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                                          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
                                              /T"y
Recycled/Recyclable
Printed with vegetable-based ink on
paper that contains a minimum of
50% post-consumer fiber content
processed chlorine free.

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                                   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.
                                          n

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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-l

B. Workshop Case Study: A Preliminary Problem Formulation for a Prospective Ecological
Risk Assessment Scenario	,	B-l

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-l
      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
                                      111

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      CONTRIBUTORS TO WORKSHOP DESIGN AND REPORT
EPA Regional Offices

Patricia Cirone (Region 10)
Robert Pepin (Region 5)
Steve Wharton (Region 8)
EPA Office of Research and Development

          Steven Bradbury
          Philip Cook
          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

       Steven Bradbury
       Philip Cook
   U.S. Department of Interior

          Tim Kubiak
          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
                                  IV

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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 PCS 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
offish 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
                                           1

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 CYP1 Al). The World Health Organization (WHO) has defined TEFs as order to half-order of
 magnitude estimates of relative potency derived from an assessment of available RePs (van den
 Berg etal., 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 dibxin 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

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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 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.PCDF-contaminated 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 offish, 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

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approaches which were subject to evaluation at the workshop. For example, the prospective case
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 - G-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

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 4.
7.
 assessment, exposure assessment, and risk characterization).  However, these
 uncertainties should be quantified better.

 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).

 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).

 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.

 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
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       a major limitation on the use of the document for risk assessment purposes.

8.      The TEF/TEQ methodology requires analytical methods to identify and quantify the
       individual dioxin, furan, and PCS 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

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       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
       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
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       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.

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.
       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.
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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 Ghair, 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 re-
examined 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
                                                                            it
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
                                          11

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toxicities in mice were also considered in the derivation of the TEF values.  For PCBs 77, 126,
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
       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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 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 Group believed that this rounding could lead to anoverestimation 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

                                           13

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(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-
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 hi chicken embryos gave 0.01); (2) systematic errors associated with
rounding TEFs to the nearest factor often 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 often 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

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 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
 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.
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       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.
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
        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."
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 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 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.
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                                         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\2:2l 1-216.

Hochstein, JR; Bursian, SJ; Aulerich, RJ. (1998) Effects of dietary exposure to 2,3,7,8-tetrachlorodibenzo-p-
dioxin in adult female mink (Mustela visori). 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 ofdioxins 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 ofdioxins 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 visori). 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 vitulind) fed Baltic Sea herring. Aquatic Tox
34(l):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). CritRev Toxicol21(l):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 Techno!
30:283-291.
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 U.S: Department of the Interior. (1995) Letter to Mr. Barry DeGraff transmitting the Section 7 Consultation
 Biological Opinion regarding the Great lakes Water Quality Initiative, from Mr. John A. Blankenship  February
 21,1995.61pp.                                                                      .            y

 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/506/6-90/00 la.

 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. EPAr (1993) Interim report on data and methods for assessment of2,3,7,8-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
(Hydropogns caspia) from the upper Great Lakes, collected in 1988. Environ Pollut 79:163-173.
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                                    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
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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.

       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

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 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.

       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

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measured in representative samples of sediments, fish, birds, and mammals from Oneofakind
Lake (Tables 1-4).

Ecological Effects and Endpoint Selection for Oneofakind Lake

       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 offish and
reproduction hi 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 offish 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

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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.

       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
                                          A-5

-------
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 tlie 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.

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

                                          A-6

-------
(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
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 TEQ^^ 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 fish-
eating 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 i- congener, (Coc),  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

                                          A-7                     •                   •

-------
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 BSAFy;,.,, 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 (TEF^) for each congener
are used to make the final conversion to a summed wet weight fish dioxin toxic equivalent
(TEQfishwclwti).

       The formula that relates the surficial sediment concentrations of PCDDs, PCDFs, and
PCBs to the expected potency of dioxin-like chemicals in the eggs offish-eating birds nesting
around Oneofakind Lake is:
(2)

           VPJ,
                                                                           egg -wet n>t.
where fM is the fractional lipid composition of the bird eggs (defined as 8 percent for Caspian
tern eggs), BMF4.eggi / is the congener-specific, lipid-normalized biomagnification factor between
fish and fish-eating bird eggs, and TEFAfr
-------
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. Ill: 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-tetrachlorodibe.nzo-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, RJ. 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. Ill: 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 B,
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., RJ. 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-
                                         A-9

-------
tetrachlorodibenzo-p-dioxin (TCDD) injected into the yolks of chicken (Callus domesticus) eggs
prior to incubation. Arch. Environ. Contain. Toxicol. 31:404-409.

Tillitt, D.E., R.W. Gale, J.C. Meadows, J.L. Zajicek, P.H. Peterman, S.N. Heaton, P.O. 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-/7-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-10

-------
               Map of One of a Kind Lake
                                                                             Yuckymuck
                                                                             River
Figure 1
                                          A-ll

-------
          Compartmental Model and Simplified Pathways of Chemicals in Oneofakind Lake.
          BSAF- and BMP-Related Compartments are Bracketed.
                             Dissolved/Paniculate
                             Chemical in Sediment
Figure 2
                                        A-12

-------
 Table 1. Concentrations of PCBs, PCDDs, and PCDFs and Calculated TEQs in Fish from Oneofakind Lake

% organic carbon
% lipid

Total PCB in ng/g

PCBs in pg/g:
77
.81
105
114
118
123
126
153
156
157
167
169
'189
PCDD in pg/g:
2378-TCDD
12378-PCDD
12478-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
PCDF in pg/g: '
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123679-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF

Total TEQ in pg/g
TEQ from PCBs
TEQ from PCDDs
TEQJrom PCDFs
Sediment
0-10 cm
n=15
dw
3.5%


110


390
11
600
88
1800
88
16
.1600
88
88
350
2
.88

1.1
0.8
0.8
0.5
2.7
1.4
47.0
369

180
95
65
95
25
0
0
10
300
16
470





Shiners
(whole)
n=25
ww

3.0%

345


308
19
5208
250
11042
125
79
19375
625
250
917
7
83

0.13
. 0.14
0.01
0.01
0.09
0.02
0.03
0.12

0.92
0.33
0.96
0.13
0.17
0.00
0.02
0.25
0.17
0.02
0.33





Lake Trout
Eggs
ri=15
ww

'5.0%

938


992
76
11905
11806
62202
337
116
12202
1389
516
1706
5
208

0.55
0.21
0.01
0.01
0.09
0.01
0.07
0.12

17.66
1.76
3.27
0.36
0.46
0.00
0.03
0.55
0.42
0.03
0.36





Fish
TEF1
consensus
(note 1)






0.0001
0.0005
<0.000005
<0.000005
<0.000005
<0.000005
0.005
na
0.000005
<0.000005
<0.000005
0.00005
<0.000005

1
1
na
0.5
0.01
0.01
0.001
na

0.05
0.05
0.5
0.1
0.1
na
0.1
0.1
0.01
0.01
0.0001





Shiners
(whole)
TEQl
ww






0.031
0.010
0.000
0.000
0.000
0.000
0.396
0.000
0.000
0.000
0.000
0.000
0.000

0.130
0.140
0.000
. 0.005
0.001
0.000
0.000
0.000

0.046
0.017
0.479
0.013
0.017
0.000
0.002
0.025
0.002
. 0.000
0.000

1.3
0.44
0.28
0.60
Lake Trout
Eggs
TEQ1
ww






0.099
0.038
0.000
0.000
0.000
0.000
0.580
0.000
0.000
0.000
0.000
0.000
0.000

0.552
0.207
0.000
0.007
0.001
0.000
0.000
0.000

0.883
0.088
1.637
0.036
0.046
0.000
0.003
0.055
0.004
0.000
0.000

4.2
0.72
0.77
2.75
Fish
TEF2
unrounded
(note 2)






0.0002
0.0006
O.000005
O.000005
<0.000005
O.000005
0.005
na
<0.000005
<0.000005
O.000005
0.00004
<0.000005

1
0.7
na
0.3
0.02
0.02
0.002
na

0.03
0.03
0.3
'0.2
0.1

0.1
0.1
0.01
0.01
na





Shiners
(whole)
TEQ2
ww






0.062
0.012
0.000
0.000
0.000
0.000
0.396
na
0.000
0.000
0.000
0.000
0.000

0.130
0.098
0.000
0.003
0.002
0.000
0.000
na

0.028
0.010
0.288
0.026
0.017
0..000
0.002
0.025
•0.002
0.000
0.000

1.1
0.47
0.23
0.40
Lake Trout
Eggs
TEQ2
ww






0.198
0.046
0.000
0.000
0.000
0.000
0.580
na
0.000
0.000
0.000
0.000
0.000

0.552
0.145
0.000
0.004
0.002
.0.000
0.000
na

0.530
0.053
0.982
0.071
0.046
0.000
0.003
0.055
0.004
0.000
0.000

3.3
0.82
0.70
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-13

-------
        Table 2. Concentrations of PCBs, PCDDs, and PCDFs and Calculated TEQs in Caspian Tern Eggs from Oneofakind Lake

% organic carbon
%lipid

Total PCS in ng/g

PCBs in pg/g:
77
81
105
114
118
123
126
153
156
157
167
169
189
PCDD in pg/g:
2378-TCDD
12378-PCDD
12478-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
PCDF in pg/g:
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123679-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF

Total TEQ in pg/g
TEQ from PCBs .
TEQ from PCDDs
TEQ from PCDFs
Sediment
0-10 cm
n=15
dw
3.5%


110


390
11
600
88
1800
88
16
1600
88
88
350
2
88

1.1
0.8
0.8
0.5
, 2.7
1.4
47.0
369

180
95
65
95
25
0
0
10
300
16
470





Shiners
(whole)
n=25 '
ww

3.0%

345


308
19
5208
250
11042
125
79
19375
625
250
917
7
83

0.13
0.14
0.01
0.01
0.09
0.02
0.03
0.12

0.92
0.33
"0.96
0.12
0.17
0.00
0.02
0.25
0.17
0.02
0.33





Caspian
Tern Egg
n=12
ww

8.0%

5667


1083
458
182083
11750
516667
2292
2750
475000
39042
11208
33417
321
6292

4.50
2.00
0.01
0.12
2.20
0.17
0.48
9.90

2.79
1.54
9.58
2.38
4.58
0.00
0.21
5.00
4.04
0.46
1.71





Bird
TEF1
consensus
(note 1)






0.05
0.1
0.0001
0.0001
0.00001
0.00001
0.1
na
0.0001
0.0001
0.00001
0.001
0.00001

1
1
na
0.05
.0.01
0.1
<0.001
na

1
0.1
1
0.1
0.1
na
0.1
0.1
0.001
0.001
0.0001





Caspian
Tern Egg
TEQ1
ww






54.17
45.83
18.21
1.18
5.17
0.02
275.00
0.00
3.90
1.12
0.33
0.32
0.06

4.50
2.00
0.00
0.01
0.02
0.02
0.00
0.00

2.79
0.15
9.58
0.24
0.46
0.00
0.02
0.50
0.00
0.00
0.00

426
405.3
6.5
13.8
Bird
TEF2
unrounded
(note 2)






0.03
0.2
0.0001
0.00008
<.000001
0.00002
0.1
na
0.0001
0.0001
0.000008
0.002
ha

1
1.2
na
0.05
0.01
0.1
0.001
•na

1
0.3
1.1
0.01
0.04
na
na
na
na
na
na





Caspian
Tern Egg
TEQ2
ww






32.50
91.67
18.21
0.94
0.00
0.05
275.00
0.00
3.90
1.12
0.27
•0.64
0.00

. 4.50
2.40
0.00
0.01
0.02
0.02
0.00
0.00

•2.79
0.46
10.54
0.02
. 0.18
0.00
0.00
0.00
0.00
0;00
0.00

445
424.3
6.9
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-14

-------
       Table 3. Concentrations of PCBs, PCDDs, and PCDFs and Calculated TEQs in Otter Livers from Oneofakind Lake

% organic carbon
% lipid

Total PCB in ng/g

PCBsinpg/g:
77
81
105
114
118
123
126
153
156
157
167
169
189
PCDDinpg/g:
2378-TCDD
12378-PCDD
12478-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
PCDFinpg/g:,
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123679-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF

Total TEQ in pg/g
TEQ from PCBs
TEQ'from PCDDs
TEQ from PCDFs
Sediment
0-10 cm
n=15
dw
3.5%


110


390
11
600
88
1800
88
16
1600
88
88
350
2
88

1.1
0.8
0.8
0.5
2.7
1.4
47.0
369

180
95
65
95
25
0
0
10
300
16
470





Shiners
(whole)
n=25
ww

3.0%

• 345


308
19
5208
250
11042
125
79
19375
625
250
917
7
83

'0.13
0.14
0.01
0.01
0.09
0.02
0.03
0.12

0.92
0.33
0.96
0.12
0.17
0.00
0.02
0.25
0.17
0.02
0.33





Otter
•Liver
n=8
ww

3.0%

1001


46
19
26042
988
47479
94
998
0
5391
2756
5225
91
756

1.43
0.88
0.00
0.00
3.02
0.00
1.13
7.48

0.00
0.00
51.85
8.05
0.00
0.00
0.00
18.95
4.58
0.00
0.00





Mammal
TEF1
consensus
(note 1)






0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
na
0.0005
0.0005
- 0.00001
0.01
0.0001

1
1
na
0.1
0.1
0.1
0.01
0.0001

0.1
0.05
0.5
0.1
0.1
na
. 0.1
0.1
0.01
0.01
0.0001





Otter
Liver
TEQ1
ww






0.00
0.00
2.60
0.49
4.75
0.01
99.75
0.00
2.70
1.38
0.05
0.91
0.08

1.43
0.88
0.00
.0.00
0.30
0.00
0.01
0.00

0.00
0.00
25.93
0.81
0.00
0.00
0.00
1.90
0.05
0.00
0.00

144
112.7
2.6
28.7
Mammal
TEF2
1994 TEFs
(note 2)






0.0005
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
na
0.0005
0.0005
0.00001
0.01
0.0001

1
0.5
na
0.1
0.1
0.1
0.01
0.001

0.1
0.05
0.5
0.1
0.1
na
0.1
0.1
0.01
0.01
0.001





Otter
Liver
TEQ2
ww






0.02
0.00
2.60
0.49
4.75
0.01
99.75
0.00
2.70
1.38
0.05
0.91
0.08

1.43
0.44
0.00
0.00
0.30
0.00
0.01
0.01

0.00
• 0.00
25.93
0.81
0.00
0.00
0.00
1.90
0.05
0.00
0.00

144
112.7
2.2
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-15

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Table 4. Organic Carbon- and Lipid-Normalized Concentrations of PCBs, PCDDs, and PCDFs in
Samples from Oneofakind Lake

% organic carbon
% lipid

Total PCB in ng/g

PCBs in pg/g:
77
81
105
114
118
123
126
153
156
157
167
169
189
PCDD in pg/g:
2378-TCDD
12378-PCDD
12478-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
PCDF in pg/g:
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123679-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
Sediment
0-10 cm
n=15
OC weight
3.5%


3,100


11,000
310
17,000
2,500
51,000
2,500
460
46,000
2,500
2,500
10,000
50
2,500

31
23
23
13
77
40
1,300
11,000

5,100
2,700
1,900
2,700
710
0
4
290
8,600
460
13,000
Shiners
(whole)
n=25
lipid weight

3.0%

12,000


10,000
640
170,000
8,300
370,000
4,200
• 2,600
650,000
21,000
8,300
31,000
220
2,800

4
5
0
0
3
1
1
4

31
11
32
4
6
0
1
8
6
1
11
Lake Trout
Eggs
n=15
lipid weight

5.0%

19,000


20,000
1,500
240,000
240,000
1,200,000
6,700
2,300
240,000
28,000
10,000
34,000
110
4,200

11
4
0
0
2
0
1
2

350
35
65
7
9
0
1
11
8
1
7
Caspian
Tern Egg
n=12
lipid weight

8.0%

71,000


14,000
5,700
2,300,000
150,000
6,500,000
27,000
34,000
5,900,000
490,000
140,000
420,000
4,000
79,000

56
25
0
2
28
2
6
120

35
.19
120
30
,57
0
T
62
51
6
21
Otter
Liver
n=8
lipid weight

3.0%

33,000


1,500
640
870,000
33,000
1,600,000
3,100
33,000
0
180,000
92,000
170,000
3,000
25,000

48
29
0
0
100
0
38
250

0
0
1,700
270
0
0
0
630
150
0
0
                                          A-16

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Table 5. No-effect thresholds (wet weight basis) for embryo mortality (fish and birds) and feto-
and 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   Reference
                  (pg/g)
                      Total
                      PCBs
                      (ug/g)
            Reference
  Fish egg     30(3-3,000)
U.S. EPA, 1993;
Elonenetal., 1997;
Wright and Tillitt,
1997
  Bird egg     100(10-100)
  Mink liver   60(10-200)
5 (1-20)
Giesyetal. 1995;       5(1-5)
Hoffman etal., 1996;
Powell etal. 1997

Tillitt etal., 1996;      2(0.1-2)
Leonards etal., 1995
Ernst, 1984;
Hendricks, 1980; U.S.
EPA 1980; Niimi,
1983

Giesyetal. 1995;
Hoffman et al., 1996;
Powell etal. 1997

Tillitt et al., 1996;
Leonards et al., 1995
                                        A-17

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                                     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 endppint 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 nrVsec) from all sources to the lake. In laboratory tests low doses of

                                         B-l

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TCDD have significantly affected egg viability and/or the survival of young fish, mammals, and
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 offish) 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 (TEqC^,) 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 co-
planar 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 offish 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

                                           B-2

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 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
 evaluate how to best apply the TCDD toxicity equivalence approach to establish the TEqTMDLs
 for protection offish, 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 (TEqQ,) standard (assumes additivity); (2) converting the
 TEqC^ standard to maximum allowable concentrations of each congener in water (MAC^ 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 MAQ  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 2.4xl010 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 1.2xl07 m3,  with

                                          B-3

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a bulk density of 2.5xl05 mg/L.

       The lake has a substantial coldwater fishery that includes introduced species such as
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-receptor-
mediated 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 (f, ~

                                           B-4

-------
 0.18) are 0.5 to 1.0 ug/g wet weight with younger adult trout (f, -0.18) at 0.2 ug/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 (ft ~ 0.08) , whitefish (f( ~ 0.06) , suckers (f( ~ 0.08), and other fish
 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  ug/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 offish, mink, and gallinaceous birds are
especially sensitive. The survival of early life stages offish 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 offish 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

                                          B-5

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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
uncontaminated tributaries. To estimate expected doses, these 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. 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 offish
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 offish,
 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.

                                           B-6

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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  •
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
lipid-normalized bioaccumulation factor for freely dissolved TCDD (BAF(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 (fd) 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

                                           B-7

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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 bfe
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
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 PCS 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

                                          B-8

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 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 l.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
 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 (BAFfd) is used, the freely dissolved (bioavailable)
 concentration of each chemical in water (C™\ may be related to the TCDD toxicity equivalence
 concentration in tissue (TEqCtjssue):
                1=1
                                                                                        (1)
Alternatively, the state water quality standards for fish, birds, and mammals, expressed as
concentrations of total TCDD in water, (C£)tcdd, may be defined as the corresponding TCDD
toxicity equivalence concentrations for total TCDD in water (TEqC^), which may be calculated
on the basis of n congeners contributing to the
                      _
                 w'tcdd
s TEqC* -   E
                                                                                        (2)
Note that equation 2 can be transformed to an expression based on freely dissolved chemical
concentrations in water through use of f^s for each chemical. Also note that each chemical's
contribution to the TEqC^, 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 BAFfd
ratios measured for fish in one ecosystem are more universal across sites and conditions than the
individual BAFfs, 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). BAFfds and BSAFs are related to each other through the sediment organic carbon-
water (freely dissolved) chemical concentration quotient (ILocw):
                                          B-9

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                                IL
' BAFf
 BSAF
(3)
       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.
BAFfs (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
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 offish (j=l),
birds 0=2), and mammals Q=3) based on TEFs, B AFs, and mass balance relationships for i
chemicals that may contribute to the (TEqC^s. 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-10

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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
offish 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-ll

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                                            Roundtail
                                            River
              Municipality
 Map of Roundtail Lake

Figure 1
                                       Roundtail River
                                B-12

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                                                  B-16

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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-
                                          C-ii

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                            TABLE OF CONTENTS
 Preface	Q_JV
 I.     Introduction	           C_l
 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
                                    C-iii

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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.
                                          C-iv

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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-/>-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
                                           C-2

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designed to establish a common frame of reference for subsequent discussions.  Brief summaries
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.
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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
compound relative to the toxicity of TCDD that is derived using careful scientific judgment after
                                      9-
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.
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    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.
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 structure-
activity 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
1,2,3,7,8-
PeCDD
OCDD
OCDF
PCB77
PCB81
PCB 170
PCB 180
Old TEF
0.5
0.001
0.001
0.0005
--
0.0001
0.0001
New TEF Explanation of Change
1 CYP1A1/A2, tumor promotion
0.000 1 misinterpretation of earlier data; exposure
versus tissue concentration
0.0001 similarity to OCDD
0.0001 EROD induction
0.0001 similarity to PCB 77
in vivo data (CYP1A1, repro) do not
support in vitro observations
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 = CYP1 A);
   •      Tier 3: biochemical effects observed in in vitro systems (endpoint = CYP1 A); 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

                                          C-6

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consensus TEFs as distinct individual values or to round them as had been done previously. For
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 offish 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.
                                          C-7

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       WHO CONSENSUS TEFs FOR MAMMALS, FISH, AND BIRDS
                            HUMANS/
                           MAMMALS
                      FISH
                 BIRDS
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
     1
     1
   0.1
   0.1
   0.1
  0.01
0.0001
    1
    1
  0.5
 0.01
 0.01
0.001
     1
     1
  0.05
  0.01
   0.1
O.001
2,3,7,8-TCDF                     0.1
1,2,3,7,8-PeCDF                 0.05
2,3,4,7,8-PeCDF                  0.5
1,2,3,4,7,8-HxCDF                0.1
1,2,3,6,7,8-HxCDF                0.1
1,2,3,7,8,9-HxCDF                0.1
2,3,4,6,7,8-HxCDF                0.1
1,2,3,4,6,7,8-HpCDF             0.01
1,2,3,4,7,8,9-HpCDF           .  0.01
OCDF                       0.0001

3,4,4',5-TCB (81)             0.0001
3,3',4,4'-TCB (77)             0.0001
3,3',4,4',5-PeCB (126)              0.1
3,3'AA',5,5'-HxCB (169)          0.01

2,3,3',4,4'-PeCB (105)          0.0001
2,3,4,4',5-PeCB(114)          0.0005
2,3',4,4',5-PeCB(118)          0.0001
2',3,4,4',5-PeCB (123)          0.0001
2,3,3',4,4',5-HxCB (156)       0.0005
2,3,3',4,4l,5l-HxCB (157)       0.0005
2,3',4,4',5,5I-HxCB (167)      0.00001
2,3,3',4,4',5,5'-HpCB (189)      0.0001
                       0.05
                       0.05
                        0.5
                        0.1
                        0.1
                        0.1
                        0,1
                       0.01
                       0.01
                     0.0001

                     0.0005
                     0.0001
                      0.005
                    0.00005

                 O.000005
                 O.000005
                 0.000005
                 O.000005
                 O.000005
                 <0.000005
                 <0.000005
                 <0.000005
                      1
                     0.1
                      1
                     0.1
                     0.1
                     0.1
                     0.1
                   0.01
                   0.01
                 0.0001

                     0.1
                   0.05
                     0.1
                  0.001

                 0.0001
                 0.0001
                0.00001
                0.00001
                 0.0001
                 0.0001
                0.00001
                0.00001
Figure 2.
                                         C-8

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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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               Map of One of a Kind Lake
Figure 3

occurred for 30 years.

    Components of the aquatic ecosystem include lake trout, Atlantic salmon, largembuth 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.
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Contaminated sediments are the primary load to the system. Biota sediment accumulation
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.
               Compartmental Model and Simplified Pathways of Chemicals in Oneofaldnd Lake.
               BSAF- and BMF-Related Compartments are Bracketed
                               Dissol ved/Part iculalc
                               Chemical in Sediment
      Figure 4.

    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 offish, 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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                                              Roundtail
                                              River
               Municipality
 Map of Roundtail Lake

Figure 5.
                                                   Municipality

                                           " '*"" Aluminum
                                        Roundtail River
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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.
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Community
Structure and
Function




Demographic
and
Social Impacts

Popu
Cha
Ecosystem
Model




Population
Model

Reproduction
Cancer
Imunotox.
Neurotox. etc.

ation
iges

Population
Changes
Population
Model >
Population
Model
Reproduction
Age-sp. Mortal.
Pred. Vulner.
Disease Resist
| Toxicity
j Hazard
•
Toxicity
Hazard


Human
Exposure .
Food
1
Consumption
Residues in
Aquatic
Organisms
Food
Reproduction
Age-sp. Mortal.
Pred. Vulner.
Disease Resist
Toxicity
Hazard
Exposure


Chemical
Loading
Chemi
Fat
Food
Water
Sediment
Consumption
Wildlife
Exposure

                             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

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                                                                                        Other Diet
         System
          Inputs
                                   Pathways of TCDD Accumulation in Roundtail Lake
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-16

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     The Basic Relationship:
                                           i = congener j = biota group (1 = fish, 2 = birds, 3 = mammals)
          TCDD-based WQ STD for
             jth Biota Group
              STD(Q,),cdd.j
                       Statement of TCDD Toxicity Equivalent Additivity Model
       TCDD Toxicity Eq. Conc.-based
         WQ STD for jth Biota. Group
              STDfTEqCJJj
                          _sr0(TE,q,), (BAF,')|PM1
                          -
          Max. Allowable Cone, of
        Each Specific Congener for
            jth Biota Group
                      Convert each (MACy,, to a MAL,, using a system-level mass balance model
         Max. Allowable Load for
         Each Specific Congener
Form
                                 Matrix
                Allocate
                                                 Fraction
                                                 TE, TMDL
                               » . [MALJ
Permit Condition
    The effluent must meet the permit condition: Z  	^	 <; 1.0
                                 1=1 Li 'allocation' MAtJ
                                      Where w,=projected load of ith congener

                         Figure 5. Process Diagram for Prospective Waste Load Allocation
    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
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studies themselves were to be reviewed. Thus, each work group would contain some individuals
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 1-1: 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
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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
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 1-2: 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
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assessment process.

   Regarding the uncertainty associated with use of the TEF/TEQ approach, some experts felt
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 1-3: The TEF values provided were based on endpoints that
             ranged from in vitro biochemical responses (e.g., induction of cytochrome P450
             1 Al) 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-o-
deethylase (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
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             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 non-
additivity would require a procedure for evaluating each compound separately. If such a
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
             class-specific TEFs be directly extrapolated to the species identified within each
             case study?
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   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
 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.
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   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 hi 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.

    •   .     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?
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   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
 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 poly chlorinated 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
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binding properties, effect profiles,  physicochemical characteristics, and estimated annual
discharge for each compound or class. Based oh 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.
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       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.
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        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 logKow
X(TCDD) 1.0 107 7
Y 0.1 106 6.5
Projected Load
0.1 g/day
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 6f congener Y, many times higher than the maximum
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concentration for TCDD.  Using a system-specific mass balance model, the details of which are
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 offish 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
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 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
 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 dioxin-
like compounds.
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   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
measured alone. As a result, environmental concentrations of the individual congeners are
known, and it is usually possible to determine BAFs for the foil 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 site-
specific 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
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 assessment process, including the uncertainty in B AFs, 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
 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-the-
table 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.
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   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
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.
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   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.

   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 better-
characterized model for the one proposed in the case study, but there was a divergence of
opinion on this issue.
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   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.

   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
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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. Bums' 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
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
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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.

    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
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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.

    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.
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               Relative Uncertainties in the Ecological Risk Assessment Including Use of TEF Values
Ranks for
uncertainty
Species/Congener


Bull
trout
1
2
3
4
5
6
7
Bald
Eagle
1
2
3
4
5
6
7
River
Otter
1
2
3
4
5
6
7


TEFs
1







4







3











BAFs
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3




Species
Sens./Extrapolation
2
1
1
1
1
1
1
1
4
1
1
1
4
• 3
3
2
3
1
1
1
2
2
2
2




Exposure
Model
2







4







4














Threshold
concentration
2







4







3





































Species specific
Congener specific























Criteria are described in the text. This approach and these values are presented for illustration only.




9
21






19
36






16
32











Total
30







55







48













Bull
Trout







Bald
Eagle







River
Otter









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 semi-
quantitative method as a decisionmaking tool.  The reason for his concern was that the weighting
of different variables may reflect subjective biases, arid 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 re-
 focusing of the regulator's attention. Another expert disagreed, suggesting that further ground-
 truthing 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.
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       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-44

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    3%b - Lake Trout Eggs
   2.

   1
                  1,7
                 0,70
                                              ,
                                           .n
    Bird - csspfen Term Egss
400
                 424
                                            S.I
                                                        3

                                                        2

                                                        i
    MammaS - Otter Liver
 ISO



M20




JO '
                 2.2


                 its
                           43
                                                          j.OO
                                                         O.S3
  Comparison of Site Data from TEQ and Total PCS Perspectives
                            C-45

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   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 screening-
level 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
                                          C-46

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 people felt that the results of the TEF approach would have to be supplemented with more
 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 BMP
          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.
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    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
 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
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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.

   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.
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    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.

    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 TEE 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.
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    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,
                                 , t                              •                '    '
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.

    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 PCS 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.
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    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.

    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.
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   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
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.
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   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
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.
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    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.

    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 cost-
 effectively 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.
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    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 BMP 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
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.
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   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
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
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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
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
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 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.

                   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 three-
 day 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
                                         C-60

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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 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.
                                          C-61

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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
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%.
                                          C-62

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   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 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.
                                         C-63

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    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.

    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 chernical 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.
                                          C-64

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    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.

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.
                                         C-65

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   3. The TEF values provided were based on endpoints that ranged from in vitro biochemical
   responses (e.g., induction of cypl Al) 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 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?

      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.
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    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-67

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   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.
                                         C-68

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      Appendix C-A
WORKSHOP PARTICIPANTS

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&EPA
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

    Bjorn Brunstrom
    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, Ml 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
        Printed on Recycled Paper
                                      C-A-1

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 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 OH3
 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-maN: cmetcalfe@trentu.ca

 Mike Meyer
 Wildlife Toxicologist
 Regional Headquarters
 Wisconsin Department
 of Natural Resources
 107 Sutliff Avenue
 Rhinelander, Wl  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; Wl 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. Box80176
 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-2

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&EPA
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
         Printed on Recycled Paper
                         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-3

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 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 Noit
 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 (SU-PR/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, Ml 48823
 517-351-8324  .
 Fax: 517-351 r1443
 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-4

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&EPA
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, MM 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
        Printed on Recycled Paper
           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. Box30712
           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 (SR-
6J)
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-5

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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, Ml 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 Cong'ress Street
Portland, ME  04102
207-774-0012
Fax: 207-774-8263
E-mail: russell_keenan@mclaren-
hart.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, Wl 53717
608-831-4444
Fax: 608-831-3334
   ail: 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
                                             C-A-6

-------
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, Wl 53717
608-831-1989, Ext.: 3338
Fax: 608-831-3334
E-mail: kirsti@rmtmsn.mitinc.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, Wl 54311
920-465-7405
Fax:920-465-7410
E-mail: ken_stromborg@mail.fws.gov

Katherine Super
Risk Assessor
IGF 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.wiiliam@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
                                              C-A-7

-------

-------
    Appendix C-B
WORKSHOP AGENDA

-------

-------
SEPA
            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:OOPM   Registration

 4:OOPM   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:OOPM   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
   Printed on Recycled Paper 6:OOPM   BREAK
                             C-B-1

-------
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:OOPM    ADJOURN



WEDNESDAY,  JANUARY  21,  1998

 8:30AM    Expertise Group Sessions:

            Toxicity Equivalency Factors (TEFs) Experts		. Dr. Richard Peterson, Facilitator
                                                                           University of Wisconsin,
                                                                                    Madison, Wl

            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    BREAK

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     BREAK

4:OOPM     Plenary Session
            Breakout groups report on Case Study 1 and discuss commonalities and differences among their
            groups

5:30PM     DINNER  BREAK

8:OOPM     Plenary Session
            Complete reports on Case Study 1 and continue plenary group discussion

9:OOPM     ADJOURN

                                             C-B-2

-------
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     LUNCH

1:30PM      Plenary Session
            Breakout groups report on Case Study 2 and discuss commonalities and differences among their
            groups
3:OOPM

3:15PM

5:OOPM
BREAK

Overall Meeting Conclusions and Wrap-Up	 Dr. Charles Menzie

ADJOURN
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.
                                          C-B-3

-------

-------
      Appendix C-C
PREMEETING COMMENTS

-------

-------
 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-i

-------

-------
                               Table of Contents

Charge to Reviewers	  C-C-5


Peer Reviewer Comments

William Adams	 . . •	• •  c-°-11
Bjorn Brunstrom	  C-C-19
Janet Burris	•	  C-C-25
Steve Bursian	• •  C-C-33
Peter deFur	  C-C-41
Joseph DePinto	  C-C-47
Lev Ginzburg	••••.-  C-C-55
Jay Gooch	•	  C-C-59
Mark Hahn	  C-C-69
Sean Kennedy	  C-C-81
Wayne Landis	-	• •  C-C-89
Lynn McCarty	• •	  C-C-99
Charles Menzie	  C-C-113
Chris Metcalfe	•	  C-C-119
Michael Meyer 	-	  C-C-125
Patrick O'Keefe	,	  C-C-131
Richard Peterson	  C-C-141
Mark Servos	  C-C-151
Martin van den Berg	 .	• • •	  C-C-157
Bert van Hattum	  C-C-163
                                      C-C-3

-------

-------
        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, KjS, 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-5

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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?
   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-6

-------
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 BAF™s used by the GLWQG. What improvement  in the accuracy of
      maximum allowable concentrations for individual congeners in water,  (MAC^y, can be
      expected through use of BAF™s 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 TEqTMDLfor 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-7

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Table 1
Parameters for PCBs, PCDDs, PCDFs

Total PCB
^>C&1248
(5(56-1264
PCB-1260
PCBs
77
81
105
114
118
123
126
153
156
157
167
169
189
PCOS
2378-TCDO
12378-PCT5D
12478-PCDO
123478-HxCGB
123678-HxGDD
123789-HxCDD
i234678-Hfc>CDD
CCDD
PCDP
2378-TCDF
12378-PCDF
2&78-PCDF
123478-HxCDF
123678-HcCW
123679-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
}234789-HpCDF
OCDF


togK^
(EPAGLO1

NA
NA
NA

6.36
6.36
6.65
NA
6.74
NA
6.89
6.92
7.18
NA
7.27
NA
7.71

7.02
7.50
NA
7.80
7780
750
£20
8.60

6UO
7.00
7.00
7.50
7.50
NA
7.50
7.50
8.00
8.00
8.8


(Eister&
Beliste, 1996)2

NA
NA
NA

6.52
6.37
6.66
6.66
7.12
6.75
6.90
7.75
7.19
7.19
7.28
7.43
7.72

NA
MA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA


(MacKay,Shiu&
Ma, 1992)3

5.8-6.3
6.1-6.8
6.3-7.5

6.5
NA
6.0
NA
NA
NA
NA
6.9
NA
NA
NA
NA
NA

6.8
NA
NA
NA
NA
NA
8.0
8.2

6.1
NA
6.5
7.0
NA
NA
NA
NA
7.4
NA
8.0


Henrys
Law
Constant

NA
NA
NA

1.72
NA
NA
NA
NA
NA
NA
42.9
NA
NA
NA
NA
NA

3.331
NA
NA
1.084
NA
NA
1.273
0.684

1.461
NA
NA
1.454
0.741
NA
NA
NA
1.425
NA
0.191


Lake Trout BSAF
(Oliver &Niimi)
(gCC/glip)
1.85
NA
NA
NA

NA
NA
2.70
NA
4.09
NA
NA
4.22
3.97
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA
NA

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA


(EPAGU)1
(gOC/glip)
NA
NA
NA
NA

0.29
0.67
4.49
NA
172
NA
3.21
1.91
NA
NA
0.69
NA
0.71

0.059
0.054
NA
0.018
0.0073
0.0081
0.0031
0.00074

0.047
0.013
0.095
0.0045
0.011
NA
0.037
0.04
0.00065
0.023
0.00099


BAF"1,
(EPAGLI)5
(L*g)
1.17E+08
NA
NA
NA

9.68E+06
2.24E+07
2.18E+08
NA
2.04E+08
NA
3.63E+08
3.31E-KI8
8.12E-K)8
NA
1.87E+08
NA
5.30E-KJ8

9.00E+06
2.49E+07
NA
1.65E407
6.71 E+05
7.44E+05
7.16E+06
4.29E-K56

2.16E+06
1.89E406
1.38E+08
2.07E+06
5.07E+06
NA
1.70E+07
1.84E+07
9.47E+05
3.35E-K37
9.10E+06


BMF
BMFbe,^
(gfislYgegg
32
NA
NA
NA

1.8
NA
20
NA
31
NA
29
48
NA
NA
NA
46
NA

20.75
9.7
NA
NA
16
NA
NA
NA

NA
NA
4.45
NA
NA
NA
NA
NA
NA
NA
NA


BWFbe,!7
(g lip/ g lip)
12
NA
NA
NA

0.7
NA
7.3
NA
11
NA
11
17
NA
NA
NA
17
NA

7.5
3.5
NA
NA
5.8
NA
NA
NA

NA
NA
1.6
NA
NA
NA
NA
NA
NA
NA
NA


BlvFdl,!8
(g lip/g lip)
2.9
NA
NA
NA

0.15
1.0
5.0
4.0
4.3
0.8
12.6
NA
8.6
11.0
5.7
13.6
9.1

11.0
6.3
NA
9.3*
33.5
15.5*
45.2
62.3

0.4*
NA
54.1
64.4
NA
54.9
NA
75.8
27.5
NA
43.3*


  References:
  1,  LSEPA,1995(EPA-820B-95O05).
  2,  Bslcr. R, and AA Beliste. 1996. Planar PCB Hazards to Rsh, Wildlife, and Inwstebrates: A Synoptic ReUew.  Nation^ Bidogicd Serwce Biological Report 31.  75pp.
  a  ^teck^,SKu& to 199Z///us/ratedHancboafcrfP/^/c^                                         Boca Raton, FU Lewis Publishers.
  4  Values tan Eis!er & Bdisle (1936) or MacKay, Shiu & Ma (1992).
  &  McanBAF", tosaftr^dsfranTaWe10oflBEPA(1995),viflhtheexcep^^^                                                                             '
  a  BMFbe.w[stha BMF from forage fish to bitd eggs on a wet weight basis from Braune, B.M., andRJ. Norstran. 1989. Dynamics of Otganochlorine Compounds in Hem'ng
     GUIs: IK. Tissue Distribution and Bioaccumulation in Lake Ontario Gulls.  Environ. Toxicol. Chem. 8:957-968.
     BMFs fcr PCS congeners 77,126, and 169 are (torn the same samples, but reported in Hodman et al.(1996).

  7, BMFs bo,l Is the BMF from foraga fish to bird eggs on  a liid 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 Iho BMF from dial to mkik 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 Ihts column are tha 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
BMF « Biomajnificalion factor
BAF * Bioaccumulation factor
EPA = Environmental Protection Agency
K^s = Octanol water partition coefficient
GLI = Great Lakes Water Quality Initiative
OC = Organic carbon
lip = lipid
g = grams
                                                                                 C-C-8

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Peer Reviewer Comments
        C-C-9

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                                                       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.
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                                                                  William J. Adams
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
                                     C-C-12

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                                                                      William J. Adams
       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
       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, whiclrhas 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 chronicno-effect concentrations
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                                                                      William J. Adams
      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.

   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
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                                                                      William J. Adams
      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
      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.
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                                                                      William J. Adams
   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 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
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                                                                      William J. Adams
       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
       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-1 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.
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                                                                 William J. Adams
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.
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                                                    Bjorn Brunstrom, Ph.D.
                                                          Associate Professor
                                        Department of Environmental Toxicology
                                                           Uppsala University
                                                             Norbyvagen 18A
                                                    S-75236 Uppsala, Sweden
                                                          011-46-18-471-2626
                                                      Fax:011-46-18-518-843
                                            E-mail: bjorn.brunstrom@etox.uu.se
Dr. Brunstrom 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.
Brunstrom is also a member of the working group preparing the report, "Endocrine
Disrupting Substances: Impairment of Reproduction and Development" for the Swedish
EPA.  Dr. Brunstrom'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.
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                                                                         Bjorn Brunstrom
 Workshop on the Application of 2,3,7,8-TCDD Toxicity Equivalency Factors (TEFs) to Fish and Wildlife

 Premeeting comments
 I.I. 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.

 1.2. 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.
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                                                                         Bjorn Brunstrom

            Concerning the exposure routes, it should be kept in mind that mammalian TEFs are mainly
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.

1.3. 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.
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                                                                          Bjorn Brunstrom
 H.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
 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 offish 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
 PCS 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.

 I V.I. 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 -ancTaTEF approach, the contribution from
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                                                                         Bjorn Brunstrom

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
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:         . ,
 !/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.
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                                                               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.M. 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),toxicityidentificationevaluations(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.
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                                                                          Janet A. Burris
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
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                                                                            Janet A. Burris
       values as point estimates provides the illusion that all of derived values are "equal" in their
       predictive ability of dioxin-Iike toxicity.

The TEF values provided were based on endpoints that ranged from in vitro biochemical responses
(e.g., induction ofcyplAl) 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 endpointfor 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 o-
       deethylase (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 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
       endpoint-specific?
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                                                                          Janet A. Burris

II.     STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE
       TEQ APPROACH

       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 ifstressor 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
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                                                                              Janet A. Burns
        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?

        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
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                                                                           Janet A. Burris
       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
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

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                                                                            Janet A. Burns
        (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.
 Additional Questions Specific to the Prospective Case Study

 The state adopted BAFjJs 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 ofBAFfdws 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 TEQTMDLfor 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
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                                                                     Janet A. Burris
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 managers 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
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.
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                                           Steven J. Bursian, Ph.D.
                                                           Professor
                                        Department of Animal Science
                                                    132 Anthony Hall
                                             Michigan State University
                                              East Lansing, Ml  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.
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                                                           Steven J. Bursian
       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

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                                                           Steven J. Burs/an
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
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
cypl A1, 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 OR 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.
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                                                            Steven J. Bursian
 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
                 Concentration       NOAEL
                   Detected        Threshold
      Mammalian Liver
Concentration      NOAEL
  Detected       Threshold
PCBs
TCDD
TEQs
5667 ng/gm
4.5 pg/gm
445 pg/gm
5000 ng/gm
100 pg/gm
1 00 ng/gm
1001 ng/gm
1 .43 pg/gm
144 pg/gm
2000 ng/gm
60 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%.
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                                                            Steven J. Bursian
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 /^g/kg
egg while a second study uses doses of 0, 1.0, and 10 //g/kg  egg. In both studies,
the bursa weight is reduced at 10 ,ug/kg egg but not at the next lowest dose. Thus,
in the first study the NOAEL is determined to be 6.4 /^g/kg egg while in the second
study the NOAEL is 1.0 /ug/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 ,ug/kg egg and the LD50 value for TCDD was 0.15 ,ug/kg egg. Thus, the
TEF value derived in this study was 0.07.  In the cormorant, the PCB  126 LD50
value was 177 ^g/kg egg while the TCDD LD50 value was 4.2 /^g/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

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                                                            Steven J. Bursian
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 non-
domesticated avian species such as cormorants or terns would be considerably
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
O-deethylase 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

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                                                           Steven J. Bursian
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 TCOD 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.
       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.
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                                               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 Disrupter 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.
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                                                                      Peter L. deFur

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
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                                                                        Peter L.deFur

 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
 included, I urge EPA to consider a follow-up case that draws on a poorly studied type of'
 situation.

 Specific Review Questions:
II.
 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)

 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
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                                                                       Peter L. deFur

           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,
           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 physico-
           chemical 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.
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V.
                                                                 Peter L. deFur

     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
     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.

 Prospective Case
 1.   This question about improvement in BAF fdw '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 half-
    life 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
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VI.
                                                                Peter L. deFur

   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.
Retrospective Case
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.
2.
                                        C-C-46

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                                            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.
                               C-C-47

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                                                                     Joseph V. DePinto
             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.
HI. EXPOSURE PROFILE
/.      To what extent does the TEF approach present challenges, introduce new uncertainties,
        or modify old uncertainties associated with modeling the exposure ofAhR 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
physico-chemical 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-
                                         C-C-48

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                                                                        Joseph V. DePinto

 activity models) these chemical properties.
        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 c'an 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
                                         C-C-49

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                                                                      Joseph V. DePinto

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.

IV. RISK CHARACTERIZATION
/.      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 strength; 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.
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                                                                       Joseph V. DePinto

        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
    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
 /.  The state adopted BAP*^ used by the GLWQG. What improvement in the accuracy of
    maximum allowable concentrations for individual congeners in water can be expected
    through use ofBAFfd,v 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 OfTCDD 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 BMP could be significant on a congener-
specific basis, which in turn might have a significant effect on the standard. Good data on the
                                         C-C-51

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                                                                      Joseph V. DePinto

fraction of the wildlife diet coming from fish and the age and size offish 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
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 PCBsfor 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
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                                                                      Joseph V. DePinto

 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 TEQ- '
 based 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 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 info the sediments.

 Conclusion
       I am strongly in favor of applying the TEF/TEQ approach for risk management offish
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.
                                       C-C-53

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                                                                           Joseph V. DePinto

Bibliography
Gong, Yuyang, J.V. DePinto, G-Y. Rhee, X. Liu. 1997. Desorption rates of two PCS 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.46:25l-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 He, 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 He,
    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.
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                                                 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.
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                                                                         Lev R. Ginzburg
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.l. 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
                                          C-C-56

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                                                                           Lev R. Ginzburg

  fairly similar to those we've seen in other inputs, and even if they're considerably bigger, it
  shouldn't 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 (Person 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.

 JTV.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 demographicaily
 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 cyplAl) 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,
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                                                                       Lev R. Ginzburg


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
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
Person, 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. Beauch'amp, 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.
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                                                   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.
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                                                                 Jay W. Gooch
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 cross-
endpoint 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).
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                                                                    Jay W. Gooch
 Table!
                                     TEF Ratio
                   In vivo RBT liver EROD/
                   RBT ELS Egg Mortality
In vitro RBT liver EROD/
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,4,6,7,8-HpCdd
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,7,8-PeCDF
2,3,7,8-TCDF
PCB 126
PCB81
PCB 77

1.0
2.7
1.5
20
33
5.9
1.7
12.5
16.7
0.4
6.5
28

1.0
3.9
4.2
10
133
5.6
4.6
6.3
6.7
41
4.8
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?
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                                                                  Jay W. Gooch

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
Speharetal.  are as follows.
Species
LC50/NOEC Ratio
Fathead Minnow
Channel Catfish
Lake Herring
Medaka
White Sucker
Northern Pike
Zebrafish	
       2.3
       1.7
       5.2
       2.4
       2.2
       2.1
       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.
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                                                                  Jay W. Gooch
 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
 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).
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                                                                  Jay W. Gooch

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
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?
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                                                                   Jay W. Gooch
 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.

 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.
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                                                                 Jay W. Gooch
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
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
                                i
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
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                                                                    JayW.Gooch
 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
 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?

 r 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.             ,         .
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                                                                 Jay W. Gooch
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 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.
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                                                    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-lnduced 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.
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                           Workshop on the Application of
                  2,3,7,8-T CDD 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://WAVw.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

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                                                                      Mark E. Halm

 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
 variability in the estimates,  indicating different  degrees of uncertainty depending  on the
 compound chosen.
                               HAH relative potencies in ish
               2378-TCDD
             1237B-PCDD
            123478-HCDD
            123678-HCDD
           1234678- HCDD
               2378-TGOF
              12378-PCDF
             23478-PCDF
            123478-HCDF
                   CB-126
                   CR-168
                   . CB-81
                    QB-77
                   CB-105
                   CB-11S
                   CB-1S8
 E
-US
              m
                                        Relative potency -
    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
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                                                                          Mark E. Hahn

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 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
steps-induction 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?
9
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                                                                           Mark E. Halm

 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.
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                                                                           Mark E. Hahn

IV. Risk Characterization
/. 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?
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                                                                           Mark.E. Hahn

   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
 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'-tetrachlofobiphenyl (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 tissue-
and species- differences in antagonistic properties of a given chemical.
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                                                                          Mark E. Hahn

  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
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-p-
dioxin (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.O. 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. demons, 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.
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                                                                         Mark E. Hahn

10. demons, J.H., Lee, L.E.J., Myers, C.R., Dixon, D.G. and Bols, N.C. (1996) Cytochrome
P4501 Al 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
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.O., 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.
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                                                                         Mark E. Hahn

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 1 Al 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
roles of polyhalogenated biphenyls in causing increases in cytochrome P450 and uroporphyrin
accumulation in cultured hepatocytes, Toxicol. Appl. Pharmacol. 146: OOO-OO'O.
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 ofortho 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.O. 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 iir
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-tetrachIorodibenzo-p-
dioxin 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. AcadSci. U.S.A. 94: in press.
33. Fujii-Kuriyama, Y., Kobayashi, A., Etna, M., Mimura, J., Morita, M. and Sogawa, K. (1997)
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                                                                          Mark E. Hahn

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.
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 OH3
                                                           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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                                                                    Sean W. Kennedy

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  arid/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
time-being, 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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                                                                     Sean W. Kennedy

 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: PCS 126, 1278-TCDD, 12378-PCDD, 2378-TCDF,
 1,2,3,7,8-PCDF arid 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.
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                                                                    Sean W. Kennedy

Prospective Scenario:
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
offish 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 no-
effect 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
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                                                                     Sean W. Kennedy

no-effect 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
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 V* 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
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                                                                     Sean W. Kennedy

 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 offish, 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 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-/?-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.
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                                                                     Sean W. Kennedy
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 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
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                                                                   Sean W. Kennedy

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.

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.
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                                                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.
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                                                            Wayne G. Landis

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 LCSOs 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.
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                                                                Wayne G. Landis

   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.
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                                                               Wayne G. Landis

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 concentration-
response 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.
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                                                                Wayne G. Landis

  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.

111. 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.
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                                                               Wayne G. Landis

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.
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                                                                Wayne G. Lantfe

 3) Get reliable concentration-response data that actually includes accurate estimates of
 effective levels of concern, not NOELs (not real) and LCSOs (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.
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                                                               Wayne G. Landis

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
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                                                              Wayne G. Landis

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 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.
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                                                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.cbm
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.
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L.S. McCarty Scientific Research & Consulting
280 Glen Oak Drive, Oakville
Ontario, Canada  L6K 2J2     905/842-6526  (phone & fax)
1156868 Ontario Inc.
 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
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                                                                                 Lynn S. McCarty
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).

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 effepts. 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,
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                                                                               Lynn S. McCarty
 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
 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.
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                                                                                 Lynn S. McCarty
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 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
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                                                                               Lynn S. McCarty
 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
 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 ug/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 ug/g.

The original TEQ analysis for birds (Table 2) finds exceedences of the provisional guidelines for total TEQ
 (100 ug/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 ug/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 (60p.g/g) by both the TEQ total and the PCBs,  but
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                                                                               Lynn S. McCarty
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 |ig/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
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 I-TEFs 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
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                                                                                Lynn S. McCarty
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-to-
field extrapolation problems discussed in the response to question 1.3, 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.

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.
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                                                                                 Lynn S. McCarty
 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 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
                                              C-C-107

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                                                                               Lynn S. McCarty
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 toxicplogical 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
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, BMP, 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
                                               C-C-108

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                                                                               Lynn S. McCarty
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 site-
specific data be of substantially greater utility.

Additional Questions Specific to the Prospective Case Study
RELATIVE TO THE EXPOSURE PROFILE
1.1 trust the question refers to BAFfd, rather than BAFfdw 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
congener-specific MACs using site-specific data for BAP61, 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
forPCDD/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
                                             C-C-109

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                                                                               Lynn S. McCarty
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/BMP and TEF values, as can be seen from the information in the included tables. These differences
are 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.
                                              C-C-110

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                                                                             Lynn S. McCarty
 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.O. 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.
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                                                          Charles Menzie, Ph.D.
                                                     Menzie-Cura & Associates, Inc.
                                                        2 Courthouse Lane - Suite 2
                                                            Chelmsford, MA01824
                                                                    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.
                                     C-C-113

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                                                                             Charles Menzie
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.
-  l
I.      STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ
       APPROACH
                                         '  C-C-114

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                                                                                  Charles Menzie

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 le,vel is being employed?
       The question suggests that there is a potential "apples and oranges" problem associated with mixing
       these different type's 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 dose-
 j*
       response curves (e.g., NOAEL values). However, these relative measures can still be combined with
       absolute toyicity 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
1 a.    To what extent does the TEF approach present challenges ....?
       The approach reduces uncertainties associated with estimating risks associated with mixtures
                                              C-C-115

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                                                                               Charles Menzie
       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
       variability is likely to provide a better estimate of effects than a method that assumes a specific
       composition.

Ib.    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
cdmplex 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?
                                             C-C-116

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                                                                                    Charles Menzie

         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
         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.
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                                                                                Charles Menzie
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-118

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                                                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.
                                    C-C-119

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                                                               Christopher D. Metcalfe
                           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
TEQ-based 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

                                         C-C-120

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                                                                Christopher D. Metcalfe
 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
 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
 ECSOs 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
 salrnonid 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 warm-
 water 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.

 III. 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
                                       C-C-121

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                                                                Christopher D. Metcalfe

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
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.
                                         C-C-122

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                                                               Christopher D. Metcalfe

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
        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
theGLWQG.

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, tn 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  PCS  congeners. This  is  especially noticeable when comparing
TEQ/PCB ratios in biotic and abiotic compartments.
                                      C-C-123

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                T-L AfeewBfc C-Q. M&exlft / The Jcamos Ufa* Tead SOTrwuwstf SI 41997} 2<*$-%?l
         0.0012
          0.001
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         0.0008
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         0.0004
         0.0002
                                           TEOflTotat PCB
                  ' Water  S*d,   Plank  Oip«f**
E5j, 11, Ratio of Tttric Equtivmlent Oaaaiidc* CTEQs) fisfcu3sE6d for tqftd macdKjjste and

total ?CB eoo«ija*tkips in waw*, «sdsK»«rt And. bioia froro 4fe« Lake OtiEiris food-web.
                                                             SeMtt   Treat


                                                                  iPOi srrasftmea fcttthw to
                                              C-C-124

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                                                     Michael W. Meyer, Ph.D.
                                                            Wildlife Toxicologist
                                            Bureau of Integrated Science Services
                                      Wisconsin Department of Natural Resources
                                                       107 Sutliff Road, Box 818
                                                         Rhinelander, Wl  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&l 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 immerj mercury exposure, reproduction, and survival in Wisconsin;
and A geographic trend in mercury exposure  measured in common loon feathers and
blood.
                                    C-C-125

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                                                                    Michael W. Meyer

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.
                                         C-C-126

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                                                                     Michael W. Meyer
 However, the calculated egg total TEQ for the same Caspian tern eggs exceeds a reported
 total 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 III.

 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
                                        C-C-127

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                                                                    Michael W. Meyer
necessary to predict risk within the various biotic and abiotic compartments of an ecosystem,
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
at. 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
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                                                                     Michael W. Meyer
              /
       classes.     .
       b      Establish standard bio-based TEQ assay and conduct research to
       understand mechanisms responsible for different results between classes.
       c.     .Establish protocol for quantifying site-specific
       biomagnification/bioaccumulation factors to quantify TEQ exppsure 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'Visk 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
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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
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.
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                                                    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.
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                                                                             Patrick W.O'Keefe
               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

HI. 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 (TEqC'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:
                                                        2A9E +07*0.08*1
                                                                      ._ooig   /£
The fd values were determined using DOC , POC and Kow values as described in EPA-820-B-95-005. The
complete set of TEqC\v 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
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                                                                              Patrick W. O'Keefe
 2,3,7,8 TCDF

 1,2,3,7,8 PeCDF

 2,3,4,7,8 HxCDF
1.2
1.9
0.026
   1.29
  2.05
. 0.028
0..183
0.289
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, values from the background literature were applied without converting
them to BAF', values using fd values. For instance the TEqC'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 (MALjj) for
each congener, several other parameters are required in addition to maximum allowable water
concentrations (MAC'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 () 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
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                                                                             Patrick W. O'Keefe
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
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 - ng/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
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                                                                              Patrick W. O'Keefe
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 offish-eating biota then food chain biomagnification can be significant and the Great Lakes
Water Quality Criteria may severely overestimate MACW values. On the other hand MAC'W 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 £2 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  ug/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 ug/g in
Caspian tern eggs results in a TEQ value of 400 pg/g, which is xlO 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 BAF',
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.
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                                                                             Patrick W. O'Keefe
        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 numbers of mink and otter are valid, and (3) the proportions offish and fish-eating biota in
the diet of mink and otter should be assessed. Consumption offish-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,
using the following equation:
                                     (BAF?)
This value can then be compared to the ratio determined from the GLWQG document. With / = 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 BMP 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 BMP 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
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                                                                             Patrick W. O'Keefe
 BAFs and the factor of 30, respectively). The BMP-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:
                NOAEL  _            IQQxlO3	
                totalBMF  0.736x21 +0.184x10 +0.056^659
=TargetDietary Concentration
                   TargetDietaryConcentration _  lS4Qpg/kgforagefish
                             BAF             172, lOOkgforagefish/L       P8   '
        It is more difficult to determine a BMP-adjusted WQS for mink since the diet-to-mink BMP is
presented on a Hpid 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 BMP for TCDD would be 5.5 and the WQS
value should be divided by this number to give a BMP-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 xlO 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
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                                                                            Patrick W.O'Keefe

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 BMP. 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:
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                                                                          Patrick W. O'Keefe
Caspian Tern Eggs
                           TEQ Calc.
                      TEQ Table 2
Otters
       PCB77
       PCB 126
       2,3,7,8-TCDD
       2,3,4,7,8-TCDF
       PCB 105
       PCB 118
       PCB 126
       2,3,4,7,8-PeCDF
29
232
2.3
4.1
TEQ Calc.
2.5
4.8
98
25.96
54
275
4.5
9.58
TEQ Table 3
2.6
4.8
99.8
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:
       PCB 77
       PCB 105
       PCB 118
       PCB 126
       2,3,7,8-TCDF
       2,3,7,8-TCDD
       2,3,4,7,8-PeCDF
BSAF shiners
Oneofakind Lake
0.91
10
7.3
5.76
0.006
0.14
0.017
BSAF lake trout
Oneofakind Lake
1.82
14
23.5
5.1
0.069
0.35
0.035
BSAF EPA
Table 1 Questions
0.29
4.49
1.72
3.21
0.047
0.059
0.095
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                                                                              Patrick W.O'Keefe

       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
savings in analytical costs by analyzing sediments for total PCBs and then determining individual Coc
values by a ratio calculation.
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                                               Richard E. Peterson, Ph.D.
                                                                  Professor
                                                      425 North Charter Street
                                                         School of Pharmacy
                                                       University of Wisconsin
                                                         Madison, Wl 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.
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                                                                   Richard E. Peterson
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 iii 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
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                                                                      Richard E. Peterson
 (AhR) agonist toxicity.  Yet the wild bird receptors of concern in the case studies, the Caspian
 tern 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 AhR-
mediated responses in a particular species it might.not be necessary to apply an  additional
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                                                                    Richard E. Peterson

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
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
offish 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 P4501 Al 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.
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                                                                    Richard E. Peterson
II.  STRESS-RESPONSE PROFILE RELATIVE TO THE APPLICATION OF THE TEQ
APPROACH
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 co-
contaminants 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 (bird-
specific 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 offish 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
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                                                                     Richard E. Peterson
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.
       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 offish 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
LCI  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 EDI 0 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 fish-
specific 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 CYP1 Al induction in vitro and in
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                                                                     Richard E. Peterson
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
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
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                                                                    Richard E. Peterson
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 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 P4501 Al 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.
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                                                                     Richard E. Peterson
3. List of specific studies that would reduce uncertainty in the case study assessments (ranked
from highest to lowest priority)
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 TCDD-
       induced 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.
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                                                                  Richard E. Peterson

Additional Questions Specific to the Prospective Case Study

RELATIVE TO THE EXPOSURE PROFILE:
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.
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                                                    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 of3-trifluoromethyl-4-nifrophenol (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.
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                                                                            Mark R. Servos

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
TCDFTEFof
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                                                                             Mark R. Servos
 trout eggs is below the threshold for effects (30) But approaching the range of reported values. The
 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 th'e 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 ECSOs 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.
HI. 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
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                                                                          Mark R. Servos

126. PCB 126 is the dominant congener of concern for otter but PCB 77 and 81 are important for
terns.

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.
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                                                                            Mark R. Servos
 •  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.
 •  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 BMP for PC 126 but the mammals have a BMP 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.
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                                              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.
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                                                               Martin van den Berg
Comments on the questions related to the Workshop on the Application of TEFs to Fish
and Wildlife.

1.1
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.

1.3
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.
11.1.
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.
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                                                              Martin van den Berg
 1.2.
 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 TGDD, can vary
 more than three orders of a magnitude. Therefore the right choice of LOAEL/NOAEL
 seems to much more essential to the process.

 111.1.
 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
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                                                              Martin van den Berg
will also be a good approach for these compounds as long as you stay with 'the more
hydrophobic compounds. In addition, .1 think that physico-chemical data can be much
easier obtained for these compounds than biological data, while further more physico-
chemical 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.
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                                                              Martin van den Berg
IV. 1.
\ 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.
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                                                  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.
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                                                                     Bert van Hattum
                                 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.
Ql,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
WHO-document 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
(Luttiketal. ,1993).
II.     Stress-response profile relative to the application of the TEQ-approach
Ql.    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
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                                                                       Bert van Hattum
 individual congeners may contribute represent at most up to 30-40% (23478-PCDF)  of the total
 diqxin-equivalent concentration for fish, and 60-70% (PCS-126) for birds and mammals.
 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 target-species. 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
 Ql.     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 species-specificity 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 lutrd) in Danish and Dutch habitats (Smit et al. , 1996; Leonards et al. , 1997),  we observed
extremely high otter-fish BMP 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
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                                                                       Bert van Hattum
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,
BMP estimates may be 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. PCB-
153, 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-exposlire 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
Ql.    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 arid 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

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                                                                      Bert van Hattum
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
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 BSAP and BMP 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
Ql.    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 biomagnificationj
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 1, 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 species-
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                                                                       Bert van Hattum

specific, extrapolation factors could be derived to express the proposed critical levels on the basis
of S 7PCBs and. of PCS-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
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 PGBs and TEQ-based concentrations may be site-and species-
specific, 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. Golpr, 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 TJ. 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. Cofmo, 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. Cofmo, 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
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                                                                         Bert van Hattum

   validated for measuring tcdd equivalents in blood plasma. Environ. Toxicol  Chem  16(8V 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 Otter-
   based Quality Objectives for PCBs (DOQOP). ISBN-90-53 83-528-8. IVM-R96/11, Institute for
   Environmental Studies, Vrije Universiteit. Amsterdam, 170 p

Tillitt, D.E., Gale, R.W., Meadows, J.C., Zajicek, J.L., Peterman, P.H., Heaton, S.N., Jones, P.O.,
   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.
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                                                                          Bert van Hattutn
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.
28

31
44
49
52
101
105
118
128
138
149
153
156
157
158
166
167
170
180
187
189
194
77
126
169
£ PCB.S
Z TEQs***
BMP*
Oude Venen
(ML)
0.044

0.049
0.014
0.022
0.016
0.066
12
15
9
26
0.16
15
30
19
4
2
6
15
123
23
50
21
1.4
70
348
14
41.
BMP**
Lymfjord
(Denmark)
0.47 (0.002 -
8.4)



0.54 (0.2-2.3)
2.1(0.03-36)
7.9 (0.7 - 50)
35(2-251)

51(4-297)

28(2-172)
37 (3 - 505)
84 (2 - 2086)


13 (2 - 83)

63 (5 - 442)

144(12-1073)

2.5 (3 - 7.9
130(4.2-900)
108 (3 - 1700)
36 (2.9 - 209)
95 (3.5 - 640)
  * Geometric mean BMFs for 5 otters from Leonards at al.  (1997); ** From Smite? 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.

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          Appendix C-D

     DETAILED SUMMARIES
OF EXPERTISE GROUP DISCUSSIONS

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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 K^s 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
                                         C-D-1

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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
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 congener-
specific 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.  K<,ws are known  reasonably
well.  Using K^s to predict K^s 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-l).
                                         C-D-2

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                     Food Web Group
                     Food Web Group
           L. Ont food web
L. Zandemeer food web
Figure D-l.  Mean ratios of CB 77/CB 153 and CB 126/CB
153 in trophic levels of Lake Zandemeer and Lake Ontario
                         C-D-3

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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 species-
specific of 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 offish 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?
                                         C-D-4

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The waste load allocation process model needs to be validated, but it is expected that this process
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-5

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                               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.
                                         C-D-6

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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
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 ECIO 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 EC,.^ 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. CYPI Al 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
                                          C-D-7

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 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.

           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 CYP1 Al. This is problematic in
 fisTi, 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?
                                          C-D-8

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 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
 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?

 Pharrnacodynamic 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 PGB 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-9

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                  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-the-
shelf 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 hi the TEF approach. The decisions are not transparent in the WHO
 document and these may affect their applicability. One should track the toxicity, assumptions,
                                         C-D-10

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 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:

           • 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.
                                         C-D-11

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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
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 PGB 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?
                                        C-D-12

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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,
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 shpuld 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
                                        C-D-13

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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.

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 willcontribute 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
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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.
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.
                                        C-D-15

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       Appendix C-E
   DETAILED SUMMARIES
OF CASE STUDY DISCUSSIONS

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Breakout Group Assignments
Wednesday, January 21,1998—10:45AM-3:45PM
Thursday, January 22,1998—8:30AM-12:30PM
Group 1
Chair: Peter deFur
Group 2
Chair: Janet Burris
Group 3
Chair: Charles Menzie
TEFs Experts
Jay Gooch (fish)
Martin van den Berg
(mammals)
Sean Kennedy (birds)
Mark Hahn (fish)
*(See EPA/DOI Planning
Group)
Bjorn Brunstrom (birds)
Richard Peterson (fish)
Bert van Hattum (mammals)
Steve Bursian (birds)
Fate & Transport/BAF Experts
William Adams
Joseph DePinto
Patrick O'Keefe
Christopher Metcalfe
Mark Servos
Lynn McCarty
Population Modeler
Lev Ginzburg
Risk Assessor
Peter deFur
Mike Meyer
Wayne Landis

Janet Burris Charles Menzie
EPA/DOI Planning Group
Gary Henningsen
Lisa Williams
Robert Pepin


*Mike Devito
Tim Kubiak
Steve Wharton
Steve Bradbury
Phil Cook
Pat Cirone
Cynthia Nolt
DonTillitt


  Printed on Recycled Paper
                     C-E-1

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                                  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

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applications. The group agreed that the greatest certainty in BAFs exists in systems where the
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
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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
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
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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
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
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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
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
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 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
 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.  Asa
 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.
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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
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 (Person and Ginzburg, 1996) can be found
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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 toxicologically-
based decisions from ecological considerations.  In practice, and in our case study, the "no-
effect" 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
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.
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                                 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.

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       Discussion of Appropriate TEF Values for Species of Interest in the Case Study (Janet
 Bums).  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 dioxin-
like 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 TMike 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.
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       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
methodology is whether the TEF should be based on the ratio of the lower end of the dose-
response 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 ED0] 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 ED0,s are fraught with uncertainty.  The ability to accurately
detect NOELs and LOELs, EDI0s, or ED0iS 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 ED0]  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 Bioassav-derived TEQs (Christopher Metcalfe). The group discussed the
potential for using bioassay-derived TEQs in the prospective risk assessment scenario.
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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
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 transportissues 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
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relatively high, but there was concern over the lack of empirically-derived Koc data, and over the
quality of K^ 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
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 offish-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 gas-
chromatography/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.
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       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
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
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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.
       When the tissue levels of concern are translated into water quality guidelines, using biota
accumulation factors, the maximum allowable total water concentrations (MAClw) 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.
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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.

       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 BMP 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 Ihe 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 (BAFX 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.,
                                         C-E-17

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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.
       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, species-
specific BMFs should be measured at the risk assessment site.

       Possible Errors in the Application of the TCDD Water Standard (Janet BurrisX  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
                                        C-E-18

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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
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/TEO 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
                                         C-E-19

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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
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.
! i
i I	"
       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.
                                         C-E-20

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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 TEO-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 endpoihts and species (Safe, 1990),
specific REP values can vary between species and across endpoints within a species.  In an ideal
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.
                                         C-E-21

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       The approach described in Figure E-l 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 species-
specific REP for the endpoint of concern.
             Framework for choosing relative potency values for use in
        Ecological Risk Assessment for fish, birds, and mammalian wildlife.

Tierl
la (endpoint of
concern)
lb (other in vivo
toxic endpoint)
Tier 2
(In vivo CYP1 A)
TierS
(in vitro CYP1A)
Tier 4
(QSAR)
Same Species
Best




Related Species
(e.g., same genus
or family)





WHO TEFs
("Class-specific")




Worst
 Figure E-l.

   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 la)
from other in vivo toxic endpoints (Tier lb). As with the WHO TEF approach, the highest
                                         C-E-22

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 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
 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
 CYP1 Al 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 ctl., 1994; Harm 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
                                         C-E-23

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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.

          The basis for the phylogenetic approach reflected in the top row of the matrix in
Figure E-l 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 endpoint-
specific 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-l 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
                                         C-E-24

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assessment for lake trout, the only "Tier la" 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
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-25

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              Matrix showing position of three choices of REP values
                        for the scenarios described above

Tierl
la(endpointof
concern)
Ib (other in vivo
toxic endpoint)
Tier 2
(i/iv/wCYPlA)
TierS
fwv//r&CYPlA)
Tier 4
(QSAR)
Same Species
?


A

Related Species
(e.g., same genus
or family)


C


WHO TEFs
("Class-specific")
B




 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 caspid). 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-26

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        TEQ values and £TEQ, determined using two different sets of REPs,
                  for Caspian terns in the retrospective scenario.

PCB-77
PCB-126
PCB-169
TCDF
£TEQ for all
congeners (including
those not shown)
BirdTEF1
0.05
0.1
0.001
1

Caspian Tern
Egg TEQ
54.17
275
0.32
2.79
426
Common Tern
REP2
0.003
0.03
0.02
0.4

Caspian Tern
Egg TEQ
0.32
82.5
6.4
1.1
184
 'WHO TEFs (van den Berg et al., 1997).
 2REP values form 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
                                         C-E-27

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use of multiple REP values and sensitivity analysis of the resulting TEQs.

       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
         pro vide 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:
                                         C-E-28

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       •  What endpoints were used?  Were in vivo endpoints used?
       •  What species-specific data was available?
       •  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
                                         C-E-29

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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
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 Bums). The group conchided 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
                                         C-E-30

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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
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."
                                    Threshold
               Exposure
(WHO TEF)
Common tern
Total PCBs
100 pg/g
lOOpg/g
5ng/g
426 pg/g
185pg/g
4.5 ng/g
                   Individual
                Hazard Quotient
                        4
                        2
       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
          TCDD
          Total PCBs
60 pg/g
60 pg/g
2.0 us/g
144 pg/g
1-4 pg/g
l.Ojig/g
                                         C-E-31

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       Action Decision (Janet Bums). 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
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 ofDioxins 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. CYP1 Al 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) Photoaffmity labeling of the Ah
receptor: Phylogenetic survey of diverse  vertebrate  and invertebrate species. Arch.  Biochem.
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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, JJ. (1996) Cytochrome
P4501A induction in avion 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.

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), dibenzp-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-33

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                                 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
                                         C-E-34

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were reduced if taxa-specific unrounded values were utilized in lieu of the "order of magnitude"
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 Toxicitv 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.
                                         C-E-35

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       Part 2: Identifying and Tracking Uncertainties

       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 B AF values;
       •      uncertainty factors associated with species extrapolations; and
       •      uncertainty factors associated with the exposure model.
                                         C-E-36

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Each of these areas is described in more detail below.

       Uncertainty Criteria for TEF Values.  Our group had two separate discussions
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 pur first attempt were:

       •       Level 1: egg injection with mortality endpoint;
       •       Level 2: whole organism with other eridpoints;
       •       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
                                         C-E-37

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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:

       •      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;
       9      LevelS: field data with no lab, or lab with no field corroboration; and
       •      Level 4: BAF based on K<,w (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;
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       •      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
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
                                         C-E-39

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   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.
               Relative Uncertainties in the Ecological Risk Assessment Including Use of TEF Values
Ranks for
uncertainty
Species/Congener


Bull
trout
1
2
3
4
5
6
7
Bald
Eagle
1
2
3
4
5
6
7
River
Otter
1
2
3
4
5
6
7


TEFs
1







4







3











BAFs
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3




Species
Sens./Extrapolation
2
1






4
1
1
1
4
3
3
2
3
1
1
1
, 2
2
2
2




Exposure
Model
2







4







4














Threshold
concentration
2







4







3





































Species specific
Congener specific























Criteria are described in the text. This approach and these values are presented for illustration only.




9
21






19
36






16
32











Total
30







55







48













Bull
Trout







Bald
Eagle







River
Otter









Figure E-4.
                                                C-E-40

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 Retrospective Case Study

        TEF/TEQ Issues Related to Measurements

        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 offish 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
                                         C-E-41

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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
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/TEO. 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.
                                        C-E-42

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       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
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
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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
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, back-
calculating will require information on ther 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.
                                        C-E-44

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       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
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
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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.

       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.
                                         C-E-46

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            Appendix C-F
WRITTEN COMMENTS FROM OBSERVERS

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 DEPARTMENT OF HEALTH AND HUMAN SERVICES
                                             National Institutes of Health
                                             National Institute of
                                             Environmental Health Sciences
                                             P. 0. Box 12233
                                             Research Triangle Park, NC 27709
 Date:

 From:

 Subject:
 To:
 Cc:
January 16, 1998

Angelique P.J.M. van Birgelen, Ph.D.

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

Chair of the Workshop on the Application of 2,3,7,8-TCDD Toxic Equivalency
Factors to Fish and Wildlife

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 dioxin-
 like 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',4r4'-Tetrachloroazobenzene  (TCAB),
 hexachlorobenzene   (HCB),   1,2,3,4,6,7-hexachioronaphthalene  (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 Diuro'n (Poland et al., 1976; Sunstrom 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 peroxide-
 producing 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
                                         C-F-1

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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
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 polychiorinated 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^ al, 1976; Schneider etaL,  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 P4501 Al, anemia, and an increase of porphyrins, in chick embryo liver cell cultures (Hsia
etal, 1980,1981,1982; Hilled/., 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                          ,
                                          C-F-2

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 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).

 Bioaccumulation
 TCAB
 TCAB has a log octanol/water partition coefficient of 5.53 to 6.69 (US-EPA, 1985; Hashimoto etol.,
 1994). The solubility in water is calculated to be 1 jig/1 (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 25°C) 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 Halo wax 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
Binding affinity to the Ah
receptor (nM)
EC50 for binding to the
mouse hepatic Ah receptor
(nM)
ED50 (nmol/kg) for induction
of aryl hydrocarbon
hydroxylase in chicken
embryos
TCDD
0.27
•1.22
0.31
TCAB
1.1
6.03
2.0
Relative potency
for TCAB
0.2
0.2
0.2
Reference
Poland etal, 1976.
Schneider et al 1995.
Poland et al, 1976.
                                         C-F-3

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LD50 (ng/egg) in chicken
embryos.
Cytochrome P4501A1
induction in the skin in a 90-
day oral gavage study in
female B6C3F1 mice with
TCDD and TCAB.
0.2

44

0.005
0.000003-0.00001
Higginbotham et al,
1968; Schrankel et al,
1982.
Hubert ef a/., 1993.
Table 2. Relative potency estimates for hexachlorobenzene (HCB).
Effect
Binding affinity to the Ah
receptor (nM)
EC50 for EROD induction in
chicken hepatocytes (nM)
EC50 for accumulation of
uroporphyrin in chicken
hepatocytes (nM)
Hepatic porphyrin
accumulation in female rats
TCDD
0.18
0.014-0.020
0.002-0.004

HCB
2100
130-150
25-35

Relative potency
for HCB
0.00009
0.00009-0.0002
0.00006-0.0002
0.0007
Reference
Hahne/a/., 1976.
Sinclair et al., 1997.
Sinclair, et al, 1997.
Cantonietal., 1981.
Table 3. Relative potency estimates for polychlorinated naphthalenes (PCNs)
Effect
AHH activity in a rat
hepatoma H-4-II cell line
CYP1A1 activity in a rat
hepatoma H-4-II cell line
Relative potency
forPCN66/PCN67
0.003
0.002
Relative potency
for PCN 73
0.003
0.003 -
Reference
Hanberg et al,
1990.
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 ng/g, this could lead to an annual
    release of 12,000 kg of TCAB into the environment due to Propanil alone (Sunstrom et al, 1978;
                                           C-F-4

-------
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 jig/g, this could lead to a production of 3,900 kg of TCAB per year in the
US (Sunstrom etal, 1978; Bunce etal, 1979; Hill etal, 1981; US-EPA, 1985). Analyses of arice
plot treated with 6.7 kg Propanil/hectare indicated a TCAB concentration of 0.09 ppm (Kearney et
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.
                                       C-F-5

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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
updated on chlorinated naphthalenes.

Literature
Ahlborg, U.G., Brouwer, A., Fingerhut, M.A.,  Jacobson, J.L., Jacobson, S.W., Kennedy, S.W., Kettrup, A.A.F.,
Koeman, J.H., Poiger, H., Rappe, C., Safe, S.H., Seegal, R.F., Tuomisto, J., and Van den Berg, M. (1992). Impact of
polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyls on human and environmental health, with special
emphasis on application of the toxic equivalency factor concept. Eur. J. Pharmacol. 228, 179-199.

Ahlborg, U.G., Becking, G.C., Birnbaum, L.S., Brouwer, A., Derks, H.J.G.M., Feeley, M., Color, G., Hanberg, A.,
Larsen, J.C. Liem, A.K.D.,  Safe, S.H., Schlatter, C., Waern, F., Younes, M.,  and Yrjanheikki, E. (1994). Toxic
equivalency factors for dioxin-like PCBs. Chemosphere 28, 1049-1067.

Asplund, L., Jansson, B., SundstrSm, G., Brandt, I., and Brinkman, U.A. (1986). Characterisation of a strongly
bioaccumulatinghexachloronaphthalene. Chemosphere 15, 619-628.

Asplund, L., Jakobsson, E., Haglund, P., and Bergman, A. (1994). 1,2,3,5,6,7-Hexachloronaphthalene and
 1,2,3,4,6,7-hexachloronaphthalene -  selective retention in rat liver and appearance in wildlife. Chemosphere 28,
2075-2086.

 Bartha, R., Linke, H.A.B., and Pramer, D. (1968). Pesticide transformations: production of chloroazobenzenes from
 chloroanilines. Science 161,  582-583.

 Bartha, R., and Pramer, D. (1969). Transformation of the herbicide methyl-n-(3,4-dichlorophenyl)-carbamate (Swep)
 in soil. Bulletin of Environmental Contamination and Toxicology 4,  240-245.

 Hsia, M.T.S., and Kreamer, B.L. (1985). Delayed wasting syndrome and alterations of liver gluconeogenic enzymes
 in rats exposed to the TCDD congener 3,3',4,4'-tetrachloroazoxybenzene. Toxicol. Lett. 25, 247-258.

 IPCS. (1997). Environmental  Health Criteria 195:  Hexachlorobenzene.  Geneva,  World  Health Organization,
 International Programme on Chemical Safety.

 Jacobsson,  E.,  Eriksson,  L.,  and  Bergman,  A.   (1992).  Synthesis  and crystallography  of   1,2,3,5,6,7-
 hexachloronaphthalene and  1,2,3,4,6,7-hexachloronaphthalene. Acta Chem. Scand 46, 527-532.

 Kearney, P.C., Smith, R.J.J., Plimmer, Jr. and Guardia, F.S. (1970). Propanil and TCAB residues in rice soils. Weed
 Sci. 18,464-466.

 Kenaga, E.E. (1980) Predicted bioconcentration factors and  soil sorption coefficients of pesticides and other
 chemicals. Ecotoxicol. Environ. Saf. 4,26-3  8.

 Lay, M.M., and Ilnicki, R.D. (1974). Peroxidase activity and propanil degradation in soil. Weed Res. 14, 111-113.

 Mansour, M., Parlar, H., and Korte,  F. (1975). Ecological chemistry. 1. Reaction behavior of 3'4-dichloroaniline and
 3,4-dichIorophenol in solution as a solid and in gas phase during UV radiation. Chemosphere 4, 235-240.

 McMillan, D.C., Leakey, J.E.A., Arlotto, M.P., McMillan, J.M., and Hinson, J.A. (1990). Metabolism of the arylamide

                                                 C-F-6

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herbicide Propanil, Toxicbl. Appl. Pharmacol. 103, 102-112.

McMillan, D.C., Bradshaw,  T.P., lemon' J.A., and  Jollow, D.J. (1991). Role of metabolites in Propanil-induced
hemolytic anemia. Toxicol. Appl. Pharmacol, 110, 70-78.

Mensink, J.A., and Strik, J.J.T.W.A. (1982). Porphyrinogenic action of tetrachloroazobenzene. Bull. Environ. Contain.
Toxicol. 28, 369-372.

Miller, G.C., Zisook, R. and Zepp, R. (1980). Photolysis of 3,4-dichloroaniline in natural waters. J. Agric. Food Chem.
28, 1053-1056.

Poland, A., Glover,  E., Kende, A.S.,  DeCamp,  M., Giandomenico,  and CM. (1976).  3A3',4*-Tetrachloro
azoxybenzene  and azobenzene: potent inducers of aryl hydrocarbon hydroxylase. Science 194, 627-630.

Rozman, K., Mueller, W., latropoulos, M., Coulston,  F., and Korte, F. (1975). Ausscheidung, Koerperverteilung und
Metabolisierung von Hexachlorbenzol nach oraler Einzeldosis in Ratten und Rhesusaffen. Chemosphere 4, 289-298.

Scheufler, E., and Rozman K.K. (1984). Comparative decontamination of hexachlorobenzene-exposed rats and rabbits
by hexadecane. J. Toxicol. Environ Health 14, 353-362.

Schlabach, M., Biseth, A., Gundersen, H., and Knutzen, J. (1995). Congener specific determination and levels of
polychlorinated naphthalenes in cod liver samples from Norway. Organohalogen Compounds 24, 489-492.

Schneider, U.A., Brown, M.M., Logan, R.A., Millar, L.C., and Bunce, N.J. (1995). Screening assay foi\dioxin-like
compounds based on competitive binding to the urine hepatic Ah receptor. 1 . Assay development. Environ.  Sci.
Technol. 29, 2595-2602.

Schrankel, K.R., Kreamer, B.L., and Hsia,  M.T.S. (1982). Embryotoxicity of 3,3',4,4'-tetrachloroazobenzene  ^nd
3'3,'4,4'-tetrachloroazoxybenzene in the chick embryo. Arch Environ. Contam. Toxicol. 11, 195-202.

Sinclair, P.R., Walton, H.S., Gorman, N., Jacobs, J.M., and Sinclair, J.F.  (1997). Multiple roles of polyhalogenated
biphenyls  in causing increases in cytochrome P450 and uroporphyrin accumulation in cultured hepatocytes. Toxicol.
Appl. Pharmacol. 147, 171-179.

Sundstrom,  G.,  Jansson,  B., and  Renberg,  L.  (1978).  Determination  of  the toxic impurities  3,3',4,4'-
tetrachloroazobenzene and 3,3',4,4'-tetrachloroazoxybe.nzene in commercial Diuron, Linuron and 3,4-dichloroaniline
samples. Chemosphere 12, 973-979.

US-EPA. (1985). Health and environmental effects profile for TCAB, TCAOB and TCHB. EPA/600/X-85/394.

US-EPA (1987). Pesticide Fact Sheet No. 149. Office of Pesticides and Toxic Substances, Washington, DC.

US-EPA. (1994). Estimating exposure to dioxin-like compounds. Volume II: Properties, sources, occurrence and
background exposure. EPA/600/6-88/005Cb. External review draft. U.S. Environmental Protection Agency,
Washington, DC.

Yamaguchi, Y., Kawano, M., and Tatsukawa, R. (1986). Tissue distribution and excretion of hexabromobenzene
(HBB) and hexachlorobenzene (HCB) administered to rats. Chemosphere 15,453-459.
                                                C-F-7

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Council
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 disrupter. 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
                                                C-F-8

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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 poly chlorinated 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 Angelique 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.
                                        C-F-9

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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.

>  StritctiiwLSimilarity to TCDD - As is shown in the figure below, HCB is a monocyclic (single-
    ringed) 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 TCDD-
    like toxicity (Hahn et al. 1989; McKinney and Singh, 1981).
              I-9.8A-]
                                                |»"~-""13. f A—~ -|
               Cl  Q
         Hexachlorobenzene
                                                 2,3,7,8-TCDD
                                        C-F-10

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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 poly cyclic 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 "equivocaP'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
   poly cyclic  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.
                                         C-F-11

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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
Human
Health
Ecological
Toxicity Value
Cancer Slope Factor
Oral Reference
Dose
Mammals
Fish
Birds
HCB
Based on liver tumors in
female rats (Eturk, 1986)
Based on liver effects in
rats (Arnold et al. 1985)
Based on survival and
reproductive effects in
mink (Rush et al.
1983;Bleavinsetal.
1984)
Based on survival in
several aquatic species
(USEPA, 1988)
Based on survival and
reproductive effects in
several species of birds
(Vosetal. 1971)
TCDD
Based on liver tumors in
female rats (Kociba et al.
1978)
Not available
Based on reproductive
effects in rats (Murray et al.
1979)
Based on survival in
rainbow trout and northern
pike (USEPA, 1993)
Based on survival,
reproductive &
developmental effects in
several species of birds
(Nosek et al. 1992; Hudson
etal. 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;
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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
basis of a toxicity reference value for this taxonomic group (Vosetal., 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)"
'] 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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