the models used to derive the

water concentrations in the criteria documents. Considering only the taxonomic extrapolations,

the following criteria were developed:
C-E-38

• Level 1: same species;

• Level 2: same genera;

• Level 3: same family; and

• Level 4: same class.

Because bull trout are in the same genus as the reference species, comparison of the TEF with

the water quality criterion was assigned an uncertainty factor of 2. Because bald eagles are

raptors and the bird standard is based on galliforms, this comparison was assigned an uncertainty

of 4. Finally, since river otter and mink are in the same family, the comparison for mammals

was given an uncertainty ranking of 3.

Uncertainty Factors for BAF Values. The following criteria were proposed:

• Level 1: site-specific measurement;

• Level 2: lab validated with field;

• Level 3: field data with no lab, or lab with no field corroboration; and

• Level 4: BAF based on Kow (prediction).

The group agreed that the BAF for 2,3,7,8-TCDD would be assigned an uncertainty value of 2. 

We also decided that the value should be 2 for the relative BAFs of all congeners.

Uncertainty Factors Associated with Species Extrapolations. The group noted some of

the differences that can occur among taxa. We decided to use the same taxonomic extrapolation

uncertainty criteria:

• Level 1: same species;

• Level 2: same genera;

• Level 3: same family; and

• Level 4: same class.

The assigned uncertainty factors were both congener- and taxa-specific. For the eagle, the three
C-E-39

dioxin congeners (Congeners 1-3) were assigned an uncertainty of 1, the first furan (Congener 4)

was assigned an uncertainty of 4, the next two furans (Congeners 5 and 6) were assigned an

uncertainty of 3, and Hx (Congener 7) was assigned an uncertainty of 2. For the bull trout, all

congeners were assigned an uncertainty of 1. For the river otter, dioxins were assigned an

uncertainty of 1 and furans were assigned an uncertainty of 2, based on metabolic

considerations.

Uncertainty Factors Associated with the Exposure Model. We noted that this was a

simplified TMDL model that incorporated Kow and Henry's Law constants, and that there are

chemical- and environment-specific factors added into these models. We noted that the model

assumes equilibrium or at least steady state; in the real world, however, non-equilibrium

conditions are likely to be present. Further, we noted that there were uncertainties associated

with the various physicochemical parameters used to predict the behavior of the chemicals.

For fish, we assigned the exposure model an uncertainty of 2 or 3, while for eagle and

otter we assigned an uncertainty of 3 or 4. The higher uncertainty associated with the eagle and

otter models reflect the anticipated increase in uncertainties associated with relating exposures at

these higher trophic levels to sources within the lake. The workgroup noted that any exposure

model is expected to have uncertainty associated with it, given all the simplifying assumptions

that need to be made.

Representation of Uncertainties

The different sources and “levels” of uncertainty can be displayed in a table and are

amenable to mathematical representation and analysis. As an example, the results of our

analysis are illustrated in Figure E-4. The table indicates which areas of uncertainty are

potentially most important. It also provides an indication of the levels of uncertainty that

accompany results of the risk analysis for specific organisms of interest.
C-E-40

Relative Uncertainties in the Ecological Risk Assessment Including Use of TEF Values

Ranks for

uncertainty

Species/Congener

TEFs BAFs

Species

Sens./Extrapolation

Exposure

Model

Threshold

concentration Total

Bull

trout 1 2 2 2 2 Species specific 9 30

Bull

Trout

1 2 1 Congener specific 21

221

321

421

521

621

721

Bald

Eagle 4 3 4 4 4 19 55

Bald

Eagle

1 3 1 36

231

331

434

533

633

732

River

Otter 3 3 3 4 3 16 48

River

Otter

1 3 1 32

231

331

432

532

632

732

Criteria are described in the text. This approach and these values are presented for illustration only.

Figure E-4.

Retrospective Case Study

TEF/TEQ Issues Related to Measurements
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The retrospective case study is based on measurements of individual PCDDs, PCDFs,

and PCBs in tissues and environmental media. The group considered several issues related to

using such information in ecological risk assessments that rely, in part, on a TEF/TEQ approach.

Accuracy and Precision of the Measurements. Because the TEF/TEQ approach relies on

information related to individual compounds, we discussed the ability of available methods to

provide accurate and precise results. Individuals familiar with the methodologies used to

identify and quantify individual PCDDs, PCDFs, and PCBs in tissues and environmental media

believed that the accuracy and precision of the measurements was good. Reanalysis of samples

gives similar results. There are certified reference standards. For the concentrations provided

in the case study, one individual noted that the measurement error was probably about 30%. 

Another reported that he observed coefficients of variation on the order of 100%. Measurement

error tends to increase with decreasing concentration. Overall, the group concluded that

analytical error was not a large source of uncertainty in the overall analysis.

Variability in data can arise from biological variability. In particular, it was noted that

different size or sex of fish would be a source of variability. Further, analytical variability in

lipid measurements could be a source of variability in data that are reported on a per lipid basis. 

Variability in analysis of environmental media can also result from variability in the physical

and chemical characteristics of these media (e.g., grain size and organic carbon content of

sediments.)

Detection levels. The group discussed detection levels with respect to the use of

concentration data for risk assessment purposes. The group concluded that available analytical

methods could achieve detection levels low enough to support the TEF/TEQ approach as it is

applied to ecological risk assessment. However, it was noted that detection levels should be

stated as part of the Data Quality Objectives and that the laboratory should be informed of the

detection levels they will need to meet. When considering the detection levels that need to be

met, one participant noted that it was important to be aware of the dose response curve. It is also

helpful to consider which compounds contribute most to the toxicity of the mixture and to be

sure that detection levels are adequate to quantify ecologically significant concentrations of these
C-E-42

compounds.

Design issues. The workgroup concluded that sampling design issues were comparable

between the TEF/TEQ method and other methods used to evaluate risks associated with PCDDs,

PCDFs, and PCBs. The case study included sample sizes of 12. Participants noted that they

would want to know values for eggs from 12 different terns or 2 nests. Such information would

be important to understanding uncertainty.

Costs. Analytical costs associated with congener analyses are higher than for total PCBs

or analysis of an individual compound.

Other Effects not Captured in TEF/TEQ. Participants noted that some PCB compounds

could affect the species of concern via toxic mechanisms other than binding to the AhR receptor.

Care must be taken to identify effects that may be important during planning stages of the

analysis.

Working with Partial Data Sets. The TEF/TEQ methodology involves an assessment of

PCDDs, PCDFs, and PCBs. However, for some situations, only one of these groups may be

important. The workgroup concluded that partial data sets for one of these three groups would

be adequate for evaluation if available information indicated that this was the only group of

importance at the site or for the application. If available information indicates that background

concentrations of other groups contribute significantly to the TEQ estimate, then those groups

would have to be included in the analysis because the TEF/TEQ approach involves comparisons

to TEQ benchmarks or dose-response curves. In such cases, it would be inappropriate to

consider the effects of any one group alone.

Utilizing Surrogate Methods in Concert with the TEF/TEQ Methodology

The workgroup concluded that there were opportunities to complement the TEF/TEQ

methodology with surrogate analytical approaches. It was noted that once you have this

information, and have calibration between individual congeners and total PCBs, you could use
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total PCBs as well. This would involve validating and calibrating as you go. Surrogate methods

could be employed during investigations or in helping guide remedial measures. Workgroup

participants noted that surrogate approaches work for the Great Lakes. It is not known how well

these methods might work in other systems.

Comparison of TEQ vs. Traditional Total PCB Approach

The workgroup concluded that the traditional PCB approach would have missed

important aspects of the problem. The traditional total PCB method would have underestimated

risks as compared to the TEF/TEQ method.

Adequacy of Available Information for Decisionmaking Purposes

The workgroup identified several pieces of information that would be desirable for

supplementing the information already at hand. These are discussed below.

Background Conditions. The case study did not provide information on background

conditions. Therefore, even though body burdens of PCBs could be explained in terms of

exposure to PCBs in the lake, there is the possibility that PCBs in the lake and in the tissues is

comparable to those found in other lake systems. This could be evaluated by examining other

lake systems or by evaluating conditions upstream of the spill. The group felt that a better

understanding of this issue would be needed before proceeding with a recommendation

concerning management options.

Reasonableness of Association Among Concentrations in the Species of Interest. The

workgroup concluded that it was important to examine the data with respect to the underlying 

conceptual model and relationships among media and receptors from other systems. Body

burdens may vary among clutches of eggs. Typically, there are higher concentrations in the

second clutch.

System and Food Web Issues. Workgroup participants concluded that additional
C-E-44

information on food web relationships would be valuable. For example, it was noted that

relationships within the case study were being inferred by assuming simple food chains. 

However, food chains could be much more complex and quite different from those assumed or

inferred from the available data.

Vertical Distribution of Compounds in Sediment Cores. Information on concentrations

in sediment cores would provide insight into the history of deposition, including pre-spill

conditions. Such information could also be used to judge the rate of recovery.

Deriving “Acceptable” Target Levels for Environmental Media

Mechanics of Back Calculating Target Levels. The workgroup concluded that this would

involve working the exposure equations backward. This would involve beginning with

“acceptable” TEQ levels in ecological receptors and deriving “acceptable” target levels in

sediments or water. The major challenge here is that the TEF/TEQ methodology involves

tracking a number of compounds. This is primarily a logistical challenge. However, backcalculating will require information on the environmental behavior of the individual compounds. 

However, it may be possible to limit back-calculation to those compounds that contribute most

to the TEQ levels in ecological receptors. Back-calculation would involve applying appropriate

mass-loading models as well as biological uptake models. These could be simple or complex. 

One participant noted that the analysis should extend beyond simple calculations of average

concentrations. Individual animals do not experience the average, but rather the overall

distribution.

Regression analyses. Apart from bioassay-based approaches (e.g., cell lines), use can be

made of regression relationships between individual congeners, which can easily be measured

with low cost GC-ECD techniques, such as PCB 153 and concentrations of toxic congeners (e.g.,

PCB 126) or total TEQ concentrations. In several ecosystem studies, relationships have been

observed for PCB 153 and total TEQ concentrations in various fish species, otter, invertebrates,

and cormorant eggs covering several orders of magnitude in PCB TEQ concentration. As a rule

of thumb, a 0.5 to 1.0 order of magnitude range of uncertainty may be involved in extrapolations
C-E-45

based on this relationship. As the regression relationships may be species- or site-specific, a

preliminary validation may be required. Members of the workgroup recommend further

exploration of this regression approach, using available data from monitoring studies, and

further assessment of the feasibility of this potentially cost-effective approach.

Body burdens in some animals are size- or age-dependent. In the case of otters, for

example, a recommendation was made to sample young carcasses.

Risk Management Options

The workgroup concluded that the decision “to clean up or not clean up” was one of

several possibilities. The workgroup discussed several possible risk management options that

could be explored using technical information.

Evaluating the Future Potential and Time Course for “Recovery”. This is the “no action”

or “limited action” option. Essentially, this option would involve providing the risk manager

with information concerning how the system may change in the future. With respect to the

TEF/TEQ approach, this will involve understanding how concentrations of individual

compounds in media and tissues will change in the future. Processes that could be involved

include burial in sediments, degradation rates, metabolism, dissolution, and loss rates via

evaporation and advection from the system.

Developing Additional Lines of Evidence. Workgroup participants discussed additional

lines of evidence that could support decisionmaking. These included direct observations of

effects on populations and bioassays. An example of observations that could be made on birds is

to look for scelma (Great Lakes edema/mortality syndrome) in tern eggs.

Identifying Alternative Remedial Strategies. Remedial options could vary in type and

magnitude. The efficacy of these alternatives could be judged by applying “what if” scenarios

utilizing the TEF/TEQ methodology.
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Integrating Lines of Evidence from Different Levels of Ecological Organization

The workgroup discussed the strengths and limitations of different lines of evidence that

could be used to complement the TEF/TEQ approach. These discussions underscored

differences in perspective related to “bottom up” approaches represented by applying the

TEF/TEQ concentration-based methodology and “top down” approaches represented by making

direct observations on populations. Bioassay methods fall in between. The group acknowledged

that these different approaches had various strengths and limitations. The group concluded that

it would be useful to explore how these different lines of evidence could be brought together to

provide an overall assessment. With respect to organisms and population biology, it would be

beneficial to foster exchanges between scientists working with the various bioassays,

conservation biologists (working with populations), and ecological risk assessors. It would be

helpful, for example, to have these groups work on a model for the first year of life for

salmonids.

For this case study, participants noted the importance of having information for several

trophic levels. In the present case, the fish populations do not appear to be at risk. However,

species at even higher trophic levels—the tern and the otter—exhibit levels that indicate

potential risks.

Appendix C-F

WRITTEN COMMENTS FROM OBSERVERS
C-F-1

National Institutes of Health

National Institute of 

Environmental Health Sciences

P. 0. Box 12233

DEPARTMENT OF HEALTH AND HUMAN SERVICES Research Triangle Park, NC 27709

Date: January 16, 1998

From: Angélique P.J.M. van Birgelen, Ph.D.

Subject: 3,3',4,4'-Tetrachloroazobenzene, hexachlorobenzene, 1,2,3,4,6,7- 

hexachloronaphthalene, 1,2,3,5,6,7-hexachloronaphthalene, and 1,2,3,4,5,6,7,-

heptachloronaphthalene as additional dioxin-like compounds for inclusion in

TEF concept

To: Chair of the Workshop on the Application of 2,3,7,8-TCDD Toxic Equivalency

Factors to Fish and Wildlife

Cc: Drs. K. Abdo, J. Bucher, and G. Lucier

Inclusion in TEF concept

A dioxin-like compound is a compound that binds to the aryl hydrocarbon receptor, results in dioxinlike effects, and bioaccumulates. These are the three factors for inclusion of dioxin-like chemicals in

the TEF scheme (Ahlborg et al., 1992, 1994). 3,3',4,4'-Tetrachloroazobenzene (TCAB),

hexachlorobenzene (HCB), 1,2,3,4,6,7-hexachloronaphthalene (PCN 66),

1,2,3,5,6,7hexachloronaphthalene (PCN 67), and 1,2,3,4,5,6,7-heptachloronaphthalene (PCN 73) are

compounds which bind to the Ah-receptor, result in dioxin-like effects, and have been shown to

bioaccumulate and should therefore be included in the TEF concept.

Sources of TCAB, HCB, and PCNs

TCAB

TCAB is present as a contaminant of 3,4-dichloroaniline (DCA) and its herbicidal derivatives

Propanil, Linuron, and Diuron (Poland et al., 1976; Sunström et al., 1978; Bunce et al., 1979; Hill et

al., 1981). In addition, environmental contamination by TCAB occurs from the degradation of

chloranilide herbicides (acylanilides, phenylcarbamates, and phenylureas) in soil by peroxideproducing microorganisms (Bartha et al., 1968; Bartha and Pramer, 1969; Lay and Ilnicki, 1974). It

is also formed by the photolysis and biolysis of 3,4-dichloroaniline (Mansour et al., 1975; Miller et

al., 1980).

HCB

HCB was used as a fungicide for crops such as wheat, barley, oats, and rye to prevent growth of fungi.

In the mid seventies the application of HCB as a fungicide was discontinued due to concerns about

adverse health effects. In Tunisia however, HCB was still used as a fungicide, seed-dressing, and

scabicide in sheep in 1986 (IPCS, 1997).

In industry, HCB has been used in the manufacture of pyrotechnics, tracer bullets, and as a fluxing

agent in the manufacture of aluminum. HCB has been used as a wood preserving agent, a porosity

control agent in the manufacture of graphite anodes, and as a peptizing agent in the production of
C-F-2

nitroso and styrene rubber for tires (IPCS, 1997).

HCB is generated as a by-product in various chemical processes such as thermal chlorination,

oxychlorination, and pyrolysis operations in the manufacture of chlorinated solvents such as carbon

tetrachloride, trichloroethylene, and tetrachloroethylene (IPCS, 1997). HCB is a by-product during

the manufacture of pesticides, such as pentachloronitrobenzene, chlorothalonil, dacthal,

pentachlorophenol, atrazine, simazine, propazine, and maleic hydrazide (IPCS, 1997). In the herbicide

Propanil it has been found in concentrations up to 10-14% (IPCS, 1997). Furthermore, HCB is

released into the environment by waste incineration. The release of HCB from all municipal

incinerators in the U.S. was estimated by the EPA to be between 57 and 454 kg per year as

documented in 1986 (IPCS, 1997).

PCNs

Polychlorinated naphthalene formulations have been used in industry as dielectric fluids in capacitors,

transformers, and cables. The products containing technical PCNs are still in use or disposed in

landfills. PCNs are also formed during production of technical mixtures of chlorobiphenyls and can

be found in concentrations up to 1% in various polychlorinated biphenyl formulations (Falandysz et

al., 1996).

Binding to the Ah-receptor

TCAB and HCB have an affinity for the Ah-receptor 5- and 10,000-fold lower that TCDD,

respectively (Hahn et al., 1976; Poland et al., 1976; Schneider et al., 1995). Preliminary results from

an Ah receptor binding assay indicate a relatively high binding activity of the hexa- and

heptachlorinated naphthalenes (Hanberg et al., 1990).

Dioxin-like effects

TCAB

TCAB exposure results in typical dioxin-like effects in rodents which include chloracne and dermal

lesions, body weight loss, thymic atrophy, hepatotoxicity, developmental toxicity, induction of

cytochrome P4501A1, anemia, and an increase of porphyrins, in chick embryo liver cell cultures (Hsia

et al., 1980, 1981, 1982; Hill et al., 1981; Mensink and Strik, 1982; Schrankel et al., 1982; Hsia and

Kreamer, 1985; McMillan et al., 1990).

HCB

HCB results in dioxin-like effects, such as induction of hepatic CYP1A1 and CYP1A2 activities,

hepatic porphyrin accumulation and excretion, alterations in thyroid hormone levels and metabolism,

alterations in retinoid levels, liver damage (hepatocellular enlargement, bile duct proliferation,

necrosis), reduction in reproduction, splenomegaly, increase in mortality, neurological alterations

(such as tremors, paralysis, weakness, hyperexcitability), teratologic effects, and immunotoxic effects

(IPCS, 1997). HCB is a carcinogen in rodents (IPCS, 1997). HCB exposure also results in

phenobarbital-like effects, such as induction of hepatic CYP2B activity (IPCS, 1997).

PCNs

PCN 73, and a mixture of PCN 66 and PCN 67 induced EROD and AHH activities in a rat hepatoma

H-4-II cell line (Hanberg et al., 1990, 1991).
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Bioaccumulation

TCAB

TCAB has a log octanol/water partition coefficient of 5.53 to 6.69 (US-EPA, 1985; Hashimoto et al.,

1994). The solubility in water is calculated to be 1 μg/l (US-EPA, 1985). In male Sprague Dawley rats

administered radiolabeled TCAB by gavage, 66% of the dose was excreted in urine and feces after 24

hours (Burant and Hsia, 1984). The pattern indicated a biphasic elimination, consisting of an early

rapid phase with a half-life of 18 hours and a slow terminal phase with a half-life greater than 20 days.

HCB

HCB has a log/octanol water partition coefficient of 5.5 (IPCS, 1997). The solubility in water has been

reported to range from 0.005 (mg/L at 25oC) to 0.035 ppm (IPCS, 1997; Kenaga, 1980). The (whole

body) half-life of HCB in male Wistar rats has been reported to be 20 days (Yamaguchi et al., 1986).

In male Sprague Dawley rats and male white rabbits, the half-life was calculated to be 24 days and

32 days, respectively (Scheufler and Rozman, 1984). The major route of excretion was via the feces

in both rats and rabbits. In rhesus monkeys, the half-life has been estimated to be 2.5 to 3 years

(Rozman et al., 1975).

PCNs

PCN 66 and PCN 67 were selectively retained in the liver of rats exposed to Halowax 1014, a

commercial mixture of PCNs (Asplund et al., 1986, 1994; Jacobsson et al. 1992). In marine mammals

such as harbour porpoise a BN9 value greater than I was observed only for the pair of PCN 66/PCN

67 (Falandysz, 1997). PCN 66/PCN 67 and PCN 73 have been found at high concentrations in cod

liver samples from Southern Norway (Schlabach et al., 1995).

Relative potency estimates for TCAB, HCB, and PCNs

Table 1. Relative potency estimates for 3,3',4,4'-tetrachloroazobenzene (TCAB).

Effect TCDD TCAB Relative potency

for TCAB

Reference

Binding affinity to the Ah

receptor (nM)

0.27 1.1 0.2 Poland et al., 1976.

EC50 for binding to the

mouse hepatic Ah receptor

(nM)

1.22 6.03 0.2 Schneider et al. 1995.

ED50 (nmol/kg) for induction

of aryl hydrocarbon

hydroxylase in chicken

embryos

0.31 2.0 0.2 Poland et al., 1976.

LD50 (ng/egg) in chicken

embryos.

0.2 44 0.005 Higginbotham et al.,

1968; Schrankel et al.,

1982.
C-F-4

Cytochrome P4501A1

induction in the skin in a 90-

day oral gavage study in

female B6C3F1 mice with

TCDD and TCAB.

0.000003-0.00001 Hébert et al., 1993.

Table 2. Relative potency estimates for hexachlorobenzene (HCB).

Effect TCDD HCB Relative potency

for HCB

Reference

Binding affinity to the Ah

receptor (nM)

0.18 2100 0.00009 Hahn et al., 1976.

EC50 for EROD induction in

chicken hepatocytes (nM)

0.014-0.020 130-150 0.00009-0.0002 Sinclair et al., 1997.

EC50 for accumulation of

uroporphyrin in chicken

hepatocytes (nM)

0.002-0.004 25-35 0.00006-0.0002 Sinclair et al., 1997.

Hepatic porphyrin

accumulation in female rats

0.0007 Cantoni et al., 1981.

Table 3. Relative potency estimates for polychlorinated naphthalenes (PCNs).

Effect Relative potency 

for PCN 66/PCN 67

Relative potency 

for PCN 73

Reference

AHH activity in a rat

hepatoma H-4-II cell line

0.003 0.003 Hanberg et al.,

1990.

CYP1A1 activity in a rat

hepatoma H-4-II cell line

0.002 0.003 Hanberg et al.,

1990,1991.

Impact on TEQ

TCAB

Based on an annual production volume of 10 million pounds of Propanil in the US and the

concentration of TCAB in Propanil ranging from 1,000 to 2,700 μg/g, this could lead to an annual

release of 12,000 kg of TCAB into the environment due to Propanil alone (Sunström et al., 1978;

Bunce et al., 1979; Hill et al., 1981; US-EPA, 1987 as cited in McMillan et al., 1991). With an

annual production volume of 0. 1 to 1 million pounds of DCA and a concentration of TCAB in DCA

ranging from 9 to 8,600 μg/g, this could lead to a production of 3,900 kg of TCAB per year in the

US (Sunström et al., 1978; Bunce et al., 1979; Hill et al., 1981; US-EPA, 1985). Analyses of a rice

plot treated with 6.7 kg Propanil/hectare indicated a TCAB concentration of 0.09 ppm (Kearney et
C-F-5

al., 1970). Six of 99 soil samples from the rice-growing states of Arkansas, California, Louisiana,

Mississippi, and Texas contained 0.01 to 0.05 ppm TCAB, whereas no residual concentration of

Propanil was detected (Carey et al., 1972). Assuming TCAB is three orders of magnitude less potent

than TCDD (to pick a number), this indicates that the concentration of TCAB in the mentioned soil

samples, calculated as TEQ could be as high as 90 ng TEQ/kg soil. For comparison, the mean level

of dioxin-like compounds (PCDDs and PCDFs only) has been estimated to be 8 ng TEQ/kg soil

(US-EPA, 1994). Using the same calculation for the production of TCAB due to Propanil and DCA,

this could lead to an annual release of 16 kg TEQ in the environment.

HCB

Levels of HCB measured in bald eagle eggs from the British Columbia coast from 1990 to 1992

ranged from 0.012 to 0.025 mg/kg wet weight (Elliott et al., 1996). Assuming HCB has a relative

potency of 0.0001 (to pick a number), this could be as high as 25 ng TEQ/kg wet weight. For

comparison, the concentration of PCDDs, PCDFs, and PCBs (planar and mono-ortho substituted)

ranged from 120 to about 320 ng TEQ/kg in bald eagle eggs from the same areas (Elliott et al.,

1996).

PCNs

The concentration of PCN 66/PCN 67 in cod liver samples from Southern Norway ranged from 927

to 123,000 pg/g wet weight (Schlabach et al., 1995). Using a relative potency value of 0.003 this

equals to 2.8 to 369 pg TEQ/g wet weight. In the same study, the TEQ based on PCDDs, PCDFs,

and non-ortho PCBs was calculated to range from 175 to 2000 pg TEQ/g wet weight. In the samples,

up to 37% of the total TEQ was derived from PCN 66/PCN 67 and 1,2,3,4,5,6,7-

heptachloronaphthalene (PCN 73), with 25% derived from PCN 66/PCN 67.

Awareness of limited data

I am very well aware of the limited data sets available to derive a TEF value. The ones available

include chicken embryos and in vitro systems in chicken hepatocytes, binding assays to the Ah

receptor, in vitro studies with a rat hepatoma cell line, and in vivo studies in rodents. However, TEF

values are interim values which will change until more data become available. By setting TEF

values for the mentioned congeners and using these preliminary values for the calculation of the total

TEQ in selected samples, scientists and regulatory agencies can be made aware of the need for

designing robust studies to derive relative potency values and continue - or even start - measuring

the mentioned compounds in biota.

Conclusion

In summary, TCAB, HCB, and PCNs should be included in the TEF concept based on binding to

the Ah-receptor, their dioxin-like effects, and their bioaccumulation. The limited data available on

environmental levels of TCAB, HCB, and PCNs suggest that these compounds could considerably

add to the total TEQ in environmental samples.

Acknowledgements

I would like to thank Dr. John Bucher for giving me the challenge in the National Toxicology

Program to be involved in the technical report on the toxicity of 3,3',4,4'-tetrachloroazobenzene, Dr.

George Becking for the opportunity to participate in the IPCS task group meeting on

hexachlorobenzene, and Prof. Bo Jansson and Dr. Jerzy Falandysz for their efforts to keep me
C-F-6

updated on chlorinated naphthalenes.

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Paul Goettlich, Board of Directors

Hoosier Environmental Council

P.O.Box 6854 South Bend IN 46660-6854

219-273-0557

gottlich@sbt.infl.net

www.envirolink.org/orgs/hecweb

22 January, 1998

The Comments of an Observer to the Expert Reviewers and EPA at the Chicago Hilton

Workshop on the Application of 2,3,7,8-TCDD Toxicity Equivalency Factors to Fish and

Wildlife

Thank you for allowing me the opportunity to be a part of this workshop as an observer. It has been an educational

event for me and something that I will build upon, especially the acronyms.

Thank you also for all your hard work and persistence in your pursuit of truth. I will feel safer knowing you are all

working so hard on the subject of this workshop. I have had conversations with many of you. Some of my

comments may have seemed vague and generally out of place. But they are not so out of place, for the questions I

raise must be asked at each step that we take in our work. We must all develop a long range outlook on our home

the earth because it is the only environment that most will have for a very long time. To look ahead 3 generations is

definitely not enough. Even 30 generations is not enough. I am not certain what is the appropriate amount of

foresight when it comes to the degradation of our environment.

The minimum time span that should be included is the useful life span of the chemical in question. How long does it

take for this chemical to break down into something that all scientists are certain is safe? Whether the chemical in

question is a PCB, dioxin or one of the latest supposedly safe chemical compounds such as glyphosate. Even that is

looking to have the characteristics of an endocrine disruptor. As long as I have your attention I'd like to throw

nuclear waste into the arena, as well, because there are agencies hard at work presently attempting to set limits on

the amount of nuclear waste that can be recycled into consumer products.

The reason I believe such prudence is due is to ensure that we are not leaving our descendants an intolerable

environmental mess. It is important that industry include the full costs of a product in its price to the consumer.

Almost never included are the costs of production waste, environmental pollution, habitat loss, health problems for

factory and field workers, health problems for the consumers and long term storage of toxic wastes.

We must stop passing these problems and costs on to future generations of unsuspecting people, many of whom will

never have any use for the products or byproducts that will degrade their bodies and environment. When we

increase a pollutant into our environment at any level it has some effect, whether it is presently measurable or not.

When a permit is granted to an industry to add additional pollutants because our legal system has deemed it safe, the

effects are felt around the world, especially when it comes to such things as dioxin and PCBs. You may think of

these effects as social or economic problems, and therefore not in your field. It is then safe to ignore the obvious

problems that we have saddled other neighborhoods, states or countries with. Maybe we cannot prove that a

particular PCB or dioxin was made by a particular factory, but we did add to the whole ecosystem by granting that

permit to increase pollutants production. As such we are just as liable as the next polluter or discharger, as the legal

people would say.

In conclusion, I must urge you all to do as much interdisciplinary communication as possible. Get really wild and

speak to a psychologist or psychiatrist about their patient that has been chemically injured and has behavioral

problems. It is with these inventive communications that your new ideas will form. Remain open at all times to new

ideas. Most of all, think in terms of many hundreds of generations in an attempt to slow down the constant

degradation.

Sincerely,

Paul Goettlich
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Date: February 25, 1998

To: Susan Brager Murphy, TEF Workshop Coordinator

cc: Charles Menzie, Linda Birnbaum

From: Brent Finley, Workshop Observer

Subject: Expanding the TEF Approach to include Hexachlorobenzene (HCB)

Introduction

As part of the effort to address potential human health risks posed by chlorinated dibenzo-p-dioxins

(CDDs) and chlorinated dibenzofurans (CDFs) in the environment, the U.S. EPA adopted an interim

procedure in 1987 based on dioxin “toxicity equivalence” factors (TEFs) for estimating the hazard

and dose-response of complex mixtures containing CDDs and CDFs in addition to 2,3,7,8-TCDD

(USEPA, 1989). The adoption of TEFs for CDD/CDF congeners was explicitly stated and

recognized by researchers to be an interim science policy measure. The technical subcommittee that

was gathered to derive and periodically update the TEF scheme noted that a general (order of

magnitude) approach was needed to characterize potential risks posed by the 209 CDD/CDF

congeners other than 2,3,7,8-TCDD because of the lack of detailed toxicity data on almost all of

these congeners. With the development of updated TEFs in 1989 (i.e., I-TEFs), it was again noted

by the subcommittee that the TEF approach was an “interim” approach and should be replaced as

soon as practicable with a bioassay method. Over the past several years, several efforts have been

made to expand the TEF approach to include mixtures of coplanar polychlorinated biphenyls (PCBs)

(Ahlborg et al. 1994; Safe et al. 1994), but a consistent set of TEF values has yet to be adopted by

USEPA for PCBs.

In the draft 1997 W.H.O. document, it was suggested that the health risks associated with exposure

to HCB (and other chemicals) could, be assessed using the 2,3,7,8-TCDD-based TEF scheme. This

suggestion was based on an apparent concern that: 1) HCB might possess “dioxin-like” properties

and, 2) it is important to understand the “total TEQ body burden” of humans and ecological species.

This suggestion was further detailed by Angélique van Birgelen at the workshop in Chicago. During

her presentation, she elaborated on the information she had written for the W.H.O. document. It was

not clear to me whether any of the other workshop participants supported or disagreed with her

position. In general, I have some serious reservations about the practicality and applicability of a

“TEF” for a chemical which exists not as a mixture but as a single compound (such as HCB), and

I specifically have some misgivings about applying a TEF to HCB, a compound whose toxicity is

already well characterized. I have detailed some of my observations and concerns below.
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1. A TEF for HCB is not Warranted Because There are no HCB “Mixtures”

The TEF approach was originally developed as an interim approach for complex mixtures of

CDD/Fs. Because it is obviously impractical to conduct long-term bioassays on each and every one

of the CDD/F congeners, the TEF approach was developed as a “short-cut” that would allow for

assessments of complex mixtures using existing congener data (in vitro studies, etc.). As discussed

in the W.H.O. report (1997), there are a large number of compounds which could contribute to the

total concentration of TCDD TEQs. These chemicals include polycyclic aromatic compounds such

as biphenyls, 2- and 3-ring polycyclic aromatic hydrocarbons and other heterocyclic compounds.

Depending on the degree of substitution, many congeners of polycyclic compounds can exist. For

most of these compounds, little or no toxicity data exists to characterize a dose-response

relationship. If in fact it can be demonstrated that some or all of the various congeners in a chemical

class are likely to possess significant “dioxin-like” activity, then perhaps development of TEFs for

this chemical class is appropriate.

On the contrary, dose-response relationships have been developed for HCB using the results of

several bioassays to characterize effects from subacute to chronic exposures. Attempting to

incorporate HCB into a TEF scheme is therefore inconsistent and contrary to the TEF concept

because it ignores the more accurate assessment techniques that have already been applied to HCB

toxicity data for characterizing adverse effects. More importantly, because there are no congeners

or isomers of HCB, its inclusion in the TEF scheme seems counterintuitive.

2. There is Insufficient Evidence to Conclude HCB Meets W.H.O.'s Definition

“Dioxin-Like”

As defined by the W.H.O.(1997) in their draft workshop proceedings, there are four criteria that

W.H.O. has proposed to determine whether a chemical might be evaluated using the TEF scheme.

Each of these criteria, as they apply to HCB, is discussed below.

> Structural Similarity to TCDD - As is shown in the figure below, HCB is a monocyclic (singleringed) aromatic compound with-full chlorine substitution, whereas TCDD is a coplanar,

polycyclic compound with chlorine substituted at four locations. As such, HCB lacks the

structural dimensions (a 6.8x13.7 Angstrom box of chlorine substitutions) required for TCDDlike toxicity (Hahn et al. 1989; McKinney and Singh, 1981).

Hexachlorobenzene 2,3,7,8-TCDD
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Extensive evidence exists on CDDs/CDFs to show that small configurational changes such as

chlorine substitution on a specific carbon atom drastically affects the potency of a compound. For

example, 2,3,5,7-TCDD is not considered as a candidate for a TEF because of the subtle difference

in the placement of a single chlorine atom. The significant structural and configurational differences

between the polycyclic CDDs/CDFs and monocyclic HCB is compelling evidence that support the

conclusion it does not meet the first criterion for a dioxin-like compounds.

> Binds to the Ah Receptor - The evidence supporting HCB binding to the Ah receptor has been

described as “equivocal”and “at best a very weak competitor” (Goldstein et al. 1986; Linko et

al. 1986). The binding affinity of HCB is far less than that of every 2,3,7,8-substituted CDD/F,

and in fact, is much less than that observed for naturally occurring aromatic compounds such as

polycyclic aromatic hydrocarbons and indole carbazoles (Ames et al. 1990; Kleman and

Gustafsson, 1996). Therefore, it would seem that HCB fails on this criterion.

> Dioxin-Like Toxic/Biochemical Responses - HCB can induce several responses that can also be

induced by TCDD, including CYPIA1/1A2 induction, thyroid hormone alterations, hepatic

retinol depletion, porphyrin accumulation, hepatic hypertrophy, and immunotoxicity. However,

the HCB doses required to elicit these effects are orders of magnitude higher than TCDD doses

required to elicit the same degree of effect. In addition, the mechanism by which HCB produces

toxic effects may be quite different than that for TCDD. For instance, oxidative metabolites of

HCB (pentachlorophenol, tetrachlorohydroquinone) have been implicated in the manifestation

of hepatic porphyria, and other effects (Rietjens et al. 1995, Schielen et al. 1995; Van Ornmen

et al. 1989). Conversely, the toxicity of TCDD is generally attributed to the interaction of the

parent compound with the Ah receptor.

> Persistence - In humans, the half-life of HCB has been estimated to be approximately 215 days

(Freeman et al. 1989), which is less than a tenth of the half-life reported for TCDD of

approximately 7.5 years (Needham et al. 1994). Clearly, the pharmacokinetics of HCB is vastly

different from that of TCDD. Indeed, one of the deficiencies of the TEF approach, particularly

with TEFS based on in vitro or acute in vivo responses, is that it does not account for differences

in kinetics, an important determinant of toxicity. In summary, it is questionable whether HCB

can be considered “persistent” relative to TCDD.

3. Numerous Chemicals Meet W.H.O.'s Criteria of “Dioxin-Like”

As noted above, there are several chemical classes which could be interpreted to meet the somewhat

arbitrary criteria of “dioxin-like,” even as defined by the use of W.H.O criteria. However, it is

unreasonable to suggest that TCDD-based TEFs will be derived for each isomer of each of these

chemical classes. Even if sufficient resources existed to establish such TEFs over the next 5-10

years, the result would be an unwieldy collection of hundreds or thousands of TEF values. This is

unlikely to occur, and therefore I believe that simply because a chemical meets a definition of

“dioxin-like” is insufficient reason to attempt to establish a TEF for that compound. This is

particularly true for HCB which, as noted above, does not satisfy W.H.O.'s criteria.
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4. A TEF is Not Warranted Because Sufficient Toxicological Criteria Exist for HCB

The TEF approach was originally developed for PCDD/F congeners since data to characterize

toxicity was limited or not available. This is not the case for HCB. Rather, the toxicity of HCB has

been extensively studied, and the dose-response relationships for various health effects have been

well characterized. Toxicity values for HCB and TCDD used for risk assessment are compared in

the table below.

Endpoints Toxicity Value HCB TCDD

Human 

Health

Cancer Slope Factor Based on liver tumors in

female rats (Eturk, 1986)

Based on liver tumors in

female rats (Kociba et al.

1978)

Oral Reference

Dose

Based on liver effects in

rats (Arnold et al. 1985)

Not available

Ecological Mammals Based on survival and

reproductive effects in

mink (Rush et al.

1983;Bleavins et al.

1984)

Based on reproductive

effects in rats (Murray et al.

1979)

Fish Based on survival in

several aquatic species

(USEPA, 1988)

Based on survival in

rainbow trout and northern

pike (USEPA, 1993)

Birds Based on survival and

reproductive effects in

several species of birds

(Vos et al. 1971)

Based on survival,

reproductive &

developmental effects in

several species of birds

(Nosek et al. 1992; Hudson

et al. 1984)

Currently, the cancer slope factor and reference dose for HCB are derived without prejudice to the

mechanism by which adverse effects were produced. As such, any “dioxin-like” activity imparted

by HCB in the critical toxicological studies is already accounted for in the existing slope factor and

reference dose.

For ecological risk assessment endpoints, there are no promulgated toxicity values as there are for

human health. However, the quantity and quality of available toxicological studies for ecological

receptors of potential concern for HCB is as good or better as that for TCDD. Controlled studies of

subchronic and chronic HCB exposures have been conducted with 10 species of mammals and five

species of birds. These include wild species such as the mink and the kestrel which are frequently

identified as receptors of interest for ecological risk assessment. These studies have assessed the

dose-related effects of HCB on the survival, growth, reproduction and development of these species;

all endpoints clearly related to risks to exposed populations. The studies with Japanese quail indicate

that hatchability of eggs and survival of chicks are sensitive endpoints for birds and provide a sound
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basis of a toxicity reference value for this taxonomic group (Vos et al., 1971; Schwetz et al., 1974).

Similarly, the studies with mink show that reductions in litter size and survival of kits are sensitive

endpoints and provide a documented basis for developing a toxicity reference value for mammals

(Bleavins et al., 1984; Rush et al., 1983). Because these studies are generally of good quality and

have assessed the dose-related effects of HCB exposure on relevant endpoints in a relatively large

number of species, there are substantial data from direct tests to evaluate HCB toxicity and little

justification, if any, for abandoning these data for this individual compound for “an order of

magnitude” TEF.

The dose-related health effects of HCB observed in long-term animal feeding studies are a far more

accurate and direct measure of HCB toxicity than an indirect “TEF-estimate” which is based on the

potency of an entirely different chemical. Chemical-specific information, based on chronic bioassays

for all endpoints of concern, is clearly a more preferable basis for risk assessment.

5. The Relative Potency (REP) of 0.0001 is Overestimated

Although an REP of 0.0001 can be calculated for HCB based on relative binding affinity to the Ah

receptor in vitro (Hahn et al. 1989), this is likely to overestimate the carcinogenic potency. Using

the relative carcinogenic potencies of HCB [(1.6 (mg/kg-day)-1] and TCDD [(156,000 (mg/kg-day)-1]

in female Sprague-Dawley rats following lifetime exposures (Eturk, 1986; Kociba et al. 1978), an

REP of 0.00001 may be viewed as a conservative upper bound (under the unlikely assumption that

all HCB-induced liver tumors are attributable to a “dioxin-like” mechanism of action). Because

HCB-induced tumors are primarily attributable to a non-dioxin-like mechanism of action, the REP

for carcinogenic effects is likely to be much less than 0.00001, and in all likelihood is closer to (if

not equal to) zero.

Conclusions

The TEF approach was developed and adopted specifically to address the potential risks posed by

related constituents (i.e., congeners) with similar structural features that might elicit a response or

toxic effect under an identical mechanism of action. HCB is a well-studied chemical for which

current risk assessment methods are superior to the TEF approach. Given that there is no discernible

benefit in adding HCB to the TEF scheme, I strongly recommend that the TEF approach not be

applied to HCB for characterizing potential risks.

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