# Methodology for Deriving Ambient

Water Quality Criteria for the Protection of Human Health (2000) United States Office of Water EPA-822-B-00-004 Environmental Protection Office of Science and Technology October 2000 Agency 4304EPA 822-B-00-004 October 2000



# Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

Final

Office of Science and Technology Office of Water U.S. Environmental Protection Agency Washington, DC 20460

## N OTICE

The policies and procedures set forth in this document are intended solely to describe EPA methods for developing or revising ambient water quality criteria to protect human health, pursuant to Section 304(a) of the Clean Water Act, and to serve as guidance to States and authorized Tribes for developing their own water quality criteria. This guidance does not substitute for the Clean Water Act or EPA’s regulations; nor is it a regulation itself. Thus, it does not impose legally-binding requirements on EPA, States, Tribes or the regulated community, and may not apply to a particular situation based upon the circumstances.

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.

iiiii 

## F OREWORD

This document presents EPA’s recommended Methodology for developing ambient water quality criteria as required under Section 304(a) of the Clean Water Act (CWA). The Methodology is guidance for scientific human health assessments used by EPA to develop, publish, and from time to time revise, recommended criteria for water quality accurately reflecting the latest scientific knowledge. The recommended criteria serve States and Tribes’ needs in their development of water quality standards under Section 303(c) of the CWA.

The term “water quality criteria” is used in two sections of the Clean Water Act, Section 304(a)(1) and Section 303(c)(2). The term has a different program impact in each section. In Section 304, the term represents a scientific assessment of ecological and human health effects that EPA recommends to States and authorized Tribes for establishing water quality standards that ultimately provide a basis for controlling discharges or releases of pollutants. Ambient water quality criteria associated with specific stream uses when adopted as State or Tribal water quality standards under Section 303 define the maximum levels of a pollutant necessary to protect designated uses in ambient waters. The water quality criteria adopted in the State or Tribal water quality standards could have the same numerical limits as the criteria developed under Section 304. However, in many situations States and authorized Tribes may want to adjust water quality criteria developed under Section 304 to reflect local environmental conditions and human exposure patterns before incorporation into water quality standards. When adopting their water quality criteria, States and authorized Tribes have four options: (1) adopt EPA’s 304(a) recommendations; (2) adopt 304(a) criteria modified to reflect site-specific conditions; (3) develop criteria based on other scientifically defensible methods; or (4) establish narrative criteria where numeric criteria cannot be determined.

EPA will use this Methodology to develop new ambient water quality criteria and to revise existing recommended water quality criteria. It also provides States and authorized Tribes the necessary guidance to adjust water quality criteria developed under Section 304 to reflect local conditions or to develop their own water quality criteria using scientifically defensible methods consistent with this Methodology. EPA enc ourages States and authorized Tribes to use this Methodology to develop or revise water quality criteria to appropriately reflect local conditions. EPA believes that ambient water quality criteria inherently require several risk management decisions that are, in many cases, better made at the State, Tribal, or regional level. Additional guidance to assist States and authori zed Tribes in the modification of criteria based on the Methodology will accompany this document in the form of three companion Technical Support Documents on Risk Assessment, Exposure Assessment, and Bioaccumulation Assessment.

___________________________ Geoffrey H. Grubbs Director Office of Science and Technologyiv [This page left blank intentionally.]v 

## A CKNOWLEDGMENTS

Project Leader Denis Borum U.S. EPA Office of Science and Technology

Coauthors

Risk Assessment Joyce M. Donohue, Ph.D.* U.S. EPA Office of Science and Technology Julie T. Du, Ph.D.* U.S. EPA Office of Science and Technology Charles O. Abernathy, Ph.D. U.S. EPA Office of Science and Technology

Exposure Denis Borum * U.S. EPA Office of Science and Technology Helen Jacobs, M.S. U.S. EPA Office of Science and Technology Henry Kahn, D.Sc. U.S. EPA Office of Science and Technology

Bioaccumulation Keith G. Sappington, M.S.* U.S. EPA Office of Science and Technology Lawrence P. Burkhard, Ph.D. U.S. EPA Office of Research and Development Philip M. Cook, Ph.D. U.S. EPA Office of Research and Development Erik L. Winchester, M.S. U.S. EPA Office of Science and Technology

U.S. EPA Technical Reviewers

William Beckwith U.S. EPA Region 1 Jeff Bigler U.S. EPA Office of Science and Technology Sally Brough U.S. EPA Region 10 Karen Clark U.S. EPA Office of General Counsel Gregory Currey U.S. EPA Office of Wastewater Management Vicki Dellarco U.S. EPA Office of Prevention, Pesticides, and Toxic Substances Charles Delos U.S. EPA Office of Science and Technology Arnold Den U.S. EPA Region 9 Catherine Eiden U.S. EPA Office of Prevention, Pesticides, and Toxic Substances Michael Firestone U.S. EPA Office of Prevention, Pesticides, and Toxic Substances Steven Galson U.S. EPA Office of Prevention, Pesticides, and Toxic Substances Sue Gilbertson U.S. EPA Office of Science and Technology Denise Hakowski U.S. EPA Region 3 Joel Hansel U.S. EPA Region 4 Wayne Jackson U.S. EPA Region 2 Annie Jarabek U.S. EPA Office of Research and Development William Jordan U.S. EPA Office of Prevention, Pesticides, and Toxic Substancesvi Margaret Kelly U.S. EPA Office of Children’s Health Protection Henry Lee U.S. EPA Office of Research and Development Sharon Lin U.S. EPA Office of Wetlands, Oceans, and Watersheds Roseanne Lorenzana U.S. EPA Region 10 Gregory McCabe U.S. EPA Region 7 Jennifer Mclain U.S. EPA Office of Ground Water and Drinking Water Bruce Mintz U.S. EPA Office of Research and Development Dave Moon U.S. EPA Region 8 William Morrow U.S. EPA Office of Science and Technology Jacqueline Moya U.S. EPA Office of Research and Development Deirdre Murphy U.S. EPA Office of Air Quality Planning and Standards Joseph Nabholz U.S. EPA Office of Prevention, Pesticides, and Toxic Substances Russell Nelson U.S. EPA Region 6 Jennifer Orme-Zavaleta U.S. EPA Offi ce of Research and Development Lynn Papa U.S. EPA Office of Research and Development Robert Pepin U.S. EPA Region 5 David Pfeifer U.S. EPA Region 5 Rita Schoeny U.S. EPA Office of Science and Technology Charles Stephan U.S. EPA Office of Research and Development Linda Teuschler U.S. EPA Office of Research and Development David Tomey U.S. EPA Region 1 Fritz Wagener U.S. EPA Region 4 Jennifer Wigal U.S. EPA Office of Science and Technology Jeanette Wiltse U.S. EPA Office of Scence and Technology Gary Wolinsky U.S. EPA Region 9 Philip Woods U.S. EPA Region 9 William Wuerthele U.S. EPA Region 8

* Principal U.S. EPA Author and Contactvii 

## E XTERNAL 

## P EER 

## R EVIEW 

## W ORKGROUP

The following professionals were part of the External Peer Review Workgroup that provided technical and scientific review regarding the content and technical approach in the July 1998 Draft Ambient Water Quality Criteria Derivation Methodology: Human Health . Their comments were reviewed and incorporated where appropriate to develop this final document.

Kenneth T. Bogen, Ph.D. Lawrence Livermore National Laboratory Paul E. Brubaker, Ph.D. P.E. Brubaker Associates Peter L. DeFur, Ph.D. Virginia Commonwealth University Karen Erstfeld, Ph.D. Rutgers University Bob Fares, Ph.D. Environmental Standards, Inc. Laura Green, Ph.D. Cambridge Environmental, Inc. Robert Hales, Ph.D. Virginia Institute of Marine Science Brendan Hickie, Ph.D. Trent University Ernest Hodgson, Ph.D. North Carolina State University Paul Locke, Ph.D. Johns Hopkins University Lynn S. McCarty, Ph.D. LS McCarty Scientific Research and Consulting Erik Rifkin, Ph.D. Rifkin and Associates, Inc. Damian Shea, Ph.D. North Carolina State University Nga Tran, Ph.D. Johns Hopkins University Curtis Travis, Ph.D. Project Performance Corp.

Potential areas for conflict of interest were inves tigated via direct inquiry with the peer reviews and review of their current affiliations. No conflicts of interest were identified.viii [This page left blank intentionally.]ix 

## T ABLE OF 

## C ONTENTS Page NOTICE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii ACKNOWLEDGMENTS .......................................................v EXTERNAL PEER REVIEW WORKGROUP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix TABLES AND FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv LIST OF ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.1 Water Quality Criteria and Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.2 Purpose of This Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.3 History of the Ambient Water Quality Criteria (AWQC) Methodology . . . . . . . . . . . . 1-2 1.4 Relationship of Water Quality Standards to AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4 1.5 Need for the AWQC Methodology Revisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4 1.5.1 Group C Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6 1.5.2 Consideration of Non-Water Sources of Exposure . . . . . . . . . . . . . . . . . . . . . . 1-7 1.5.3 Cancer Risk Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-8 1.6 Overview of the AWQC Methodology Revisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9 1.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-13

2. CLARIFICATIONS ON THE METHODOLOGY, RISK CHARACTERIZATION, AND OTHER ISSUES FOR DEVELOPING CRITERIA . . . . . . . . . . . . . . . . . . . 2-1

2.1 Identifying the Population Subgroup that the AWQC Should Protect . . . . . . . . . . . . . 2-1 2.2 Science, Science Policy, and Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 2.3 Setting Criteria to Protect Against Multiple Exposures From Multiple Chemicals (Cumulative Risk) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.4 Cancer Risk Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6 2.5 Microbiological Ambient Water Quality Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 2.6 Risk Characterization Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 2.7 Discussion of Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11 2.7.1 Observed Range of Toxicity Versus Range of Environmental Exposure . . . . 2-11 2.7.2 Continuum of Preferred Data/Use of Defaults . . . . . . . . . . . . . . . . . . . . . . . . . 2-11 2.7.3 Significant Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11 2.8 Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13 2.8.1 Minimum Data Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13 2.8.2 Site-Specific Criterion Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13 2.8.3 Organoleptic Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-14 2.8.4 Criteria for Chemical Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 2.8.5 Criteria for Essential Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16 2.9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16x 3. RISK ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Cancer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1.1 Background on EPA Cancer Risk Assessment Guidelines . . . . . . . . . . . . . . . 3-1 3.1.2 EPA's Proposed Guidelines for Carcinogen Risk Assessment and the Subsequent July, 1999 Draft Revised Cancer Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.1.3 Methodology for Deriving AWQC by the 1999 Draft Revised Cancer Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 3.1.3.1 Weight-of-Evidence Narrative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5 3.1.3.2 Mode of Action-General Considerations and Framework for Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6 3.1.3.3 Dose Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7 A. Determining the Human Equivalent Dose . . . . . . . . . . . . . . . . . . . 3-7 B. Dose-Response Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7 3.1.3.4 Characterizing Dose-Response Relationships in the Range of Observation and at Low Environmentally Relevant Doses . . . . . . . . . . . . . . . . . . . . 3-8 A. Extrapolation to Low, Environmentally Relevant Doses . . . . . . . . 3-9 B. Biologically-Based Modeling Approaches . . . . . . . . . . . . . . . . . . 3-9 C. Default Linear Extrapolation Approach . . . . . . . . . . . . . . . . . . . 3-10 D. Default Nonlinear Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11 E. Both Linear and Nonlinear Approaches . . . . . . . . . . . . . . . . . . . 3-13 3.1.3.5 AWQC Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13 A. Linear Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13 B. Nonlinear Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14 3.1.3.6 Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14 3.1.3.7 Use of Toxicity Equivalence Factors (TEF) and Relative Potency Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15 3.1.4 References for Cancer Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16 3.2 Noncancer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17 3.2.1 1980 AWQC National Guidelines for Noncancer Effects . . . . . . . . . . . . . . . . 3-17 3.2.2 Noncancer Risk Assessment Developments Since 1980 . . . . . . . . . . . . . . . . . 3-18 3.2.3 Issues and Recommendations Concerning the Derivation of AWQC for Noncarcinogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20 3.2.3.1 Using the Current NOAEL/UF-Based RfD Approach or Adopting More Quantitative Approaches for Noncancer Risk Assessment . . . . . . . . . 3-20 A. The Benchmark Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-22 B. Categorical Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24 C. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25 3.2.3.2 Presenting the RfD as a Single Point or as a Range for Deriving AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25 3.2.3.3 Guidelines to be Adopted for Derivation of Noncancer Health Effects Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27 3.2.3.4 Treatment of Uncertainty Factors/Severity of Effects During the RfD Derivation and Verification Process . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27 3.2.3.5 Use of Less-Than-90-Day Studies to Derive RfDs . . . . . . . . . . . . . . . 3-27xi 3.2.3.6 Use of Reproductive/Developmental, Immunotoxicity, and Neurotoxicity Data as the Basis for Deriving RfDs . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28 3.2.3.7 Applicability of Toxicokinetic Data in Risk Assessment . . . . . . . . . . 3-28 3.2.3.8 Consideration of Linearity (or Lack of a Threshold) for Noncarcinogenic Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29 3.2.3.9 Minimum Data Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29 3.2.4 References for Noncancer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

4. EXPOSURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1. Exposure Policy Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1.1 Sources of Exposure Associated with Ambient Water . . . . . . . . . . . . . . . . . . . 4-2 4.1.1.1 Appropriateness of Including the Drinking Water Pathway in AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2 4.1.1.2 Setting Separate AWQC for Drinking Water and Fish Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2 4.1.1.3 Incidental Ingestion from Ambient Surface Waters . . . . . . . . . . . . . . . 4-3 4.2. Consideration of Non-Water Sources of Exposure When Setting AWQC . . . . . . . . . . 4-3 4.2.1 Policy Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3 4.2.2 The Exposure Decision Tree Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5 4.2.2.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9 4.2.2.2 Data Adequacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10 4.2.2.3 Regulatory Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13 4.2.2.4 Apportionment Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13 4.2.3 Additional Points of Clarification on the Exposure Decision Tree Approach for Setting AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15 4.2.4 Quantification of Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16 4.2.5 Inclusion of Inhalation and Dermal Exposures . . . . . . . . . . . . . . . . . . . . . . . . 4-16 4.3 Exposure Factors Used in the AWQC Computation . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17 4.3.1 Human Body Weight Values for Dose Calculations . . . . . . . . . . . . . . . . . . . . 4-18 4.3.1.1 Rate Protective of Human Health from Chronic Exposure . . . . . . . . . 4-19 4.3.1.2 Rates Protective of Developmental Human Health Effects . . . . . . . . . 4-20 4.3.2 Drinking Water Intake Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21 4.3.2.1 Rate Protective of Human Health from Chronic Exposure . . . . . . . . . 4-23 4.3.2.2 Rates Protective of Developmental Human Health Effects . . . . . . . . . 4-24 4.3.2.3 Rates Based on Combining Drinking Water Intake and Body Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-24 4.3.3 Fish Intake Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25 4.3.3.1 Rates Protective of Human Health from Chronic Exposure . . . . . . . . 4-25 4.3.3.2 Rates Protective of Developmental Human Health Effects . . . . . . . . . 4-29 4.3.3.3 Rates Based on Combining Fish Intake and Body Weight . . . . . . . . . 4-30 4.4 References for Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-30

5. BIOACCUMULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1xii 5.1.1 Important Bioaccumulation and Bioconcentration Concepts . . . . . . . . . . . . . . 5-2 5.1.2 Goal of the National BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3 5.1.3 Changes to the 1980 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3 5.1.3.1 Overall Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.1.3.2 Lipid Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.1.3.3 Bioavailability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.1.3.4 Trophic Level Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.1.3.5 Site-Specific Adjustments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.1.4 Organization of This Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6 5.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6 5.3 Framework for Determining National Bioaccumulation Factors . . . . . . . . . . . . . . . 5-10 5.3.1 Four Different Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10 5.3.2 Overview of BAF Derivation Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12 5.3.3 Defining the Chemical of Concern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 5.3.4 Collecting and Reviewing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 5.3.5 Classifying the Chemical of Concern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15 5.4 National Bioaccumulation Factors for Nonionic Organic Chemicals . . . . . . . . . . . . . 5-16 5.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-16 5.4.2 Selecting the BAF Derivation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18 5.4.2.1 Chemicals with Moderate to High Hydrophobicity . . . . . . . . . . . . . . . 5-18 5.4.2.2 Chemicals with Low Hydrophobicity . . . . . . . . . . . . . . . . . . . . . . . . . 5-19 5.4.2.3 Assessing Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-20 5.4.3 Deriving National BAFs Using Procedure #1 . . . . . . . . . . . . . . . . . . . . . . . . . 5-22 5.4.3.1 Calculating Individual Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . 5-23 A. Baseline BAF R fdfrom Field-Measured BAFs . . . . . . . . . . . . . . . . 5-23 B. Baseline BAF R fdDerived from BSAFs . . . . . . . . . . . . . . . . . . . . . 5-28 C. Baseline BAF R fdfrom a Laboratory-Measured BCF t T and FCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-32 D. Baseline BAF R fdfrom a K
ow and FCM . . . . . . . . . . . . . . . . . . . . . . 5-38 5.4.3.2 Selecting Final Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39 5.4.3.3 Calculating National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-41 5.4.4 Deriving National BAFs Using Procedure #2 . . . . . . . . . . . . . . . . . . . . . . . . . 5-44 5.4.4.1 Calculating Individual Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . 5-45 A. Baseline BAF R fdfrom Field-Measured BAFs . . . . . . . . . . . . . . . . 5-45 B. Baseline BAF R fdDerived from Field-Measured BSAFs . . . . . . . . 5-46 C. Baseline BAF R fdfrom a Laboratory-Measured BCF . . . . . . . . . . 5-46 5.4.4.2 Selecting Final Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-46 5.4.4.3 Calculating the National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-47 5.4.5 Deriving National BAFs Using Procedure #3 . . . . . . . . . . . . . . . . . . . . . . . . . 5-47 5.4.5.1 Calculating Individual Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . 5-47 A. Baseline BAF R fdfrom Field-Measured BAFs . . . . . . . . . . . . . . . . 5-48 B. Baseline BAF R fdfrom a Laboratory-Measured BCF . . . . . . . . . . 5-48 C. Baseline BAF R fdfrom a K
ow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-49 5.4.5.2 Selecting Final Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-49 5.4.5.3 Calculating the National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-50 5.4.6 Deriving National BAFs Using Procedure #4 . . . . . . . . . . . . . . . . . . . . . . . . . 5-51xiii 5.4.6.1 Calculating Individual Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . 5-52 A. Baseline BAF R fdfrom Field-measured BAFs . . . . . . . . . . . . . . . . 5-52 B. Baseline BAF R fdfrom a Laboratory-measured BCF . . . . . . . . . . . 5-53 5.4.6.2 Selecting Final Baseline BAF R fd s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-53 5.4.6.3 Calculating National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54 5.5 National Bioaccumulation Factors for Ionic Organic Chemicals . . . . . . . . . . . . . . . . 5-55 5.6 National Bioaccumulation Factors for Inorganic and Organometallic Chemicals . . . 5-57 5.6.1 Selecting the BAF Derivation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-57 5.6.2 Bioavailability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58 5.6.3 Deriving BAFs Using Procedure #5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58 5.6.3.1 Determining Field-Measured BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-59 5.6.3.2 Determining Laboratory-Measured BCFs . . . . . . . . . . . . . . . . . . . . . . 5-60 5.6.3.3 Determining the National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-60 5.6.4 Deriving BAFs Using Procedure #6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-61 5.6.4.1 Determining Field-Measured BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-62 5.6.4.2 Determining Laboratory-Measured BCFs . . . . . . . . . . . . . . . . . . . . . . 5-62 5.6.4.3 Determining the National BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-62 5.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-63xiv 

## T ABLES AND 

## F IGURES Page Table 3-1. Uncertainty Factors and the Modifying Factor . . . . . . . . . . . . . . . . . . . . . . . . 3-19

Figure 4-1. Exposure Decision Tree for Defining Proposed RfD (or POD/UF) Apportionment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

Figure 5-1 Framework for Deriving a National BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-13

Figure 5-2 BAF Derivation for Nonionic Organic Chemicals . . . . . . . . . . . . . . . . . . . . . 5-17

Table 5-1 Food-Chain Multipliers for Trophic Levels 2, 3 and 4 . . . . . . . . . . . . . . . . . . 5-36xv 

## L IST OF 

## A CRONYMS

ADI Acceptable Daily Intake ARAR Applicable or Relevant and Appropriate Requirements ASTM American Society of Testing and Materials AWQC Ambient Water Quality Criteria BAF Bioaccumulation Factor BAF R fd Baseline Bioaccumulation Factor BCF Bioconcentration Factor BCF R fd Baseline Bioconcentration Factor BCF t T Bioconcentration Factor Based on Total Concentrations in Tissue and Water BMD Benchmark Dose BMDL Lower-Bound Confidence Limit on the BMD BMF Biomagnification Factor BMR Benchmark Response BSAF Biota-Sediment Accumulation Factors BW Body Weight C R Lipid-normalized Concentration C
soc Organic Carbon-normalized Concentration C t Concentration of the Chemical in the Specified Wet Tissue C
w Concentration of the Chemical in Water CDC U.S. Centers for Disease Control and Prevention CSFII Continuing Survey of Food Intake by Individuals CWA Clean Water Act DDT 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane DDE 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene DDD 1,1-dichloro-2,2-bis(p-chlorophenyl)ethane DI Drinking Water Intake DNA Deoxyribonucleic Acid DNOC 2,4-dinitro-o-cresol DOC Dissolved Organic Carbon ED 10 Dose Associated with a 10 Percent Extra Risk EPA Environmental Protection Agency f fd Fraction Freely Dissolved f R Fraction Lipid FCM Food Chain Multiplier FEL Frank Effect Level FI Fish Intake FIFRA Federal Insecticide, Fungicide, and Rodenticide Act GLI Great Lakes Water Quality Initiative HCBD Hexachlorobutadiene IARC International Agency for Research on Cancer II Incidental Ingestion ILSI International Life Sciences Institutexvi IRIS Integration Risk Information System kg kilogram K
ow Octanol-Water Partition Coefficient L Liter LAS Linear Alkylbenzesulfonate LED 10 The Lower 95 Percent Confidence Limit on a Dose Associated with a 10 Percent Extra Risk LMS Linear Multistage Model LOAEL Lowest Observed Adverse Effect Level M R Mass of Lipid in Specified Tissue M t Mass of Specified Tissue (Wet Weight) MCL Maximum Contaminant Level MCLG Maximum Contaminant Level Goal MF Modifying Factor mg Milligrams ml Milliliters MOA Mode of Action MOE Margin of Exposure NCHS National Center for Health Statistics NCI National Cancer Institute NFCS Nationwide Food Consumption Survey NHANES National Health and Nutrition Examination Survey NOAEL No Observed Adverse Effect Level NOEL No Observed Effect Level NPDES National Pollutant Discharge Elimination System PAH Polycyclic Aromatic Hydrocarbon PCB Polychlorinated Biphenyls POD Point of Departure POC Particulate Organic Carbon RDA Recommended Daily Allowance RfC Reference Concentration RfD Reference Dose RfD DT Reference Dose for Developmental Effects RPF Relative Potency Factor RSC Relative Source Contribution RSD Risk-Specific Dose SAB Science Advisory Board SDWA Safe Drinking Water Act SF Safety Factor STORET Storage Retrieval TEAM Total Exposure Assessment Methodology TEF Toxicity Equivalency Factor TMDL Total Maximum Daily Load TSD Technical Support Document USDA United States Department of Agriculture USEPA United States Environmental Protection Agencyxvii UF Uncertainty Factor WQBEL Water Quality-Based Effluent Limitsxviii [This page left blank intentionally.]1-1 

## 1. I NTRODUCTION

1.1 WATER QUALITY CRITERIA AND STANDARDS

Pursuant to Section 304(a)(1) of the Clean Water Act (CWA), the U.S. Environmental Protection Agency (EPA) is required to publish, and from time to time thereafter revise, criteria for water quality accurately reflecting the latest scientific knowledge on the kind and extent of all identifiable effects on human health which may be expected from the presence of pollutants in any body of water.

Historically, the ambient water quality criteria (AWQC or 304(a) criteria) provided two essential types of information: (1) discussions of available scientific data on the effects of the pollutants on public health and welfare, aquatic life, and recreation; and (2) quantitative concentrations or qualitative assessments of the levels of pollutants in water which, if not exceeded, will generally ensure adequate water quality for a specified water use. Water quality criteria developed under Section 304(a) are based solely on data and scientific judgments on the relationship between pollutant concentrations and environmental and human health effects. The 304(a) criteria do not reflect consideration of economic impacts or the technological feasibility of meeting the criteria in ambient water. These 304(a) criteria may be used as guidance by States and authorized Tribes to establish water quality standards, which ultimately provide a basis for controlling discharges or releases of pollutants into ambient waters.

In 1980, AWQC were derived for 64 pollutants using guidelines developed by the Agency for calculating the impact of waterborne pollutants on aquatic organisms and on human health. Those guidelines consisted of systematic procedures for assessing valid and appropriate data concerning a pollutant’s acute and chronic adverse effects on aquatic organisms, nonhuman mammals, and humans.

1.2 PURPOSE OF THIS DOCUMENT

The Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000) (hereafter the “2000 Human Health Methodology”) addresses the development of AWQC to protect human health. The Agency intends to use the 2000 Human Health Methodology both to develop new AWQC for additional pollutants and to revise existing AWQC. Within the next several years, EPA intends to focus on deriving AWQC for chemicals of high priority (including, but not limited to, me rcury, arsenic, PCBs, and dioxin). Furthermore, EPA anticipates that 304(a) criteria development in the future will be for bioaccumulative chemicals and pollutants considered highest priority by the Agency. The 2000 Human Health Methodology is also intended to provide States and authorized Tribes flexibility in establishing water quality standards by providing scientifically valid options for developing their own water quality criteria that consider local conditions. States and authorized Tribes are strongly encouraged to use this Methodology to derive their own AWQC. However, the 2000 Human Health Methodology also defines the default factors EPA intends to use in evaluating and determining consistency of State water quality standards with the requirements of the CWA. The Agency intends to use these default factors to calculate national water quality criteria under1-2 Section 304(a) of the Act. EPA will also use this Methodology as guidance when promulgating water quality standards for a State or Tribe under Section 303(c) of the CWA.

This Methodology does not substitute for the CWA or EPA’s regulations; nor is it a regulation itself. Thus, the 2000 Human Health Methodology cannot impose legally-binding requirements on EPA, States, Tribes or the regulated community, and may not apply to a particular situation based upon the circumstances. EPA and State/Tribal decision-makers retain the discretion to use different, scientifically defensible, methodologies to develop human health criteria on a case-by-case basis that differ from this Methodology where appropriate. EPA may change the Methodology in the future through intermittent refinements as advances in science or changes in Agency policy occur.

The 2000 Human Health Methodology incorporates scientific advancements made over the past two decades. The use of this Methodology is an important component of the Agency’s efforts to improve the quality of the Nation’s waters. EPA believes the Methodology will enhance the overall scientific basis of water quality criteria. Further, the Methodology should help States and Tribes address their unique water quality issues and risk management decisions, and afford them greater flexibility in developing their water quality programs.

There are three companion Technical Support Document (TSD) volumes for the 2000 Human Health Methodology: a Risk Assessment TSD; an Exposure Assessment TSD; and a Bioaccumulation TSD. These documents are intended to further support States and Tribes in developing AWQC to reflect local conditions. The Risk Assessment TSD (USEPA, 2000) is being published concurrently with this Methodology. Publication of the Exposure Assessment and Bioaccumulation TSDs are anticipated in 2001.

1.3 HISTORY OF THE AMBIENT WATER QUALITY CRITERIA (AWQC) METHODOLOGY

In 1980, EPA published AWQC for 64 pollutants/pollutant classes identified in Section 307(a) of the CWA and provided a methodology fo r deriving the criteria (USEPA, 1980). These 1980 AWQC National Guidelines (or the “1980 Methodology”) for developing AWQC for the protection of human health addressed three types of endpoints: noncancer, cancer, and organoleptic (taste and odor) effects. Criteria for protection against noncancer and cancer effects were estimated by using risk assessment-based procedures, including extrapolation from animal toxicity or human epidemiological studies. Basic human exposure assumptions were applied to the criterion equation.

The risk assessment-based procedures used to derive the AWQC to protect human health were specific to whether the endpoint was cancer or noncancer. When using cancer as the critical risk assessment endpoint (which had been assumed not to have a threshold), the AWQC were presented as a range of concentrations a ssociated with specified incremental lifetime risk1 Throughout this document, the term “ris k level” regarding a cancer assessment using linear approach refers to an upper-bound estimate of excess lifetime cancer risk.

1-3 levels 1 . When using noncancer effects as the critical endpoint, the AWQC reflected an assessment of a “no-effect” level, since noncancer effects were assumed to have a threshold. The key features of each procedure are desc ribed briefly in the following paragraphs.

Cancer effects. If human or animal studies on a contaminant indicated that it induced a statistically significant carcinogenic response, the 1980 AWQC National Guidelines treated the contaminant as a carcinogen and derived a low-dose cancer potency factor from available animal data using the linearized multistage model (LMS). The LMS, which uses a linear, nonthreshold assumption for low-dose risk, was used by the Agency as a science policy choice in protecting public health, and represented a plausible upper limit for low-dose risk. The cancer potency factor, which expresses incremental, lifetime risk as a function of the rate of intake of the contaminant, was then combined with exposure assumptions to express that risk in terms of an ambient water concentration. In the 1980 AWQC National Guidelines, the Agency presented a range of contaminant concentrations corresponding to incremental cancer risks of 10 -7 to 10 -5 (that is, a risk of one additional case of cancer in a population of ten million to one additional cancer case in a population of one hundred thousand, respectively).

Noncancer effects. If the pollutant was not considered to have the potential for causing cancer in humans (later defined as a known, pr obable, or possible human carcinogen by the 1986 Guidelines for Carcinogen Risk Assessment, USEPA, 1986d), the 1980 AWQC National Guidelines treated the contaminant as a noncarcinogen; a criterion was derived using a threshold concentration for noncancer adverse effects. The criteria derived from noncancer data were based on the Acceptable Daily Intake (ADI) (now termed the reference dose [RfD]). ADI values were generally derived using a no-observed-advers e-effect level (NOAEL) from animal studies, although human data were used whenever available. The ADI was calculated by dividing the NOAEL by an uncertainty factor to account for uncertainties inherent in extrapolating limited toxicological data to humans. In accordance with the National Research Council recommendations of 1977 (NRC, 1977), safety fact ors (SFs) (later redefined as uncertainty factors) of 10, 100, or 1,000 were used, depending on the quality of the data.

Organoleptic effects. Organoleptic characteristics were also used in developing criteria for some contaminants to control undesirable taste and/or odor imparted by them to ambient water. In some cases, a water quality criterion based on organoleptic effects would be more stringent than a criterion based on toxicologi c endpoints. The 1980 AWQC National Guidelines emphasized that criteria derived for organol eptic endpoints are not based on toxicological information, have no direct relationship to adve rse human health effects and, therefore, do not necessarily represent approximations of acceptable risk levels for humans.1-4 1.4 RELATIONSHIP OF WATER QUALITY STANDARDS TO AWQC

Under Section 303(c) of the CWA, States have the primary responsibility for establishing water quality standards, defined under the Act as designated beneficial uses of a water segment and the water quality criteria necessary to support those uses. Additionally, Native American Tribes authorized to administer the water quality standards program under 40 CFR 131.8 establish water quality standards for waters within their jurisdictions. This statutory framework allows States and authorized Tribes to work with local communities to adopt appropriate designated uses and to adopt criteria to protectthose designated uses. Section 303(c) provides for EPA review of water quality standards and for promulgation of a superseding Federal rule in cases where State or Tribal standards are not consistent with the applicable requirements of the CWA and the implementing Federal regulations, or where the Agency determines Federal standards are necessary to meet the requirements of the Act. Section 303(c)(2)(B) specifically requires States and authorized Tribes to adopt water quality criteria for toxics for which EPA has published criteria under Section 304(a) and for which the discharge or presence could reasonably be expected to interfere with the designated use adopted by the State or Tribe. In adopting such criteria, States and authorized Tribes must establish numerical values based on one of the following: (1) 304(a) criteria; (2) 304(a) criteria m odified to reflect site-specific conditions; or, (3) other scientifically defensible methods. In addition, States and authorized Tribes can establish narrative criteria where numeric criteria cannot be determined.

It must be recognized that the Act uses the term “criteria” in two different ways. In Section 303(c), the term is part of the definition of a water quality standard. Specifically, a water quality standard is composed of designated uses and the criteria necessary to protect those uses. Thus, States and authorized Tribes are requi red to adopt regulations which contain legally enforceable criteria. However, in Section 304(a) the term criteria is used to describe the scientific information that EPA develops to be used as guidance by States, authorized Tribes and EPA when establishing water quality standards pursuant to 303(c). Thus, two distinct purposes are served by the 304(a) criteria. The first is as guidance to the States and authorized Tribes in the development and adoption of water quality criteria which will protect designated uses, and the second is as the basis for promulgation of a superseding Federal rule when such action is
necessary.

1.5 NEED FOR THE AWQC METHODOLOGY REVISIONS

Since 1980, EPA risk assessment practices have evolved significantly in all of the major Methodology areas: that is, cancer and noncancer risk assessments, exposure assessments, and bioaccumulation. When the 1980 Methodology was developed, EPA had not yet developed formal cancer or noncancer risk assessment guidelines. Since then, EPA has published several risk assessment guidelines. In cancer risk assessment, there have been advances in the use of mode of action (MOA) information to support both the identification of potential human carcinogens and the selection of procedures to characterize risk at low, environmentally relevant exposure levels. EPA published Proposed Guidelines for Carcinogen Risk Assessment (USEPA, 1996a, hereafter the “1996 proposed cancer guidelines”). These guidelines presented revised procedures to quantify cancer risk at low doses, replacing the current default use of the LMS model. Following review by the Agency’s Science Advisory Board (SAB), EPA published the1-5 revised Guidelines for Carcinogen Risk Assessment–Review Draft in July 1999 (USEPA, 1999a, hereafter the “1999 draft revised cancer guidelines”). In noncancer risk assessment, the Agency is moving toward the use of the benchmark dose (BMD) and other dose-response approaches in place of the traditional NOAEL approach to estimat e an RfD or Reference Concentration (RfC). Guidelines for Mutagenicity Risk Assessment were published in 1986 (USEPA, 1986b). In 1991, the Agency published Guidelines for Developmental Toxicity Risk Assessment (USEPA, 1991), and it issued Guidelines for Reproductive Toxicity Risk Assessment in 1996 (USEPA, 1996b). In 1998, EPA published final Guidelines for Neurotoxicity Risk Assessment (USEPA, 1998), and in 1999 it issued the draft Guidance for Conducting Health Risk Assessment of Chemical Mixtures (USEPA, 1999b).

In 1986, the Agency made available to the public the Integrated Risk Information System (IRIS). IRIS is a database that contains risk information on the cancer and noncancer effects of chemicals. The IRIS assessments are peer reviewed and represent EPA consensus positions across the Agency’s program and regional offices.

New studies have addressed water consump tion and fish tissue consumption. These studies provide a more current and comprehensive description of national, regional, and specialpopulation consumption patterns that EPA has reflected in the 2000 Human Health Methodology. In addition, more formalized procedures are now available to account for human exposure from multiple sources when setting health goals such as AWQC that address only one exposure source. In 1986, the Agency published the Total Exposure Assessment Methodology (TEAM) Study: Summary and Analysis, Volume I, Final Report (USEPA, 1986c), which presents a process for conducting comprehensive evaluation of human exposures. In 1992, EPA published the revised Guidelines for Exposure Assessment (USEPA, 1992), which describe general concepts of exposure assessment, including definitions and associated units, and provide guidance on planning and conducting an exposure assessment. The Exposure Factors Handbook was updated in 1997 (USEPA, 1997a). Also in 1997, EPA developed Guiding Principles for Monte Carlo Analysis (USEPA, 1997b) and published its Policy for Use of Probabilistic Analysis in Risk Assessment (see http://www.epa.gov/ncea/mcpolicy.htm ). The Monte Carlo guidance can be applied to exposure assessments and risk assessments. The Agency has recently developed the Relative Source Contribution (RSC) Policy for assessing total human exposure to a contaminant and apportioning the RfD among the media of concern, published for the first time in this Methodology.

The Agency has moved toward the use of a bioaccumulation factor (BAF) to reflect the uptake of a contaminant from all sources (e.g., ingestion, sediment) by fish and shellfish, rather than just from the water column as reflected by the use of a bioconcentration factor (BCF) in the 1980 Methodology. The Agency has also developed detailed procedures and guidelines for estimating BAF values.

Another reason for the 2000 Human Health Methodology is the need to bridge the gap between the differences in the risk assessment and risk management approaches used by EPA’s Office of Water for the derivation of AWQC under the authority of the CWA and Maximum Contaminant Level Goals (MCLGs) under the Safe Drinking Water Act (SDWA). Three notable differences are the treatment of chemicals designated as Group C, possible human carcinogens1-6 under the 1996 proposed cancer guidelines, the consideration of non-water sources of exposure when setting an AWQC or MCLG for a noncarcinogen, and cancer risk ranges. Those three differences are described in the three subsections below, respectively.

1.5.1 Group C Chemicals

Chemicals were typically classified as Group C–i.e., possible human carcinogens–under the existing (1986) EPA cancer classification scheme for any of the following reasons:

1) Carcinogenicity has been documented in only one test species and/or only one cancer bioassay and the results do not meet the requirements of “sufficient evidence.”

2) Tumor response is of marginal statistical significance due to inadequate design or reporting.

3) Benign, but not malignant, tumors occur with an agent showing no response in a variety of short-term tests for mutagenicity.

4) There are responses of marginal statistical significance in a tissue known to have a high or variable background rate.

The 1986 Guidelines for Carcinogen Risk Assessment (hereafter the “1986 cancer guidelines”) specifically recognized the need for fl exibility with respect to quantifying the risk of Group C, possible human carcinogens. The 1986 cancer guidelines noted that agents judged to be in Group C, possible human carcinogens, may generally be regarded as suitable for quantitative risk assessment, but that case-by-case judgments may be made in this regard.

The EPA Office of Water has historically treated Group C chemicals differently under the CWA and the SDWA. It is important to note that the 1980 AWQC National Guidelines for setting AWQC under the CWA predated EPA’s carcinogen classification system, which was proposed in 1984 (USEPA, 1984) and finalized in 1986 (USEPA, 1986a). The 1980 AWQC National Guidelines did not explicitly differentiate among agents with respect to the weight of evidence for characterizing them as likely to be carcinogenic to humans. For all pollutants judged as having adequate data for quantifying carcinogenic risk–including those now classified as Group C–AWQC were derived based on data on cancer incidence. In the1980 AWQC National Guidelines, EPA emphasized that the AWQC for carcinogens should state that the recommended concentration for maximum protection of human health is zero. At the same time, the criteria published for specific carcinogens presented water concentrations for these pollutants corresponding to individual lifetime excess cancer risk levels in the range of 10 -7 to 10 -5

. In the development of national primary drinking water regulations under the SDWA, EPA is required to promulgate a health-based MCLG for each contaminant. The Agency policy has been to set the MCLG at zero for chemicals with strong evidence of carcinogenicity associated with exposure from water. For chemicals with limited evidence of carcinogenicity, including many Group C agents, the MCLG was usually obtained using an RfD based on the1-7 pollutant’s noncancer effects with the application of an additional uncertainty factor of 1 to 10 to account for carcinogenic potential of the chemical. If valid noncancer data for a Group C agent were not available to establish an RfD but adequate data are available to quantify the cancer risk, then the MCLG was based upon a nominal lifetime excess cancer risk in the range of 10 -6 to10 -5 (ranging from one case in a population of one million to one case in a population of one hundred thousand). Even in those cases where the RfD approach has been used for the derivation of the MCLG for a Group C agent, the drinking water concentrations associated with excess cancer risks in the range of 10 -6 to 10 -5 were also provided for comparison.

It should also be noted that EPA’s pesticides program has applied both of the previously described methods for addressing Group C chemicals in actions taken under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and finds both methods applicable on a case-by-case basis. Unlike the drinking water program, however, the pesticides program does not add an extra uncertainty factor to account for potential carcinogenicity when using the RfD approach.

In the 1999 draft revised cancer guidelines, there are no more alphanumeric categories. Instead, there will be longer narratives for hazard characterization that will use consistent descriptive terms when assessing cancer risk.

1.5.2 Consideration of Non-water Sources of Exposure

The 1980 AWQC National Guidelines recommended that contributions from non-water sources, namely air and non-fish dietary intake, be subtracted from the Acceptable Daily Intake (ADI), thus reducing the amount of the ADI “available” for water-related sources of intake. In practice, however, when calculating human health criteria, these other exposures were generally not considered because reliable data on these exposure pathways were not available. Consequently, the AWQC were usually derived such that drinking water and fish ingestion accounted for the entire ADI (now called RfD).

In the drinking water program, a similar “subtraction” method was used in the derivation of MCLGs proposed and promulgated in drinking water regulations through the mid-1980s. More recently, the drinking water program has used a “percentage” method in the derivation of MCLGs for noncarcinogens. In this approach, the percentage of total exposure typically accounted for by drinking water, referred to as the relative source contribution (RSC), is applied to the RfD to determine the maximum amount of the RfD “apportioned” to drinking water reflected by the MCLG value. In using this percentage procedure, the drinking water program also applies a ceiling level of 80 percent of the RfD and a floor level of 20 percent of the RfD. That is, the MCLG cannot account for more than 80 percent of the RfD, nor less than 20 percent of the RfD.

The drinking water program usually takes a conservative approach to public health by applying an RSC factor of 20 percent to the RfD when adequate exposure data do not exist, assuming that the major portion (80 percent) of the total exposure comes from other sources, such as diet.1-8In the 2000 Human Health Methodology, guidance for the routine consideration of nonwater sources of exposure [both ingestion expos ures (e.g., food) and exposures other than the oral route (e.g., inhalation)] is presented. The approach is called the Exposure Decision Tree. Relative source contribution estimates will be made by EPA using this approach, which allows for use of either the subtraction or percentage methods, depending on chemical-specific circumstances, within the 20 to 80 percent range described above.

1.5.3 Cancer Risk Ranges

In addition to the different risk assessment approaches discussed above for deriving AWQC and MCLGs for Group C agents, there have been different risk management approaches by the drinking water and surface water programs on lifetime excess risk values when setting health-based criteria for carcinogens. The surface water program has derived AWQC for carcinogens that generally corresponded to lifetime excess cancer risk levels of 10 -7 to 10 -5 . The drinking water program has set MCLGs for Group C agents based on a slightly less stringent risk range of 10 -6 to 10 -5 , while MCLGs for chemicals with strong evidence of carcinogenicity (that is, classified as Group A, known, or B probable, human carcinogen) are set at zero. The drinking water program is now following the principles of the 1999 draft revised cancer guidelines to determine the type of low-dose extrapolation based on mode of action.

It is also important to note that under the drinking water program, for those substances having an MCLG of zero, enforceable Maximum Contaminant Levels (MCLs) have generally been promulgated to correspond with cancer risk levels ranging from 10 -6 to 10 -4 . Unlike AWQC and MCLGs which are strictly health-based criteria, MCLs are developed with consideration given to the costs and technological feasibility of reducing contaminant levels in water to meet those standards.

With the 2000 Human Health Methodology, EPA will publish its national 304(a) water quality criteria at a 10 -6 risk level, which EPA considers appropriate for the general population. EPA is increasing the degree of consistency between the drinking water and ambient water programs, given the somewhat different requirements of the CWA and SDWA.2 Although appearing in this equation as a factor to be multip lied, the RSC can also be an amount subtracted. Refer to the explanation key below the equations.

1-9 (Equation 1-1)

(Equation 1-2) 1.6 OVERVIEW OF THE AWQC METHODOLOGY REVISIONS

The following equations for deriving AWQC include toxicological and exposure assessment parameters which are derived from scientific analysis, science policy, and risk management decisions. For example, values for parameters such as a field-measured BAF or a point of departure from an animal study [in the form of a lowest-observed-adverse-effect level (LOAEL)/no-observed -adverse-effect level (NOAEL)/lower 95 percent confidence limit on a dose associated with a 10 percent extra risk (LED 10 )] are empirically measured using scientific methods. By contrast, the decision to use animal effects as surrogates for human effects involves judgment on the part of the EPA (and similarly, by other agencies) as to the best practice to follow when human data are lacking. Such a decision is, therefore, a matter of science policy. The choice of default fish consumption rates for protection of a certain percentage (i.e., the 90 th percentile) of the general population is clearly a risk management decision. In many cases, the Agency has selected parameter values using its best judgment regarding the overall protection afforded by the resulting AWQC when all parameters are combined. For a longer discussion of the differences between science, science policy, and risk management, please refer to Section 2 of this document. Section 2 also provides further details with regard to risk characterization for this Methodology, with emphasis placed on explaining the uncertainties in the overall risk
assessment.

The generalized equations for deriving AWQC based on noncancer effects are:

Noncancer Effects 2

Cancer Effects: Nonlinear Low-Dose Extrapolation1-10 (Equation 1-3) Cancer Effects: Linear Low-Dose Extrapolation

where:

AWQC = Ambient Water Quality Criterion (mg/L) RfD = Reference dose for noncancer effects (mg/kg-day) POD = Point of departure for carcinogens based on a nonlinear low-dose extrapolation (mg/kg-day), usually a LOAEL, NOAEL, or LED 10 UF = Uncertainty Factor for carcinogens based on a nonlinear low-dose extrapolation (unitless) RSD = Risk-specific dose for carcinogens based on a linear low-dose extrapolation (mg/kg-day) (dose associated with a target risk, such as 10 -6 ) RSC = Relative source contribution factor to account for non-water sources of exposure. (Not used for linear carcinogens.) May be either a percentage (multiplied) or amount subtracted, depending on whether multiple criteria are relevant to the chemical. BW = Human body weight (default = 70 kg for adults) DI = Drinking water intake (default = 2 L/day for adults) FI i = Fish intake at trophic level (TL) I (I = 2, 3, and 4) (defaults for total intake = 0.0175 kg/day for general adult population and sport anglers, and 0.1424 kg/day for subsistence fishers). Trophic level breakouts for the general adult population and sport anglers are: TL2 = 0.0038 kg/day; TL3 = 0.0080 kg/day; and TL4 = 0.0057 kg/day. BAF i = Bioaccumulation factor at trophic level I (I=2, 3 and 4), lipid normalized (L/kg)

For highly bioaccumulative chemicals where ingestion from water might be considered negligible, EPA is currently evaluating the feasibility of developing and implementing AWQCs that are expressed in terms of concentrations in tissues of aquatic organisms. Such tissue residue criteria might be used as an alternative to AWQCs which are expressed as concentrations in water, particularly in situations where AWQCs are at or below the practical limits for quantifying a chemical in water. Even though tissue residue criteria would not require the use of a BAF in their derivation, implementing such criteria would still require a mechanism for relating chemical loads and concentrations in water and sediment to concentrations in tissues of appropriate fish and shellfish (e.g., a BAF or bi oaccumulation model). At this time, no revisions are planned to the Methodology to provide speci fic guidance on developing fish tissue-based water quality criteria. However, guidance may be provided in the future either as a separate document or integrated in a specific 304(a) water quality criteria document for a chemical that warrants such an approach.1-11 AWQC for the protection of human health are designed to minimize the risk of adverse effects occurring to humans from chronic (lifetime) exposure to substances through the ingestion of drinking water and consumption of fish obt ained from surface waters. The Agency is not recommending the development of additional water quality criteria similar to the “drinking water health advisories” that focus on acute or short-term effects; these are not seen as routinely having a meaningful role in the water quality criteria and standards program. However, as discussed below, there may be some instances where the consideration of acute or short-term toxicity and exposure in the derivation of AWQC is warranted.

Although the AWQC are based on chronic health effects data (both cancer and noncancer effects), the criteria are intended to also be protective against adverse effects that may reasonably be expected to occur as a result of elevated acute or short-term exposures. That is, through the use of conservative assumptions with respect to both toxicity and exposure parameters, the resulting AWQC should provide adequate protec tion not only for the general population over alifetime of exposure, but also for special subpopulations who, because of high water- or fishintake rates, or because of biological sensitivities, have an increased risk of receiving a dose that would elicit adverse effects. The Agency recognizes that there may be some cases where the AWQC based on chronic toxicity may not provi de adequate protection for a subpopulation at special risk from shorter-term exposures. The Agency encourages States, Tribes, and others employing the 2000 Human Health Methodology to give consideration to such circumstances in deriving criteria to ensure that adequate protection is afforded to all identifiable subpopulations. (See Section 4.3, Factors Used in the AWQC Computation, for additional discussion of these subpopulations.)

The EPA is in the process of revising its cancer guidelines, including its descriptions of human carcinogenic potential. Once final guidelines are published, they will be the basis for assessment under this methodology. In the meanwhile, the 1986 guidelines are used and extended with principles discussed in EPA’s 1999 Guidelines for Carcinogen Risk Assessment - Review Draft (hereafter “1999 draft revised cancer guidelines”). These principles arise from new science about cancer discovered in the last 15 years and from EPA policy of recent years supporting full characterization of hazard and risk both for the general population and potentially sensitive groups such as children. These principles are incorporated in recent and ongoing assessments such as the reassessment of dioxin, consistent with the 1986 guidelines. Until final guidelines are published, information is presented to describe risk under both the old guidelines and draft revisions. Dose-response assessment under the 1986 guidelines employs a linearized multistage model to extrapolate tumor dose-response observed in animal or human studies down to zero dose, zero extra risk. The dose-response assessment under EPA’s 1999 draft revised cancer guidelines is a two-step process. In the first step, the response data are modeled in the range of empirical observation. Modeling in the observed range is done with biologically based or appropriate curve-fitting m odeling. In the second step, extrapolation below the range of observation is accomplished by biologically based modeling if there are sufficient data or by a default procedure (linear, nonlinear , or both). A point of departure (POD) for extrapolation is estimated from modeling observed data. The lower 95 percent confidence limit on a dose associated with 10 percent extra risk (LED 10 ) is the standard POD for low-dose extrapolation. The linear default procedure is a straight line extrapolation to the origin (i.e., zero dose, zero extra risk) from the POD, which is the LED 10 identified in the observable response1-12 range. The result of this procedure is generally comparable (within 2-fold) to that of using a linearized multistage model under existing, 1986 guidelines. The linear low-dose extrapolation applies to agents that are best characterized by the assumption of linearity (e.g., direct DNA reactive mutagens) for their MOA. A linear approach would also be applied when inadequate or no information is available to explain the carcinogenic MOA; this is a science policy choice in the interest of public health. If it is determined that the MOA understanding fully supports a nonlinear extrapolation, the AWQC is derived using the nonlinear default which is based on a margin of exposure (MOE) analysis using the LED 10 as the POD and applying uncertainty factors (UFs) to arrive at an acceptable MOE. There may be situations where it is appropriate to apply both the linear and nonlinear default procedures (e.g., for an agent that is both DNA reactive and active as a promoter at higher doses).

For substances that are carcinogenic, particularly those for which the MOA suggests nonlinearity at low doses, the Agency recommends that an integrated approach be taken in looking at cancer and noncancer effects. If one effect does not predominate, AWQC values should be determined for both carcinogenic and noncarcinogenic endpoints. The lower of the resulting values should be used for the AWQC.

When deriving AWQC for noncarcinogens and carcinogens based on a nonlinear lowdose extrapolation, a factor is included to account for other non-water exposure sources [both ingestion exposures (e.g., food) and exposures othe r than the oral route (e.g., inhalation)] so that the entire RfD, or POD/UF, is not apportioned to drinking water and fish consumption alone. Guidance is provided in the 2000 Human Health Methodology for determining the factor (i.e., the RSC) to be used for a particular chemical. The Agency is recommending the use of an Exposure Decision Tree procedure to support the determination of the appropriate RSC value for a given water contaminant. In the absence of data, the Agency intends to use 20 percent of the RfD (or POD/UF) as the default RSC in calcu lating 304(a) criteria or promulgating State or Tribal water quality standards under Section 303(c).

With AWQC derived for carcinogens based on a linear low-dose extrapolation, the Agency will publish recommended criteria values at a 10 -6 risk level. States and authorized Tribes can always choose a more stringent risk level, such as 10 -7 . EPA also believes that criteria based on a 10 -5 risk level are acceptable for the general population as long as States and authorized Tribes ensure that the risk to more highly exposed subgroups (sportfishers or subsistence fishers) does not exceed the 10 -4 level. Clarification on this risk management decision is provided in Section 2 of this document.

The default fish consumption value for the general adult population in the 2000 Human Health Methodology is 17.5 grams/day, which represents an estimate of the 90 thpercentile consumption rate for the U.S. adult population based on the U.S. Department of Agriculture’s (USDA’s) Continuing Survey of Food Intake by Individuals (CSFII) 1994-96 data (USDA, 1998). EPA will use this default intake rate with future national 304(a) criteria derivations or revisions. This default value is chosen to be protective of the majority of the general population. However, States and authorized Tribes are urge d to use a fish intake level derived from local data on fish consumption in place of this default value when deriving AWQC, ensuring that the fish intake level chosen is protective of highl y exposed individuals in the population. EPA has1-13 provided default values for States and authorizedTribes that do not have adequate information on local or regional consumption patterns, based on numerous studies that EPA has reviewed on sport anglers and subsistence fishers. EPA’s defaults for these population groups are estimates of their average consumption. EPA recommends a default of 17.5 grams/day for sport anglers as an approximation of their average consumption and 142.4 grams/day for subsistence fishers, which falls within the range of averages for this group. Consumption rates for women of childbearing age and children younger than 14 are also provided to maximize protection in those cases where these subpopulations may be at greatest risk.

In the 2000 Human Health Methodology, criteria are derived using a BAF rather than a BCF. To derive the BAF, States and author ized Tribes may use EPA’s Methodology or any method consistent with this Methodology. EPA’ s highest preference in developing BAFs are BAFs based on field-measured data from local/regional fish.

1.7 REFERENCES

NRC (National Research Council). 1977. Drinking Water and Health . Safe Drinking Water Committee. National Academy of Sciences, National Academy Press. Washington, DC.

USDA. 1998. U.S. Department of Agriculture. 1994–1996 Continuing Survey of Food Intakes by Individuals and 1994–1996 Diet and Health Knowledge Survey . Agricultural Research Service, USDA. NTIS CD–ROM, accession number PB98–500457.

USEPA (U.S. Environmental Protection Agency). 1980. Guidelines and methodology used in the preparation of health effect assessment chapters of the consent decree water criteria documents. Federal Register 45: 79347, Appendix 3.

USEPA (U.S. Environmental Protection Agency). 1984. Proposed guidelines for carcinogen risk assessment. Federal Register 49:46294.

USEPA (U.S. Environmental Protection Agency). 1986a. Guidelines for carcinogen risk
assessment. Federal Register 51:33992-34003.

USEPA (U.S. Environmental Protection Agency). 1986b. Guidelines for mutagenicity risk
assessment. Federal Register 51:34006-34012.

USEPA (U.S. Environmental Protection Agency). 1986c. Total Exposure Assessment Model (TEAM) Study: Summary and Analysis, Volume I. Final Report. EPA/600/6-87/002a.

USEPA (U.S. Environmental Protection Agency). 1986d. Guidelines for exposure assessment. Federal Register 51:34042-34054.

USEPA (U.S. Environmental Protection Agency). 1991. Guidelines for developmental toxicity risk assessment. Federal Register 56:63789- 63826.1-14 USEPA (U.S. Environmental Protection Agency). 1992. Guidelines for exposure assessment. Federal Register 57:22888-22938.

USEPA (U.S. Environmental Protection Agen cy). 1996a. Proposed guidelines for carcinogen risk assessment. Federal Register 61:17960-18011.

USEPA (U.S. Environmental Protection Agency). 1996b. Guidelines for reproductive toxicity risk assessment. Federal Register 61: 6274-56322.

USEPA (U.S. Environmental Protection Agency). 1997a. Exposure Factors Handbook . Office of Research and Development. Washington, DC. EPA/600/P-95/002Fa..

USEPA (U.S. Environmental Protection Agency). 1997b. Guiding Principles for Monte Carlo Analysis . Risk Assessment Forum. Washington, DC. EPA/630/R-97/001.

USEPA (U.S. Environmental Protection Agency). 1998. Guidelines for neurotoxicity risk
assessment. Federal Register 63: 26926.

USEPA (U.S. Environmental Protection Agency). 1999a. 1999 Guidelines for Carcinogen Risk Assessment. Review Draft . Office of Research and Development. Washington, DC. NCEA-F-0644.

USEPA (U.S. Environmental Protection Agency). 1999b. Guidance for Conducting Health Risk Assessment of Chemical Mixtures . Final Draft. Risk Assessment Forum Technical Panel. Washington, DC. EPA/NCEA-C-0148. September. Website: http://www.epa.gov/ncea/raf/rafpub.htm

USEPA (U.S. Environmental Protection Agency). 2000. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

. Technical Support Document Volume 1: Risk Assessment. Office of Science and Technology, Office of Water. Washington, DC. EPA-822-B-00-005. August.2-1 

### 2. C LARIFICATIONS ON 

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### M ETHODOLOGY 

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### C HARACTERIZATION 

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### O THER 

### I SSUES 

### F OR 

### D EVELOPING 

### C RITERIA

2.1 IDENTIFYING THE POPULATION SUBGROUP THAT THE AWQC SHOULD PROTECT

Water quality criteria are derived to establish ambient concentrations of pollutants which, if not exceeded, will protect the general population from adverse health impacts from those pollutants due to consumption of aquatic organisms and water, including incidental water consumption related to recreational activities. For each pollutant, chronic criteria are derived to reflect long-term consumption of food and water. An important decision to make when setting AWQC is the choice of the particular population to protect. For instance, criteria could be set to protect those individuals who have average or “t ypical” exposures, or the criteria could be set so that they offer greater protection to those i ndividuals who are more highly exposed. EPA has selected default parameter values that are representative of several defined populations: adults in the general population; sport (recreational) fisher s; subsistence fishers; women of childbearing age (defined as ages 15-44); and children (up to the age of 14). In deciding on default parameter values, EPA is aware that multiple parameters are used in combination when calculating AWQC (e.g., intake rates and body weight). EPA describes the estimated population percentiles that are represented by each of the default exposure parameter values in Section 4.

EPA’s national 304(a) criteria are usually derived to protect the majority of the general population from chronic adverse health effects. EPA has used a combination of median values, mean values, and percentile estimates for the parameter value defaults to calculate its national 304(a) criteria. EPA believes that its assumptions afford an overall level of protection targeted at the high end of the general population (i.e., the target population or the criteria-basis population). EPA also believes that this is reasonably conservative and appropriate to meet the goals of the CWA and the 304(a) criteria program. EPA considers that its target protection goal is satisfied if the population as a whole will be adequately protected by the human health criteria when the criteria are met in ambient water. However, associating the derived criteria with a specific population percentile is far more difficult, and such a quantitative descriptor typically requires detailed distributional exposure and dose information. EPA’s Guidelines For Exposure Assessment (USEPA, 1992) describes the extreme difficulty in making accurate estimates of exposures and indicates that uncertainties at the more extreme ends of the distribution increase greatly. On quantifying population exposures/risks, the guidelines specifically state:

In practice, it is difficult even to establish an accurate mean health effect risk for a population. This is due to many complications, including uncertainties in usinganimal data for human dose-response relationships, nonlinearities in the doseresponse curve, projecting incidence data from one group to another dissimilar group, etc. Although it has been common practice to estimate the number of cases of disease, especially cancer, for populations exposed to chemicals, it should be understood that these estimates are not meant to be accurate estimates of real (or actuarial) cases of disease. The estimate’s value lies in framing2-2 hypothetical risk in an understandable way rather than in any literal interpretation of the term “cases.”

Although it is not possible to subject the estimates to such a rigorous analysis (say, for example, to determine what criterion value provides protection of exactly the 90 thpercentile of the population), EPA believes that the combination of parameter value assumptions achieves its target goal, without being inordinately conservative. The standard assumptions made for the national 304(a) criteria are as follows. The assumed body weight value used is an arithmetic mean, as are the RSC intake estimates of other exposures (e.g., non-fish dietary), when data are available. The BAF component data (e.g., for lipid values, for particulate and dissolved organic carbon) are based on median (i.e., 50 thpercentile) values. The drinking water intake values are approximately 90 thpercentile estimates and fish intake values are 90 thpercentile estimates. EPA believes the use of these values will result in 304( a) criteria that are protective of a majority of the population; this is EPA’s goal.

However, EPA also strongly believes that Stat es and authorized Tribes should have the flexibility to develop criteria, on a site-specific basis, that provide additional protection appropriate for highly exposed populations. EPA is aware that exposure patterns in general, and fish consumption in particular, vary subs tantially. EPA understands that highly exposed populations may be widely distributed geographi cally throughout a given State or Tribal area. EPA recommends that priority be given to iden tifying and adequately protecting the most highly exposed population. Thus, if the State or Tribe determines that a highly exposed population is at greater risk and would not be adequately protected by criteria based on the general population, and by the national 304(a) criteria in particular, EPA recommends that the State or Tribe adopt more stringent criteria using alternative exposure assumptions.

EPA has provided recommended default intake rates for various population groups for State and Tribal consideration. EPA does not intend for these alternative default values to be prescriptive. EPA strongly emphasizes its preferen ce that States and Tribes use local or regional data over EPA’s defaults, if they so choose, as being more representative of their population groups of concern.

In the course of updating the 2000 Human Health Methodology, EPA received some questions regarding the population groups for whic h the criteria would be developed. EPA does not intend to derive multiple 304(a) criteria for all subpopulation groups for every chemical. As stated above, criteria that address chronic adverse health effects are most applicable to the CWA Section 304(a) criteria program and the chemicals evaluated for this program. If EPA determined that pregnant women/fetuses or young children were the target population (or criteria basis population) of a chemical’s RfD or POD/UF , then the 304(a) criteria would be developed using exposure parameters for that subgroup. This would only be relevant for acute or subchronic toxicity situations. This does not conf lict with the fact that chronic health effects potentially reflect a person’s exposure during both childhood and adult years.

For RfD-based and POD/UF-based chemicals, EPA’s policy is that, in general, the RfD (or POD/UF) should not be exceeded and the exposure assumptions used should reflect the population of concern. It is recommended that when a State or authorized Tribe sets a2-3 waterbody-specific AWQC, they consider the popul ations most exposed via water and fish. EPA’s policy on cancer risk management goals is discussed in Section 2.4.

Health Risks to Children

In recognition that children have a special vulnerability to many toxic substances, EPA’s Administrator directed the Agency in 1995 to explicitly and consistently take into account environmental health risks to infants and children in all risk assessments, risk characterizations, and public health standards set for the United States. In April 1997, President Clinton signed Executive Order 13045 on the protection of children from environmental health risks, which assigned a high priority to addressing risks to children. In May 1997, EPA established the Office of Children’s Health Protection to ensure the implementation of the President’s Executive Order. EPA has increased efforts to ensure its guidance and regulations take into account risks to children. Circumstances where risks to children should be considered in the context of the 2000 Human Health Methodology are discussed in the Section 3.2, Noncancer Effects (in terms of developmental and reproductive toxicity) and in Section 4, Exposure (for appropriate exposure intake parameters).

Details on risk characterization and the guidi ng principles stated above are included in EPA’s March 21, 1995 policy statement and the discussion of risk characterization (USEPA, 1995) and the 1999 Guidelines for Carcinogen Risk Assessment. Review Draft (USEPA, 1999a) and the Reproductive and Toxicity Risk Assessment Guidelines of 1996 (USEPA, 1996b).

2.2 SCIENCE, SCIENCE POLICY, AND RISK MANAGEMENT

An important part of risk characterization, as described later in Section 2.7, is to make risk assessments transparent. This means that conclusions drawn from the science are identified separately from policy judgments and risk management decisions, and that the use of default values or methods, as well as the use of assumptions in risk assessments, are clearly articulated. In this Methodology, EPA has attempted to separate scientific analysis from science policy and risk management decisions for clarity. This should allow States and Tribes (who are also prospective users of this Methodology) to understand the elements of the Methodology accurately and clearly, and to easily separate out the scientific decisions from the science policy and risk management decisions. This is important so that when questions are asked regarding the scientific merit, validity, or apparent stringency or leniency of AWQC, the implementer of the criteria can clearly explain what judgments were made to develop the criterion in question and to what degree these judgments were based on science, science policy, or risk management. To some extent this process will also be displayed in future AWQC documents.

When EPA speaks of science or scientific analysis, it is referring to the extraction of data from toxicological or exposure studies and surv eys with a minimum of judgment being used to make inferences from the available evidence. For example, if EPA is describing a POD from an animal study (e.g., a LOAEL), this is usually determined as a lowest dose that produces an observable adverse effect. This would constitute a scientific determination. Judgments applying science policy, however, may enter this determination. For example, several scientists may differ in their opinion of what is adverse, and this in turn can influence the selection of a LOAEL2-4 in a given study. The use of an animal study to predict effects in a human in the absence of human data is an inherent science policy decision. The selection of specific UFs when developing an RfD is another example of science policy. In any risk assessment, a number of decision points occur where risk to humans can only be inferred from the available evidence. Both scientific judgments and policy choices may be involved in selecting from among several possible inferences when conducting a risk assessment.

Risk management is the process of selecti ng the most appropriate guidance or regulatory actions by integrating the results of risk assessment with engineering data and with social, economic, and political concerns to reach a decision. In this Methodology, the choice of a default fish consumption rate which is protective of 90 percent of the general population is a risk management decision. The choice of an acceptable cancer risk by a State or Tribe is a risk management decision.

Many of the components in the 2000 Human Health Methodology are an amalgam of science, science policy, and/or risk management. For example, most of the default values chosen by EPA are based on examination of scientific data and application of either science policy or risk management. This includes the default assumption of 2 liters a day of drinking water; the assumption of 70 kilograms for an adult body weight; the use of default percent lipid and particulate organic carbon/dissolved organic carbon (POC/DOC) for developing national BAFs; the default fish consumption rates for the general population and sport and subsistence anglers; and the choice of a default cancer risk level. Some decisions are more grounded in science and science policy (such as the choice of default BAFs) and others are more obviously risk management decisions (such as the determination of default fish consumption rates and cancer risk levels). Throughout the 2000 Human Health Methodology, EPA has identified the kind of decision necessary to develop defaults and what the basis for the decision was. More details on the concepts of science analysis, science policy, risk management, and how they are introduced into risk assessments are included in Risk Assessment in the Federal Government: Managing the Process (NRC, 1983).

2.3 SETTING CRITERIA TO PROTEC T AGAINST MULTIPLE EXPOSURES FROM MULTIPLE CHEMICAL S (CUMULATIVE RISK)

EPA is very much aware of the complex issues and implications of cumulative risk and has endeavored to begin developing an overall approach at the Agency-wide level. Assuming that multiple exposures to multiple chemicals are additive is scientifically sound if they exhibit the same toxic endpoints and modes of action. There are numerous publications relevant to cumulative risk that can assist States and Tribes in understanding the complex issues associated with cumulative risk. These include the following:



### < Durkin, P.R., R.C. Hertzberg, W. Stiteler, and M. Mumtaz. 1995. The identification and testing of interaction patterns. Toxicol. Letters 79:251-264.



### < Hertzberg, R.C., G. Rice, and L.K. Teuschler. 1999. Methods for health risk assessment of combustion mixtures. In: Hazardous Waste Incineration: Evaluating the Human2-5 Health and Environmental Risks . S. Roberts, C. Teaf and J. Bean, (eds). CRC Press LLC, Boca Raton, FL. Pp. 105-148.



### < Rice, G., J. Swartout, E. Brady-Roberts, D. Reisman, K. Mahaffey, and B. Lyon. 1999. Characterization of risks posed by combustor emissions. Drug and Chem. Tox. 22:221- 240.



### < USEPA. 1999. Guidance for Conducting Health Risk Assessment of Chemical Mixtures

. Final Draft. Risk Assessment Forum Technical Panel. Washington, DC. NCEA-C0148. September. Web site: http://www.epa.gov/ncea/raf/rafpub.htm



### < USEPA. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions . (Update to EPA/600/6-90/003 Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions ). National Center for Environmental Assessment. Washington, DC. EPA600-R-98-137. Website http://www.epa.gov/ncea/combust.htm



### < USEPA. 1996. PCBs: Cancer Dose-Response Assessment and Application to Environmental Mixtures . National Center for Environmental Assessment. Washington, DC. EPA/600/P-96/001F.



### < USEPA. 1993. Review Draft Addendum to the Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions . Office of Health and Environmental Assessment, Office of Research and Development. Washington, DC. EPA/600/AP-93/003. November.



### < USEPA. 1993. Provisional Guidance for Quantitative Risk Assessment of Polycyclic Aromatic Hydrocarbons . Office of Research and Development. Washington, DC. EPA/600/R-93/089. July.



### < USEPA. 1990. Technical Support Document on Health Risk Assessment of Chemical Mixtures . Office of Research and Developm ent. Washington, DC. EPA/600/8/90/064. August.



### < USEPA. 1989a. Risk Assessment Guidance for Superfund. Vol. 1. Human Health Evaluation Manual (Part A) . Office of Emergency and Remedial Response. Washington, DC. EPA/540/1-89/002.



### < USEPA. 1989b. Interim Procedures for Estimating Risks Associated with Exposures to Mixtures of Chlorinated Dibenzo-p-Diox ins and -Dibenzofurans (CDDs and CDFs) and 1989 Update . Risk Assessment Forum. Washington, DC. EPA/625/3-89/016. March.

The Agency’s program offices are also engaged in on-going discussions of the great complexities, methodological challenges, data adequacy needs and other information gaps, as well as the science policy and risk management decisions that will need to be made, as they pursue developing a sound strategy and, eventually, specific guidance for addressing cumulative2-6 risks. As a matter of internal policy, EPA is committed to refining the Methodology as advances in relevant aspects of the science improve, as part of the water quality criteria program.

2.4 CANCER RISK RANGE

For deriving 304(a) criteria or promulgating water quality criteria for States and Tribes under Section 303(c) based on the 2000 Human Health Methodology, EPA intends to use the 10

-
6 risk level, which the Agency believes reflects an appropriate risk for the general population. EPA’s program office guidance and regulatory actions have evolved in recent years to target a 10

! 6 risk level as an appropriate risk for the general population. EPA has recently reviewed the policies and regulatory language of other Agency mandates (e.g., the Clean Air Act Amendments of 1990, the Food Quality Protection Act) and believes the target of a 10

! 6 risk level is consistent with Agency-wide practice.

EPA believes that both 10

! 6 and 10

! 5 may be acceptable for the general population and that highly exposed populations should not exceed a 10

! 4 risk level. States or Tribes that have adopted standards based on criteria at the 10

! 5 risk level can continue to do so, if the highly exposed groups would at least be protected at the 10

! 4 risk level. However, EPA is not automatically assuming that 10

! 5 will protect “the highest consumers” at the 10

! 4 risk level. Nor is EPA advocating that States and Tribes automatically set criteria based on assumptions for highly exposed population groups at the 10

! 4 risk level. The Agency is simply endeavoring to add that a specific determination should be made to ensure that highly exposed groups do not exceed a 10

! 4 risk level. EPA understands that fish consumption rates vary considerably, especially among subsistence populations, and it is such great variation among these population groups that may make either 10

! 6 or 10

! 5 protective of those groups at a 10

! 4 risk level. Therefore, depending on the consumption patterns in a given State or Tribal jurisdiction, a 10

! 6 or 10

! 5 risk level could be appropriate. In cases where fish consumption among highly exposed population groups is of a magnitude that a 10

! 4 risk level would be exceeded, a more protective risk level should be chosen. Such determinations should be made by the State or Tribal authorities and are subject to EPA’s review a nd approval or disapproval under Section 303(c) of the CWA.

Adoption of a 10

! 6 or 10

! 5 risk level, both of which States and authorized Tribes have chosen in adopting water quality standards to date, represents a generally acceptable risk management decision, and EPA intends to continue providing this flexibility to States and Tribes. EPA believes that such State or Tribal d ecisions are consistent with Section 303(c) if the State or authorized Tribe has identified the most highly exposed subpopulation, has demonstrated that the chosen risk level is adequately protective of the most highly exposed subpopulation, and has completed all necessary public participation. States and authorized Tribes also have flexibility in how they demonstrate this protectiveness and obtain such information. A State or authorized Tribe may use existing information as well as collect new information in making this determination. In addition, if a State or authorized Tribe does not believe that the 10

! 6 risk level adequately protects the exposed subpopulations, water quality criteria based on a more stringent risk level may be adopted. This discretion includes combining the 10

! 6 risk level with fish consumption rates for highly exposed population groups.2-7 It is important to understand that criteria for carcinogens are based on chosen risk levels that inherently reflect, in part, the exposure parameters used to derive those values. Therefore, changing the exposure parameters also changes the risk. Specifically, the incremental cancer risk levels are relative , meaning that any given criterion associated with a particular cancer risk level is also associated with specific exposure parameter assumptions (e.g., intake rates, body weights). When these exposure parameter values change, so does the relative risk. For a criterion derived on the basis of a cancer risk level of 10

! 6 , individuals consuming up to 10 times the assumed fish intake rate would not exceed a 10

! 5 risk level. Similarly, individuals consuming up to 100 times the assumed rate would not exceed a 10

! 4 risk level. Thus, for a criterion based on EPA’s default fish intake rate (17.5 gm/day) and a risk level of 10

! 6 , those consuming a pound per day (i.e., 454 grams/day) would potentially experience between a 10

! 5 and a 10

! 4 risk level (closer to a 10

! 5 risk level). (Note: Fish consumers of up to 1,750 gm/day would not exceed the 10

! 4 risk level.) If a criterion were based on high-end intake rates and the relative risk of 10

! 6 , then an average fish consumer would be protected at a cancer risk level of approximately 10

! 8 . The point is that the risks for different population groups are not the same.

2.5 MICROBIOLOGICAL AMBIENT WATER QUALITY CRITERIA

Guidance for deriving microbiological AWQC is not a part of this Methodology. In 1986, EPA published Ambient Water Quality Criteria for Bacteria - 1986 (USEPA, 1986a), which updated and revised bacteriological criteria previously published in 1976 in Quality Criteria for Water (USEPA, 1976). The inclusion of guidance for deriving microbiological AWQC was considered in the 1992 national workshop that initiated the effort to revise the 1980 Methodology and was recommended by the SAB in 1993. Since that time, however, efforts separate from these Methodology revisions have addressed microbiological AWQC concerns. The purpose of this section is to briefly describe EPA’s current recommendations and activities.

EPA’s Ambient Water Quality Criteria for Bacteria - 1986 recommends the use of Escherichia coli and enterococci rather than fecal co liforms (USEPA, 1986a). EPA’s criteria recommendations are:

• Fresh water: E. coli not to exceed 126/100 ml or enterococci not to exceed 33/100 ml; and

• Marine water: enterococci not to exceed 35/100 ml.

These criteria should be calculated as the geometric mean based on five equally spaced samples taken over a 30-day period.

In addition, EPA recommends that States adopt a single sample maximum, based on the expected frequency of use. No sample taken should exceed this value. EPA specifies appropriate single sample maximum values in the 1986 criteria document.

Current Activities and Plans for Future Work2-8 EPA has identified development of microbial water quality criteria as part of its strategy to control waterborne microbial disease, by controlling pathogens in waterbodies and by protecting designated uses, such as recreation and public water supplies. The program fosters an integrated approach to protect both ground-water and surface water sources. EPA plans to conduct additional monitoring for Cryptosporidium parvum and E. coli , and determine action plans in accordance with the results of this monitoring.

EPA recommends no change at this time in the stringency of its bacterial criteria for recreational waters; existing criteria and methodologies from 1986 will still apply. The recommended methods for E. coli and enterococci have been improved. As outlined in the Action Plan for Beaches and Recreational Waters (Beach Action Plan, see below), the Agency plans to conduct national studies on improving indi cators together with epidemiology studies for new criteria development (USEPA, 1999b). The Agency is also planning to establish improved temporal and spatial monitoring protocols.

In the Beach Action Plan, EPA identifies a multi-year strategy for monitoring recreational water quality and communicating public health risks associated with potentially pathogen-contaminated recreational rivers, lakes, and ocean beaches. It articulates the Agency’s rationale and goals in addressing specific problems and integrates all associated program, policy, and research needs and directions. The Beach Action Plan also provides information on timing, products and lead organization for each activity. These include activities and products in the areas of program development, risk communication, water quality indicator research, modeling and monitoring research, and exposure and health effects research.

Recently, EPA approved new 24-hour E. coli and enterococcus tests for recreational waters that may be used as an alternative to the 48-hour test (USEPA, 1997). EPA anticipates proposing these methods for inclusion in the 40 CRF 136 in the Fall of 2000. EPA has also published a video with accompanying manual on the original and newer methods for enterococci and E. coli (USEPA, 2000).

As part of the Beach Action Plan, EPA made the following recommendations for further Agency study:

• Future criteria development should consider the risk of diseases other than gastroenteritis. EPA intends to consider and evaluate such water-related exposure routes as inhalation and dermal absorption when addressing microbial health effects. The nature and significance of other than the classical waterborne pathogens are to some degree tied to the particular type of waste sources.

• A new set of indicator organisms may need to be developed for tropical water if it is proven that the current fecal indicators can maintain viable cell populations in the soil and water for significant periods of time in uniform tropical conditions. Some potential alternative indicators to be fully explored are coliphage, other bacteriophage, and Clostridium perfringens

.2-9 • Because animal sources of pathogens of concern for human infection such as Giardia lamblia

, Cryptosporidium parvum , and Escherichia coli 0157:H7 may be waterborne or washed into water and thus become a potential source for infection, they should not be ignored in risk assessment. A likely approach would be phylogenetic differentiation; that is, indicators that are specific to, or can discriminate among, animal sources.

• EPA intends to develop additional data on secondary infection routes and infection rates from prospective epidemiology studies and outbreaks from various types of exposure (e.g., shellfish consumption, drinking water, recreational exposure).

• EPA needs to improve sampling strategies for recreational water monitoring including consideration of rainfall and pollution events to trigger sampling.

2.6 RISK CHARACTERIZATION CONSIDERATIONS

On March 21, 1995, EPA’s Administrator issued the EPA Risk Characterization Policy and Guidance (USEPA, 1995). This policy and guidance is intended to ensure that characterization information from each stage of a risk assessment is used in forming conclusions about risk and that this information is communicated from risk assessors to risk managers, and from EPA to the public. The policy also provide s the basis for greater clarity, transparency, reasonableness, and consistency in risk assessments across EPA programs. The fundamental principles which form the basis for a risk characterization are as follows:

• Risk assessments should be transparent, in that the conclusions drawn from the science are identified separately from policy judgments, and the use of default values or methods and the use of assumptions in the risk assessment are clearly articulated.

• Risk characterizations should include a summary of the key issues and conclusions of each of the other components of the risk assessments, as well as describe the likelihood of harm. The summary should include a description of the overall strengths and limitations (including uncertainties) of the assessment and conclusions.

• Risk characterizations should be consistent in general format, but recognize the unique characteristics of each specific situation.

• Risk characterizations should include, at least in a qualitative sense, a discussion of how a specific risk and its context compares with similar risks. This may be accomplished by comparisons with other pollutants or situations on which the Agency has decided to act, or other situations with which the public may be familiar. The discussion should highlight the limitations of such comparisons.

• Risk characterization is a key component of risk communication, which is an interactive process involving exchange of informa tion and expert opinion among individuals, groups, and institutions.

Additional guiding principles include:2-10• The risk characterization integrates the information from the hazard identification, doseresponse, and exposure assessments, using a combination of qualitative information, quantitative information, and information regarding uncertainties.

• The risk characterization includes a discussion of uncertainty and variability in the risk
assessment.

• Well-balanced risk characterizations present conclusions and information regarding the strengths and limitations of the assessment for other risk assessors, EPA decision-makers, and the public.

In developing the methodology presented here, EPA has closely followed the risk characterization guiding principles listed above. As States and Tribes adopt criteria using the 2000 Human Health Methodology, they are strongly encouraged to follow EPA’s risk characterization guidance. There are a number of areas within the Methodology and criteria development process where risk characterization principles apply:

• Integration of cancer and noncancer assessments with exposure assessments, including bioaccumulation potential determinations, in essence, weighing the strengths and weaknesses of the risk assessment as a whole when developing a criterion.

• Selecting a fish consumption rate, either locally derived or the national default value, within the context of a target population (e.g., sensitive subpopulations) as compared to the general population.

• Presenting cancer and/or noncancer risk assessment options.

• Describing the uncertainty and variability in the hazard identification, the dose-response, and the exposure assessment. 2.7 DISCUSSION OF UNCERTAINTY

2.7.1 Observed Range of Toxicity Versus Range of Environmental Exposure

When characterizing a risk assessment, an important distinction to make is between the observed range of adverse effects (from an epidemiology or animal study) and the environmentally observed range of exposure (or anticipated human exposure) to the contaminant. In many cases, EPA intends to apply default factors to account for uncertainties or incomplete knowledge in developing RfDs or cancer risk assessments using nonlinear low-dose extrapolation to provide a margin of protection. In reality, the actual effect level and the environmental exposure levels may be separated by several orders of magnitude. The difference between the dose causing some observed response and the anticipated human exposure should be described by risk assessors and managers, especially when comparing criteria to environmental levels of a contaminant.

2.7.2 Continuum of Preferred Data/Use of Defaults2-11 In both toxicological and exposure assessments, EPA has defined a continuum of preferred data for toxicological assessments ranging from a highest preference for chronic human data (e.g., studies that examine a long-term exposure of humans to a chemical, usually from occupational and/or residential exposure) and actual field data for many of the exposure parameter values (e.g., locally derived fish consumption rates, waterbody-specific bioaccumulation rates), to default values which are at the lower end of the preference continuum. EPA has supplied default values for all of the risk assessment parameters in the 2000 Human Health Methodology; however, it is important to note that when default values are used, the uncertainty in the final risk assessment may be higher, and the final resulting criterion may not be as applicable to local conditions, than is a risk assessment derived from human/field data. Using defaults assumes generalized conditions and may not capture the actual variability in the population (e.g., sensitive subpopulations/high-end consumers). If defaults are chosen as the basis for criteria, these inherent uncertainties should be communicated to the risk manager and the public. While this continuum is an expression of preference on the part of EPA, it does not imply in any way that any of the choices are unacceptable or scientifically indefensible.

2.7.3 Significant Figures

The number of significant figures in a numeric value is the number of certain digits plus one estimated digit. Digits should not be confused with decimal places. For example, 15.1, 0.0151, and 0.0150 all have 3 significant figures. Decimal places may have been used to maintain the correct number of significant figures, but in themselves they do not indicate significant figures (Brinker, 1984). Since the num ber of significant figures must include only one estimated digit, the sources of input parameters (e.g., fish consumption and water consumption rates) should be checked to determine the number of significant figures associated with data they provide. However, the original measured values may not be available to determine the number of significant figures in the input parameters. In these situations, EPA recommends utilizing the data as presented.

When developing criteria, EPA recommends rounding the number of significant figures at the end of the criterion calculation to the same number of significant figures in the least precise parameter. This is a generally accepted practice which can be found described in greater detail in APHA (1992) and Brinker (1984). The general rule is that for multiplication or division, the resulting value should not possess any more significant figures than is associated with the factor in the calculation with the least precision. When numbers are added or subtracted, the number that has the fewest deci mal places, not necessarily the fewest significant figures, puts the limit on the number of places that justifiably may be carried in the sum or difference. Rounding off a number is the process of dropping one or more digits so that the value contains only those digits that are significant or necessary in subsequent computations (Brinker, 1984). The following rounding procedures are recommended: (1) if the digit 6, 7, 8, or 9 is dropped, increase the preceding digit by one unit; (2) if the digit 0, 1, 2, 3, or 4 is dropped, do not alter the preceding digit; and (3) if the digit 5 is dropped, round off the preceding digit to the nearest even number (e.g., 2.25 becomes 2.2 and 2.35 becomes 2.4) (APHA, 1992; Brinker, 1984).2-12 (Equation 2-1) EPA recommends that calculations of water quality criteria be performed without rounding of intermediate step values. The resulting criterion may be rounded to a manageable number of decimal places. However, in no case should the number of digits presented exceed the number of significant figures implied in the data and calculations performed on them. The term “intermediate step values” refers to values of the parameters in Equations 1-1 through 1-3. The final step is considered the resulting AWQC. Although AWQC are, in turn, used for purposes of establishing water quality-based effluent limits (WQBELs) in National Pollutant Discharge Elimination System (NPDES) permits, calculating total maximum daily loads (TMDLs), and applicable or relevant and appropriate requirements (ARARs) for Superfund, they are considered the final step of this Methodology and, for the purpose of this discussion, where the rounding should occur.

The determination of appropriate significant figures inevitably involves some judgment given that some of the equation parameters are adopted default exposure values. Specifically, the default drinking water intake rate of 2 L/day is a value adopted to represent a majority of the population over the course of a lifetime. Although supported by drinking water consumption survey data, this value was adopted as a policy decision and, as such, does not have to be considered in determining the parameter with the least precision. That is, the resulting AWQC need not always be reduced to one significant digit. Similarly, the 70-kg adult body weight has been adopted Agency-wide and represents a default policy decision.

The following example with a simplified AWQC equation illustrates the rule described above. The example is for hexachlorobutadiene (HCBD), which EPA used to demonstrate the 1998 draft Methodology revisions (USEPA, 1998b). The parameters that were calculated (i.e., not policy adopted values) include values with significant figures of two (the POD and RSC), three (the UF), and four (the FI and BAF). Based on the 2000 Human Health Methodology, the final criterion should be rounded to two significan t figures. The bold numbers in parentheses indicate the number of significant figures and thos e with asterisks also indicate Agency adopted policy values.

Example [Refer to draft HCBD document for details on the POD/UF, RSC and BAF data (EPA 822-R-98-004). Also note that the fish intake rate in this example is the revised value.]:2-13 AWQC = 7.3 × 10 -5 mg/L (0.073 μg/L, rounded from 7.285 × 10 -2 μg/L) * represents Agency adopted policy value

A number of the values used in the equation may result in intermediate step values that have more than four figures past the decimal place and may be carried throughout the calculation. However, carrying more than four figures past the decimal place (equivalent to the most precise parameter) is unnecessary as it has no effect on the resulting criterion value.

2.8 OTHER CONSIDERATIONS

2.8.1 Minimum Data Considerations

For many of the preceding technical areas, considerations have been presented for data quality in developing toxicological and exposure assessments. For greater detail and discussion of minimum data recommendations, the reader is referred to the specific sections in the Methodology on cancer and noncancer risk assessments (and especially to the referenced EPA risk assessment guidelines documents), exposure assessment, and bioaccumulation assessment, in addition to the TSD volumes for each.

2.8.2 Site-Specific Criterion Calculation

The 2000 Human Health Methodology allows for site-specific modifications by States and Tribes to reflect local environmental conditions and human exposure patterns. “Local” may refer to any appropriate geographic area where common aquatic environmental or exposure patterns exist. Thus “local” may signify Statewide, regional, a river reach, or an entire river.

Such site-specific criteria may be developed as long as the site-specific data, either toxicological or exposure-related, is justifiable. For example, when using a site-specific fish consumption rate, a State should use a value that represents at least the central tendency of the population surveyed (either sport or subsistence, or both). If a site-specific fish consumption rate for sport anglers or subsistence anglers is lower than an EPA default value, it may be used in calculating AWQC. However, to justify such a level (either higher or lower than EPA defaults), the State should assemble appropriate survey data to arrive at a defensible site-specific fish consumption rate.

Such data must also be submitted to EPA for its review when approving or disapprovingState or Tribal water quality standards under Section 303(c). The same conditions apply to sitespecific calculations of BAF, percent fish lipid, or the RSC. In the case of deviations from toxicological values (i.e., IRIS values: verifi ed noncancer and cancer assessments), EPA strongly recommends that the data upon which the deviation is based be presented to and approved by the Agency before a criterion is developed.

Additional guidance on site-specific modifications to the 2000 Human Health Methodology is provided in each of the three TSD volumes.

2.8.3 Organoleptic Criteria2-14 Organoleptic criteria define concentrations of chemicals or materials which impart undesirable taste and/or odor to water. Organolep tic effects, while significant from an aesthetic standpoint, are not a significant health concern. In developing and utilizing such criteria, two factors must be appreciated: (1) the limitations of most organoleptic data; and (2) the human health significance of organoleptic properties. In the past, EPA has developed organoleptic criteria if organoleptic data were available for a specific contaminant. The 1980 AWQC National Guidelines made a clear distinction that organoleptic criteria and toxicity-based criteria are derived from completely different endpoi nts, and that organoleptic criteria have no demonstrated relationship to potential adverse human health effects because there is no toxicological basis. EPA acknowledges that if organoleptic effects (i.e., objectionable taste and odor) cause people to reject the water and its designated uses, then the public is effectively deprived of the natural resource. It is also possible that intense organoleptic characteristics could result in depressed fluid intake which, in turn, mightlead to an indirect human health effect via decreased fluid consumption. Although EPA has de veloped organoleptic criteria in the past and may potentially do so in the future, this will not be a significant part of the water quality criteria program. EPA encourages the development of organoleptic criteria when States and Tribes believe they are needed. However, EPA cautions States and Tribes that the quality of organoleptic data is often significantly less than that of toxicologic data used in establishing health-based criteria. Therefore, a comprehensive evaluation of available organoleptic data should be made, and the selection of the most appropriate database for the criterion should be based on sound scientific judgment.

In 1980, EPA provided recommended criteria summary language when both types of data are available. The following format was used and is repeated here:

For comparison purposes, two approaches were used to derive criterion levels for ____. Based on available toxicity data, for the protection of public health the derived level is ____. Using available organoleptic data, for controlling undesirable taste and odor quality of ambient water the estimated level is ____. It should be recognized that organoleptic data as a basis for establishing a water quality criteria have no demonstrated relationship to potential adverse human health effects.

Similarly, the 1980 Methodology recommended that in those instances where a level to limit toxicity cannot be derived, the following statement should be provided:

Sufficient data are not available for ____ to derive a level which would protect against the potential toxicity of this compound.

2.8.4 Criteria for Chemical Classes

The 2000 Human Health Methodology also allows for the development of a criterion for classes of chemicals, as long as a justification is provided through the analysis of mechanistic data, toxicokinetic data, structure-activity relationship data, and limited acute and chronic toxicity data. When potency differences between members of a class is great (such as in the case2-15 of chlorinated dioxins and furans), toxic ity equivalency factors (TEFs) may be more appropriately developed than one class criterion.

A chemical class is defined as any group of chemical compounds which are similar in chemical structure and biological activity, and which frequently occur together in the environment usually because they are generated by the same commercial process. In criterion development, isomers should be regarded as part of a chemical class rather than as a single compound. A class criterion, therefore, is an estimate of risk/safety which applies to more than one member of a class. It involves the use of available data on one or more chemicals of a class to derive criteria for other compounds of the same class in the event that there are insufficient data available to derive compound-specific criteria. The health-based criterion may apply to the water concentration of each member of the class, or may apply to the sum of the water concentrations of the compounds within the class. Because relatively minor structural changes within the class of compounds can have pronounced effects on their biological activities, reliance on class criteria should be minimized depending on the data available.

The following guidance should also be followed when considering the development of a class criterion.

• A detailed review of the chemical and physical properties of the chemicals within the group should be made. A close relationship within the class with respect to chemical activity would suggest a similar potential to reach common biological sites within tissues. Likewise, similar lipid solubilities would suggest the possibility of comparable absorption and distribution.

• Qualitative and quantitative toxicological data for chemicals within the group should be examined. Adequate toxicological data on a number of compounds within a group provides a more reasonable basis for extrapolation to other chemicals of the same class than minimal data on one chemical or a few chemicals within the group.

• Similarities in the nature of the toxicological response to chemicals in the class provides additional support for the prediction that the response to other members of the class may be similar. In contrast, where the biological response has been shown to differ markedly on a qualitative and quantitative basis for chemicals within a class, the extrapolation of a criterion to other members is not appropriate.

• Additional support for the validity of extrapolation of a criterion to other members of a class could be provided by evidence of similar metabolic and toxicokinetic data for some members of the class.

Additional guidance is described in the Technical Support Document on Health Risk Assessment of Chemical Mixtures (USEPA, 1990).2-16 2.9.5 Criteria for Essential Elements2-17 Developing criteria for essential elements, particularly metals, must be a balancing act between toxicity and the requirement for good health. The AWQC must consider essentiality and cannot be established at levels that would result in deficiency of the element in the human population. The difference between the recommended daily allowance (RDA) and the daily doses causing a specified risk level for carcinogens or the RfDs for noncarcinogens defines the spread of daily doses within which the criterion may be derived. Because errors are inherent in defining both essential and adverse-effect levels, the criterion is derived from a dose level near the center of such dose ranges.

The process for developing criteria for essential elements should be similar to that used for any other chemical with minor modifications. The RfD represents concern for one end of the exposure spectrum (toxicity), whereas the RDA represents the other end (minimum essentiality). While the RDA and RfD values might occasionally appear to be similar in magnitude to one another, it does not imply incompatibility of the two methodological approaches, nor does it imply inaccuracy or error in either calculation.

2.9 REFERENCES

APHA. American Public Health Association. 1992. Standard Methods: For the Examination of Water and Wastewater. 18th Edition. Prepared and published jointly by: American Public Health Association, American Water Works Association, and Water Environment Federation. Washington, DC.

Brinker, R.C. 1984. Elementary Surveying. 7th Edition. Cliff Robichaud and Robert Greiner, Eds. Harper and Row Publishers, Inc. New York, NY.

NRC (National Research Council). 1983. Risk Assessment in the Federal Government: Managing the Process. National Academy Press. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 1976. Quality Criteria for Water . Office of Water and Hazardous Materials. Washington, DC. July.

USEPA (U.S. Environmental Protection Agency). 1986a. Ambient Water Quality Criteria for Bacteria – 1986. Office of Water Regulations and Standards. Washington, DC. EPA/440/5-84/002. January.

USEPA (U.S. Environmental Protection Agency). 1986b. Test Methods for Escherichia coli and Enterococci in Water by the Membrane Filter Procedure . Office of Research and Development. Cincinnati, OH. EPA/600/4-85/076.

USEPA (U.S. Environmental Protection Agency). 1990. Technical Support Document on Health Risk Assessment of Chemical Mixtures . Office of Research and Development. Washington, DC. EPA/600/8-90/064. August.

USEPA (U.S. Environmental Protection Agency). 1992. Guidelines for exposure assessment. Federal Register 57:22888-22938.2-18 USEPA (U.S. Environmental Protection Agency). 1995. Policy for Risk Characterization

. Memorandum of Carol M. Browner, Administrator. March 21, 1995. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 1996a. Draft revisions to guidelines for carcinogen risk assessment. Federal Register 61:17960.

USEPA (U.S. Environmental Protection Agency). 1996b. Guidelines for reproductive toxicity risk assessment. Federal Register 61:6274-56322.

USEPA (U.S. Environmental Protection Agency). 1997. Method 1600: Membrane Filter Test Method for Enterococci in Water . Office of Water. Washington, DC. EPA/821/R97/004. May.

USEPA (U.S. Environmental Protection Agency). 1998a. Draft Water Quality Criteria Methodology: Human Health . Office of Water. Washington, DC. EPA-822-Z-98-001. (Federal Register 63:43756)

USEPA (U.S. Environmental Protection Agency). 1998b. Ambient Water Quality Criteria for the Protection of Human Health. Hexachlorobutadiene (HCBD). Draft . Office of Water. Washington, DC. EPA 882-R-98-004. July.

USEPA (U.S. Environmental Protection Agency). 1999a. 1999 Guidelines for Carcinogen Risk Assessment. Review Draft . Office of Research and Development. Washington, DC. NCEA-F-0644.

USEPA (U.S. Environmental Protection Agency). 1999b. Action Plan for Beaches and Recreational Waters. Reducing Exposures to Waterborne Pathogens. Office of Research and Development and Office of Water. Washington, DC. EPA-600-R-98-079. March.

USEPA (U.S. Environmental Protection Agency). 2000. Improved Enumeration Methods for the Recreational Water Quality Indicators: Enterococci and Escherichia coli . Office of Water, Office of Science and Tec hnology. Washington, DC. EPA-821-R-97-004. March.3-1 

### 3. R ISK 

### A SSESSMENT

This section describes the methods used to estimate ambient water quality criteria (AWQC) for the protection of human health for carcinogenic chemicals (Section 3.1) and for noncarcinogenic chemicals (Section 3.2).

3.1 CANCER EFFECTS

3.1.1 Background on EPA Cancer Risk Assessment Guidelines

The current EPA Guidelines for Carcinogen Risk Assessment were published in 1986 (USEPA, 1986a, hereafter the “1986 cancer guidelines”). The 1986 cancer guidelines categorize chemicals into alpha-numerical Groups: A, know n human carcinogen (sufficient evidence from epidemiological studies or other human studi es); B, probable human carcinogen (sufficient evidence in animals and limited or inadequate evidence in humans); C, possible human carcinogen (limited evidence of carcinogenicity in animals in the absence of human data); D, not classifiable (inadequate or no animal evidence of carcinogenicity); and E, evidence of noncarcinogenicity for humans (no evidence of carcinogenicity in at least two adequate animal tests in different species or in both adequate ep idemiological and animal studies). Within Group B there are two subgroups, Groups B1 and B2. Group B1 is reserved for agents for which there is limited evidence of carcinogenicity from epidemiological studies. Group B2 is generally for agents for which there is sufficient evidence from animal studies and for which there is inadequate evidence or no data from epidemiological studies (USEPA, 1986). The system was similar to that used by the International Agency for Research on Cancer (IARC).

The 1986 cancer guidelines include guidance on what constitutes sufficient, limited, or inadequate evidence. In epidemiological studies, sufficient evidence indicates a causal relationship between the agent and human cancer; limited evidence indicates that a causal relationship is credible, but that alternative explanations, such as chance, bias, or confounding, could not adequately be excluded; inadequate evidence indicates either lack of pertinent data, or a causal interpretation is not credible. In general, although a single study may be indicative of a cause-effect relationship, confidence in inferring a causal association is increased when several independent studies are concordant in showing the association. In animal studies, sufficient evidence includes an increased incidence of malignant tumors or combined malignant and benign tumors:

• In multiple species or strains;

• In multiple experiments (e.g., with different routes of administration or using different dose levels);

• To an unusual degree in a single experiment with regard to high incidence, unusual site or type of tumor, or early age at onset;

• Additional data on dose-response, short-term tests, or structural activity relationships.3-2 In the 1986 cancer guidelines, hazard identification and the weight-of-evidence process focus on tumor findings. The weight-of-evidence approach for making judgments about cancer hazard analyzes human and animal tumor data separately, then combines them to make the overall conclusion about potential human carcinogenicity. The next step of the hazard analysis is an evaluation of supporting evidence (e.g., mutagenicity, cell transformation) to determine whether the overall weight-of-evidence conclusion should be modified.

For cancer risk quantification, the 1986 cancer guidelines recommend the use of linearized multistage model (LMS) as the only default approach. The 1986 cancer guidelines also mention that a low-dose extrapolation model other than the LMS might be considered more appropriate based on biological grounds. Howeve r, no guidance is given in choosing other approaches. The 1986 cancer guidelines recommended the use of body weight raised to the 2/3 power (BW 2/3 ) as a dose scaling factor between species.

3.1.2 EPA’s Proposed Guidelines for Carcinogen Risk Assessment and the Subsequent July, 1999 Draft Revised Cancer Guidelines

In 1996, EPA published Proposed Guidelines for Carcinogen Risk Assessment (USEPA, 1996a, hereafter the “1996 proposed cancer guidelines”). After the publication of the 1996 proposed cancer guidelines and a February, 1997 and January, 1999 Science Advisory Board (SAB) review, a revision was made in July, 1999 Guidelines for Carcinogen Risk Assessment - Review Draft (hereafter the “1999 draft revised cancer guidelines”; USEPA, 1999a), and an SAB meeting was convened to review this revised document. When final guidelines are published, they will replace the 1986 cancer guidelines. These revisions are designed to ensure that the Agency’s cancer risk assessment methods reflect the most current scientific information and advances in risk assessment methodology.

In the meanwhile, the 1986 guidelines are used and extended with principles discussed in the 1999 draft revised cancer guidelines. These principles arise from scientific discoveries concerning cancer made in the last 15 years and from EPA policy of recent years supporting full characterization of hazard and risk both for the general population and potentially sensitive groups such as children. These principles are incorporated in recent and ongoing assessments such as the reassessment of dioxin, consistent with the 1986 guidelines. Until final guidelines are published, information is presented to describe risk under both the 1986 guidelines and 1999 draft revisions.

The 1999 draft revised cancer guidelines call for the full use of all relevant information to convey the circumstances or conditions under which a particular hazard is expressed (e.g., route, duration, pattern, or magnitude of exposure). They emphasize understanding the mode of action (MOA) whereby the agent induces tumors. The MOA underlies the hazard assessment and provides the rationale for dose-response assessments.

The key principles in the 1999 draft revised cancer guidelines include:3-3 a) Hazard assessment is based on the analysis of all biological information rather than just tumor findings.

b) An agent’s MOA in causing tumors is emphasized to reduce the uncertainty in describing the likelihood of harm and in determining the dose-response approach(es).

c) The 1999 draft revised cancer guidelines emphasize the conditions under which the hazard may be expressed (e.g., route, pattern, duration and magnitude of exposure). Further, the guidelines call for a hazard characterization to integrate the data analysis of all relevant studies into a weight-of-evidence conclusion of hazard and to develop a working conclusion regarding the agent’s mode of action in leading to tumor development.

d) A weight-of-evidence narrative with accompanying descriptors (listed in Section 3.1.3.1 below) would replace the current alphanumeric classification system. The narrative summarizes the key evidence for carcinogenicity, describes the agent’s MOA, characterizes the conditions of hazard expression, including route of exposure, describes any disproportionate effects on subgroups of the human population (e.g., children), and recommends appropriate dose-response approach(es). Significant strengths, weaknesses, and uncertainties of contributing evidence are also highlighted.

e) Biologically based extrapolation models are the preferred approach for quantifying risk. These models integrate data and conclusions about events in the carcinogenic process throughout the dose-response range from high to low doses. It is anticipated, however, that the necessary data for the parameters used in such models will not be available for most chemicals. The 1999 draft revised cancer guidelines allow for alternative quantitative methods, including several default approaches.

f) Dose-response assessment is a two-step process. In the first step, response data are modeled in the observable range of data and a determination is made of the point of departure (POD) from the observed range to extrapolate to low doses. The second step is extrapolation from the POD to estimate dose-response at lower doses. In addition to modeling tumor data, the 1999 draft revised cancer guidelines call for the use and modeling of other kinds of responses if they are considered to be more informed measures of carcinogenic risk. Nominally, these responses reflect key events in the carcinogenic process integral to the MOA of the agent.

g) Three default approaches are provided–linear, nonlinear, or both when adequate data are unavailable to generate a biologically based model. As the first step for all approaches, curve fitting in the observed range is used to determine a POD. A standard POD is the effective dose corresponding to the lower 95 percent limit on3 Use of the LED 10 as the point of departure is recommended with this Methodology, as it is with the 1999 draft revised cancer guidelines.

4 Additional information regarding the revised me thod for assessing carcinogens may be found in theMethodology for Deriving Ambient Water Quality Criteria for the Protec tion of Human Health (2000). Technical Support Document

, Volume 1: Risk Assessment (USEPA, 2000).

3-4 a dose associated with 10 percent extra risk (LED 10 ). 3 Linear : The linear default is a straight line extrapolation from the response at LED 10 to the origin (zero dose, zero extra risk). Nonlinear : The nonlinear default begins with the identified POD and provides a margin of exposure (MOE) analysis rather than estimating the probability of effects at low doses. The MOE analysis is used to determine the appropriate margin between the POD and the exposure level of interest, in this Methodology, the AWQC. The key objective of the MOE analysis is to describe for the risk manager how rapidly responses may decline with dose. Other factors are also considered in the MOE analysis (i.e., nature of the response, slope of the dose-response curve, human sensitivity compared with experimental animals, nature and extent of human variability in sensitivity and human exposure). Linear and nonlinear : Section 3.1.3.4E describes the situations when both linear and nonlinear defaults are used.

h) The approach used to calculate an oral human equivalent dose when assessments are based on animal bioassays has been refined and includes a change in the default assumption for interspecies dose scaling. The 1999 draft revised cancer guidelines use body weight raised to the 3/4 power.

EPA health risk assessment practices for both cancer and noncancer endpoints are beginning to come together with recent proposals to emphasize MOA understanding in risk assessment and to model response data in the observable range to derive PODs for data sets and benchmark doses (BMDs) for individual studies. The modeling of observed response data to identify PODs in a standard way will help to harmonize cancer and noncancer dose-response approaches and permit comparisons of cancer and noncancer risk estimates.

3.1.3 Methodology for Deriving AWQC 4 by the 1999 Draft Revised Cancer Guidelines

Following the publication of the Draft Water Quality Criteria Methodology: Human Health (USEPA, 1998a) and the accompanying TSD (USEPA, 1998b), EPA received comments from the public. EPA also held an external peer review of the draft Methodology. Both the peer reviewers and the public recommended that EPA incorporate the new approaches into the AWQC Methodology.

Until new guidelines are published, the 1986 cancer guidelines will be used along with principles of the 1999 draft revised cancer guidelines. The 1986 guidelines are the basis for IRIS risk numbers which were used to derive the current AWQC. Each new assessment applying the principles of the 1999 draft revised cancer guidelines will be subject to peer review before being used as the basis of AWQC.5 The weight-of-evidence narrative is intended for the risk ma nager, and thus explains in nontechnical language the key data and conclusions, as well as the cond itions for hazard expression. Conclusions about potential human carcinogenicity are presented by route of exposure. Containe d within this narrative are simple likeli hood descriptors that essentially distinguish whether there is enough evidence to make a projection about human hazard (i.e., Carcinogenic to humans; Likely to be carcinogenic to humans; Suggestive eviden ce of carcinogenicity but not sufficient to assess human carcinogenic potential; Data are inadequate for an assessment of human carcinogenic potential; and Not likely to be carcinogenic to humans). Because one encounters a variety of data sets on agents, these descriptors ar e not meant to stand alone; rather, the context of the weight- ofevidence narrative is intended to provide a transparent explan ation of the biological evidence and how the conclusions were derived. Moreover, these descri ptors should not be viewed as classification cat egories (like the alphameric system), which oft en obscure key scientific differences among ch emicals. The new weight-of-evidence narra tive also presents conclusions about how the agent induces tumors and the relevan ce of the mode of action to humans, and recommends a dose-response approach based on the MOA understanding (USEPA, 1996a, 1999a).

3-5 The remainder of Section 3 illustrates the methodology for deriving numerical AWQC for carcinogens applying the 1999 draft revised cancer guidelines (USEPA, 1999a). This discussion of the revised methodology for carcinogens focuses primarily on the quantitative aspects of deriving numerical AWQC values. It is important to note that the cancer risk assessment process outlined in the 1999 draft revised cancer guidelines is not limited to the quantitative aspects. A numerical AWQC value derived for a carcinogen is to be based on appropriate hazard characterization and accompan ied by risk characterization information.

This section contains a discussion of the weight-of-evidence narrative, that describes all information relevant to a cancer risk evaluation, followed by a discussion of the quantitative aspects of deriving numerical AWQC values for carcinogens. It is assumed that data from an appropriately conducted animal bioassay or hum an epidemiological study provide the underlyingbasis for deriving the AWQC value. The discussion focuses on the following: (1) the weight-ofevidence narrative; (2) general considerations and framework for analysis of the MOA; (3) dose estimation; (4) characterizing dose-response relationships in the range of observation and at low, environmentally relevant doses; (5) calculating the AWQC value; (6) risk characterization; and (7) use of Toxicity Equivalent Factors (TEF) and Relative Potency Estimates. The first three topics encompass the quantitative aspects of deriving AWQC for carcinogens.

3.1.3.1 Weight-of-Evidence Narrative 5

The 1999 draft revised cancer guidelines include a weight-of-evidence narrative that is based on an overall judgment of biological and chemical/physical considerations. Hazardassessment information accompanying an AWQC value for a carcinogen in the form of a weightof-evidence narrative is described in the footnote. Of particular importance is that the weight-ofevidence narrative explicitly provides adequate support based on human studies, animal bioassays, and other key evidence for the conclusion whether the substance is or is likely to be carcinogenic to humans from exposures through dr inking water and/or fish ingestion. The Agency emphasizes the importance of providing an explicit discussion of the MOA for the substance in the weight-of-evidence narrative if data are available, including a discussion that relates the MOA to the quantitative procedures used in the derivation of the AWQC.

3.1.3.2 Mode of Action - General Considerations and Framework for Analysis6 A “key event” is an empirically observable, precursor step that is itself a necessary element of the mode of action, or is a marker for such an element.

3-6 An MOA is composed of key events and processes starting with the interaction of an agent with a cell, through operational and anatomical changes, resulting in cancer formation. “Mode” of action is contrasted with “mechanism” of action, which implies a more detailed, molecular description of events than is meant by MOA.

Mode of action analysis is based on physical, chemical, and biological information that helps to explain key events 6 in an agent’s influence on development of tumors. Inputs to MOA analysis include tumor data in humans, animals, and among structural analogues as well as the other key data.

There are many examples of possible modes of carcinogenic action, such as mutagenicity, mitogenesis, inhibition of cell death, cytotoxicity with reparative cell proliferation, and immune suppression. All pertinent studies are reviewed in analyzing an MOA, and an overall weighing of evidence is performed, laying out the strengths, weaknesses, and uncertainties of the case as well as potential alternative positions and rationales. Identifying data gaps and research needs is also part of the assessment.

Mode of action conclusions are used to address the question of human relevance of animal tumor responses, to address differences in anticipated response among humans such as between children and adults or men and women, and as the basis of decisions about the anticipated shape of the dose-response relationship.

In reaching conclusions, the question of “general acceptance” of an MOA will be tested as part of the independent peer review thatEPA obtains for its assessment and conclusions.

Framework for Evaluating a Postulated Carcinogenic Mode(s) of Action

The framework is intended to be an analytic tool for judging whether available data support a mode of carcinogenic action postulated for an agent and includes nine elements:

1. Summary description of postulated MOA 2. Identification of key events 3. Strength, consistency, specificity of association 4. Dose-response relationship 5. Temporal relationship 6. Biological plausibility and coherence 7. Other modes of action 8. Conclusion 9. Human relevance, including subpopulations

3.1.3.3 Dose Estimation3-7 A. Determining the Human Equivalent Dose by the Oral Route

An important objective in the dose-response assessment is to use a measure of internal or delivered dose at the target site where possible. This is particularly important in those cases where the carcinogenic response information is being extrapolated to humans from animal studies. Generally, by the oral exposure route, the measure of a dose provided in the underlying human studies or animal bioassays is the applied dose, typically given in terms of unit mass per unit body weight per unit time, (e.g., mg/kg-day). When animal bioassay data are used, it is necessary to make adjustments to the applied dose values to account for differences in toxicokinetics between animals and humans that affect the relationship between applied dose and delivered dose at the target organ.

In the estimation of a human equivalent dose, the 1999 draft revised cancer guidelines recommend that when adequate data are available, the doses used in animal studies can be adjusted to equivalent human doses using toxicokinetic information on the particular agent. However, in most cases, there are insufficient data available to compare dose between species. In these cases, the estimate of a human equivalent dose is based on science policy default assumptions. To derive an equivalent human oral dose from animal data, the default procedure in the 1999 draft revised cancer guidelines is to scale daily applied oral doses experienced for a lifetime in proportion to body weight raised to the 3/4 power (BW 3/4 ). The adjustment factor is used because metabolic rates, as well as most rates of physiological processes that determine the disposition of dose, scale this way. Thus, the rationale for this factor rests on the empirical observation that rates of physiological processes consistently tend to maintain proportionality with body weight raised to 3/4 power (USEPA, 1992a, 1999a).

The use of BW 3/4 is a departure from the scaling factor of BW 2/3 that was based on surface area adjustment and was included in the 1980 AWQC National Guidelines as well as the 1986 cancer guidelines.

B. Dose-Response Analysis

If data on the agent are sufficient to support the parameters of a biologically based model and the purpose of the assessment is such as to justify investing resources supporting its use, this is the preferred approach for both the observed tumor and related response data and for extrapolation below the range of observed data in either animal or human studies.

3.1.3.4 Characterizing Dose-Response Relationships in the Range of Observation and at Low Environmentally Relevant Doses

The first quantitative component in the derivation of AWQC for carcinogens is the doseresponse assessment in the range of observation. For most agents, in the absence of adequate data to generate a biologically based model, dose-response relationships in the observed range can be addressed through curve-fitting procedures for response data. It should be noted that the 1999 draft revised cancer guidelines call for modeling of not only tumor data in the observable range, but also other responses thought to be important events preceding tumor development (e.g., DNA adducts, cellular proliferation, receptor binding, hormonal changes). The modeling of3-8 these data is intended to better inform the dose-response assessment by providing insights into the relationships of exposure (or dose) below the observable range for tumor response. These non-tumor response data can only play a role in the dose-response assessment if the agent’s carcinogenic mode of action is reasonably understood, as well as the role of that precursor event.

The 1999 draft revised cancer guidelines recommend calculating the lower 95 percent confidence limit on a dose associated with an estimated 10 percent increased tumor or relevant non-tumor response (LED 10 ) for quantitative modeling of dose-response relationships in the observed range. The estimate of the LED 10 is used as the POD for low-dose extrapolations discussed below. This standard point of departure (LED 10 ) is adopted as a matter of science policy to remain as consistent and comparable from case to case as possible. It is also a convenient comparison point for noncancer endpoints. The rationale supporting use of the LED 10 is that a 10 percent response is at or just below the limit of sensitivity for discerning a statistically significant tumor response in most long-term rodent studies and is within the observed range for other toxicity studies. Use of lower limit takes experimental variability and sample size into account. The ED 10 (central estimate) is also presented as a reference for comparison uses, especially for use in relative hazard/potency ranking among agents for priority setting.

For some data sets, a choice of the POD other than the LED 10 may be appropriate. The objective is to determine the lowest reliable part of the dose-response curve for the beginning of the second step of the dose-response assessment—determine the extrapolation range. Therefore, if the observed response is below the LED 10 , then a lower point may be a better choice (e.g., LED 5 ). Human studies more often support a lower POD than animal studies because of greater sample size.

The POD may be a NOAEL when a margin of exposure analysis is the nonlinear doseresponse approach. The kinds of data available and the circumstances of the assessment both contribute to deciding to use a NOAEL or LOAEL wh ich is not as rigorous or as ideal as curve fitting, but can be appropriate. If several data sets for key events and tumor response are available for an agent, and they are a mixture of continuous and incidence data, the most practicable way to assess them together is often through a NOAEL/LOAEL approach.

When an LED value estimated from animal data is used as the POD, it is adjusted to the human equivalent dose using an interspecies dose adjustment or a toxicokinetic analysis as described in Section 3.1.3.3.

Analysis of human studies in the observed range is designed on a case-by-case basis depending on the type of study and how dose and response are measured in the study.

A. Extrapolation to Low, Environmentally Relevant Doses

In most cases, the derivation of an AWQC will require an evaluation of carcinogenic risk at environmental exposure levels substantially lower than those used in the underlying study. Various approaches are used to extrapolate risk outside the range of observed experimental data. In the 1999 draft revised cancer guidelines, the choice of extrapolation method is largely7 For discussion of the cancer risk range, see Section 2.4.

3-9 dependent on the mode of action. It should be noted that the term “mode of action” (MOA) is deliberately chosen in the 1999 draft revised cancer guidelines in lieu of the term “mechanism” to indicate using knowledge that is sufficient to draw a reasonable working conclusion without having to know the processes in detail as the term mechanism might imply. The 1999 draft revised cancer guidelines favor the choice of a biologically based model, if the parameters of such models can be calculated from data sources independent of tumor data. It is anticipated that the necessary data for such parameters will not be available for most chemicals. Thus, the 1999 draft revised cancer guidelines allow for several default extrapolation approaches (low-dose linear, nonlinear, or both).

B. Biologically Based Modeling Approaches

If a biologically based approach has been used to characterize the dose-response relationships in the observed range, and the confidence in the model is high, it may be used to extrapolate the dose-response relationship to environmentally relevant doses. For the purposes of deriving AWQC, the environmentally relevantdose would be the risk-specific dose (RSD) associated with incremental lifetime cancer risks in the 10 -6 to 10 -4 range for carcinogens for which a linear extrapolation approach is applied. 7 The use of the RSD and the POD/UF to compute the AWQC is presented in Section 3.1.3.5, below. Although biologically-based approaches are appropriate both for characterizing observed dose-response relationships and extrapolating to environmentally relevant doses, it is not expected that adequate data will be available to support the use of such approaches for most substances. In the absence of such data, the default linear approach, the nonlinear (MOE) approach, or both linear and nonlinear approaches will be used.3-10 C. Default Linear Extrapolation Approach

The default linear approach replaces the LMS approach that has served as the default for EPA cancer risk assessments. Any of the following conclusions leads to selection of a linear dose-response assessment approach:



### C There is an absence of sufficient tumor MOA information.

• The chemical has direct DNA mutagenic reactivity or other indications of DNA effects that are consistent with linearity.

• Human exposure or body burden is high and near doses associated with key events in the carcinogenic process (e.g., 2,3,7,8-tetrachlorodibenzo-p-dioxin).

• Mode of action analysis does not support direct DNA effects, but the doseresponse relationship is expected to be linear (e.g., certain receptor-mediated effects).

The procedures for implementing the default linear approach begin with the estimation of a POD as described above. The point of departure, LED 10 , reflects the interspecies conversion to the human equivalent dose and the other adjustments for less-than-lifetime experimental duration. In most cases, the extrapolation for estimating response rates at low, environmentally relevant exposures is accomplished by drawing a straight line between the POD and the origin (i.e., zero dose, zero extra risk). This is mathematically represented as:

y = mx + b (Equation 3-1) b = 0

where:

y = Response or incidence m = Slope of the line (cancer potency factor) = 

### a y/ 

### a x x = Dose b = Slope intercept

The slope of the line, “m” (the estimated cancer potency factor at low doses), is computed as:

(Equation 3-2)

The RSD is then calculated for a specific incremental targeted lifetime cancer risk (in the range of 10 -6 to 10 -4 ) as:8 In 1980, the target lifetime cancer risk range was set at 10- 7 to 10-5. However, both the expert panel for the AWQC workshop (USEPA, 1993) and the peer review workshop expert s (USEPA,1999c) recommended that EPA change the risk range to 10-6 to 10-4, to be consistent with SDWA program decisions. See Sec tion 2.4 for more details.

3-11 (Equation 3-3)

where:

RSD = Risk-specific dose (mg/kg-day) Target Incremental Cancer Risk 8 = Value in the range of 10 -6 to10 -4 m = Cancer potency factor (mg/kg-day) -1

The use of the RSD to compute the AWQC is described in Section 3.1.3.5 below.

D. Default Nonlinear Approach

As discussed in the 1999 draft revised cancer guidelines, any of the following conclusions leads to a selection of a nonlinear (MOE) approach to dose-response assessment:

• A tumor MOA supporting nonlinearity applies (e.g., some cytotoxic and hormonal agents such as disruptors of hormonal homeostasis), and the chemical does not demonstrate mutagenic effects consistent with linearity.

• An MOA supporting nonlinearity has been demonstrated, and the chemical has some indication of mutagenic activity, but it is judged not to play a significant role in tumor causation.

Thus, a default assumption of nonlinearity is appropriate when there is no evidence for linearity and sufficient evidence to support an assumption of nonlinearity. The MOA may lead to a dose-response relationship that is nonlinear, with response falling much more quickly than linearly with dose, or being most influenced by individual differences in sensitivity. Alternatively, the MOA may theoretically have a threshold (e.g., the carcinogenicity may be a secondary effect of toxicity or of an inducedphysiological change that is itself a threshold phenomenon).

The nonlinear approach may be used, for instance, in the case of a bladder tumor inducer, where the chemical is not mutagenic and causes only stone formation in male rat bladders at high doses. This dynamic leads to tumor formation only at the high doses. Stone and subsequent tumor formation are not expected to occur at doses lower than those that induce the physiological changes that lead to stone formation. (More detail on this chemical is provided in the cancer section of the Risk Assessment TSD; USEPA, 2000). EPA does not generally try to distinguish between modes of action that might imply a “true threshold” from others with a3-12 nonlinear dose-response relationship, because there is usually not sufficient information to distinguish between those possibilities empirically.

The nonlinear MOE approach in the 1986 proposed cancer guidelines compares an observed response rate such as the LED 10 , NOAEL, or LOAEL with actual or nominal environmental exposures of interest by computing the ratio between the two. In the context of deriving AWQC, the environmentally relevant exposures are nominal targets rather than actual
exposures.
If the evidence for an agent indicates nonlinearity (e.g., when carcinogenicity is secondary to another toxicity for which there is a threshold), the MOE analysis for the toxicity is similar to what is done for a noncancer endpoint, a nd an RfD or RfC for that toxicity may also be estimated and considered in the cancer assessment. However, a threshold of carcinogenic response is not necessarily assumed. It should be noted that for cancer assessment, the MOE analysis begins from a POD that is adjusted fo r toxicokinetic differences between species to give a human equivalent dose.

To support the use of the MOE approach, risk assessment information provides evaluation of the current understanding of the phenomena that may be occurring as dose (exposure) decreases substantially below the observed data. This gives information about the risk reduction that is expected to accompany a lowering of exposure. The various factors that influence the selection of the UF in an MOE approach are also discussed below.

There are two main steps in the MOE approach. The first step is the selection of a POD. The POD may be the LED 10 for tumor incidence or a precursor, or in some cases, it may also be appropriate to use a NOAEL or LOAEL value. When animal data are used, the POD is a human equivalent dose or concentration arrived at by interspecies dose adjustment (as discussed in Section 3.1.3.3) or toxicokinetic analysis.

The second step in using MOE analysis to establish AWQC is the selection of an appropriate margin or UF to apply to the POD. This is supported by analyses in the MOE discussion in the risk assessment. The following issues should be considered when establishing the overall UF for the derivation of AWQC using the MOE approach (others may be found appropriate in specific cases):

• The nature of the response used for the dose-response assessment, for instance, whether it is a precursor effect or a tumor response. The latter may support a greater MOE.

• The slope of the observed dose-response relationship at the POD and its uncertainties and implications for risk reduction associated with exposure reduction. (A steeper slope implies a greater reduction in risk as exposure decreases. This may support a smaller MOE).

• Human sensitivity compared with that of experimental animals.

• Nature and extent of human variability and sensitivity.3-13 (Equation 3-4) • Human exposure. The MOE evaluation also takes into account the magnitude, frequency, and duration of exposure. If the population exposed in a particular scenario is wholly or largely composed of a subpopulation of special concern (e.g., children) for whom evidence indicates a special sensitivity to the agent’s MOA, an adequate MOE would be larger than for general population exposure.

E. Both Linear and Nonlinear Approaches

Any of the following conclusions leads to selection of both a linear and nonlinear approach to dose-response assessment. Relative support for each dose-response method and advice on the use of that information needs to be documented for the AWQC. In some cases, evidence for one MOA is stronger than for the other, allowing emphasis to be placed on that dose-response approach. In other cases, both modes of action are equally possible, and both dose-response approaches should be emphasized.



### C Modes of action for a single tumor type support both linear and nonlinear dose response in different parts of the dose-response curve (e.g., 4,4' methylene chloride).



### C A tumor mode of action supports different approaches at high and low doses; e.g., at high dose, nonlinearity, but, at low dose, linearity (e.g., formaldehyde).



### C The agent is not DNA-reactive and all plausible modes of action are consistent with nonlinearity, but not fully established.



### C Modes of action for different tumor types support differing approaches, e.g., nonlinear for one tumor type and linear for another due to lack of MOA information (e.g., trichloroethylene).

3.1.3.5 AWQC Calculation

A. Linear Approach

The following equation is used for the calculation of the AWQC for carcinogens where an RSD is obtained from the linear approach:

AWQC = Ambient water quality criterion (mg/L) RSD = Risk-specific dose (mg/kg-day) BW = Human body weight (kg) DI = Drinking water intake (L/day)9 Although appearing in this equation as a factor to bemultiplied, the RSC can also be an amount subtracted.

3-14 (Equation 3-5) FI i = Fish intake at trophic level I (I = 2, 3, and 4) (kg/day) BAF i = Bioaccumulation factor for trophic level I (I = 2, 3, and 4), lipid normalized (L/kg)

B. Nonlinear Approach

In those cases where the nonlinear, MOE approach is used, a similar equation is used to calculate the AWQC 9

where variables are defined as for Equation 3-4 and:

POD = Point of departure (mg/kg-day) UF = Uncertainty factor (unitless) RSC = Relative source contribution (percentage or subtraction)

Differences between the AWQC values obtained using the linear and nonlinear approaches should be noted. First, the AWQC value obtained using the default linear approach corresponds to a specific estimated incremental lifetime cancer risk level in the range of 10 -4 to 10 -6 . In contrast, the AWQC obtained using the nonlinear approach does not describe a specific cancer risk. The AWQC calculations shown above are appropriate for waterbodies that are used as sources of drinking water.

The actual AWQC chosen for the protection of human health is based on a review of all relevant information, including cancer and noncancer data. The AWQC may, or may not, utilize the value obtained from the cancer analysis in the final AWQC value. The endpoint selected for the AWQC will be based on consideration of the weight of evidence and a complete analysis of all toxicity endpoints.

3.1.3.6 Risk Characterization

Risk assessment is an integrative process that is documented in a risk characterization summary. Risk characterization is the final step of the risk assessment process in which all preceding analyses (i.e., hazard, dose-response, and exposure assessments) are tied together to convey the overall conclusions about potential human risk. This component of the risk assessment process characterizes the data in nontechnical terms, explaining the extent and weight of evidence, major points of interpretation and rationale, and strengths and weaknesses of3-15 the evidence, and discussing alternative approaches, conclusions, uncertainties, and variability that deserve serious consideration.

Risk characterization information accompanies the numerical AWQC value and addresses the major strengths and weaknesses of the assessment arising from the availability of data and the current limits of understanding the process of cancer causation. Key issues relatingto the confidence in the hazard assessment and the dose-response analysis (including the lowdose extrapolation procedure used) are discussed. Whenever more than one interpretation of the weight of evidence for carcinogenicity or the dose-response characterization can be supported, and when choosing among them is difficult, the alternative views are provided along with the rationale for the interpretation chosen in the derivation of the AWQC value. Where possible, quantitative uncertainty analyses of the data are provided; at a minimum, a qualitative discussion of the important uncertainties is presented.

3.1.3.7 Use of Toxicity Equivalence Factors and Relative Potency Estimates

The 1999 draft revised cancer guidelines state:

A toxicity equivalence factor (TEF) procedure is one used to derive quantitative dose-response estimates for agents that are members of a category or class of agents. TEFs are based on shared characteristics that can be used to order the class members by carcinogenic potency when cancer bioassay data are inadequate for this purpose. The ordering is by reference to the characteristics and potency of a well-studied member or members of the class. Other class members are indexed to the reference agent(s) by one or more shared characteristics to generate their TEFs.

In addition, the 1999 draft revised cancer guidelines state that TEFs are generated and used for the limited purpose of assessment of agents or mixtures of agents in environmental media when better data are not available. When better data become available for an agent, the TEF should be replaced or revised. To date, adequate data to support use of TEFs have been found only for dibenzofurans (dioxins) and coplanar polyc hlorinated biphenyls (PCBs) (USEPA, 1989, 1999b).

The uncertainties associated with TEFs must be described when this approach is used. This is a default approach to be used when tumor data are not available for individual components in a mixture. Relative potency factors (RPFs) can be similarly derived and used for agents with carcinogenicity or other supporting data. The RPF is conceptually similar to TEFs, but does not have the same level of data to support it and thus has a less rigorous definition compared with the TEF. TEFs and RPFs are used only when there is no better alternative. When they are used, assumptions and uncertainties associated with them are discussed. As of today, there are only three classes of compounds for which relative potency approaches have been examined by EPA: dibenzofurans (dioxi ns), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). There are limitations to the use of TEF and RFP approaches, and caution should be exercised when using them. More guidance can be found in the draft document for conducting health risk assessment of chemical mixtures, published by the EPA Risk Assessment Forum (USEPA,1999b).3-16 3.1.4 References for Cancer Section

Barnes, D.G., G.P Daston, J.S. Evans, A.M. Jarabek, R.J. Kavlock, C.A. Kimmel, C. Park, and H.L. Spitzer. 1995. Benchmark dose workshop: Criteria for use of a benchmark dose to estimate a reference dose. Regul. Toxicol. Pharmacol . 21:296-306.

USEPA (U.S. Environmental Protection Agency). 1980. Water quality criteria documents. Federal Register 45: 79318-79379.

USEPA (U.S. Environmental Protection Agency). 1986. Guidelines for carcinogen risk
assessment. Federal Register 51:33992-34003.

USEPA (U.S. Environmental Protection Agency). 1989. Interim Procedures for Estimating Risks Associated with Exposures to Mixtur es of Chlorinated Dibenzo-p-dioxins and - Dibenzofurans (CDDs and CDFs) and 1989 Update . Risk Assessment Forum. Washington, DC. EPA/625/3-89/016.

USEPA (U.S. Environmental Protection Agency). 1992a. Draft report: a cross-species scaling factor for carcinogen risk assessment based on equivalence of mg/kg 3/4/day. Federal Register 57: 24152-24173.

USEPA (U.S. Environmental Protection Agency). 1993. Revision of Methodology for Deriving National Ambient Water Quality Criteria for the Protection of Human Health: Report of Workshop and EPA’s Preliminary Recommendations for Revision. Submitted to EPA Science Advisory Board Drinking Water Committee, January 8, 1993. Office of Science and Technology, Office of Water. Water Docket W-97-20.

USEPA (U.S. Environmental Protection Agency). 1996. Proposed Guidelines for Carcinogen Risk Assessment. Office of Research and Development. Washington, DC. EPA/600/P92/003C. ( Federal Register 61:17960)

USEPA (U.S. Environmental Protection Agency). 1998a. Draft Water Quality Criteria Methodology: Human Health. Federal Register Notice . Office of Water. Washington, DC. EPA-822-Z-98-001.

USEPA (U.S. Environmental Protection Agency). 1998b. Ambient Water Quality Criteria Derivation Methodology - Human Health. Technical Support Document. Final Draft

. Office of Water. Washington, DC. EPA-822-B-98-005.

USEPA (U.S. Environmental Protection Agency). 1999a. Guidelines for Carcinogen Risk Assessment. Review Draft . Risk Assessment Forum. Washington, DC. EPA/NCEA-F0644. July.

USEPA (U.S. Environmental Protection Agency). 1999b. Guidance for Conducting Health Risk Assessment of Chemical Mixtures. External Peer Review Draft . Risk Assessment Forum. Washington, DC. EPA/NCEA-C-0148. April.3-17 USEPA (U.S. Environmental Protection Agency). 1999c. Revisions to the Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health. Peer Review Workshop Summary Report . Office of Water. Washington, DC. EPA-822-R-99- 015. September.

USEPA (U.S. Environmental Protection Agency). 2000. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

. Technical Support Document Volume 1: Risk Assessment. Office of Science and Technology, Office of Water. Washington, DC. EPA-822-B-00-005. August.

3.2 NONCANCER EFFECTS

3.2.1 1980 AWQC National Guidelines for Noncancer Effects

In the 1980 AWQC National Guidelines, the Agency evaluated noncancer human health effects from exposure to chemical contaminants using Acceptable Daily Intake (ADI) levels. ADIs were calculated by dividing NOAELs by safety factors (SFs) to obtain estimates of doses of chemicals that would not be expected to cause adverse effects over a lifetime of exposure. In accordance with the National Research Council report of 1977 (NRC, 1977), EPA used SFs of 10, 100, or 1,000, depending on the quality and quantity of the overall database. In general, a factor of 10 was suggested when good-quality data identifying a NOAEL from human studies were available. A factor of 100 was suggested if no human data were available, but the database contained valid chronic animal data. For chemicals with no human data and scant animal data, a factor of 1,000 was recommended. Intermediate SFs could also be used for databases that fell between these categories.

AWQC were calculated using the ADI levels together with standard exposure assumptions about the rates of human ingestion of water and fish, and also accounting for intake from other sources (see Equation 1-1 in the Introduction). Surface water concentrations at or below the calculated criteria concentrations would be expected to result in human exposure levels at or below the ADI. Inherent in these calculations is the assumption that, generally, adverse effects from noncarcinogens exhibit a threshold.

3.2.2 Noncancer Risk Assessment Developments Since 1980

Since 1980, the risk assessment of noncarcinogenic chemicals has changed. To remove the value judgments implied by the words “acceptable” and “safety,” the ADI and SF terms have been replaced with the terms RfD and UF/modifying factor (MF), respectively.

For the risk assessment of general systemic toxicity, the Agency currently uses the guidelines contained in the IRIS background document entitled Reference Dose (RfD): Description and Use in Health Risk Assessments (hereafter the “IRIS background document”. That document defines an RfD as “an estimate (with uncertainty spanning approximately an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without appreciable risk of deleterious effects over a lifetime” (USEPA,3-18 (Equation 3-6) 1993a). The most common approach for deriving the RfD does not involve dose-response modeling. Instead, an RfD for a given chemical is usually derived by first identifying the NOAEL for the most sensitive known toxicity endpoint, th at is, the toxic effect that occurs at the lowest dose. This effect is called the critical effect. Factors such as the study protocol, the species of experimental animal, the nature of the toxicity endpoint assessed and its relevance to human effects, the route of exposure, and exposure duration are critically evaluated in order to select the most appropriate NOAEL from among all available studies in the chemical’s database. If no appropriate NOAEL can be identified from any study, then the LOAEL for the critical effect endpoint is used and an uncertainly fact or for LOAEL-to-NOAEL extrapolation is applied. Using this approach, the RfD is equal to the NOAEL (or LOAEL) divided by the product of UFs and, occasionally, an MF:

The definitions and guidance for use of the UFs and the MFs are provided in the IRIS background document and are repeated in Table 3-1.

The IRIS background document on the RfD (USEPA, 1993a) provides guidance for critically assessing noncarcinogenic effects of ch emicals and for deriving the RfD. Another reference on this topic is Dourson (1994). Furthermore, the Agency has also published separate guidelines for assessing specific toxic endpoints, such as developmental toxicity (USEPA, 1991a), reproductive toxicity (USEPA, 1996a), and neurotoxicity risk assessment (USEPA, 1995). These endpoint-specific guidelines will be used for their respective areas in the hazard assessment step and will complement the overall toxicological assessment. It should be noted, however, that an RfD, derived using the most sensitive known endpoint, is considered protective against all noncarcinogenic effects.3-19 T ABLE 3-1. U NCERTAINTY F ACTORS AND THE M ODIFYING F ACTOR

Uncertainty Factor Definition

UF H Use a 1, 3, or 10-fold factor when extrapolating from valid data in studies using long-term exposure to average hea lthy humans. This factor is intended to account for the variation in sensitivity (intraspecies variation) among the members of the human population.

UF A Use an additional factor of 1, 3, or 10 when extrapolating from valid results of long-term studies on experimental animals when results of studies of human exposure are not available or are inadequa te. This factor is intended to account for the uncertainty involved in extrapolating from animal data to humans (interspecies variation).

UF S Use an additional factor of 1, 3, or 10 when extrapolating from less-thanchronic results on experimental animals when there are no useful long-term human data. This factor is intended to account for the uncertainty involved in extrapolating from less-than-chronic NOAELs to chronic NOAELs.

UF L Use an additional factor of 1, 3, or 10 when deriving an RfD from a LOAEL, instead of a NOAEL. This factor is intended to account for the uncertainty involved in extrapolating from LOAELs to NOAELs.

UF D Use an additional 3- or 10-fold factor when deriving an RfD from an "incomplete" database. This factor is meant to account for the inability of any single type of study to consider all toxic endpoints. The intermediate factor of 3 (approximately 1⁄2 log 10 unit, i.e., the square root of 10) is often used when there is a single data gap exclusive of chronic data. It is often designated as UF D . Modifying Factor

Use professional judgment to determine the MF, whic h is an additional uncertainty factor that is greater than zero and less than or equal to 10. The magnitude of the MF depends upon the professional assessment of scientific uncertainties of the study and database not explicitly treated above (e.g., the number of species tested). The default value for the MF is 1.

Note: With each UF or MF assignment, it is recognized that professional scientific judgment must be used. The total product of the uncertainty f actors and modifying factor should not exceed 3,000.3-20 Similar to the procedure used in the 1980 AWQC National Guidelines, the revised method of deriving AWQC for noncarcinogens uses the RfD together with various assumptions concerning intake of the contaminant from both water and non-water sources of exposure. The objective of an AWQC for noncarcinogens is to ensure that human exposure to a substance related to its presence in surface water, combined with exposure from other sources, does not exceed the RfD. The algorithm for deriving AWQC for noncarcinogens using the RfD is presented as Equation 1-1 in the Introduction.

3.2.3 Issues and Recommendations Concerning the Derivation of AWQC for Noncarcinogens

During a review of the 1980 AWQC National Guidelines (USEPA, 1993b), the Agency identified several issues that must be resolved in order to develop a final revised methodology for deriving AWQC based on noncancer effects. These issues, as discussed below, mainly concern the derivation of the RfD as the basis for such an AWQC. Foremost among these issues is whether the Agency should revise the present method or adopt entirely new procedures that use quantitative dose-response modeling for the derivation of the RfD. Other issues include the following:

• Presenting the RfD as a single point value or as a range to reflect the inherent imprecision of the RfD;

• Selecting specific guidance documents for derivation of noncancer health effect levels;

• Considering severity of effect in the development of the RfD;

• Using less-than-90-day studies as the basis for RfDs;

• Integrating reproductive/developmental, immunotoxicity, and neurotoxicity data into the RfD calculation;

• Applying toxicokinetic data in risk assessments; and

• Considering the possibility that some noncarcinogenic effects do not exhibit a threshold.

3.2.3.1 Using the Current NOAEL/UF-Based RfD Approach or Adopting More Quantitative Approaches for Noncancer Risk Assessment

The current NOAEL/UF-based RfD methodology, or its predecessor ADI/SF methodology, have been used since 1980. This approach assumes that there is a threshold exposure below which adverse noncancer health effects are not expected to occur. Exposures above this threshold are believed to pose some risk to exposed individuals; however, the current approach does not address the nature and magnitude of the risk above the threshold level (i.e., the shape of the dose-response curve above the threshold). The NOAEL/UF-based RfD approach is intended primarily to ensure that the RfD value derived from the available data falls below the population effects threshold. Howe ver, the NOAEL/UF-based RfD procedure has3-21 limitations. In particular, this method requires that one of the actual experimental doses used by the researchers in the critical study be selected as the NOAEL or LOAEL value. The determination that a dose is a NOAEL or LOAEL will depend on the biological endpoints used and the statistical significance of the data. Statistical significance will depend on the number and spacing of dose groups and the numbers of animals used in each dose group. Studies using a small number of animals can limit the ability to distinguish statistically significant differences among measurable responses seen in dose groups and control groups. Furthermore, the determination of the NOAEL or LOAEL also depends on the dose spacing of the study. Doses are often widely spaced, typically differing by f actors of three to ten. A study can identify a NOAEL and a LOAEL from among the doses studied, but the “true” effects threshold cannot be determined from those results. The study size and dose spacing limitations also limit the ability to characterize the nature of the expected response to exposures between the observed NOAEL and LOAEL values.

The limitations of the NOAEL/UF approach have prompted development of alternative approaches that incorporate more quantitative dose-response information. The traditional NOAEL approach for noncancer risk assessment has often been a source of controversy and has been criticized in several ways. For example, experiments involving fewer animals tend to produce higher NOAELs and, as a consequence, may produce higher RfDs. Larger sample sizes, on the other hand, should provide greater expe rimental sensitivity and lower NOAELs. The focus of the NOAEL approach is only on the dose that is the NOAEL, and the NOAEL must be one of the experimental doses. It also ignores the shape of the dose-response curve. Thus, the slope of the dose-response plays little role in determining acceptable exposures for human beings. Therefore, in addition to the NOAEL/UF-based RfD approach described above, EPA will accept other approaches that incorporate more quantitative dose-response information in appropriate situations for the evaluation of noncancer effects and the derivation of RfDs. However, the Agency wishes to emphasize that it still believes the NOAEL/UF RfD methodology is valid and can continue to be used to develop RfDs.

Two alternative approaches that may have relevance in assisting in the derivation of the RfD for a chemical are the BMD and the categorical regression approaches. These alternative approaches may overcome some of the inherent limitations in the NOAEL/UF approach. For example, the BMD analyses for developmental effects show that NOAELs from studies correlate well with a 5 percent response level (Allen et al., 1994). The BMD and the categorical regression approaches usually have greater data requirements than the RfD approach. Thus, it is unlikely that any one approach will apply to every circumstance; in some cases, different approaches may be needed to accommodate the varying databases for the range of chemicals for which water quality criteria must be developed. Acceptable approaches will satisfy the following criteria: (1) meet the appropriate risk assessment goal; (2) adequately describe the toxicity database and its quality; (3) characterize the endpoints properly; (4) provide a measure of the quality of the “fit” of the model when a model is used for dose-response analysis; and (5) describe the key assumptions and uncertainties.3-22 A. The Benchmark Dose3-23 The BMD is defined as the dose estimated to produce a predetermined level of change in response (the Benchmark Response level, or BMR) relative to control. The BMDL is defined as the statistical lower confidence limit on the BMD. In the derivation of an RfD, the BMDL is used as the dose to which uncertainty factors are applied instead of the NOAEL. The BMD approach first models a dose-response curve for the critical effect(s) using available experimental data. Several mathematical algorithms can be used to model the dose-response curve, such as polynomial or Weibull functions. To define a BMD from the modeled curve for quantal data, the assessor first selects the BMR. The choice of the BMR is critical. For quantal endpoints, a particular level of response is chosen (e.g., 1 percent, 5 percent, or 10 percent). For continuous endpoints, the BMR is the degree of change from controls and is based on what is considered a biologically significant change. The BMD is derived from the BMR dose by applying the desired confidence limit calculation. The RfD is obtained by dividing the BMD by one or more uncertainty factors, similar to the NOAEL approach. Because the BMD is used like the NOAEL to obtain the RfD, the BMR should be selected at or near the low end of the range of increased risks that can be detected in a study of typical size. Generally, this falls in the range between the ED 01 and the ED 10 . The Agency will accept use of a BMD approach to derive RfDs for those agents for which there is an adequate database. There are a number of technical decisions associated with the application of the BMD technique. These include the following:

• The definition of an adverse response;

• Selection of response data to model;

• The form of the data used (continuous versus quantal);

• The choice of the measures of increased risk (extra risk versus additional risk);

• The choice of mathematical model (including use of nonstandard models for unusual data sets);

• The selection of the BMR;

• Methods for calculating the confidence interval;

• Selection of the appropriate BMD as the basis for the RfD (when multiple endpoints are modeled from a single study, when multiple models are applied to a single response, and when multiple BMDs are calculated from different studies); and

• The use of uncertainty factors with the BMD approach.

These topics are discussed in detail in Crump et al. (1995) and in the Risk Assessment TSD Volume (USEPA, 2000). The use of the BMD approach has been discussed in general terms by several authors (Gaylor, 1983; Crump, 1984; Dourson et al., 1985; Kimmel and Gaylor, 1988; Brown and Erdreich, 1989; Kimmel, 1990). The International Life Sciences Institute3-24 (ILSI) also held a major workshop on the BMD in September 1993; the workshop proceedings are summarized in ILSI (1993) and in Barnes et al. (1995). For further information on these technical issues, the reader is referred to the publications referenced above.

The BMD approach addresses several of the quantitative or statistical criticisms of the NOAEL approach. These are discussed at great er length in Crump et al. (1995) and are summarized here. First, the BMD approach uses all the dose-response information in the selected study rather than just a single data point, such as the NOAEL or LOAEL. By using response data from all of the dose groups to model a dose-response curve, the BMD approach allows for consideration of the steepness of the slope of the curve when estimating the ED 10 . The use of the full data set also makes the BMD approach less sensitive to small changes in data than the NOAEL approach, which relies on the statistical comparison of individual dose groups. The BMD approach also allows consistency in the consideration of the level of effect (e.g., a 10 percent response rate) across endpoints.

The BMD approach accounts more appropriately for the size of each dose group than the NOAEL approach. Laboratory tests with fewe r animals per dose group tend to yield higher NOAELs, and thus higher RfDs, because statistically significant differences in response rates are harder to detect. Therefore, in the NOAEL approach, dose groups with fewer animals lead to a higher (less conservative) RfD. In contrast, with the BMD approach, smaller dose groups will tend to have the effect of extending the confidence interval around the ED 10 ; therefore, the lower confidence limit on the ED 10 (the BMD) will be lower. With the BMD approach, greater uncertainty (smaller test groups) leads to a lower (more conservative) RfD.

There are some issues to be resolved before the BMD approach is used routinely. These were identified in a 1996 Peer Consultation Workshop (USEPA, 1996b). Methods for routine use of the BMD are currently under development by EPA. Several RfCs and RfDs based on the BMD approach are included in EPA’s IRIS database. These include reference values for methylmercury based on delayed postnatal development in humans; carbon disulfide based on neurotoxicity; 1,1,1,2-tetrafluoroethane based on testicular effects in rats; and antimony trioxide based on chronic pulmonary interstitial inflammation in female rats.

Various mathematical approaches have been proposed for modeling developmental toxicity data (e.g., Crump, 1984; Kimmel and Ga ylor, 1988; Rai and Van Ryzin, 1985; Faustman et al., 1989), which could be used to calculate a BMD. Similar methods can be used to model other types of toxicity data, such as neurot oxicity data (Gaylor and Slikker, 1990, 1992; Glowa and MacPhail, 1995). The choice of the mathematical model may not be critical, as long as estimation is within the observed dose range. Since the model fits a mathematical equation to the observed data, the assumptions in a particular model regarding the existence or absence of a threshold for the effect may not be pertinent (USEPA, 1997). Thus, any model that suitably fits the empirical data is likely to provide a reasonable estimate of a BMD. However, research has shown that flexible models that are nonsymmetric (e.g., the Weibull) are superior to symmetric models (e.g., the probit) in estimating the BMD because the data points at the higher doses have less influence on the shape of the curve than at low doses. In addition, models should incorporate fundamental biological factors where such factors are known (e.g., intralitter correlation for developmental toxicity data) in order to account for as much variability in the3-25 data as possible. The Agency is currently using the BMD approach in risk assessments where the data support its use. Draft guidelines for application of the BMD approach also are being developed by the Agency.

Use of BMD methods involves fitting mathematical models to dose-response data obtained primarily from toxicology studies. When considering available models to use for a BMD analysis, it is important to select the model that fits the data the best and is the most biologically appropriate. EPA has developed soft ware following several years of research and development, expert peer review, public comment, subsequent revision, and quality assurance testing. The software (BMDS, Version 1.2) can be downloaded from http://www.epa.gov/ncea/bmds.htm . BMDS facilitates these operations by providing simple data-management tools, a comprehensive help manual, an online help system, and an easy-to-use interface to run multiple models on the same dose-response data.

As part of this software package, EPA has in cluded sixteen (16) different models that are appropriate for the analysis of dichotomous (quantal) data (Gamma, Logistic, Log-Logistic, Multistage, Probit, Log-Probit, Quantal-Linear, Quantal-Quadratic, Weibull), continuous data (Linear, Polynomial, Power, Hill), and nested de velopmental toxicology data (NLogistic, NCTR, Rai & Van Ryzin). Results from all models include a reiteration of the model formula and model run options chosen by the user, goodness-of-fit information, the BMD, and the estimate of the lower-bound confidence limit on the benchmark dose (BMDL). Model results are presented in textual and graphical output files which can be printed or saved and incorporated into other documents.

B. Categorical Regression

Categorical regression is an emerging technique that may have relevance for the derivation of RfDs or for estimating risk above the RfD (Dourson et al., 1997; Guth et al., 1997). The categorical regression approach, like the BMD approach, can be used to estimate a dose that corresponds to a given probability of adverse effects. This dose would then be divided by UFs to establish an RfD. However, unlike the BMD approach, the Categorical regression approach can incorporate information on different health endpoints in a single dose-response analysis. For those health effects for which studies exist, responses to the substance in question are grouped into severity categories; for example (1) no effect, (2) no adverse effect, (3) mild-to-moderate adverse effect, and (4) frank effect. These categories correspond to the dose categories currently used in setting the RfD, namely, the no-obser ved-effect level (NOEL), NOAEL, LOAEL, and frank-effect level (FEL), respectively. Logistic transformation or other applicable mathematical operations are used to model the probability of experiencing effects in a certain category as a function of dose (Harrell, 1986; Hertzberg, 1989). The “acceptability” of the fit of the model to the data can be judged using several statistical measures, including the 

### P 2 statistic, correlation coefficients, and the statistical significance of its model parameter estimates.

The resulting mathematical equation can be used to find a dose (or the lower confidence bound on the dose) at which the probability of experiencing adverse effects does not exceed a selected level, e.g., 10 percent. This dose ( like the NOAEL or BMD) would then be divided by3-26 relevant UFs to calculate an RfD. For more detail on how to employ the categorical regression approach, see the discussion in the Risk Assessment TSD (USEPA, 2000).

As with the BMD approach, the categorical regression approach has the advantage of using more of the available dose-response data to account for response variability as well as accounting for uncertainty due to sample size through the use of confidence intervals. Additional advantages of categorical regression include the combining of data sets prior to modeling, thus allowing the calculation of the slope of a dose-response curve for multiple adverse effects rather than only one effect at a time. Another advantage is the ability to estimate risks for different levels of severity from exposures above the RfD.

On the other hand, as with BMD, opinions differ over the amount and adequacy of data necessary to implement the method. The categorical regression approach also requires judgments regarding combining data sets, judging goodness-of-fit, and assigning severity to a particular effect. Furthermore, this approach is still in the developmental stage. It is not recommended for routine use, but may be used when data are available and justify the extensive analyses required.

C. Summary

Whether a NOAEL/UF-based methodology, a BMD, a categorical regression model, or other approach is used to develop the RfD, the dose-response-evaluation step of a risk assessment process should include additional discussion about the nature of the toxicity data and its applicability to human exposure and toxicity. The discussion should present the range of doses that are effective in producing toxicity for a given agent; the route, timing, and duration of exposure; species specificity of effects; and any toxicokinetic or other considerations relevant to extrapolation from the toxicity data to human-health-based AWQC. This information should always accompany the characterization of the adequacy of the data.

3.2.3.2 Presenting the RfD as a Single Point or as a Range for Deriving AWQC

Although the RfD has traditionally been presented and used as a single point, its definition contains the phrase “. . . an estimate (with uncertainty spanning perhaps an order of magnitude) . . .” (USEPA, 1993a). Underlying this concept is the reasoning that the selection of the critical effect and the total uncertainty factor used in the derivation of the RfD is based on the “best” scientific judgment, and that competent scientists examining the same database could derive RfDs which varied within an order of magnitude.

In one instance, IRIS presented the RfD as a point value within an accompanying range. EPA derived a single number as the RfD for arsenic (0.3 

### : g/kg-day), but added that “strong scientific arguments can be made for various valu es within a factor of 2 or 3 of the currently recommended RfD value, i.e., 0.1 to 0.8 

### : g/kg/day” (USEPA, 1993c). EPA noted that regulatory managers should be aware of the flexibility afforded them through this action.

There are situations in which the risk manager can select an alternative value to use in place of the RfD in the AWQC calculations. The domain from which this alternative value can3-27 be selected is restricted to a defined range around the point estimate. As explained further below, the Agency is recommending that sometimes the use of a value other than the calculated RfD point estimate is appropriate in characterizing risk. The selection of an alternative value within an appropriate range must be determined for each individual situation, since several factors affect the selection of the alternative va lue. Observing similar effects in several animal species, including humans, can increase confidence in the selection of the critical effect and thereby narrow the range of uncertainty. There are other factors that can affect the precision. These include the slope of the dose-response curve, seriousness of the observed effect, dose spacing, and possibly the route for the experimental doses. Dose spacing and the number of animals in the study groups used in the experiment can also affect the confidence in the RfD.

To derive the AWQC, the calculated point estimate of the RfD is the default. Based on consideration of the available data, the use of another number within the range defined by the product of the UF(s) (and MF, if used) could be justified in some specific situations. This means that there are risk considerations which indicate that some value in the range other than the point estimate may be more appropriate, based on human health or environmental fate considerations. For example, the bioavailability of the contaminant in fish tissues is one factor to consider. If bioavailability from fish tissues is much lower than that from water and the RfD was derived from a study in which the contaminant exposure was from drinking water, the alternative to the calculated RfD could be selected from the high end of the range and justified using the quantitative difference in bioavailability.

Most inorganic contaminants, particularly divalent cations, have bioavailability values of 20 percent or less from a food matrix, but are much more available (about 80 percent or higher) from drinking water. Accordingly, the external dose necessary to produce a toxic internal dose would likely be higher for a study where the exposure occurred through the diet rather than the drinking water. As a result, the RfD from a diet ary study would likely be higher than that for the drinking water study if equivalent external doses had been used. Conversely, in cases where the NOAEL that was the basis for the RfD came from a dietary study, the alternative value could be slightly lower than the calculated RfD.

Because the uncertainty around the dose-response relationship increases as extrapolation below the observed data increases, the use of an alternative point within the range may be more appropriate in characterizing the risk than the use of the calculated RfD, especially in situations when the uncertainty is high. Therefore, as a matter of policy, the 2000 Human Health Methodology permits the selection of a single point within a range about the calculated RfD to be used as the basis of the AWQC if an adequate justification of the alternative point is provided. More complete discussion of this option, including limitations on the span of the range, is provided in the Risk Assessment TSD (USEPA, 2000).

3.2.3.3 Guidelines to be Adopted for Derivation of Noncancer Health Effects Values

The Agency currently is using the IRIS background document as the general basis for the risk assessment of noncarcinogenic effects of chemicals (USEPA, 1993a). EPA recommends continued use of this document for this purpose. However, it should be noted that the process for evaluating chemicals for inclusion in IRIS is undergoing revision (USEPA, 1996c). The3-28 revised assessments for many chemicals are now available on IRIS and can be consulted as examples of the RfD development process and required supporting documentation.

3.2.3.4 Treatment of Uncertainty Factors/Severity of Effects During the RfD Derivation and Verification Process

During the RfD derivation and toxicology revi ew process, EPA considers the uncertainty in extrapolating between animal species and within individuals of a species, as well as specific uncertainties associated with the completeness of the database. The Agency’s RfD Work Group has always considered the severity of the observed effects induced by the chemical under review when choosing the value of the UF with a LOAEL. For example, during the derivation and verification of the RfD for zinc (USEPA, 1992), an uncertainty factor less than the standard factor of 10 (UF of 3) was assigned to the re latively mild decrease in erythrocyte superoxide dismutase activity in human subjects. EPA recommends that the severity of the critical effect be assessed when deriving an RfD and that risk managers be made aware of the severity of the effect and the weight placed on this attribut e of the effect when the RfD was derived.

3.2.3.5 Use of Less-Than-90-Day Studies to Derive RfDs

Generally, less-than-90-day experimental studies are not used to derive an RfD. This is based on the rationale that studies lasting for less than 90 days may be too short to detect various toxic effects. However, EPA, has in certain circumstances, derived an RfD based on a less-than90-day study. For example, the RfD for nonradioactive effects of uranium is based on a 30-day rabbit study (USEPA, 1989). The short-term exposure period was used, because it was adequate for determining doses that cause chronic toxicity. In other cases, it may be appropriate to use a less-than-90-day study because the critical effect is expressed in less than 90 days. For example, the RfD for nitrate was derived and verified us ing studies that were less than 3-months in duration (USEPA, 1991b). For nitrate, the criti cal effect of methemoglobinemia in infants occurs in less than 90 days. When it can be demonstrated from other data in the toxicological database that the critical adverse effect is expressed within the study period and that a longer exposure duration would not exacerbate the observed effect or cause the appearance of some other adverse effect, the Agency may choose to use less-than-90-day studies as the basis of the RfD. Such values would have to be used with care because of the uncertainty in determining if other effects might be expressed if expos ure was of greater duration than 90 days.

3.2.3.6 Use of Reproductive/Developmental, Immuno toxicity, and Neurotoxicity Data as the Basis for Deriving RfDs

All relevant toxicity data have some bear ing on the RfD derivation and verification and are considered by EPA. The “critical” effect is the adverse effect most relevant to humans or, in the absence of an effect known to be relevant to humans, the adverse effect that occurs at the lowest dose in animal studies. If the critical effect is neurotoxicity, EPA will use that endpoint as the basis for the derivation and verification of an RfD, as it did for the RfD for acrylamide. Moreover, the Agency is continually revising its procedures for noncancer risk assessment. For example, EPA has released guidelines for deriving developmental RfDs (RfD DT , USEPA, 1991a), for using reproductive toxicity (USEPA, 1996a), and neurotoxicity (USEPA, 1995) data3-29 in risk assessments. The Agency is currently working on guidelines for using immunotoxicity data to derive RfDs. In addition, the Agency is proceeding with the process of generating acceptable emergency health levels for hazardous substances in acute exposure situations based on established guidelines (NRC, 1993).

3.2.3.7 Applicability of Toxicokinetic Data in Risk Assessment

All pertinent toxicity data should be used in the risk assessment process, including toxicokinetic and mechanistic data. The Agency has used toxicokinetic data in deriving the RfD for cadmium and other compounds and currently is using toxicokinetic data to better characterize human inhalation exposures from animal inhala tion experiments during derivation/verification of RfCs. In analogy to the RfD, the RfC is consideredto be an estimate of a concentration in the air that is not anticipated to cause adverse noncancer effects over a lifetime of inhalation exposure (USEPA, 1994; Jarabek, 1995a). For RfCs, different dosimetry adjustments are made to account for the differences between laboratory animals and humans in gas uptake and disposition or in particle clearance and retention. This procedure results in calculation of a “human equivalent concentration.” Based on the use of these procedures, an interspecies UF of 3 (i.e., approximately 10 0.5 ), instead of the standard factor of 10, is used in the RfC derivation (Jarabek, 1995b).

Toxicokinetics and toxicodynamics of a chemical each contribute to a chemical’s observed toxicity, and specifically, to obser ved differences among species in sensitivity. Toxicokinetics describes the disposition (i.e., deposition, absorption, distribution, metabolism, and elimination of chemicals in the body) and can be approximated using toxicokinetic models. Toxicodynamics describes the toxic interaction of the agent with the target cell. In the absence of specific data on their relative contributions to the toxic effects observed in species, each is considered to account for approximately one-hal f of the difference in observed effects for humans compared with laboratory animals. The implication of this assumption is that an interspecies uncertainty factor of 3 rather than 10 could be used for deriving an RfD when valid toxicokinetic data and models can be applied to obtain an oral “human equivalent applied dose” (Jarabek, 1995b). If specific data exist on the relative contribution of either element to observed effects, that proportion will be used. The role exposure duration may play, and whether or not the chemical or its damage may accumulate over time in a particular scenario, also requires careful consideration (Jarabek, 1995c).

3.2.3.8 Consideration of Linearity (or Lack of a Threshold) for Noncarcinogenic Chemicals

It is quite possible that there are chemicals with noncarcinogenic endpoints that have no threshold for effects. For example, in the cas e of lead, it has not been possible to identify a threshold for effects on neurological developmen t. Other examples could include genotoxic teratogens and germline mutagens. Genotoxic teratogens act by causing mutational events during organogenesis, histogenesis, or other stages of development. Germline mutagens interact with germ cells to produce mutations which may be transmitted to the zygote and expressed during one or more stages of development. However, there are few chemicals which currently have sufficient mechanistic information about these possible modes of action. It should be recognized that although an MOA consistent with linearity is possible (especially for agents3-30 known to be mutagenic), this has yet to be reasonably demonstrated for most toxic endpoints other than cancer.

EPA has recognized the potential for nonthreshold noncarcinogenic endpoints and discussed this issue in the Guidelines for Developmental Toxicity Risk Assessment (USEPA, 1991a) and in the 1986 Guidelines for Mutagenicity Risk Assessment (USEPA, 1986). An awareness of the potential for such teratogenic/mutagenic effects should be established in order to deal with such data. However, without adequate data to support a genetic or mutational basis for developmental or reproductive effects, the default becomes a UF or MOA approach, which are procedures utilized for noncarcinogens assumed to have a threshold. Therefore, genotoxic teratogens and germline mutagens should be considered an exception while the traditional uncertainty factor approach is the general rule for calculating criteria or values for chemicals demonstrating developmental/reproductive effects. For the exceptional cases, since there is no well-established mechanism for calculating criteria protective of human health from the effects of these agents, criteria will be established on a case-by-case basis. Other types of nonthreshold noncarcinogens must also be handled on a case-by-case basis.

3.2.3.9 Minimum Data Guidance

For details on minimum data guidance for RfD development, see the Risk Assessment TSD (USEPA, 2000).3-31 3.2.4 References for Noncancer Effects

Allen, B.C., R.T. Kavlock, C.A. Kimmel, and E.M. Faustman. 1994. Dose-response assessment for developmental toxicity. Fund. Appl. Toxicol . 23:496-509.

Barnes, D.G., G.P Daston, J.S. Evans, A.M. Jarabek, R.J. Kavlock, C.A. Kimmel, C. Park, and H.L. Spitzer. 1995. Benchmark dose workshop: criteria for use of a benchmark dose to estimate a reference dose. Reg. Toxicol. Pharmacol . 21:296-306.

Brown, K.G. and L.S. Erdreich. 1989. Statistical uncertainty in the no-observed-adverse-effect level. Fund. Appl. Toxicol. 13:235-244.

Crump, K.S., B. Allen, and E. Faustman. 1995. The Use of the Benchmark Dose Approach in Health Risk Assessment . Prepared for U.S. Environmental Protection Agency’s Risk Assessment Forum. EPA/630/R-94/007.

Crump, K.S. 1984. A new method for determining acceptable daily intakes. Fund. Appl. Toxicol . 4:854-871.

Dourson, M.L. 1994. Methodology for establishing oral reference doses (RfDs). In: Risk Assessment of Essential Elements. W. Mertz, C.O. Abernathy, and S.S. Olin (eds.) ILSI Press. Washington, DC. Pp. 51-61.

Dourson, M.L., R.C. Hertzberg, R. Hartung and K. Blackburn. 1985. Novel approaches for the estimation of acceptable daily intake. Toxicol. Ind. Health 1:23-41.

Dourson, M.L., L.K. Teuschler, P.R. Durkin, and W.M. Stiteler. 1997. Categorical regression of toxicity data, a case study using aldicarb. Regul. Toxicol. Pharmacol . 25:121-129.

Faustman, E.M., D.G. Wellington, W.P. Smith and C.A. Kimmel. 1989. Characterization of a developmental toxicity dose-response model. Environ. Health Perspect. 79:229-241.

Gaylor, D.W. 1983. The use of safety factors for controlling risk. J. Toxicol. Environ. Health 11:329-336.

Gaylor, D.W. and W. Slikker. 1990. Risk asse ssment for neurotoxic effects. Neurotoxicology 11:211-218.

Gaylor, D.W. and W. Slikker. 1992. Risk assessment for neurotoxicants. In: Neurotoxicology. H. Tilson and C. Mitchel (eds). Raven Press. New York, NY. Pp. 331-343.

Glowa, J.R. and R.C. MacPhail. 1995. Quantitative approaches to risk assessment in neurotoxicology. In: Neurotoxicology: Approaches and Methods . Academic Press. New York, NY. Pp. 777-787.3-32 Guth, D.J., R.J. Carroll, D.G. Simpson, and H. Zhou. 1997. Categorical regression analysis of acute exposure to tetrachloroethylene. Risk Anal. 17(3):321-332.

Harrell, F. 1986. The logist procedure. SUGI Supplemental Library Users Guide , Ver. 5 thed. SAS Institute. Cary, NC.

Hertzberg, R.C. 1989. Fitting a model to categorical response data with application to species extrapolation of toxicity. Health Physics 57: 405-409.

ILSI (International Life Sciences Institute). 1993. Report of the Benchmark Dose Workshop

. ISLI Risk Science Institute. Washington, DC.

Jarabek, A.M. 1995a. The application of dosimetry models to identify key processes and parameters for default dose-response assessment approaches. Toxicol. Lett. 79:171-184.

Jarabek, A.M. 1995b. Interspecies extrapolation based on mechanistic determinants of chemical disposition. Human Eco. Risk Asses . 1(5):41-622.

Jarabek, A.M. 1995c. Consideration of temporal toxicity challenges current default assumptions. Inhalation Toxicol. 7:927-946.

Kimmel, C.A. 1990. Quantitative approaches to human risk assessment for noncancer health effects. Neurotoxicology 11: 189-198.

Kimmel, C.A. and D.W. Gaylor. 1988. Issues in qualitative and quantitative risk analysis for developmental toxicity. Risk Anal . 8: 15-20.

NRC (National Research Council). 1977. Decision Making in the Environmental Protection Agency

. Vol. 2. National Academy of Sciences. Washington, DC. Pp. 32-33 and 241- 242.

NRC (National Research Council). 1993. Guidelines for Developing Emergency Exposure Levels for Hazardous Substances . Subcommittee on Guidelines for Developing Community Emergency Exposure Levels (CEELs) for Hazardous Substances. Committee on Toxicology, NRC. National Academy Press. Washington, DC.

Rai, K. and J. Van Ryzin. 1985. A dose-respons e model for teratological experiments involving quantal responses. Biometrics 41: 1-10.

USEPA (U.S. Environmental Protection Agency). 1986. Guidelines for mutagenicity
assessment. Federal Register 51:34006-34012. September 24.

USEPA (U.S. Environmental Protection Agency). 1989. Reference dose (RfD) for oral exposure for uranium (soluble salts). Integrated Risk Information System (IRIS). Online

. (Verification date 10/1/89). Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH.3-33 USEPA (U.S. Environmental Protection Agency). 1991a. Final guidelines for developmental toxicity risk assessment. Federal Register 56:63798-63826. December 5.

USEPA (U.S. Environmental Protection Agency). 1991b. Reference dose (RfD) for oral exposure for nitrate. Integrated Risk Information System (IRIS). Online . (Verification date 10/01/91). Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 1992. Reference dose (RfD) for oral exposure for inorganic zinc. Integrated Risk Information System (IRIS). Online

. (Verification date 10/1/92). Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 1993a. Reference dose (RfD): Description and use in health risk assessments. Integrated Risk Information System (IRIS). Online. Intra-Agency Reference Dose (RfD) Work Group, Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH. March 15.

USEPA (U.S. Environmental Protection Agency). 1993b. Revision of Methodology for Deriving National Ambient Water Quality Criteria for the Protection of Human Health: Report of Workshop and EPA’s Preliminary Recommendations for Revision . Submitted to the EPA Science Advisory Board by the Human Health Risk Assessment Branch, Health and Ecological Criteria Division, Office of Science and Technology, Office of Water. Washington, DC. January 8.

USEPA (U.S. Environmental Protection Agency). 1993c. Reference dose (RfD) for oral exposure for inorganic arsenic. Integrated Risk Information System (IRIS). Online. (Verification date 02/01/93). Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 1994. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry . Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Research Triangle Park, NC. EPA/600/8-90/066F.

USEPA (U.S. Environmental Protection Agency). 1995. Proposed guidelines for neurotoxicity risk assessment

. Federal Register 60:52032-52056. October 4.

USEPA (U.S. Environmental Protection Agency). 1996a. Reproductive toxicity risk assessment guidelines. Federal Register 61:56274-56322. October 31.

USEPA (U.S. Environmental Protection Agency). 1996b. Report on the Benchmark Dose Peer Consultation Workshop. Risk Assessment Forum. Washington, DC. EPA/630/R96/011.3-34 USEPA (U.S. Environmental Protection Agency). 1996c. Integrated Risk Information System (IRIS); announcement of pilot program; request for information . Federal Register . 61: 14570. April 2.

USEPA (U.S. Environmental Protection Agency). 1997. Mercury Study: Report to Congress. Volume 5: Health Effects of Mercury and Mercury Compounds. Office of Air Quality Planning and Standards, and Office of Resear ch and Development. Research Triangle Park, NC. EPA-452-R-97-007.

USEPA (U.S. Environmental Protection Agency). 2000. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

. Technical Support Document Volume 1: Risk Assessment. Office of Science and Technology, Office of Water. Washington, DC. EPA-822-B-00-005. August.3-35 [This page left blank intentionally.]4-1 

### 4. E XPOSURE

The derivation of AWQC for the protection of human health requires information about both the toxicological endpoints of concern for water pollutants and the pathways of human exposure to those pollutants. The two primary pathways of human exposure to pollutants present in a particular ambient waterbody that have been considered in deriving AWQC are direct ingestion of drinking water obtained from that waterbody and the consumption of fish/shellfish obtained from that waterbody. Th e water pathway also includes other exposures from household uses (e.g., showering). The deri vation of an AWQC involves the calculation of the maximum water concentration for a pollutant (i.e., the water quality criteria level) that ensures drinking water and/or fish ingestion exposures will not result in human intake of that pollutant in amounts that exceed a specified level based upon the toxicological endpoint of

concern.
The equation for noncancer effects is presented again here, in simplified form, to emphasize the exposure-related parameters (in bold). [Note: the RSC parameter also applies to nonlinear low-dose extrapolation for cancer effects and the other exposure parameters apply to all three of the equations (see Section 1.6).]

(Equation 4-1) 

## ( ) ( ) 

# [ ] AWQC RfD BAF =• • + • RSC BW
DI FI

where: AWQC = Ambient Water Quality Criterion (mg/L) RfD = Reference dose for noncancer effects (mg/kg-day) RSC = Relative source contribution factor to account for non-water sources of exposure BW = Human body weight (kg) DI = Drinking water intake (L/day) FI = Fish intake (kg/day) BAF = Bioaccumulation factor (L/kg)

The following subsections discuss exposure issues relevant to the 2000 Human Health Methodology: exposure policy issues; consideration of non-water sources of exposure (the Relative Source Contribution approach); and the factors used in AWQC computation. In relevant sections, science policy and risk management decisions made by EPA are discussed.

4.1 EXPOSURE POLICY ISSUES

This section discusses broad policy issues related to exposure concerning the major objectives that the Agency believes should be met in setting AWQC.4-2 An Exposure Assessment TSD provides greater detail on numerous topics discussed in this guidance: suggested sources of contaminant concentration and exposure intake information; suggestions of survey methods for obtaining and analyzing exposure data necessary for deriving AWQC; summaries of studies on fish consumpti on among sport fishers and subsistence fishers; more detailed presentation of parameter values (e.g., fish consumption rates, body weights); and additional guidance on the application of the RSC approach.

4.1.1 Sources of Exposure Associated With Ambient Water

4.1.1.1 Appropriateness of Including the Drinking Water Pathway in AWQC

EPA intends to continue including the drinking water exposure pathway in the derivation of its national default human health criteria (AWQC), as has been done since the 1980 AWQC National Guidelines were first published.

EPA recommends inclusion of the drinki ng water exposure pathway where drinking water is a designated use for the following reasons: (1) Drinking water is a designated use for surface waters under the CWA and, therefore, criteria are needed to assure that this designated use can be protected and maintained. (2) Although rare, there are some public water supplies that provide drinking water from surface water sources without treatment. (3) Even among the majority of water supplies that do treat surface waters, existing treatments may not necessarily be effective for reducing levels of particular contaminants. (4) In consideration of the Agency’s goals of pollution prevention, ambient waters should not be contaminated to a level where the burden of achieving health objectives is shifted away from those responsible for pollutant discharges and placed on downstream users to bear the costs of upgraded or supplemental water
treatment.

This policy decision has been supported by the States, most of the public stakeholders, and by external peer reviewers. As with the other exposure parameters, States and authorized Tribes have the flexibility to use alternative intake rates if they believe that drinking water consumption is substantively different than EP A’s recommended default assumptions of 2 L/day for adults and 1 L/day for children. EPA recommends that States and authorized Tribes use an intake rate that would be protective of a majority of consumers and will consider whether an alternative assumption is adequately protective of a State’s or Tribe’s population based on the information or rationale provided at the time EPA reviews State and Tribal water quality standards submissions.

4.1.1.2 Setting Separate AWQC for Drinking Water and Fish Consumption

In conjunction with the issue of the appropriateness of including the drinking water pathway explicitly in the derivation of AWQC for the protection of human health, EPA intends to continue its practice of setting a single AW QC for both drinking water and fish/shellfish consumption, and a separate AWQC based on ingestion of fish/shellfish alone. This latter criterion applies in those cases where the designated uses of a waterbody include supporting fishable uses under Section 101(a) of the CWA and, thus, fish or shellfish for human consumption, but not as a drinking water supply source (e.g., non-potable estuarine waters).4-3 EPA does not believe that national water quality criteria for protection of drinking water uses only are particularly useful for two reasons . First, State and Tribal standards for human health are set to protect Section 101(a) uses (e.g., “fishable, swimmable uses”) under the CWA. Second, most waters have multiple designated uses. Additionally, the water quality standards program protects aquatic life. The 2000 Human Health Methodology revisions do not change EPA’s policy to apply aquatic life criteria to protect aquatic species where they are more sensitive (i.e., when human health criteria would not be protective enough) or where human health via fish or water ingestion is not an issue.

4.1.1.3 Incidental Ingestion from Ambient Surface Waters

The 2000 Human Health Methodology does not routinely include criteria to address incidental ingestion of water from recreational uses. EPA has considered whether there are cases where water quality criteria for the protection of human health based only on fish ingestion (or only criteria for the protection of aquatic life) ma y not adequately protect recreational users from health effects resulting from incidental water ingestion.

EPA reviewed information that provided estimates of incidental water ingestion rates averaged over time. EPA generally believes that the averaged amount is negligible and will not have any impact on the chemical criteria values representative of both drinking water and fish ingestion. A lack of impact on the criteria values would likely also be true for chemical criteria based on fish consumption only, unless the chemical exhibits no bioaccumulation potential. However, EPA also believes that incidental/accide ntal water ingestion could be important for the development of microbial contaminant water quality criteria, and for either chemical or microbial criteria for States where recreational uses such as swimming and boating are substantially higher than the national average. EPA also notes that some States have indicated they already have established incidental ingestion rates for use in developing criteria. Therefore, although EPA will not use this intake parameter when deriving its national 304(a) chemical criteria, limited guidance is provided in the Exposure Assessment TSD volume in order to assist States and authorized Tribes that face situations where this intake parameter could be of significance.

4.2 CONSIDERATION OF NON-WATERSOURCES OF EXPOSURE WHEN SETTING AWQC

4.2.1 Policy Background

The 2000 Human Health Methodology uses different approaches for addressing nonwater exposure pathways in setting AWQC for the protection of human health depending upon the toxicological endpoint of concern. With those substances for which the appropriate toxic endpoint is carcinogenicity based on a linear low-dose extrapolation, only the two water sources (i.e., drinking water and fish ingestion) are c onsidered in the derivation of the AWQC. Nonwater sources are not considered explicitly. In the case of carcinogens based on linear low-dose extrapolation, the AWQC is being determined with respect to the incremental lifetime risk posed by a substance’s presence in water, and is not being set with regard to an individual’s total risk from all sources of exposure. Thus, the AWQC represents the water concentration that would be4-4 expected to increase an individual’s lifetime risk of carcinogenicity from exposure to the particular pollutant by no more than one chance in one million, regardless of the additional lifetime cancer risk due to exposure, if any, to that particular substance from other sources.

Furthermore, health-based criteria values for one medium based on linear low-dose extrapolation typically vary from values for other media in terms of the concentration value, and often the associated risk level. Therefore, the RSC concept could not even theoretically apply unless all risk assessments for a particular carcinogen based on linear low-dose extrapolation resulted in the same concentration value and same risk level; that is, an apportionment would need to be based on a single risk value and level.

In the case of substances for which the AWQC is set on the basis of a carcinogen based on a nonlinear low-dose extrapolation or for a noncancer endpoint where a threshold is assumed to exist, non-water exposures are considered when deriving the AWQC using the RSC approach. The rationale for this approach is that for pollutants exhibiting threshold effects, the objective of the AWQC is to ensure that an individual’s total exposure does not exceed that threshold level.

There has been some discussion of whether it is, in fact, necessary in most cases to explicitly account for other sources of exposure when computing the AWQC for pollutants exhibiting threshold effects. It has been argued that because of the conservative assumptions generally incorporated in the calculation of RfDs (or POD/UF values) used as the basis for the AWQC derivation, total exposures slightly exceeding the RfD are unlikely to produce adverse effects.

EPA emphasizes that the purpose of the RSC is to ensure that the level of a chemical allowed by a criterion or multiple criteria, when combined with other identified sources of exposure common to the population of concern, will not result in exposures that exceed the RfDor the POD/UF. The policy of considering multiple sources of exposure when deriving healthbased criteria has become common in EPA’s progr am office risk characterizations and criteria and standard-setting actions. Numerous EPA wo rkgroups have evaluated the appropriateness of factoring in such exposures, and the Agency concludes that it is important for adequately protecting human health. Consequently, EPA ri sk management policy has evolved significantly over the last six years. Various EPA program initiatives and policy documents regarding aggregate exposure and cumulative risk have been developed, including the consideration of inhalation and dermal exposures. Additionally, accounting for other exposures has been included in recent mandates (e.g., the Food Quality Protection Act) and, thus, is becoming a requirement for the Agency. The Exposure Decision Tree approach has been shared with other EPA offices, and efforts to coordinate policie s on aggregate exposure, where appropriate, have begun. EPA intends to continue developing policy guidance on the RSC issue and guidance to address the concern that human health may not be adequately protected if criteria allow for higher levels of exposure that, combined, may exceed the RfD or POD/UF. EPA also intends to refine the 2000 Human Health Methodology in the future to incorporate additional guidance on inhalation and dermal exposures. As stated prev iously, EPA is required to derive national water quality criteria under Section 304(a) of the CWA and does not intend to derive site-specific criteria. However, States and authorized Tribes have the flexibility to make alternative exposure and RSC estimates based on local data, and EPA strongly encourages this.4-5 Uncertainty factors used in the derivation ofthe RfD (or POD/UF) to account for intraand interspecies variability and the incompleteness of the toxicity data set(s)/animal studies are specifically relevant to the chemical’s internal toxicological action, irrespective of the sources of exposure that humans may be experiencing. The Agency’s policy is to consider and account for other sources of exposure in order to set protective health criteria. EPA believes that multiple route exposures may be particularly important when uncertainty factors associated with the RfD are small. Although EPA is well aware that RfDs are not all equivalent in their derivation, EPA does not believe that uncertainty in the toxicological data should result in less stringent criteria by ignoring exposure sources. However, the RSC policy approach does allow less stringent assumptions when multiple sources of exposure are not anticipated.

The AWQC are designed to be protective criteria, generally applicable to the waters of the United States. While EPA cannot quantitatively predict the actual human health risk associated with combined exposures above the RfD or POD/UF, a combination of health criteria for multiple media exceeding the RfD or POD/UF ma y not be sufficiently protective. Therefore, EPA’s policy is to routinely account for all sources and routes of non-occupational exposure when setting AWQC for noncarcinogens and for carcinogens based on nonlinear low-dose extrapolations. EPA believes that maintaining total exposure below the RfD (or POD/UF) is a reasonable health goal and that there are circumstances where health-based criteria for a chemical should not exceed the RfD (or POD/UF), either alone (if only one criterion is relevant, along with other intake sources considered as background exposures) or in combination. EPA believes its RSC policy ensures this goal.

Also, given the inability to reasonably predict future changes in exposure patterns, the uncertainties in the exposure estimates due to typical data inadequacy, possible unknown sources of exposure, and the potential for some populations to experience greater exposures than indicated by the available data, EPA believes that utilizing the entire RfD (or POD/UF) does not ensure adequate protection.

4.2.2 The Exposure Decision Tree Approach

As indicated in Section 1, EPA has, in the past, used a “subtraction” method to account for multiple sources of exposure to pollutants. In the subtraction method, other sources of exposure (i.e., those other than the drinking wate r and fish exposures) are subtracted from the RfD (or POD/UF). However, EPA also previously used a “percentage” method for the same purpose. In this approach, the percentage of total exposure typically accounted for by the exposure source for which the criterion is being determined, referred to as the relative source contribution (RSC), is applied to the RfD to determine the maximum amount of the RfD “apportioned” to that source. With both procedures, a “ceiling” level of 80 percent of the RfD and a “floor level” of 20 percent of the RfD are applied.

The subtraction method is considered acceptable when only one criterion is relevant for a particular chemical. The percentage method is recommended in the context of the above goals when multiple media criteria are at issue. The percentage method does not simply depend on the amount of a contaminant in the prospective criterion source only. It is intended to reflect health considerations, the relative portions of other sources, and the likelihood for ever-changing levels4-6 in each of those multiple sources (due to ever-changing sources of emissions and discharges). Rather than simply defaulting in every instance, the Agency attempts to compare multiple source exposures with one another to estimate their relative contribution to the total–given that understanding the degree to which their concentrations vary, or making any distributional analysis, is often not possible. The criteria levels, when multiple criteria are at issue, are based on the actual levels, with an assumption that there may be enough relative variability such that an apportionment (relating that percentage to the RfD) is a reasonable way of accounting for the uncertainty regarding that variability.

The specific RSC approach recommended by EPA, which we will use for the derivation of AWQC for noncarcinogens and carcinogens a ssessed using nonlinear low-dose extrapolation, is called the Exposure Decision Tree and is described below. To account for exposures from other media when setting an AWQC (i.e., non-dr inking water/non-fish ingestion exposures, and inhalation or dermal exposures), the Exposur e Decision Tree for determining proposed RfD or POD/UF apportionments represents a method of comprehensively assessing a chemical for water quality criteria development. This method considers the adequacy of available exposure data, levels of exposure, relevant sources/media of exposure, and regulatory agendas (i.e., whether there are multiple health-based criteria or regulatory standards for the same chemical). The Decision Tree addresses most of the disadvantages a ssociated with the exclusive use of either the percentage or subtraction approaches, because they are not arbitrarily chosen prior to determining the following: specific population(s) of concern, whether these populations are relevant to multiple-source exposures for the chemical in question (i.e., whether the population is actually or potentially experiencing exposure from multiple sources), and whether levels of exposure, regulatory agendas, or other circ umstances make apportionment of the RfD or POD/UF desirable. Both subtraction and percentage methods are potentially utilized under different circumstances with the Exposure D ecision Tree approach, and the Decision Tree is recommended with the idea that there is enough flexibility to use other procedures if information on the contaminant in question suggests it is not appropriate to follow the Decision Tree. EPA recognizes that there may be other valid approaches in addition to the Exposure Decision Tree.

The Exposure Decision Tree approach allows flexibility in the RfD (or POD/UF) apportionment among sources of exposure. When adequate data are available, they are used to make protective exposure estimates for the population(s) of concern. When other sources or routes of exposure are anticipated but data are not adequate, there is an even greater need to make sure that public health protection is achieved. For these circumstances, a series of qualitative alternatives is used (with the less adequate data or default assumptions) that allow for the inadequacies of the data while protecting human health. Specifically, the Decision Tree makes use of chemical information when actual monitoring data are inadequate. It considers information on the chemical/physical properties, uses of the chemical, and environmental fate and transformation, as well as the likelihood of occurrence in various media. Review of such information, when available, and determination of a reasonable exposure characterization for the chemical will result in a water quality criterion that more accurately reflects exposures than automatically using a default value. Although the 20 percent default will still generally be used when information is not adequate, the need for using it should be reduced. There may also be some situations where EPA would consider the use of an 80 percent default (see Section 4.2.3).4-7 The Decision Tree also allows for use of either the subtraction or percentage method to account for other exposures, depending on whether one or more health-based criterion is relevant for the chemical in question. The subtraction method is considered acceptable when only one criterion is relevant for a particular chemical. In these cases, other sources of exposure can be considered “background” and can be subtracted from the RfD (or POD/UF).

EPA cautions States and Tribes when using the subtraction method in these circumstances. The subtraction method results in a criterion allowing the maximum possible chemical concentration in water after subtracting other sources. As such, it removes any cushion between pre-criteria levels (i.e., actual “current” levels) and the RfD, thereby setting criteria at the highest levels short of exceeding the RfD. It is somewhat counter to the goals of the CWA for maintaining and restoring the nation’s waters. It is also directly counter to Agency policies, explicitly stated in numerous programs, regarding pollution prevention. EPA has advocated that it is good health policy to set criteria such that exposures are kept low when current levels are already low. The subtraction method generally results in criteria levels of a contaminant in a particular medium at significantly higher levels than the percentage method and, in this respect, is contradictory to such goals. In fact, many chemicals have pre-criteria levels in environmental media substantially lower (compared to the RfD) than the resulting criteria allow.

When more than one criterion is relevant to a particular chemical, apportioning the RfD (or POD/UF) via the percentage method is considered appropriate to ensure that the combination of criteria and, thus, the potential for resulting exposures do not exceed the RfD (or POD/UF). The Exposure Decision Tree (with numbered boxes) is shown in Figure 4-1. The explanation in the text on the following pages must be read in tandem with the Decision Tree figure; the text in each box of the figure only nominally identifies the process and conditions for determining the outcome for that step of the Decision Tree. The underlying objective is to maintain total exposure below the RfD (or POD/UF) while generally avoiding an extremely low limit in a single medium that represents just a nominal fraction of the total exposure. To meet this objective, all proposed numeric limits lie between 80 percent and 20 percent of the RfD (or POD/UF). Again, EPA will use the Exposure Decision Tree approach when deriving its AWQC but also recognizes that departures from the appr oach may be appropriate in certain cases. EPA understands that there may be situations where the Decision Tree procedure is not practicable or4-8 Are exposures from multiple sources (due to a sum of sources or an individual source) potentially at levels near (i.e., over 80%), at or in excess of the RfD (or POD/UF)? Exposure Decision Tree for Defining Proposed RfD (or POD/UF) Apportionment

1. Identify population(s) of

concern.

2. Identify relevant exposure sources/pathways. *

3.

4. Are there sufficient data, physical/chemical property information, fate and transport information, and/or generalized information available to characterize the likelihood of exposure to relevant sources?

Is there some information available on each sourceto make a characterization of exposure? Apportion the RfD (or POD/ UF) including 80% ceiling/20% floor using the percentage approach (with ceiling and floor). Is there more than one regulatory action (i.e., criteria, standard, guidance) relevant for the chemical in question? Describe exposures,uncertainties, toxicityrelated information, control issues, and other information for management decision. Perform calculations associated with Boxes 12 or 13 as applicable. No Yes 9.

Yes 10.

11.

Use subtraction of appropriate intake levels fr om sources other than source of concern, including 80% ceiling/20% floor. 12.

13. Are there significant known or potential uses/sources other than the source of concern?

Use 50% of the RfD (or POD/UF). 7. 8A. No

No

Yes Yes

Yes No Are adequate data available to describe central tendencies and high-ends for relevant exposure sources/pathways?

No 

### Problem Formulation

Use 20% of the RfD (or POD/UF). 8B. No 8C. Yes 5A.

6. Figure 4-1

Perform apportionment as described in Box 12 or 13, with a 50% ceiling/ 20% floor. 5B.

Gather

moreinformationand rereview Use 20% of the RfD

or POD/UF OR

* Sources and pathways include both ingestion and routes other than oral for water-related exposures, and nonwater sources of exposure, including ingestion exposures (e.g., food), inhalation, and/or dermal.4-9 may be simply irrelevant after considering the properties, uses, and sources of the chemical in question. EPA endorses such flexibility by Stat es and authorized Tribes when developing alternative water quality criteria in order to choose other procedures that are more appropriate for setting health-based criteria and, perhaps, apportioning the RfD or POD/UF, as long as reasons are given as to why it is not appropriate to follow the Exposure Decision Tree approach and as long as the steps taken to evaluate the potential sources and levels of exposure are clearly described. Often, however, the common situation of multiple exposure sources for a chemical is likely to merit a Decision Tree evaluation for the purpose of developing human health water quality criteria for a given chemical.

It is clear that this will be an interactive process; input by exposure assessors will be provided to, and received from, risk managers throughout the process, given that there may be significant implications regarding control issues (i.e., cost/feasibility), environmental justice issues, etc. In cases where the Decision Tree is not chosen, communication and concurrence about the decision rationale and the alternative water quality criteria are of great importance.

Descriptions of the boxes within the Decision Tree are separated by the following process headings to facilitate an understanding of the major considerations involved. The decision to perform, or not to perform, an appor tionment could actually be made at several points during the Decision Tree process. Working through the process is most helpful for identifying possible exposure sources and the potential for exposure, determining the relevancy of the Decision Tree to developing an AWQC for a particular chemical and, possibly, determining the appropriateness of using an alternative approach to account for overall exposure. “Relevancy” here means determining whether more than one criterion, standard, or other guidance is being planned or is in existence for the chemical in question. Additional guidance for States and Tribes that wish to use the Exposure Decision Tree is provided in the Exposure Assessment TSD.

4.2.2.1 Problem Formulation

Initial Decision Tree discussion centers around the first two boxes: identification of population(s) of concern (Box 1) and identifica tion of relevant exposure sources and pathways (Box 2). The term “problem formulation” refe rs to evaluating the population(s) and sources of exposure in a manner that allows determination of the potential for the population of concern to experience exposures from multiple sources for the chemical in question. Also, the data for the chemical in question must be representative of each source/medium of exposure and be relevant to the identified population(s). Evaluation includes determining whether the levels, multiple criteria or regulatory standards, or other ci rcumstances make apportionment of the RfD or POD/UF reasonable. The initial problem formulation also determines the exposure parameters chosen, the intake assumptions chosen for each route, and any environmental justice or other social issues that aid in determining the population of concern. The term “data,” as used here and discussed throughout this section, refers to ambient sampling data (whether from Federal, regional, State, or area-specific studies) and not internal human exposure measurements.4-10 4.2.2.2 Data Adequacy

In Box 3, it is necessary that adequate data exist for the relevant sources/pathways of exposure if one is to avoid using default procedures. The adequacy of data is a professional judgment for each individual chemical of concern, but EPA recommends that the minimum acceptable data for Box 3 are exposure distributions that can be used to determine, with an acceptable 95 percent confidence interval, the central tendency and high-end exposure levels for each source. In fact, distributional data may exist for some or most of the sources of exposure.

There are numerous factors to consider in order to determine whether a dataset is adequate. These include: (1) sample size (i.e., the number of data points); (2) whether the data set is a random sample representative of the target population (if not, estimates drawn from it may be biased no matter how large the sample); (3) the magnitude of the error that can be tolerated in the estimate (estimator precision); (4) the sample size needed to achieve a given precision for a given parameter (e.g., a larger sample is needed to precisely estimate an upper percentile than a mean or median value); (5) an acceptable analytical method detection limit; and (6) the functional form and variability of the underlying distribution, which determines the estimator precision (e.g., whether the distribution is normal or lognormal and whether the standard deviation is 1 or 10). Lack of information may prevent assessment of each of these factors; monitoring study reports often fail to include background information or sufficient summary statistics (and rarely the raw data) to completely characterize data adequacy. Thus, a case-by-case determination of data adequacy may be necessary.

That being stated, there are some guidelines, as presented below, that lead to a rough rule-of-thumb on what constitutes an “adequate” sample size for exposure assessment. Again, first and foremost, the representativeness of the data for the population evaluated and the analytical quality of the data must be acceptable. If so, the primary objective then becomes estimating an upper percentile (e.g., say the 90 th ) and a central tendency value of some exposure distribution based on a random sample from the distribution. Assuming that the distribution of exposures is unknown, a nonparametric estimate of the 90 thpercentile is required. The required estimate, based on a random sample of n observations from a target population, is obtained by ranking the data from smallest to largest and selecting the observation whose rank is 1 greater than the largest integer in the product of 0.9 times n . For example, in a data set of 25 points, the nonparametric estimate of the 90 thpercentile is the 23rd largest observation.

In addition to this point estimate, it is useful to have an upper confidence bound on the 90 thpercentile. To find the rank of the order statistic that gives an upper 95 percent confidence limit on the 90 thpercentile, the smallest value of r

that satisfies the following formula is determined:

(Equation 4-2)4-11 where:

r = the rank order of the observation n = the number of observations I = integer from 0 to r - 1

For relatively small data sets, the above formula will lead to selecting the largest observation as the upper confidence limit on the 90 thpercentile. However, the problem with using the maximum is that, in many environmental datasets, the largest observation is an outlier and would provide an unrealistic upper bound on the 90 thpercentile. It would, therefore, be preferable if the sample size n were large enough so that the formula yielded the second largest observation as the confidence limit (see for example Gibbons, 1971).

This motivates establishing the following criterion for setting an “adequate” sample size: pick the smallest n such that the nonparametric upper 95 percent confidence limit on the 90 th percentile is the second largest value. Application of the above formula with r set to n -1 yields n = 45 for this minimum sample size.

For the upper 95 percent confidence limit to be a useful indicator of a high-end exposure, it must not be overly conservative (too large relative to the 90 thpercentile). It is, therefore, of interest to estimate the expected magnitude of the ratio of the upper 95 percent confidence limit to the 90 thpercentile. This quantity generally cannot be computed, since it is a function of the unknown distribution. However, to get a rough idea of its value, consider the particular case of a normal distribution. If the coefficient of varia tion (i.e., the standard deviation divided by the mean) is between 0.5 and 2.0, the expected value of the ratio in samples of 45 will be approximately 1.17 to 1.31; i.e., the upper 95 percent confidence limit will be only about 17 to 31 percent greater than the 90 thpercentile on the average.

It should be noted that the nonparametric estimate of the 95 percent upper confidence limit based on the second largest value can be obtained even if the data set has only two detects(it is assumed that the two detects are greater than the detection limit associated with all nondetects). This is an argument for using nonparametric rather than parametric estimation, sinceuse of parametric methods would require more detected values. On the other hand, if nondetects were not a problem and the underlying di stribution were known, a parametric estimate of the 90 thpercentile would generally be more precise.

As stated above, adequacy also depends on whether the samples are relevant to and representative of the population at risk. Data may, therefore, be adequate for some decisions and inadequate for others; this determination requires some professional judgment.

If the answer to Box 3 is no, based on the above determination of adequacy, then the decision tree moves to Box 4. As suggested by the separate boxes, the available data that will be reviewed as part of Box 4 do not meet the requirements necessary for Box 3. In Box 4, any limited data that are available (in addition to information about the chemical/physical properties, uses, and environmental fate and transformation, as well as any other information that would characterize the likelihood of exposure from various media for the chemical) are evaluated to4-12 make a qualitative determination of the relation of one exposure source to another. Although this information should always be reviewed at the outset, it is recommended that this information also be used to estimate the health-based water quality criteria. The estimate should be rather conservative (as indicated in the Decision Tree), given that it is either not based on actual monitoring data or is based on data that has been considered to be inadequate for a more accurate quantitative estimate. Therefore, greater uncertainties exist and accounting for variability is not really possible. Whether the available data are adequate and sufficiently representative will likely vary from chemical to chemical and may depend on the population of concern. If there are some data and/or other information to make a characterization of exposure, a determination can be made as to whether there are significant known or potential uses for the chemical/sources of exposure other than the source of concern (i.e., in this case, the drinking water and fish intakes relevant to developing an AWQC) that would allow one to anticipate/quantify those exposures (Box 6). If there are not, then it is recommended that 50 percent of the RfD or POD/UF can be safely apportioned to the source of concern (Box 7). While this leaves half of the RfD or POD/UF unapportioned, it is recommended as the maximum apportionment due to the lack of data needed to more accurately quantify actual or potential exposures. If the answer to the question in Box 6 is yes (there is multiple source information available for the exposures of concern), and some information is available on each source of exposure (Box 8A), apply the procedure in either Box 12 or Box 13 (depending on whether one or more criterion is relevant to the chemical), using a 50 percent ceiling (Box 8C)–agai n due to the lack of adequate data. If the answer to the question in Box 8A is no (there is no available information to characterize exposure), then the 20 percent default of the RfD or POD/UF is used (Box 8B).

If the answer to the question in Box 4 is no; that is, there are not sufficient data/information to characterize exposure, EPA intends to generally use the “default” assumption of 20 percent of the RfD or POD/UF (Box 5A) wh en deriving or revising the AWQC. It may be better to gather more data or information and re-review when this information becomes available (Box 5B). EPA has done this on occasion when resources permit the acquisition of additional data to enable better estimates of exposure instead of the default. If this is not possible, then the assumption of 20 percent of the RfD or POD/UF (Box 5A) should be used. Box 5A is likely to be used infrequently with the Exposure Decision Tree approach, given that the information described in Box 4 should be available in most cases. However, EPA intends to use 20 percent of the RfD (or POD/UF), which has also been used in past water program regulations, as the default value.

4.2.2.3 Regulatory Actions

If there are adequate data available to describe the central tendencies and high ends from each exposure source/pathway, then the levels of exposure relative to the RfD or POD/UF are compared (Box 9). If the levels of exposure for the chemical in question are not near (currently defined as greater than 80 percent), at, or in excess of the RfD or POD/UF, then a subsequent determination is made (Box 11) as to whether there is more than one health-based criterion or regulatory action relevant for the given chemical (i.e., more than one medium-specific criterion,4-13 standard or other guidance being planned, performed or in existence for the chemical). The subtraction method is considered acceptable when only one criterion (standard, etc.) is relevant for a particular chemical. In these cases, other sources of exposure can be considered “background” and can be subtracted from the RfD (or POD/UF). When more than one criterion is relevant to a particular chemical, apportioning the RfD (or POD/UF) via the percentage method is considered appropriate to ensure that the combination of health criteria, and thus the potential for resulting exposures, do not exceed the RfD (or POD/UF).

As indicated in Section 2, for EPA’s national 304(a) criteria, the RSC intake estimates of non-water exposures (e.g., non-fish dietary exposures) will be based on arithmetic mean values when data are available. The assumed body weight used in calculating the national criteria will also be based on average values. The drinking water and fish intake values are 90 thpercentile estimates. EPA believes that these assumptions will be protective of a majority of the population and recommends them for State and Tribal use. Ho wever, States and authorized Tribes have the flexibility to choose alternative intake rate and exposure estimate assumptions to protect specific population groups that they have chosen.

4.2.2.4 Apportionment Decisions

If the answer to the question in Box 11 is no (there is not more than one relevantmedium-specific criterion/regulatory action), then the recommended method for setting a healthbased water quality criterion is to utilize a subtraction calculation (Box 12). Specifically, appropriate intake values for each exposure source other than the source of concern are subtracted out. EPA will rely on average values commonly used in the Agency for food ingestion and inhalation rates, combined with mean contaminant concentration values, for calculating RSC estimates to subtract. Alternatively, contaminant concentrations could be selected based on the variability associated with those concentrations for each source. This implies that a case-by-case determination of the variability and the resulting intake chosen would be made, as each chemical evaluated can be expected to have different variations in concentration associated with each source of intake. However, EPA anticipates that the available data for most contaminants will not allow this for determination (based on past experience). Guidance addressing this possibility is addressed in the Exposure Assessment TSD. EPA does not recommend that high-end intakes be subtracted for every exposure source, since the combination may not be representative of any actually exposed population or individual. The subtraction method would also include an 80 percent ceiling and a 20 percent floor.

If the answer to the question in Box 11 is yes (there is more than one medium-specific criterion/regulation relevant), then the recommended method for setting health-based water quality criteria is to apportion the RfD or POD/UF among those sources for which health-based criteria are being set (Box 13). This is done via a percentage approach (with a ceiling and floor). This simply refers to the percentage of overall exposure contributed by an individual exposure source. For example, if for a particular chemical, drinking water were to represent half of total exposure and diet were to represent the other half, then the drinking water contribution (or RSC) would be 50 percent. The health-based criteria would, in turn, be set at 50 percent of the RfD or POD/UF. This method also utilizes an appropriate combination of intake values for each4-14 exposure source based on values commonly used in the Agency for food ingestion and inhalation rates, combined with mean contaminant concentration values.

Finally, if the levels of exposure for the chemical in question are near (currently defined as greater than 80 percent), at, or in excess of the RfD or POD/UF (i.e., the answer in Box 9 is yes), then the estimates of exposures and related uncertainties, recommended apportionment (either box 12 or 13), toxicity-related information, control issues, and other information are to be presented to managers for a decision (Box 10). The high levels referred to in Box 9 may be due to one source contributing that high level (while other sources contribute relatively little) or due to more than one source contributing levels that, in combination, approach or exceed the RfD or POD/UF. Management input may be necessary due to the control issues (i.e., cost and feasibility concerns), especially when multiple criteria are at issue. In practice, risk managers are routinely a part of decisions regarding regulatory acti ons and will be involved with any recommended outcome of the Exposure Decision Tree or, for that matter, any alternative to the Exposure Decision Tree. However, because exposures approach or exceed the RfD or POD/UF and because the feasibility of controlling different sources of exposure are complicated issues, risk managers will especially need to be directly involved in final decisions in these circumstances.

It is emphasized here that the procedures in these circumstances are not different than the procedures when exposures are not at or above th e RfD (or POD/UF). Therefore, in these cases, estimates should be performed as with Boxes 11, 12, and 13. The recommendation should be made based on health-based considerations only, just as when the chemical in question was not a Box 10 situation. If the chemical is relevant to one health criterion or regulatory action only, the other sources of exposure could be subtracted fr om the RfD or POD/UF to determine if there is any leftover amount for setting the criterion. If the chemical is a multiple media criteria issue, then an apportionment should be made, even though it is possible that all sources would need to be reduced. Regardless of the outcome of Box 9, all apportionments made (via the methods of Boxes 12 or 13) should include a presentation of the uncertainty in the estimate and in the RfD or POD/UF for a more complete characterization.

The process for a Box 10 situation (versus a situation that is not) differs in that the presentations for Boxes 12 and 13 are based on apportionments (following the review of available information and a determination of appropriate exposure parameters) that must address additional control issues and may result in more selective reductions. With Box 10, one or several criteria possibilities (“scenarios”) could be presented for comparison along with implications of the effects of various control options. It is appropriate to present information in this manner to risk managers given the complexity of these additional control issues.

4.2.3 Additional Points of Clarification on the Exposure Decision Tree Approach for Setting AWQC

As with Box 9, if a determination is made in Box 8A (i.e., information is available to characterize exposure) that exposures are near, at, or above the RfD (or POD/UF) based on the available information, the apportionments made need to be presented to risk managers for decision. If information is lacking on some of the multiple exposure sources, then EPA would use a default of 20 percent of the RfD or POD/UF (Box 8B).4-15 Results of both Boxes 12 and 13 rely on the 80 percent ceiling and 20 percent floor. The 80 percent ceiling was implemented to ensure that the health-based goal will be low enough to provide adequate protection for individuals whose total exposure to a contaminant is, due to any of the exposure sources, higher than currently indicated by the available data. This also increases the margin of safety to account for possible unknown sources of exposure. The 20 percent floor has been traditionally rationalized to prevent a situation where small fractional exposures are being controlled. That is, below that point, it is more appropriate to reduce other sources of exposure, rather than promulgating standards for de minimus reductions in overall
exposure.
If it can be demonstrated that other sources and routes of exposure are not anticipated for the pollutant in question (based on information about its known/anticipated uses and chemical/physical properties), then EPA would use the 80 percent ceiling. EPA qualifies this policy with the understanding that as its policy on cumulative risk assessment continues to develop, the 80 percent RSC may prove to be underprotective.

In the cases of pollutants for which substantial data sets describing exposures across all anticipated pathways of exposure exist, and probabilistic analyses have been conducted based on those data, consideration will be given to the results of those assessments as part of the Exposure Decision Tree approach for setting AWQC.

For many chemicals, the rate of absorption from ingestion can differ substantially from absorption by inhalation. There is also available information for some chemicals that demonstrates appreciable differences in gastrointestinal absorption depending on whether the chemical is ingested from water, soil, or food. For some contaminants, the absorption of the contaminant from food can differ appreciably for plant compared with animal food products. Regardless of the apportionment approach used, EPA recommends using existing data on differences in bioavailability between water, air, soils, and different foods when estimating total exposure for use in apportioning the RfD or POD/UF. The Agency has developed such exposure estimates for cadmium (USEPA, 1994). In the absence of data, EPA will assume equal rates of absorption from different routes and sources of exposure.

4.2.4 Quantification of Exposure

When selecting contaminant concentration values in environmental media and exposure intake values for the RSC analysis, it is important to realize that each value selected (including those recommended as default assumptions in the AWQC equation) may be associated with a distribution of values for that parameter. Determining how various subgroups fall within the distributions of overall exposure and how the combination of exposure variables defines what population is being protected is a complicated and, perhaps, unmanageable task, depending on the amount of information available on each exposure factor included. Many times, the default assumptions used in EPA risk assessments are derived from the evaluation of numerous studies and are considered to generally represent a particular population group or a national average. Therefore, describing with certainty the exact percentile of a particular population that is protected with a resulting criteria is often not possible.4-16 By and large, the AWQC are derived to protect the majority of the general population from chronic adverse health effects. Howeve r, as stated above in Section 4.1.1.1, States and authorized Tribes are encouraged to consider protecting population groups that they determine are at greater risk and, thus, would be better protected using alternative exposure assumptions. The ultimate choice of the contaminant concentrations used in the RSC estimate and the exposure intake rates requires the use of professional judgment. This is discussed in greater detail in the Exposure Assessment TSD.

4.2.5 Inclusion of Inhalation and Dermal Exposures

EPA intends to develop policy guidelines to apply to this Methodology for explicitly incorporating inhalation and dermal exposures. When estimating overall exposure to pollutants for AWQC development, EPA believes that the sources of inhalation and dermal exposures considered should include, on a case-by-case basi s, both non-oral exposures from water and other inhalation and dermal sources (e.g., ambient or indoor air, soil). When the policy guidelines are completed, this Methodology will be refined to include that guidance.

A number of drinking water contaminants are volatile and thus diffuse from water into the air where they may be inhaled. In addition, drinking water is used for bathing and, thus, there is at least the possibility that some contaminants in water may be dermally absorbed. Volatilization may increase exposure via inhalation and decrease exposure via ingestion and dermal absorption. The net effect of volatilization and dermal absorption upon total exposure to volatile drinking water contaminants is unclear in some cases and varies from chemical to chemical. Dermal exposures are also important to consider for certain population groups, such as children and other groups with high soil contact.

With regard to additional non-water related exposures, it is clear that the type and magnitude of toxicity produced via inhalation, inge stion, and dermal contact may differ; that is, the route of exposure can affect absorption of a chemical and can otherwise modify its toxicity. For example, an inhaled chemical such as hydrogen fluoride may produce localized effects on the lung that are not observed (or only observed at much higher doses) when the chemical is administered orally. Also, the active form of a chemical (and principal toxicity) can be the parent compound and/or one or more metabolites. With this Methodology, EPA recommends that differences in absorption and toxicity by di fferent routes of exposure be determined and accounted for in dose estimates and applied to the exposure assessment. EPA acknowledges that the issue of whether the doses received from inhalation and ingestion exposures are cumulative (i.e., toward the same threshold of toxicity) is complicated. Such a determination involves evaluating the chemical’s physical characteristics, speciation, and reactivity. A chemical may also exhibit different metabolism by inhalation versus oral exposure and may not typically be metabolized by all tissues. In addition, a metabolite may be much more or much less toxic than the parent compound. Certainly with a systemic effect, if the chemical absorbed via different routes enters the bloodstream, then there is some likelihood that it will contact the same target organ. Attention also needs to be given to the fact that both the RfD and RfC are derived based on the administered level. Toxicologists generally believe that the effective concentration of the active form of a chemical(s) at the site(s) of action determines the toxicity. If specific differences between routes of exposure are not known, it may be reasonable to assume that the4-17 internal concentration at the site from any route contributes as much to the same effect as any other route. A default of assuming equal absorption has often been used. However, for many of the chemicals that the Agency has reviewed, there is a substantial amount of information already known to determine differences in rates of absorption. For example, absorption is, in part, a function of blood solubility (i.e., Henry’s Constant) and better estimations than the default can be made.

The RSC analyses that accompany the 2000 Human Health Methodology accommodate inclusion of inhalation exposures. Even if diffe rent target organs are involved between different routes of exposure, a conservative policy may be appropriate to keep all exposures below a certain level. A possible alternative is to set allowable levels (via an equation) such that the total of ingestion exposures over the ingestion RfD adde d to the total of inhalation exposures over the inhalation RfC is not greater than 1 (Note: the RfD is typically presented in mg/kg-day and the RfC is in mg/m 3 ). Again, EPA intends to develop guidance for this Methodology to explicitly incorporate inhalation and dermal exposures, and will refine the Methodology when that guidance is completed.

4.3 EXPOSURE FACTORS USED IN THE AWQC COMPUTATION

This section presents values for the specific exposure factors that EPA will use in the derivation of AWQC. These include human body we ight, drinking water consumption rates, and fish ingestion rates.

When choosing exposure factor values to include in the derivation of a criterion for a given pollutant, EPA recommends considering values that are relevant to population(s) that is (are) most susceptible to that pollutant. In addition, highly exposed populations should be considered when setting criteria. In general, exposure factor values specific to adults and relevant to lifetime exposures are the most appropriate values to consider when determining criteria to protect against effects from long-term exposure which, by and large, the human health criteria are derived to protect. However, infants and children may have higher rates of water and food consumption per unit body weight compared with adults and also may be more susceptible to some pollutants than adults (USEPA, 1997a). There may be instances where acute or subchronic developmental toxicity makes children the population group of concern. In addition, exposure of pregnant women to certain toxic chemicals may cause developmental effects in the fetus (USEPA, 1997b). Exposures resulting in developmental effects may be of concern for some contaminants and should be considered along with information applicable to long-termhealth effects when setting AWQC. (See Section 3.2 for further discussion of this issue.) Shortterm exposure may include multiple intermittent or continuous exposures occurring over a week or so. Exposure factor values relevant for c onsidering chronic toxicity, as well as exposure factor values relevant for short-term exposure developmental concerns, that could result in adverse health effects are discussed in the sections below. In appropriate situations, EPA may consider developing criteria for developmental health effects based on exposure factor values specific to children or to women of childbearing age. EPA encourages States and Tribes to do the same when health risks are associated with short-term exposures.4-18 EPA believes that the recommended exposure factor default intakes for adults in chronic exposure situations are adequately protective of the population over a lifetime. In providing additional exposure intake values for highly exposed subpopulations (e.g., sport anglers, subsistence fishers), EPA is providing flexibility for States and authorized Tribes to establish criteria specifically targeted to provide additiona l protection using adjusted values for exposure parameters for body weight, drinking water intake , and fish consumption. The exposure factor values provided for women of childbearing age and children would only be used in the circumstances indicated above.

Each of the following sections recommends exposure parameter values for use in developing AWQC. These are based on both science policy decisions that consider the best available data, as well as risk management judgments regarding the overall protection afforded by the choice in the derivation of AWQC. These will be used by EPA to derive new, or revise existing, 304(a) national criteria.

4.3.1 Human Body Weight Values for Dose Calculations

The source of data for default human body weights used in deriving the AWQC is the third National Health and Nutrition Examination Survey (NHANES III). NHANES III represents a very large interview and examination endeavor of the National Center for Health Statistics (NCHS) and included participation from the Centers for Disease Control (CDC). The NHANES III was conducted on a nationwide probability sample of over 30,000 persons from the civilian, non-institutionalized population of the United States. The survey began in October 1988 and was completed in October 1994 (WESTAT, 2000; McDowell, 2000). Body weight data were taken from the NHANES III Examination Data File. Sampling weights were applied to all persons examined in the Mobile Examination Centers (MECs) or at home, as was recommended by the NHANES data analysts (WESTAT, 2000).

The NHANES III survey has numerous strengths and very few weaknesses. Its primary strengths are the national representativeness, large sample size, and precise estimates due to this large sample size. Another strength is its high response rate; the examination rate was 73 percent overall, 89 percent for children under 1 year old, and approximately 85 percent for children 1 to 5 years old (McDowell, 2000). Interview response rates were even higher, but the body weight data come from the NHANES examinations; that is, all body weights were carefully measured by survey staff, rather than the use of self-reported body weights. The only significant potential weakness of the NHANES data is the fact that the data are now between 6 and 12 years old. Given that there were upward trends in body weight from NHANES II to NHANES III, and that NCHS has indicated the prevalence of overweight people increased in all age groups, the data could underestimate current body weights if that trend has continued (WESTAT, 2000).

The NHANES III collected standard body measurements of sample subjects, including height and weight, that were made at various tim es of the day and in different seasons of the year. This technique was used because one’s weight may vary between winter and summer and may fluctuate with recency of food and water in take and other daily activities (McDowell, 2000).4-19 As with the other exposure assumptions, States and authorized Tribes are encouraged to use alternative body weight assumptions for population groups other than the general population and to use local or regional data over default values as more representative of their target population group(s).

4.3.1.1 Rate Protective of Human Health from Chronic Exposure

EPA recommends maintaining the default body weight of 70 kg for calculating AWQC as a representative average value for both male and female adults. As previously indicated, exposure factor values specific to adults are recommended to protect against effects from longterm exposure. The value of 70 kg is based on the following information. In the analysis of the NHANES III database, median and mean values for female adults 18-74 years old are 65.8 and 69.5 kg, respectively (WESTAT, 2000). For males in the same age range, the median and mean values are 79.9 and 82.1 kg, respectively. The mean body weight value for men and women ages 18 to 74 years old from this survey is 75.6 kg (WESTAT, 2000). This mean value is higher than the mean value for adults ages 20-64 years old of 70.5 kg from a study by the National Cancer Institute (NCI) which primarily measured drinking water intake (Ershow and Cantor, 1989). The NCI study is described in the subsection on Drinking Water Intake Rates that follows (Section 4.3.2). The value from the NHANES III database is also higher than the value given in the revised EPA Exposure Factors Handbook (USEPA, 1997b), which recommends 71.8 kg for adults, based on the older NHANES II data. The Handbook also acknowledges the commonly used 70 kg value and encourages risk assessors to use values which most accurately reflect the exposed population. However, the point is also made that the 70 kg value is used in the derivation of cancer slope factors and unit risks that appear in IRIS. Consistency is advocated between the dose-response relationship and exposure factors assumed. Therefore, if a value higher than 70 kg is used, the assessor needs to adjust the dose-response relationship as described in the Appendix to Chapter 1, Volume 1 of the Handbook (USEPA, 1997b).

4.3.1.2 Rates Protective of Developmental Human Health Effects

As noted above, pregnant women may represent a more appropriate population for which to assess risks from exposure to chemicals in ambient waters in some cases, because of the potential for developmental effects in fetuses. In these cases, body weights representative of women of childbearing age may be appropriate to adequately protect offspring from such health effects. To determine a mean body weight value appropriate to this population, separate body weight values for women in individual age groups within the range of 15 to 44 years old were analyzed from the NHANES III data (WESTAT, 2000). The resulting median and mean body weight values are 63.2 and 67.3 kg, respectivel y. Ershow and Cantor (1989) present body weight values specifically for pregnant women included in the survey; median and mean weights are 64.4 and 65.8 kilograms, respectively. Ershow and Cantor (1989), however, do not indicate the ages of these pregnant women. Based on this information for women of childbearing age and pregnant women, EPA recommends use of a body weight value of 67 kg in cases where pregnant women are the specific population of concern and the chemical of concern exhibits reproductive and/or developmental effects (i.e., the critical effect upon which the RfD or POD/UF is based). Using the 67 kg assumption would result in lower (more protective) criteria than criteria based on 70 kg.4-20 As discussed earlier, because infants and children generally have a higher rate of water and food consumption per unit body weight compared with adults, a higher intake rate per unit body weight may be needed when comparing estimated exposure doses with critical doses when RfDs are based on health effects in children. To calculate intake rates relevant to such effects, the body weight of children should be used. As with the default body weight for pregnant women, EPA is not recommending the development of additional AWQC (i.e., similar to drinking water health advisories) that focus on acute or short-term effects, since these are not seen routinely as having a meaningful role in the water quality criteria program. However, there may be circumstances where the consideration of exposures for these groups is warranted. Although the AWQC generally are based on chronic health effects data, they are intended to also be protective with respect to adverse effects that may reasonably be expected to occur as a result of elevated shorter-term exposures. EPA acknowledges this as a potential course of action and is, therefore, recommending these default values which EPA would consider in an appropriate circumstance and for States and authorized Tribes to utilize in such situations.

EPA is recommending an assumption of 30 kg as a default child’s body weight to calculate AWQC to provide additional protection for children when the chemical of concern indicates health effects in children are of predominant concern (i.e., test results show children are more susceptible due to less developed immune systems, neurological systems, and/or lower body weights). The value is based on the mean body weight value of 29.9 kg for children ages 1 to14 years old, which combines body weight values for individual age groups within this largergroup. The mean value is based on body weight information from NHANES III for individualyear age groups between one and 14 years old (WESTAT, 2000). A mean body weight of 28 kg is obtained using body weight values from Ershow and Cantor (1989) for five age groups within this range of 0-14 years and applying a wei ghting method for different ages by population percentages from the U.S. Bureau of the Census. The 30 kg assumption is also consistent with the age range for children used with the estimated fish intake rates. Unfortunately, fish intake rates for finer age group divisions are not possible due to the limited sampling base from the fish intake survey; there is limited confidence in calculated values (e.g., the mean) for such fine age groups. Given this limitation, the broad age category of body weight for children is suitable for use with the default fish intake assumption.

Given the hierarchy of preferences regarding the use of fish intake information (see Section 4.3.3), States may have more comprehensive data and prefer to target a more narrow, younger age group. If States choose to specifically evaluate toddlers, EPA recommends using 13 kg as a default body weight assumption for children ages 1 to 3 years old. The median and mean values of body weight for children 1 to 3 years old are 13.2 and 13.1 kg, respectively, based on an analysis of the NHANES III database (WESTAT, 2000). The NHANES III median and mean values for females between 1 and 3 years old are 13.0 and 12.9 kg, respectively, and are 13.4 and 13.4 kg for males, respectively. Median and mean body weight values from the earlier Ershow and Cantor (1989) study for children ages 1 to 3 years old were 13.6 and 14.1 kg, respectively. Finally, if infants are specifically evaluate d, EPA recommends a default body weight of 7 kg based on the NHANES III analysis. Median and mean body weights for both male and female infants (combined) 2 months old were 6.3 and 6.3 kg, respectively, and for infants 3 months old were 7.0 and 6.9 kg, respectively. With the broader age category of males and females 2 to 6 months old, median and mean body weights were 7.4 and 7.4 kg, respectively. The NHANES4-21 analysis did not include infants under 2 months of age. Although EPA is not recommending body weight values for newborns, the NCHS Nati onal Vital Statistics Report indicates that, for 1997, the median birth weight ranged from 3 to 3.5 kg, according to WESTAT (2000).

Body weight values for individual ages within the larger range of 0-14 years are listed in the Exposure Assessment TSD for those States and authorized Tribes who wish to use body weight values for these individual groups. States and Tribes may wish to consider certain general developmental ages (e.g., infants, pre-adolescents, etc.), or certain specific developmental landmarks (e.g., neurological development in the first four years), depending on the chemical of concern. EPA encourages Stat es and authorized Tribes to choose a body weight intake from the tables presented in the TSD, if they believe a particular age subgroup is more appropriate.

4.3.2 Drinking Water Intake Rates

The basis for the drinking water intake rates (also for the fish intake rates presented in Section 4.3.3) is the 1994-96 Continuing Survey of Food Intake by Individuals ( CSFII) conducted by the U.S. Department of Agriculture (USDA, 1998). The CSFII survey collects dietary intake information from nationally representative samples of non-institutionalized persons residing in United States households. Households in these national surveys are sampled from the 50 states and the District of Columbia. Each survey collects daily consumption records for approximately 10,000 food codes across nine food groups. These food groups are (1) milk and milk products; (2) meat, poultry, and fish; (3) eggs; (4) dry beans, peas, legumes, nuts, and seeds; (5) grain products; (6) fruit; (7) vegetables; (8) fats, oils, and salad dressings; and (9) sweets, sugars, and beverages. The survey also asks each respondent how many fluid ounces of plain drinking water he or she drank during each of the survey days. In addition, the CSFII collects household information, including the source of plain drinking water, water used to prepare beverages, and water used to prepare foods. Data provide “up-to-date information on food intakes by Americans for use in policy formation, regulation, program planning and evaluation, education, and research.” The survey is “the cornerstone of the National Nutritional Monitoring and Related Research Program, a set of related federal activities intended to provide regular information on the nutritional status of the United States population” (USDA, 1998).

The 1994-96 CSFII was conducted according to a stratified, multi-area probability sample organized using estimates of the 1990 United States population. Stratification accounted for geographic location, degree of urbanization, and socioeconomics. Each year of the survey consisted of one sample with oversampling for low-income households.

Survey participants provided two non-consecutive, 24-hour days of dietary data. Both days’ dietary recall information was collected by an in-home interviewer. Interviewers provided participants with an instructional booklet and standard measuring cups and spoons to assist them in adequately describing the type and amount of food ingested. If the respondent referred to a cup or bowl in their own home, a 2-cup measuring cup was provided to aid in the calculation of the amount consumed. The sample person could fill their own bowl or cup with water to represent the amount eaten or drunk, and the interviewer could then measure the amount consumed by pouring it into the 2-cup measure. The Day 2 interview occurred three to 10 days4-22 after the Day 1 interview, but not on the same day of the week. The interviews allowed participants “three passes” through the daily intake record to maximize recall (USDA, 1998). Proxy interviews were conducted for children aged six and younger and sampled individuals unable to report due to mental or physical limitations. The average questionnaire administration time for Day 1 intake was 30 minutes, while Day 2 averaged 27 minutes.

Two days of dietary recall data were provided by 15,303 individuals across the three survey years. This constitutes an overall two-day response rate of 75.9 percent. Survey weights were corrected by the USDA for nonresponse.

All three 1994-96 CSFII surveys are multistage, stratified-cluster samples. Sample weights, which project the data from a samp led individual to the population, are based on the probability of an individual being sampled at each stage of the sampling design. The sample weights associated with each individual reporting tw o days of consumption data were adjusted to correct for nonresponse bias.

The 1994-96 CSFII surveys have advantages and limitations for estimating per capita water (or fish) consumption. The primary advantage of the CSFII surveys is that they were designed and conducted by the USDA to support unbiased estimation of food consumption across the population in the United States and the District of Columbia. Second, the survey is designed to record daily intakes of foods and nutrients and support estimation of food consumption.

One limitation of the 1994-96 CSFII surveys is that individual food consumption data were collected for only two days–a brief period wh ich does not necessarily depict “usual intake.” Usual dietary intake is defined as “the long-run average of daily intakes by an individual.” Upper percentile estimates may differ for short-term and longer-term data because short-term food consumption data tend to be inherently more variable. It is important to note, however, that variability due to duration of the survey does not result in bias of estimates of overall mean consumption levels. Also, the multistage survey design does not support interval estimates for many of the subpopulations of interest because of sparse representation in the sample. Subpopulations with sparse representation include Native Americans on reservations and certain ethnic groups. While these individuals are participants in the survey, they are not present in sufficient numbers to support consumption estimates.

Despite these limitations, the CSFII is considered one of the best sources of current information on consumption of water and fish-containing foods. The objective of estimating per capita water and fish consumption by the United States population is compatible with the statistical design and scope of the CSFII survey.

4.3.2.1 Rate Protective of Human Health from Chronic Exposure

EPA recommends maintaining the default drinking water intake rate of 2 L/day to protect most consumers from contaminants in drinking water. EPA believes that the 2 L/day assumption is representative of a majority of the population over the course of a lifetime. EPA also notes that there is comparatively little variability in water intake within the population compared with4-23 fish intake (i.e., drinking water intake varies, by and large, by about a three-fold range, whereas fish intake can vary by 100-fold). EPA believes that the 2 L/day assumption continues to represent an appropriate risk management decision. The results of the 1994-96 CSFII analysis indicate that the arithmetic mean, 75 th , and 90 thpercentile values for adults 20 years and older are 1.1, 1.5, and 2.2 L/day, respectively (USEPA, 2000a). The 2 L/day value represents the 86th percentile for adults. These values can also be compared to data from an older National Cancer Institute (NCI) study, which estimated intakes of tapwater in the United States based on the USDA’s 1977-78 Nationwide Food Consumption Survey (NFCS). The arithmetic mean, 75 th

, and 90 thpercentile values for adults 20 - 64 years old were 1.4, 1.7, and 2.3 L/day, respectively (Ershow and Cantor, 1989). The 2 L/day value represents the 88 thpercentile for adults from the NCI study.

The 2 L/day assumption was used with the original 1980 AWQC National Guidelines and has also been used in EPA’s drinking water program. EPA believes that the newer studies continue to support the use of 2 L/day as a reasonable and protective consumption rate that represents the intake of most water consumers in the general population. However, individuals who work or exercise in hot climates could ha ve water consumption rates significantly above 2 L/day, and EPA believes that States and Trib es should consider regional or occupational variations in water consumption.

4.3.2.2 Rates Protective of Developmental Human Health Effects

Based on the 1994-96 CSFII study data, EPA also recommends 2 L/day for women of childbearing age. The analysis for women of childbearing age (ages 15-44) indicate mean, 75 th

, and 90 thpercentile values of 0.9, 1.3, and 2.0 L/day, respectively. These rates compare well with those based on an analysis of tapwater intake by pregnant and lactating women by Ershow et al. (1991), based on the older USDA data, for women ages 15-49. Arithmetic mean, 75 thand 90 th percentile values were 1.2, 1.5, and 2.2 L/day, respectively, for pregnant women. For lactating women, the arithmetic mean, 75 thand 90 thpercentile values were 1.3, 1.7, and 1.9 L/day, respectively.

As noted above, because infants and children have a higher daily water intake per unit body weight compared with adults, a water consumption rate measured for children is recommended for use when RfDs are based on health effects in children. Use of this water consumption rate should result in adequate protection for infants and children when setting criteria based on health effects for this target population. EPA recommends a drinking water intake of 1 L/day to, again, represent a majority of the population of children that consume drinking water. The results of the 1994-96 CSFII analysis indicate that for children from 1 to 10 years of age, the arithmetic mean, 75 th , and 90 thpercentile values are 0.4, 0.6, and 0.9 L/day, respectively (USEPA, 2000a). The 1 L/day value represents the 93rd percentile for this group. The arithmetic mean, 75 th , and 90 thpercentile values for smaller children, ages 1 to 3 years, are 0.3, 0.5, and 0.7 L/day, respectively. The 1 L/day value represents the 97th percentile of the group ages 1 to 3 years old. For the category of infants under 1 year of age, the arithmetic mean, 75 th , and 90 thpercentile values are 0.3, 0.7, and 0.9 L/day, respectively. These data can similarly be compared to those of the older National Cancer Institute (NCI) study. The arithmetic mean, 75 th , and 90 thpercentile values for children 1 to 10 years old were 0.74, 0.96, and 1.3 L/day,4-24 respectively. The mean, 75 th , and 90 thpercentile values for children 1 to 3 years old in the NCI study were 0.6, 0.8, and 1.2 L/day, respectively. Finally, the mean, 75 th , and 90 thpercentile values for infants less than 6 months old were 0.3, 0.3, and 0.6 L/day, respectively (Ershow and Cantor, 1989).

4.3.2.3 Rates Based on Combining Drinking Water Intake and Body Weight

As an alternative to considering body weight and drinking water intake rates separately, EPA is providing rates based on intake per unit body weight data (in units of ml/kg) in the Exposure Assessment TSD, with additional discussion on their use. These rates are based on self-reported body weights from the CSFII survey respondents for the 1994-96 data. While EPA intends to derive or revise national default criteria on the separate intake values and body weights, in part due to the strong input receive d from its State stakeholders, the ml/kg-BW/day values are provided in the TSD for States or author ized Tribes that prefer their use. It should be noted that in their 1993 review, EPA’s Science Advisory Board (SAB) felt that using drinking water intake rate assumptions on a per unit body weight basis would be more accurate, but did not believe this change would appreciably affect the criteria values (USEPA, 1993).

4.3.3 Fish Intake Rates

The basis for the fish intake rates is the 1994-96 CSFII conducted by the USDA, and described above in Section 4.3.2.

4.3.3.1 Rates Protective of Human Health from Chronic Exposure

EPA recommends a default fish intake rate of 17.5 grams/day to adequately protect the general population of fish consumers, based on the 1994 to 1996 data from the USDA’s CSFII Survey. EPA will use this value when deriving or revising its national 304(a) criteria. This value represents the 90 thpercentile of the 1994-96 CSFII data. This value also represents the uncooked weight estimated from the CSFII data, and represents intake of freshwater and estuarine finfish and shellfish only. For deriving AWQC, EPA has also considered the States’ and Tribes’ needs to provide adequate protection from adverse health effects to highly exposed populations such as recreational and subsistence fishers, in addition to the general population. Based on available studies that characterize consumers of fish, recreational fishers and subsistence fishers are two distinct groups whose intake rates may be greater than the general population. It is, therefore, EPA’s decision to discuss intakes for these two groups, in addition to the general population.

EPA recommends default fish intake rates for recreational and subsistence fishers of 17.5 grams/day and 142.4 grams/day, respectively. These rates are also based on uncooked weights for fresh/estuarine finfish and shellfish only. However, because the level of fish intake in highly exposed populations varies by geographical loca tion, EPA suggests a four preference hierarchy for States and authorized Tribes to follow when deriving consumption rates that encourages use of the best local, State, or regional data ava ilable. A thorough discussion of the development of this policy method and relevant data sources is contained in the Exposure Assessment TSD. The hierarchy is also presented here because EPA strongly emphasizes that States and authorized4-25 Tribes should consider developing criteria to protect highly exposed population groups and use local or regional data over the default values as more representative of their target population group(s). The four preference hierarchy is: (1) use of local data; (2) use of data reflecting similar geography/population groups; (3) use of data from na tional surveys; and (4) use of EPA’s default intake rates.

The recommended four preference hierarchy is intended for use in evaluating fish intake from fresh and estuarine species only. Therefore, to protect humans who additionally consume marine species of fish, the marine portion should be considered an other source of exposure when calculating an RSC for dietary intake. Refer to the Exposure Assessment TSD for further discussion. States and Tribes need to ensure that when evaluating overall exposure to a contaminant, marine fish intake is not double-counted with the other dietary intake estimate used. Coastal States and authorized Tribes that believe accounting for total fish consumption (i.e., fresh/estuarine andmarine species) is more appropriate for protecting the population of concern may do so, provided that the marine intake component is not double-counted with the RSC estimate. Tables of fish consumption intakes based on the CSFII in the TSD provide rates for fresh/estuarine species, marine species, and total (combined) values to facilitate this option for States and Tribes. Throughout this section, the terms “fish intake” or “fish consumption” are used. These terms refer to the consumption of finfish andshellfish, and the CSFII survey includes both. States and Tribes should ensure th at when selecting local or regionally-specific studies, both finfish and shellfish are included when the population exposed are consumers of both types.

EPA’s first preference is that States and author ized Tribes use the results from fish intake surveys of local watersheds within the State or Tribal jurisdiction to establish fish intake rates that are representative of the defined populations being addressed for the particular waterbody. Again, EPA recommends that data indicative of fresh/estuarine species only be used which is, by and large, most appropriate for developing AWQC. EPA also recommends the use of uncooked weight intake values, which is discussed in greaterdetail with the fourth preference. States and authorized Tribes may use either high-end values (such as the 90 thor 95 thpercentile values) or average values for an identified population that they plan to protect (e.g., subsistence fishers, sport fishers, or the general population). EPA generally recommends that arithmetic mean values should be the lowest value considered by States or Tribes when choosing intake rates for use in criteria derivation. When considering geometric mean (median) values from fish consumption studies, States and authorized Tribes need to ensure that the distribution is based on survey respondents who reported consuming fish because surveys based on both consumers and nonconsumers can often result in median values of zero. If a State or Tribe chooses values (whether the central tendency or high-end values) from studies that particularly target high-end consumers, these values should be compared to high-end fish intake rates for the general population to make sure that the high-end consumers within the general population would be protected by the chosen intake rates. EPA believes this is a reasonable procedure and is also consistent with the recent Great Lakes Water Quality Initiative (known as the “GLI”) (USEPA, 1995). States and authorized Tribes may wish to conduct their own surveys of fish intake, and EPA guidance is available on methods to conduct such studies in Guidance for Conducting Fish and Wildlife Consumption Surveys (USEPA, 1998). Results from broader geographic regions in which the State or Tribe is located can also be used, but may not be as applicable as results from4-26 local watersheds. Since such studies would ultimately form the basis of a State or Tribe’s AWQC, EPA would review any surveys of fish intake for consistency with the principles of EPA’s guidance as part of the Agency’s review of water quality standards under Section 303(c).

If surveys conducted in the geographic area of the State or Tribe are not available, EPA’s second preference is that States and authorized Tribes consider results from existing fish intake surveys that reflect similar geography and popul ation groups (e.g., from a neighboring State or Tribe or a similar watershed type), and follow the method described above regarding target values to derive a fish intake rate. Again, EPA recommends the use of uncooked weight intake values and the use of fresh/estuarine species data only. Results of existing local and regional surveys are discussed in greater detail in the TSD.

If applicable consumption rates are not available from local, State, or regional surveys, EPA’s third preference is that States and authori zed Tribes select intake rate assumptions for different population groups from national food c onsumption surveys. EPA has analyzed one such national survey, the 1994-96 CSFII. As described in Section 4.3.2, this survey, conducted annually by the USDA, collects food consumption information from a probability sample of the population of all 50 states. Respondents to the surv ey provide two days of dietary recall data. A detailed description of the combined 1994-96 CSFII survey, the statistical methodology, and the results and uncertainties of the EPA analyses are provided in a separate EPA report (USEPA, 2000b). The Exposure Assessment TSD for this Methodology presents selected results from this report including point and interval estimates of combined finfish and shellfish consumption for the mean, 50 th(median), 90 th , 95 th , and 99 thpercentiles. The estimated fish consumption rates are by fish habitat (i.e., freshwater/estuarine, marine and all habitats) for the following population groups: (1) all individuals; (2) individuals age 18 and over; (3) women ages 15-44; and (4) children age 14 and under. Three kinds of estimated fish consumption rates are provided: (1) per capita rates (i.e., rates based on consumers and nonconsumers of fish from the survey period– refer to the TSD for further discussion); (2) consumers-only rates (i.e., rates based on respondents who reported consuming finfish or shellfish during the two-day reporting period); and (3) per capita consumption by body weight (i.e., per capita rates reported as milligrams of fish per kilogram of body weight per day).

EPA’s fourth preference is that States a nd authorized Tribes use as fish intake assumptions the following default rates, based on the 1994-96 CSFII data, that EPA believes are representative of fish intake for different popul ation groups: 17.5 grams/day for the general adult population and sport fishers, and 142.4 grams/day for subsistence fishers. These are risk management decisions that EPA has made after evaluating numerous fish intake surveys. These values represent the uncooked weight intake of freshwater/estuarine finfish and shellfish. As with the other preferences, EPA requests that St ates and authorized Tribes routinely consider whether there is a substantial population of sport fishers or subsistence fishers when developing site-specific estimates, rather than automatically basing them on the typical individual. Because the combined 1994-96 CSFII survey is national in scope, EPA will use the results from this survey to estimate fish intake for deriving national criteria. EPA has recognized the data gaps and uncertainties associated with the analysis of the 1994-96 CSFII survey in the process of making its default recommendations. The estimated mean of freshwater and estuarine fish ingestion for adults is 7.50 grams/day, and the median is 0 grams/day. The estimated 90 th4-27 percentile is 17.53 grams/day; the estimated 95 thpercentile is 49.59 grams/day; and the estimated 99 thpercentile is 142.41 grams/day. The median value of 0 grams/day may reflect the portion of individuals in the population who never eat fish as well as the limited reporting period (2 days) over which intake was measured. By applying as a default 17.5 grams/day for the general adult population, EPA intends to select an intake rate that is protective of a majority of the population (again, the 90 thpercentile of consumers and nonconsumers according to the 1994-96 CSFII survey data). Trophic level breakouts are: TL2 = 3.8 grams/day; TL3 = 8.0 grams/day; and TL4 = 5.7 grams/day. EPA further considers 17.5 grams/day to be indicative of the average consumption among sport fishers based on averages in the studies reviewed, which are presented in the Exposure Assessment TSD. Similarly, EPA believes that the assumption of 142.4 grams/day is within the range of average consumption estimates for subsistence fishers based on the studies reviewed. Experts at the 1992 National Workshop that initiated the effort to revise this Methodology acknowledged that the national survey high-end values are representative of average rates for highly exposed groups such as subsistence fishermen, specific ethnic groups, or other highly exposed people. EPA is aware that some local and regional studies indicate greater consumption among Native American, Pacific Asian American, and other subsistence consumers, and recommends the use of those studies in appropriate cases, as indicated by the first and second preferences. Again, States and authorized Tribes have the flexibility to choose intake rates higher than an average value for these population groups. If a State or authorized Tribe has not identified a separate well-define d population of high-end consumers and believes that the national data from the 1994-96 CSFII are representative, they may choose these recommended rates.

As indicated above, the default intake values are based on the uncooked weights of the fish analyzed. There has been some question regarding whether to use cooked or uncooked weights of fish intake for deriving the AWQC. Studies show that, typically, with a filet or steak of fish, the weight loss in cooking is about 20 percent; that is, the uncooked weight is approximately 20 percent higher (Jacobs et al., 1998). This obviously means that using uncooked weights results in a slightly higher intake rate and slightly more stringent AWQC. In researching consumption surveys for this proposal, EPA has found that some surveys have reported rates for cooked fish, others have reported uncooked rates, and many more are unclear as to whether cooked or uncooked rates are used. The basis of the CSFII survey was prepared or as consumed intakes; that is, the survey respondents estimated the weight of fish that they consumed. This was also true with the GLI (w hich was specifically based on studies describing consumption rates of cooked fish) and, by and large, cooked fish is what people consume. However, EPA’s Guidance For Assessing Chemical Contaminant Data For Use In Fish Advisories recommends analysis and advisories based on uncooked fish (USEPA, 1997a). EPA considered the potential confusion over the fact that the uncooked weights are used in the fish advisory program. Further, the measures of a contaminant in fish tissue samples that are applicable to compliance monitoring and the permitting program are related to the uncooked weights. The choice of intakes is also complicat ed by factors such as the effect of the cooking process, the different parts of a fish where a chemical may accumulate, and the method of preparation.

After considering all of the above (in addition to public input received), EPA will derive its national default criteria based on the uncooked weight fish intakes. The Exposure4-28 Assessment TSD provides additional guidance on s ite-specific modifications. Specifically, an alternate approach is described for calculating AWQC with the as consumed weight–which is more directly associated with human exposure and risk–and then adjusting the value by the approximate 20 percent loss to an uncooked equivalent (thereby representing the same relative risk as the as consumed value). This approach results in a different AWQC value (than using the uncooked weights) and represents a more direct translation of the as consumed risk to the uncooked equivalent. However, EPA understands that it is more scientifically rigorous and may be too intensive of a process for States and Tribes to rely on. The option is presented in the TSD to offer States and authorized Tribes greater flexibility with their water quality standards
program.

The default fish intake values also reflect specific designations of species classified in accordance with information regarding the life history of the species or based on landings information form the National Marine Fisheries Service. Most significantly, salmon has been reclassified from a freshwater/estuarine species to a marine species. As marine harvested salmon represents approximately 99 percent of salmon consumption in the 1994-96 CSFII Survey, removal reduces the overall fresh/estuarine fish consumption rate by 13 percent. Although they represent a very small percentage of freshwater/estuarine intake, land-locked and farm-raised salmon consumed by 1994-96 CSFII respondents are still included. The rationale for the default intake species designations is explained in the Exposure Assessment TSD. Once again, EPA emphasizes the flexibility for States and authorized Tribes to use alternative assumptions based on local or regional data to better represent their population groups of concern.

4.3.3.2 Rates Protective of Developmental Human Health Effects

Exposures resulting in health effects in children or developmental effects in fetuses may be of primary concern. As discussed at the be ginning of this section on exposure factors used, in a situation where acute or sub-chronic toxicity and exposure are the basis of an RfD (or POD/UF), EPA will consider basing its national default criteria on children or women of childbearing age, depending on the target population at greatest risk. EPA recommends that States and authorized Tribes use exposure fact ors for children or women of childbearing age in these situations. As stated previously, EPA is not recommending the development of additional AWQC but is acknowledging that basing a criterion on these population groups is a potential course of action and is, therefore, recommending the following default intake rates for such situations.

EPA’s preferences for States and authorized Tribes in selecting values for intake rates relevant for children is the same as that discussed above for establishing values for average daily consumption rates for chronic effects; i.e., in decreasing order of preference, results from fish intake surveys of local watersheds, results from existing fish intake surveys that reflect similar geography and population groups, the distribution of intake rates from nationally based surveys (e.g., the CSFII), or lastly, the EPA default rates. When an RfD is based on health effects in children, EPA recommends a default intake rate of 156.3 grams/day for assessing those contaminants that exhibit adverse effects. This represents the 90 thpercentile consumption rate for actual consumers of freshwater/estuarine finfish and shellfish for children ages 14 and under using the combined 1994 to 1996 results from the CSFII survey. The value was calculated based4-29 on data for only those children who ate fish during the 2-day survey period, and the intake was averaged over the number of days during which fish was actually consumed. EPA believes that by selecting the data for consumers only, the 90 thpercentile is a reasonable intake rate to approximate consumption of fresh/estuarine finfis h and shellfish within a short period of time for use in assessments where adverse effects in children are of primary concern. As discussed previously, EPA will use a default body weight of 30 kg to address potential acute or subchronic effects from fish consumption by children. EPA is also providing these default intake values for States and authorized Tribes that choose to provide additional protection when developing criteria that they believe should be based on health effects in children. This is consistent with the rationale in the recent GLI (USEPA, 1995) and is an approach that EPA believes is reasonable. Distributional information on intake values relevant for assessing exposure when health effects to children are of concern is presented in the Exposure Assessment TSD.

There are also cases in which pregnant women may be the population of most concern, due to the possibility of developmental effects that may result from exposures of the mother to toxicants. In these cases, fish intake rates specific to females of childbearing age are most appropriate when assessing exposures to developmental toxicants. When an RfD is based on developmental toxicity, EPA proposes a default intake rate of 165.5 grams/day for assessing exposures for women of childbearing age from contaminants that cause developmental effects. This is equivalent to the 90 thpercentile consumption rate for actual consumers of freshwater/ estuarine finfish and shellfish for women ages 15 to 44 using the combined 1994 to1996 results from the CSFII survey. As with the rate for children, this value represents only those women who ate fish during the 2-day survey period. As discussed previously, EPA will use a default body weight of 67 kg for women of childbearing age.

4.3.3.3 Rates Based on Combining Fish Intake and Body Weight

As with the drinking water intake values, EPA is providing values for fish intake based on a per unit body weight basis (in units of mg/kg) in the Exposure Assessment TSD. These rates use the self-reported body weights of the 1994-96 CSFII survey. Again, while EPA intends to derive or revise national default criteria on the separate intake values and body weights, the mg/kg-BW/day values are provided in the TSD for St ates or authorized Tribes that prefer their

use.

4.4 REFERENCES FOR EXPOSURE

Ershow A.G., Brown L.M. and Cantor K.P. 1991. Intake of tapwater and total water by pregnant and lactating women. Am. J. Public Health. 81:328-334.

Ershow A.G. and K.P. Cantor. 1989. Total Water and Tap Water Intake in the United States: Population-based Estimates of Quantities and Sources . National Cancer Institute. Bethesda, MD. Order #263-MD-810264.

Gibbons, J.D. 1971. Nonparametric Statistical Inference. Chapter 2: Order Statistics. McGraw-Hill, Inc. New York, NY.4-30 Jacobs, H.L., H.D. Kahn, K.A. Stralka, and D.B. Phan. 1998. Estimates of per capita fish consumption in the U.S. based on the continuing survey of food intake by individuals (CSFII). Risk Analysis: An International Journal18(3).

McDowell, M. 2000. Personal communication between Denis R. Borum, U.S. Environmental Protection Agency, and Margaret McDowell, Health Statistician, National Health and Nutrition Examination Survey, National Center for Health Statistics. March 24, 2000.

USDA. 1998. U.S. Department of Agriculture. 1994–1996 Continuing Survey of Food Intakes by Individuals and 1994–1996 Diet and Health Knowledge Survey . Agricultural Research Service, USDA. NTIS CD–ROM, accession number PB98–500457. [Available from the National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22161. Phone: (703) 487–4650.]

USEPA. 1993. Review of the Methodology for Developing Ambient Water Quality Criteria for the Protection of Human Health. Prepared by the Drinking Water Committee of the Science Advisory Board. EPA-SAB-DWC

USEPA. 1994. Reference dose (RfD) for oral exposure for cadmium. Integrated Risk Information System (IRIS). Online. (Verification date 02/01/94.) Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH.

USEPA. 1995. Great Lakes Water Quality Initiative Technical Support Document for the Procedure to Determine Bioaccumulation Factors . Office of Water. Washington, DC. EPA/820/B-95/005.

USEPA. 1997a. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories. Volume II: Risk Assessment and Fish Consumption Limits . Second Edition. Office of Water. Washington DC. EPA/823/B-97/009.

USEPA. 1997b. Exposure Factors Handbook . National Center for Environmental Assessment, Office of Research and Development. Wa shington, DC. EPA/600/P-95/002Fa. August.

USEPA. 1998. Guidance for Conducting Fish and Wildlife Consumption Surveys . Office of Science and Technology, Office of Wate r. Washington, DC. EPA-823-B-98-007. November.

USEPA. 2000a. Estimated Per Capita Water Ingestion in the United States: Based on Data Collected by the United States Department of Agriculture’s 1994-96 Continuing Survey of Food Intakes by Individuals. Office of Science and Technology, Office of Water. Washington, DC. EPA-822-00-008. April.

USEPA. 2000b. Estimated Per Capita Fish Consumption in the United States: Based on Data Collected by the United States Department of Agriculture’s 1994-1996 Continuing4-31 Survey of Food Intake by Individuals. Office of Science and Technology, Office of Water, Washington, DC. March.

WESTAT. 2000. Memorandum on Body Weight Estimates Based on NHANES III data, Including Data Tables and Graphs . Analysis conducted and prepared by WESTAT, under EPA Contract No. 68-C-99-242. March 3, 2000.5-1 (Equation 5-1) 

### 5. B IOACCUMULATION

5.1 INTRODUCTION

Aquatic organisms can accumulate certain chemicals in their bodies when exposed to these chemicals through water, their diet, and other sources. This process is called bioaccumulation. The magnitude of bioaccumulation by aquatic organisms varies widely depending on the chemical but can be extremely high for some highly persistent and hydrophobic chemicals. For such highly bioaccumulative chemicals, concentrations in aquatic organisms may pose unacceptable human health risks from fish and shellfish consumption even when concentrations in water are too low to cause unacceptable health risks from drinking water consumption alone. These chemicals may also biomagnify in aquatic food webs, a process whereby chemical concentrations increase in aquatic organisms of each successive trophic level due to increasing dietary exposures (e.g., increasi ng concentrations from algae, to zooplankton, to forage fish, to predatory fish).

In order to prevent harmful exposures to waterborne chemicals through the consumption of contaminated fish and shellfish, national 304(a) water quality criteria for the protection of human health must address the process of chemical bioaccumulation in aquatic organisms. For deriving national 304(a) criteria to protecthuman health, EPA accounts for potential bioaccumulation of chemicals in fish and shellfish through the use of national bioaccumulation factors (BAFs). A national BAF is a ratio (in L/kg) that relates the concentration of a chemical in water to its expected concentration in commonly consumed aquatic organisms in a specified trophic level. An illustration of how national BAFs are used in the derivation of 304(a) criteria for carcinogens using linear low-dose extrapolation is shown in the following equation:

where:

RSD = Risk specific dose (mg/kg-day) BW = Human body weight (kg) DI = Drinking water intake (L/day) FI i = Fish intake at trophic level I, where I=2, 3, and 4; BAF i = National bioaccumulation factor at trophic level I, where I=2, 3, and 4

The purpose of this chapter is to present EPA’s recommended methodology for deriving national bioaccumulation factors for setting national 304(a) water quality criteria to protect human health. A detailed scientific basis of the recommended national BAF methodology is provided in the Bioaccumulation TSD. While the methodology detailed in this chapter is5-2 intended to be used by EPA for deriving nationa l BAFs, EPA encourages States and authorized Tribes to derive BAFs that are specific to cer tain regions or waterbodies, where appropriate. Guidance to States and authorized Tribes for deriving site-specific BAFs is provided in the Biaccumulation TSD.

5.1.1 Important Bioaccumulation and Bioconcentration Concepts

Several attributes of the bioaccumulation process are important to understand when deriving national BAFs for use in setting national 304(a) criteria. First, the term “bioaccumulation” refers to the uptake and retention of a chemical by an aquatic organism from all surrounding media (e.g., water, food, sediment). The term “bioconcentration” refers to the uptake and retention of a chemical by an aquatic organism from water only. For some chemicals (particularly those that are highly persistent and hydrophobic), the magnitude of bioaccumulation by aquatic organisms can be substantially greater than the magnitude of bioconcentration. Thus, an assessment of bioconcentration alone would underestimate the extent of accumulation in aquatic biota for these chemicals. Accordingly, EPA’s guidelines presented in this chapter emphasize the measurement of chemical bioaccumulation by aquatic organisms, whereas EPA’s 1980 Methodology emphasized the measurement of bioconcentration.

Another noteworthy aspect of bioaccumu lation process is the issue of steady-state conditions. Specifically, both bioaccumulation a nd bioconcentration can be viewed simply as the result of competing rates of chemical uptake and depuration (chemical loss) by an aquatic organism. The rates of chemical uptake and depuration can be affected by various factors including the properties of the chemical, the physiology of the organism in question, water quality and other environmental conditions, ecological characteristics of the waterbody (e.g., food web structure), and the concentration and loadings history of the chemical. When the rates of chemical uptake and depuration are equal, tissue concentrations remain constant over time andthe distribution of the chemical between the organism and its source(s) is said to be at steadystate. For constant chemical exposures and other conditions, the steady-state concentration in the organism represents the highest accumulation potential of the chemical in that organism under those conditions. The time required for a chemical to achieve steady state has been shown to vary according to the properties of the chemical and other factors. For example, some highly hydrophobic chemicals can require long periods of time to reach steady state between environmental compartments (e.g., many months), while highly hydrophilic chemicals usually reach steady-state relatively quickly (e.g., hours to days).

Since national 304(a) criteria for the protection of human health are typically designed to protect humans from harmful lifetime or long-term exposures to waterborne contaminants, the assessment of bioaccumulation that equals or approximates steady-state accumulation is one of the principles underlying the derivation of national BAFs. For some chemicals that require relatively long periods of time to reach steady-state in tissues of aquatic organisms, changes in water column concentrations may occur on a much more rapid time scale compared to the corresponding changes in tissue concentrations. Thus, if the system departs substantially from steady-state conditions and water concentrations are not averaged over a sufficient time period, the ratio of the tissue concentration to a water concentration may have little resemblance to the steady-state ratio and have little predictive value of long-term bioaccumulation potential.5-3 Therefore, BAF measurements should be based on water column concentrations which are averaged over a sufficient period of time (e.g., a duration comparable to the time required for the chemical to reach steady-state). In addition, BAF measurements should be based on adequate spatial averaging of both tissue and water column concentrations for use in deriving 304(a) criteria for the protection of human health.

For this reason, a BAF is defined in this Methodology as representing the ratio (in L/kgtissue) of a concentration of a chemical in tissue to its concentration in the surrounding water in situations where the organism and its food are exposed and the ratio does not change substantially over time (i.e., the ratio which reflects bioaccumulation at or near steady-state). A bioconcentration factor (BCF) is the ratio (in L/kg-tissue) of the concentration of a substance in tissue of an aquatic organism to its concentration in the ambient water, in situations where the organism is exposed through the water only and the ratio does not change substantially over time.

5.1.2 Goal of the National BAF

The goal of EPA’s national BAF is to represent the long-term, average bioaccumulation potential of a chemical in edible tissues of aquatic organisms that are commonly consumed by humans throughout the United States. National BAFs are not intended to reflect fluctuations in bioaccumulation over short time periods (e.g., a few days) because 304(a) human health criteria are generally designed to protect humans from l ong-term exposures to waterborne chemicals. National BAFs are also intended to account for some major chemical, biological, and ecological attributes that can affect bioaccumulation in bodies of water across the United States. For example, separate procedures are provided fo r deriving national BAFs depending on the type of chemical (i.e., nonionic organic, ionic organic, inorganic and organometallic). In addition, EPA’s national BAFs are derived separately for each trophic level to account for potential biomagnification of some chemicals in aquatic food webs and broad physiological differences between trophic levels that may influence bioaccumulation. Because lipid content of aquatic organisms and the amount of organic carbon in the water column have been shown to affect bioaccumulation of nonionic organic chemicals, EPA’ s national BAFs are adjusted to reflect the lipid content of commonly consumed fish and shellfish and the freely dissolved fraction of the chemical in ambient water for these chemicals.

5.1.3 Changes to the 1980 Methodology

Numerous scientific advances have occurred in the area of bioaccumulation since the publication of the 1980 Methodology for deriving AWQC for the protection of human health (USEPA, 1980). These advances have significantly increased our ability to assess and predict the bioaccumulation of chemicals in aquatic biota. As a result, EPA has revised the bioaccumulation portion of the 1980 Methodology to reflect the current state of the science and to improve accuracy in assessing bioaccumulation for setting 304(a) criteria for the protection of human health. The changes contained in the bioaccumulation portion of the 2000 Human Health Methodology are mostly designed to:5-4 

### C Improve the ability to incorporate chemical exposure from sediments and aquatic food webs in assessing bioaccumulation potential,



### C Expand the ability to account for site-specific factors which affect bioaccumulation, and



### C Incorporate new data and assessment tools into the bioaccumulation assessment process.

A summary of the key changes that have been incorporated into the bioaccumulation portion of the 2000 Human Health Methodology and appropriate comparisons to the1980 Methodology are provided below.

5.1.3.1 Overall Approach

The 1980 Methodology for deriving 304(a) criteria for the protection of human health emphasized the assessment of bioconcentration (uptake from water only) through the use of the BCF. Based on the 1980 Methodology, measured BCFs were usually determined from laboratory data unless field data demonstrated consistently higher or lower accumulation compared with laboratory data. In these cases, “field BCFs” (currently termed field-measured BAFs) were recommended for use. For lipophilic chemicals where lab or field-measured data were unavailable, EPA recommended predicting BCFs from the octanol-water partition coefficient and the following equation from Veith et al. (1979): “log BCF = (0.85 log K
ow ) - 0.70".

The 2000 Human Health Methodology revisions contained in this chapter emphasize the measurement of bioaccumulation (uptake from water, sediment, and diet) through the use of the BAF. Consistent with the 1980 Methodology, measured data are preferred over predictive approaches for determining the BAF (i.e., field-measured BAFs are generally preferred over predicted BAFs). However, the 2000 Human Health Methodology contains additional methods for deriving a national BAF that were not available in 1980. The preference for using the BAF methods also differs depending on the type and pr operties of the chemical. For example, the BAF derivation procedure differs for each of th ree broadly defined chemical categories: (1) nonionic organic, (2) ionic organic, and (3 ) inorganic and organometallic chemicals. Furthermore, within the category of nonionic organi c chemicals, different procedures are used to derive the BAF depending on a chemicals’ hydrophobicity and extent of chemical metabolism that would be expected to occur in aquatic biota.

5.1.3.2 Lipid Normalization

In the 1980 Methodology, BCFs for lipophilic chemicals were normalized by the lipid fraction in the tissue of fish and shellfish used to determine the BCF. Lipid normalization enabled BCFs to be averaged across tissues and organisms. Once the average lipid-normalized BCF was determined, it was adjusted by the consumption-weighted lipid content of commonly consumed aquatic organisms in the United States to obtain an overall consumption-weighted BCF. A similar procedure has been retained in the 2000 Human Health Methodology, wherebyBAFs for nonionic organic chemicals are lipid normalized and adjusted by the consumptionweighted lipid content of commonly consumed organisms to obtain a BAF for criteria5-5 calculations. However, the 2000 Human Health Methodology uses more up-to-date lipid data and consumption data for deriving the consumption-weighted BAFs.

5.1.3.3 Bioavailability

Bioconcentration factors derived according to the 1980 Methodology were based on the total concentration of the chemical in water, for both lipophilic and nonlipophilic chemicals. In the 2000 Human Health Methodology, BAFs for nonionic organic chemicals are derived using the most bioavailable fraction (i.e., the freely di ssolved fraction) to account for the influence of particulate and dissolved organic carbon on a chemical’s bioavailability. Such BAFs are then adjusted to reflect the expected bioavailability at the sites of interest (i.e., by adjusting for organic carbon concentrations at the sites of intere st). Procedures for accounting for the effect of organic carbon on bioaccumulation were published previously by EPA under the Great Lakes Water Quality Initiative (GLWQI or GLI) rulemaking (USEPA, 1995a,b). Bioavailability is also considered in developing BAFs for the other chemical classes defined in the 2000 Human Health Methodology (e.g., ionic organics, inorganics/orga nometallics) but is done so on a chemical-bychemical basis.

5.1.3.4 Trophic Level Considerations

In the 1980 Methodology, BCFs were determined and used for criteria derivation without explicit regard to the trophic level of the aquatic organism (e.g., benthic filter feeder, forage fish, predatory fish). Over the past two decades, much information has been assembled which demonstrates that an organism’s trophic position in the aquatic food web can have an important effect on the magnitude of bioaccumulation of certain chemicals. In order to account for the variation in bioaccumulation that is due to trophic position of the organism, the 2000 HumanHealth Methodology recommends that BAFs be determined and applied on a trophic levelspecific basis.

5.1.3.5 Site-Specific Adjustments

The 1980 Methodology contained little guidance for making adjustments to the national BCFs to reflect site- or region-specific c onditions. The 2000 Human Health Methodology has greatly expanded the guidance to States and authorized Tribes for making adjustments to national BAFs to reflect local conditions. This guidance is contained in the Bioaccumulation TSD. In the Bioaccumulation TSD, guidance and data are provided for adjusting national BAFs to reflect the lipid content in locally consumed aquatic biota and the organic carbon content in the waterbodies of concern. This guidance also allows the use of appropriate bioaccumulation models for deriving site-specific BAFs. EPA also plans to publish detailed guidance on designing and conducting field bioaccumulation studi es for measuring BAFs and biota-sediment accumulation factors (BSAFs). In general, EPA encourages States and authorized Tribes to make site-specific modifications to EPA’s national BAFs provided such adjustments are scientifically defensible and adequately protect the designated use of the waterbody.

While the aforementioned revisions are new to EPA’s Methodology for deriving national 304(a) criteria for the protection of human health, many of these refinements have been5-6 (Equation 5-2) incorporated in prior Agency guidance and regulations. For example, the use of food chain multipliers to account for the biomagnification of nonionic organic chemicals in aquatic food webs when measured data are unavailable was introduced by EPA in three documents : Technical Support Document for Water Quality-Based Toxics Control (USEPA, 1991), a draft document entitled Assessment and Control of Bioconcentratable Contaminants in Surface Waters (USEPA, 1993), and in the Great Lakes Water Quality Initiative (GLI) (USEPA, 1995b). Similarly, procedures for predicting BAFs using BSAFsand incorporating the effect of organic carbon on bioavailability were used to derive water quality criteria under the GLI.

5.1.4 Organization of This Section

The methodology for deriving national BAFs for use in deriving National 304(a) Human Health AWQC is provided in the following sections. Important terms used throughout this chapter are defined in Section 5.2. Section 5.3 provides an overview of the BAF derivation guidelines. Detailed procedures for deriving national BAFs are provided in Section 5.4 for nonionic organic chemicals, in Section 5.5 for i onic organic chemicals, and in Section 5.6 for inorganics and organometallic chemicals. Literature cited is provided in Section 5.7.

5.2 DEFINITIONS

The following terms and definitions are used throughout this chapter.

Bioaccumulation. The net accumulation of a substance by an organism as a result of uptake from all environmental sources.

Bioconcentration. The net accumulation of a substance by an aquatic organism as a result of uptake directly from the ambient water, through gill membranes or other external body surfaces.

Bioaccumulation Factor (BAF). The ratio (in L/kg-tissue) of the concentration of a substance in tissue to its concentration in the ambient water, in situations where both the organism and its food are exposed and the ratio does not change substantially over time. The BAF is calculated

as:

where:

C t = Concentration of the chemical in the specified wet tissue C
w = Concentration of chemical in water5-7 (Equation 5-3)

(Equation 5-4) Bioconcentration Factor (BCF). The ratio (in L/kg-tissue) of the concentration of a substance in tissue of an aquatic organism to its concentration in the ambient water, in situations where the organism is exposed through the water only and the ratio does not change substantially over time. The BCF is calculated as:

where:

C t = Concentration of the chemical in the specified wet tissue C
w = Concentration of chemical in water

Baseline BAF (BAF R fd ). For nonionic organic chemicals (and certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior applies), a BAF (in L/kg-lipid) that is based on the concentration of freely dissolved chemical in the ambient water and the lipid normalized concentration in tissue.

Baseline BCF (BCF R fd ). For nonionic organic chemicals (and certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior applies), a BCF (in L/kg-lipid) that is based on the concentration of freely dissolved chemical in the ambient water and the lipid normalized concentration in tissue.

Biomagnification. The increase in tissue concentration of a chemical in organisms at successive trophic levels through a series of predator-prey associations, primarily through the mechanism of dietary accumulation.

Biomagnification Factor (BMF). The ratio (unitless) of the tissue concentration of a chemical in a predator at a particular trophic level to the tissue concentration in its prey at the next lower trophic level for a given waterbody and chemical exposure. For nonionic organic chemicals (and certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior applies), a BMF can be calculated using lipid-normalized concentrations in the tissue of organisms at two successive trophic levels as:

where:

C R (TL, n) = Lipid-normalized concentration in appropriate tissue of predator organism at a given trophic level (TL “n”)5-8 (Equation 5-5)

(Equation 5-6) C R (TL, n-1) = Lipid-normalized concentration in appropriate tissue of prey organism at the next lower trophic level from the predator (TL “n-1”)

For inorganic, organometallic, and certain ioni c organic chemicals where lipid and organic carbon partitioning does not apply, a BMF can be calculated using chemical concentrations in the tissue of organisms at two successive trophic levels as:

where:

C t (TL, n) = Concentration in appropriate tissue of predator organism at trophic level “n” (may be either wet weight or dry weight concentration so long as both the predator and prey concentrations are expressed in the same manner) C t (TL, n-1) = Concentration in appropriate tissue of prey organism at the next lower trophic level from the predator (may be either wet weight or dry weight concentration so long as both the predator and prey concentrations are expressed in the same manner)

Biota-Sediment Accumulation Factor (BSAF). For nonionic organic chemicals (and certain ionic organic chemicals where similar lipid a nd organic carbon partitioning behavior applies), the ratio of the lipid-normalized concentration of a substance in tissue of an aquatic organism to its organic carbon-normalized concentration in surface sediment (expressed as kg of sediment organic carbon per kg of lipid), in situations where the ratio does not change substantially over time, both the organism and its food are exposed, and the surface sediment is representative of average surface sediment in the vicinity of the organism. The BSAF is defined as:

where:

C R = The lipid-normalized concentration of the chemical in tissues of the biota (μg/g lipid) C
soc = The organic carbon-normalized concentration of the chemical in the surface sediment (μg/g sediment organic carbon)

Depuration. The loss of a substance from an organism as a result of any active or passive
process.5-9 (Equation 5-7)

(Equation 5-8) Food Chain Multiplier (FCM). For nonionic organic chemicals (and certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior applies), the ratio of a baseline BAF R fdfor an organism of a particular trophic level to the baseline BCF R fd(usually determined for organisms in trophic level one). For inorganic, organometallic, and certain ionic organic chemicals where lipid and organic carbon partitioning does not apply, a FCM is based on total (wet or dry weight) concentrations of the chemical in tissue.

Freely Dissolved Concentration. For nonionic organic chemicals, the concentration of the chemical that is dissolved in ambient water, excluding the portion sorbed onto particulate or dissolved organic carbon. The freely dissolved concentration is considered to represent the most bioavailable form of an organic chemical in water and, thus, is the form that best predicts bioaccumulation. The freely dissolved concentration can be determined as:

where:

C
w f d = Freely dissolved concentration of the organic chemical in ambient water C
w

t = Total concentration of the organic chemical in ambient water f fd = Fraction of the total chemical in ambient water that is freely dissolved

Hydrophilic. A term that refers to the extent to which a chemical is attracted to partitioning into the water phase. Hydrophilic organic chemicals have a greater tendency to partition into polar phases (e.g., water) compared to chemicals of hydrophobic chemicals.

Hydrophobic. A term that refers to the extent to which a chemical avoids partitioning into the water phase. Highly hydrophobic organic chemicals have a greater tendency to partition into nonpolar phases (e.g., lipid, organic carbon) compar ed with chemicals of lower hydrophobicity.

Lipid-normalized Concentration (C R ). The total concentration of a contaminant in a tissue orwhole organism divided by the lipid fraction in that tissue or whole organism. The lipidnormalized concentration can be calculated as:

where:

C t = Concentration of the chemical in the wet tissue (either whole organism or specified tissue)5-10 (Equation 5-9) f R = Fraction lipid content in the organism or specified tissue

Octanol-water Partition Coefficient (K
ow ). The ratio of the concentration of a substance in then-octanol phase to its concentration in the aqueous phase in an equilibrated two-phase octanolwater system. For log K
ow , the log of the octanol-water partition coefficient is a base 10 logarithm.

Organic Carbon-normalized Concentration (C
soc ). For sediments, the total concentration of a contaminant in sediment divided by the fracti on of organic carbon in sediment. The organic carbon-normalized concentration can be calculated as:

where:

C
s = Concentration of chemical in sediment f
oc = Fraction organic carbon in sediment

Uptake. Acquisition by an organism of a substance from the environment as a result of any active or passive process.

5.3 FRAMEWORK FOR DETERMINING NATIONAL BIOACCUMULATION FACTORS

5.3.1 Four Different Methods

Bioaccumulation factors used to derive national BAFs can be measured or predicted using some or all of the following four methods, depending on the type of chemical and its properties. These methods are:

(1) a measured BAF obtained from a fi eld study (i.e., a field-measured BAF);

(2) a BAF predicted from a field-measured BSAF;

(3) a BAF predicted from a laboratory-measur ed BCF (with or without adjustment by an FCM); and

(4) a BAF predicted from a chemical’s octanol-water partition coefficient (K
ow ), with or without adjustment using an FCM.5-11 A brief summary of each of the four methods is provided below. Additional details on the use of these four methods is provided in Section 5.4 (for nonionic organics), Section 5.5 (for ionic organics) and Section 5.6 (for inorganics and organometallics).

1. Field-Measured BAF. Use of a field-measured BAF, which is the most direct measure of bioaccumulation, is the only method that can be used to derive a national BAF for all types of chemicals (i.e., nonionic organi c, ionic organic, and inorganic and organometallic chemicals). A field-measured BAF is determined from a field study using measured chemical concentrations in the aquatic organism and its surrounding water. Because field studies are conducted in natural aquatic ecosystems, a field-measured BAF reflects an organism’s exposure to a chemical through all relevant exposure pathways (i.e., water, sediment, and diet). A field-m easured BAF also reflects any metabolism of achemical that might occur in the aquatic organism or its food web. Therefore, fieldmeasured BAFs are appropriate for all chemicals, regardless of the extent of chemical metabolism in biota.

2. Field-measured BSAF. For nonionic organic chemicals (and certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior applies), a BAF can also be predicted from BSAFs. A BSAF is similar to a field-measured BAF in that the concentration of a chemical in biota is measured in the field and reflects an organism’s exposure to all relevant exposure routes. A BSAF also reflects any chemical metabolism that might occur in the aquatic organism or its food web. However, unlike a field-measured BAF which references the biota concentration to the water concentration, a BSAF references the biota concentration to the sediment concentration. Use of the BSAF procedure is restricted to organic chemicals which are classified as being moderately to highly hydrophobic.

3. Lab-measured BCF. A laboratory-measured BCF can also be used to estimate a BAF for organic and inorganic chemicals. Howe ver, unlike a field-measured BAF or a BAF predicted from a field-measured BSAF, a laboratory-measured BCF only reflects the accumulation of chemical through the water exposure route. Laboratory-measured BCFs may therefore under estimate BAFs for chemicals where accumulation from sediment or dietary sources is important. In these cases, laboratory-measured BCFs can be multiplied by a FCM to reflect accumulation from non-aqueous (i.e., food chain) pathways of exposure. Since a laboratory-measured BCF is determined using the measured concentration of a chemical in an aquatic organism and its surrounding water, a laboratory-measured BCF reflects any metabolism of the chemical that occurs in the organism, but not in the food web.

4. K
ow . A chemical’s octanol-water partition coefficient, or K
ow , can also be used to predict a BAF for nonionic organic chemicals. This procedure is appropriate only for nonionic organic chemicals (and certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior applies). The K
ow has been extensively correlated with the BCF for nonionic organic chemicals that are poorly metabolized by aquatic organisms. Therefore, where substantial metabolism is known to occur in biota, the K
ow is not used5-12 to predict the BAF. For nonionic organic chemicals where chemical exposure through the food web is important, use of the K
ow alone will under predict the BAF. In such cases, the K
ow is adjusted with a FCM similar to the BCF procedure above.

5.3.2 Overview of BAF Derivation Framework

Although up to four methods can be used to derive a BAF as described in the previous section, it is evident that these methods do not apply equally to all types of chemicals. In addition, experience demonstrates that the required data will usually not be available to derive a BAF value using all of the applicable methods. As a result, EPA has developed the following guidelines to direct users in selecting the mo st appropriate method(s) for deriving a national BAF. Figure 5-1 shows the overall framework of EPA’s national BAF methodology. This framework illustrates the major steps and decisions that will ultimately lead to calculating a national BAF using one of six hierarchical proce dures shown at the bottom of Figure 5-1. Each procedure contains a hierarchy of the BAF de rivation methods discussed above, the composition of which depends on the chemical type and certain chemical properties (e.g., its degree of hydrophobicity and expected degree of metabolism and biomagnification). The number assigned to each BAF method within a procedure indicates its general order of preference for deriving a national BAF value. The goal of the framework and accompanying guidelines is to enable full use of available data and methods for deriving a national BAF value while appropriately restricting the use of certain methods to reflect their inherent limitations.

The first step in the framework is to define the chemical of concern. As described in Section 5.3.3, the chemical used to derive the national BAF should be consistent with the chemical used to derive the critical health assessment value. The second step is to collect and review all relevant data on bioconcentration and bioaccumulation of the chemical of concern (see Section 5.3.4). Once pertinent data are reviewed, the third step is to classify the chemical of concern into one of three broadly defined chemical categories: (1) nonionic organic chemicals, (2) ionic organic chemicals, and (3) and inorganic and organometallic chemicals. Guidance for classifying chemicals into these three categories is provided in Section 5.3.5.

After a chemical has been classified into one of the three categories, other information is used to select one of six hierarchical proce dures to derive the national BAF. The specific procedures for deriving a BAF for each chemical group are discussed in Section 5.4 for nonionic organics, Section 5.5 for ionic organics, and S ection 5.6 for inorganics and organometallics.5-13 CLASSIFY CHEMICAL OF CONCERN

Nonionic Organic 

### Figure 5-1. Framework for Deriving a National BAF

Ionic Organic Inorganic & Organometalic

Yes (Log KOW> 4) (Log K OW <4)

PROCEDURE #4 HYDROPHOBICITY?

METABOLISM?

Low High Low High METABOLISM?

1. Field BAF or PROCEDURE #3

Lab BCF

2. KOW

1. Field BAF PROCEDURE #2

3. Lab BCF 1. Field BAF PROCEDURE #1

2. BSAF

4. KOW *FCM BIOMAGNIFICATION?

PROCEDURE #6 3. Lab BCF*FCM Low COLLECT & REVIEW DATA DEFINE CHEMICAL OF CONCERN

Moderate-High

PROCEDURE #5 No IONIZATION NEGLIGIBLE?

No Yes

2. BSAF 1. Field BAF or Lab BCF 1. Field BAF or Lab BCF 1. Field BAF

2. Lab BCF*FCM5-14 Detailed guidance concerning the first three step s of the derivation process (i.e, defining the chemical of concern, collecting and reviewing data , and classifying the chemical of concern) is provided in the following three sections.

5.3.3 Defining the Chemical of Concern

Defining the chemical of concern is the first step in deriving a national BAF. This step involves precisely defining the form(s) of the chemical upon which the national BAF value will be derived. Although this step is usually strai ghtforward for single chemicals, complications can arise when the chemical of concern occurs as a mixture. The following guidelines should be followed for defining the chemical of concern.

1. Information for defining the chemical of concern should be obtained from the health and exposure assessment portions of the criteria derivation effort. The chemical(s) used to derive the national BAF should be consistent with the chemical(s) used to derive the reference dose (RfD), point of departure/un certainty factor (POD/UF), or cancer potency factor.

2. In most cases, the RfD, POD/UF, or cancer potency factor will be based on a single chemical. In some cases, the RfD, POD/UF, or cancer potency factor will be based on a mixture of compounds, typically within the same chemical class (e.g., toxaphene, chlordane). In these situations, the national BAF should be derived in a manner that is consistent with the mixture used to express the health assessment.

a. If sufficient data are available to reliably assess the bioaccumulation of each relevant compound contained in the mixture, then the national BAF(s) should be derived using the BAFs for the individual compounds of the mixture and appropriately weighted to reflect the mixture composition used to establish the RfD, POD/UF, or cancer potency factor. An example of this approach is shown in the derivation of BAFs for PCBs in the GLI Rulemaking (USEPA, 1997).

b. If sufficient data are not available to reliably assess the bioaccumulation of individual compounds of the mixture, then the national BAF(s) should be derived using BAFs for the same or appropriately similar chemical mixture as that used to establish the RfD, POD/UF, or cancer potency value.

5.3.4 Collecting and Reviewing Data

The second step in deriving a national BAF is to collect and review all relevant bioaccumulation data for the chemical of concern. The following guidance should be followed for collecting and reviewing bioaccumulation data for deriving national BAFs.

1. All data on the occurrence and accumulation of the chemical of concern in aquatic animals and plants should be collected and reviewed for adequacy.5-15 2. A comprehensive literature search strategy should be used for gathering bioaccumulation-related data. An example of a comprehensive literature search strategy is provided in the Bioaccumulation TSD.

3. All data that are used should contain sufficient supporting information to indicate that acceptable measurement procedures were used and that the results are probably reliable. In some cases it may be appropriate to obtain additional written information from the investigator.

4. Questionable data, whether published or unpublished, should not be used. Guidance for assessing the acceptability of bioaccumulation and bioconcentration studies is found in Sections 5.4, 5.5, and 5.6.

5.3.5 Classifying the Chemical of Concern

The next step in deriving a national BAF consis ts of classifying the chemical of concern into one of three categories: nonionic organic, ionic organic, and inorganic and organometallic (Figure 5-1). This step helps to determine which of the four methods described in Section 5.3.1 are appropriate for deriving BAFs. The following guidance applies for classifying the chemical of concern.

1. Nonionic Organic Chemicals. For the purposes of the 2000 Human Health Methodology, nonionic organic chemicals are those organic compounds that do not ionize substantially in natural bodies of water. These chemicals are also referred to as neutral or nonpolar organics in the scientific literature. Due to their neutrality, nonionic organic chemicals tend to associate with other neutral (or near neutral) compartments in aquatic ecosystems (e.g., lipid, organic carbon). Examples of nonionic organic chemicals which have been widely studied in terms of their bioaccumulation include polychlorinated biphenyls (PCBs), polychlorin ated dibenzo-p-dioxins and furans, many chlorinated pesticides, and polynuclear aromatic hydrocarbons (PAHs). Procedures for deriving a national BAF for nonionic organi c chemicals are provided in Section 5.4.

2. Ionic Organic Chemicals. For the purposes of the 2000 Human Health Methodology, ionic organic chemicals are considered to in clude those chemicals that contain functional groups with exchangeable protons such as hydroxyl, carboxylic, and sulfonic groups and functional groups that readily accept protons such as amino and aromatic heterocyclic nitrogen (pyridine) groups. Ionic organic chemicals undergo ionization in water, the extent of which depends on pH and the pKa of the chemical. Because the ionized species of these chemicals behave differently from the neutral species, separate guidance is provided for deriving BAFs for ionic organi c chemicals. Procedures for deriving national BAFs for ionic organic chemicals are provided in Section 5.5.

3. Inorganic and Organometallic Chemicals. The inorganic and organometallic category is considered to include inorganic minerals, other inorganic compounds and elements, metals (e.g., copper, cadmium, chromium, zinc), metalloids (selenium, arsenic) and5-16 organometallic compounds (e.g., methylmercury, tributyltin, tetraalkyllead). Procedures for deriving BAFs for inorganic and organometallic chemicals are provided in Section 5.6.

5.4 NATIONAL BIOACCUMULATION FACT ORS FOR NONIONIC ORGANIC CHEMICALS

5.4.1 Overview

This section contains the methodology for deriving national BAFs for nonionic organic chemicals as defined in Section 5.3.5. The four general steps of this methodology are:

1. Selecting the BAF derivation procedure, 2. Calculating individual baseline BAF R fd s, 3. Selecting the final baseline BAF R fd s, and 4. Calculating the national BAFs from the final baseline BAF R fd s.
A schematic of this four-step process is shown in Figure 5-2.

Step 1 of the methodology (selecting the BAF derivation procedure) determines which of the four BAF procedures summarized in Figure 5-1 will be appropriate for deriving the national BAF. Step 2 involves calculating individual, species-specific BAF R fd s using all of the methods available within the selected BAF derivation procedure. Calculating the individual baseline BAF R fd s involves using data from the field site or laboratory where the original data were collected to account for site-specific factors whic h affect the bioavailability of the chemical to aquatic organisms (e.g., lipid content of study organisms and freely dissolved concentration in study water). Step 3 of the methodology consists of selecting the final baseline BAF R fd s from the individual baseline BAF R fd s by taking into account the uncertainty in the individual BAFs and the data preference hierarchy selected in Step 1. The final step is to calculate a BAF (or BAFs) that will be used in the derivation of 304(a) criteria (i.e., referred to as the national BAF). This step involves adjusting the final baseline BAF R fd (s) to reflect certain factors that affect bioavailablity of the chemical to aquatic organisms in waters to which the national 304(a) criteria will apply (e.g., the freely dissolved fraction expected in U.S. waters and the lipid content of consumed aquatic organisms). Baseline BAF R fd s are not used directly in the derivation of the 304(a) criteria because they do not reflect the conditions that affect bioavailability in U.S. waters.

Section 5.4.2 below provides detailed guidance for selecting the appropriate BAF derivation procedure (Step 1 of the process). Guidance on calculating individual baseline BAF R fd s, selecting the final baseline BAF, and calculating the national BAF (Steps 2 through 4 of the process) is provided in separate sections under each of the four BAF derivation procedures.5-175-185-19 5.4.2 Selecting the BAF Derivation Procedure

This section describes the decisions that should be made to select one of the four available hierarchical procedures for derivi ng a national BAF for nonionic organic chemicals (Procedures #1 through #4 of Figure 5-1). As s hown in Figure 5-1, two decision points exist in selecting the BAF derivation procedure. The first decision point requires knowledge of the chemical’s hydrophobicity (i.e., the K
ow of the chemical). Guidance for selecting the K
ow for a chemical is provided in the Bioaccumulation TSD. The K
ow provides an initial basis for assessing whether biomagnification may be a c oncern for nonionic organic chemicals. The second decision point is based on the rate of metabolism for the chemical in the target organism. Guidance for assessing whether a high or low rate of metabolism is likely for a chemical of concern is provided below in Section 5.4.2.3. With the appropriate information for these two decision points, the BAF derivation procedure s hould be selected using the following guidelines.

5.4.2.1 Chemicals with Moderate to High Hydrophobicity

1. For the purposes of the 2000 Human Health Methodology, nonionic organic chemicals with log K
ow values equal to or greater than 4.0 should be classified as moderately to highly hydrophobic. For moderately to highly hydrophobic nonionic organic chemicals, available data indicate that exposure through the diet and other non-aqueous routes can become important in determining chemical residues in aquatic organisms (e.g., Russell et al., 1999; Fisk et al., 1998; Oliver and Niimi, 1983; Oliver and Niimi, 1988; Niimi, 1985; Swackhammer and Hites, 1988). Dietary and other non-aqueous exposure can become extremely important for those nonionic organic chemicals that are poorly metabolized by aquatic biota (e.g., certain PCB congeners, chlorinated pesticides, and polychlorinated dibenzo-p-dioxins and furans).

2. Procedure #1 should be used to derive national BAFs for moderately to highly hydrophobic nonionic organic chemicals in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be sufficiently low such that biomagnification is of concern, or

(b) the rate of chemical metabolism by target aquatic organisms is not sufficiently known.

Procedure #1 accounts for non-aqueous exposure and the potential for biomagnification in aquatic food webs through the use of field-measured values for bioaccumulation (i.e., field measured BAF or BSAF) and FCMs whenappropriate field data are unavailable. Guidance on deriving national BAFs using Procedure #1 is found below in Section 5.4.3.

3. Procedure #2 should be used to derive the national BAFs for moderately to highly hydrophobic nonionic organic chemicals in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be sufficiently high such that biomagnification is not of concern.5-20 Procedure #2 relaxes the requirement of using FCMs and eliminates the use of K
ow -based estimates of the BAF, two procedures that are most appropriate for poorly metabolized nonionic organic chemicals. Guidance on de riving national BAFs using Procedure #2 is found below in Section 5.4.4.

5.4.2.2 Chemicals with Low Hydrophobicity

1. For the purposes of these guidelines, nonionic organic chemicals with log K
ow values less than 4.0 should be classified as exhib iting low hydrophobicity. For nonionic organic chemicals that exhibit low hydrophobicity (i.e., log K
ow < 4.0), available information indicates that non-aqueous exposure to these chemicals is not likely to be important in determining chemical residues in aquatic or ganisms (e.g., Fisk et al., 1998; Gobas et al., 1993; Connolly and Pedersen, 1988; Thomann, 1989). For this group of chemicals, laboratory-measured BCFs and K
ow -predicted BCFs do not require adjustment with FCMs for determining the national BAF (Procedures #3 and #4), unless other appropriate data indicate differently.

Other appropriate data include studies cl early indicating that non-aqueous exposure is important such that use of a BCF would substantially underestimate residues in aquatic organisms. In these cases, Procedure #1 should be used to derive the BAF for nonionic organic chemicals with log K
ow < 4.0. Furthermore, the data supporting the K
ow determination should be carefully reviewed for accuracy and appropriate interpretation, since the apparent discrepancy may be due to errors in determining K
ow .

2. Procedure #3 should be used to derive national BAFs for nonionic organic chemicals of low hydrophobicity in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be negligible, such that tissue residues of the chemical of concern are not substantially reduced compared to an assumption of no metabolism, or

(b) the rate of chemical metabolism by target aquatic organisms is not sufficiently known.

Procedure #3 includes the use of K
ow -based estimates of the BCF to be used when lab or field data are absent. Guidance on derivi ng national BAFs using Procedure #3 is found below in Section 5.4.5.

3. Procedure #4 should be used to derive national BAFs for nonionic organic chemicals of low hydrophobicity in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be sufficiently high, such that tissue residues of the chemical of concern are substantially reduced compared with an assumption of no metabolism.5-21 Procedure #4 eliminates the option of using K
ow -based estimates of the BAF because the K
ow may over-predict accumulation when a chemical is metabolized substantially by an aquatic organism. Guidance on deriving national BAFs using Procedure #4 is found below in Section 5.4.6.

5.4.2.3 Assessing Metabolism

Currently, assessing the degree to which a chemical is metabolized by aquatic organisms is confounded by a variety of factors. First, conclusive data on chemical metabolism in aquatic biota are largely lacking. Such data include whole organism studies where the metabolic rates and breakdown products are quantified in fish and other aquatic organisms relevant to human consumption. However, the majority of information on metabolism is derived from in vitro liver microsomal preparations in which primary and secondary metabolites may be identified and their rates of formation may or may not be quantified. Extrapolating results from in vitro studies to the whole organism involves considerable uncertainty. Second, there are no generally accepted procedures for reliably predicting chemical metabolism by aquatic organisms in the absence of measured data. Third, the rate at which a chemical is metabolized by aquatic organisms can be species and temperature dependent. For example, PAHs are known to be metabolized readily by vertebrate aquatic species (primarily fish), although at rates much less than those observed for mammals. However, the degree of metabolism in invertebrate species is generally much less than the degree in vertebrate species (James, 1989). One hypothesis for this difference is that the invertebrate species lack the detoxifying enzymes and pathways that are present in many vertebrate species.

Given the current limitations on assessing the degree of chemical metabolism by aquatic organisms, the assessment of metabolism should be made on a case-by-case basis using a weight-of-evidence approach. When assessing a chemical’s likelihood to undergo substantial metabolism in a target aquatic organism, the following data should be carefully evaluated:

(1) in vivo chemical metabolism data, (2) bioconcentration and bioaccumulation data, (3) data on chemical occurrence in target aquatic biota, and (4) in vitro chemical metabolism data.

1. In vivo Data. In vivo data on metabolism in aquatic organisms are from studies of chemical metabolism using whole organisms. These studies are usually conducted using large fish from which blood, bile, urine, and individual tissues can be collected for the identification and quantification of metabolites formed over time. In vivo studies are considered the most useful for evaluating a chemical’s degree of metabolism in an organism because both oxidative (Phase I) and conjugative (Phase II) metabolism can be assessed in these studies. Mass-balance studies, in which parent compound elimination is quantified separately from biotransformation and elimination of metabolites, allow calculation of conversion rate of parent to metabolite as well as metabolite elimination. This information might be used to estimate loss due to metabolism separately from that due to elimination of the parent compound for adjustment of K
ow -predicted BAFs. However, due to the analytical and experimental challenges these studies pose, data of5-22 this type are limited. Less rigorous in vivo metabolism studies might include the use of metabolic blockers to demonstrate the influence of metabolism on parent compound kinetics. However, caution should be used in interpretation of absolute rates from these data due to the lack of specificity of mammalian derived blockers in aquatic species (Miranda et al., 1998).

2. Bioconcentration or Bioaccumulation Data. Data on chemical bioconcentration or bioaccumulation in aquatic organisms can be used indirectly for assessing metabolism. This assessment involves comparing acceptable lab-measured BCFs or field-measured BAFs (after converting to baseline values using procedures below) with the chemical’s predicted value based on K
ow . The theoretical basis of bioconcentration and bioaccumulation for nonionic organic chemicals indicates that a chemical’s baseline BCF should be similar to its K
ow -predicted value if metabolism is not occurring or is minimal (see the Bioaccumulation TSD). This theory also indicates that baseline BAFs should be similar to or higher than the K
ow for poorly metabolized organic chemicals, with highly hydrophobic chemicals often exhibiting higher baseline BAFs than K
ow values. Thus, if a chemical’s baseline BCF or BAF is substantially lower than its K
ow , this may be an indication that the chemical is being metabolized by the aquatic organism of concern. Note, however, that this difference may also indicate problems in the experimental design or analytical chemistry, and that it may be difficult to discern the difference.

3. Chemical Occurrence Data. Although by no means definitive, data on the occurrence of chemicals in aquatic biota (i.e., residue studies) may offer another useful line of evidence for evaluating a chemical’s likelihood to undergo substantial metabolism. Such studies are most useful if they have been conducted repeatedly over time and over wide geographical areas. Such studies might indicate a chemical is poorly metabolized if datashow that the chemical is being biomagnified in the aquatic food web (i.e., higher lipidnormalized residues in successive trophic levels). Conversely, such studies might indicate a chemical is being metabolized substantially if residue data show a decline in residues with increasing trophic level. Again, other reasons for increases or decreases in concentrations with increasing trophic level might exist and should be carefully evaluated (e.g., incorrect food web assumptions, differences in exposure concentrations).

4. In vitro Data. In vitro metabolism data include data from studies where specific subcellular fractions (e.g., microsomal, cytosolic), cells, or tissues from an organism are tested outside the body (i.e., in test-tubes, cell- or tissue-culture). Compared with in vivo studies of chemical metabolism in aquatic organisms, in vitro studies are much more plentiful in the literature, with the majority of studies characterizing oxidative (Phase I) reactions de-coupled from conjugative (Phase II) metabolism. Cell, tissue, or organ level in vitro studies are less common but provide a more complete assessment of metabolism. While such studies are particularly useful for identifying the pathways, rates of formation, and metabolites formed, as well as the enzymes involved and differences in the temperature dependence of metabolism across aquatic species, they suffer from uncertainty when results are extrapolated to the whole organism. This uncertainty results from the fact that dosimetry (i.e., delivery of the toxicant to, and removal of metabolite5-23 from, the target tissue) cannot currently be adequately reproduced in the laboratory or easily modeled.

When assessing chemical metabolism using the above information, the following guidelines apply.

a. A finding of substantial metabolism should be supported by two or more lines of evidence identified using the data described above.

b. At least one of the lines of evidence should be supported by either in vivo metabolism data or acceptable bioconcentration or bioaccumulation data.

c. A finding of substantial metabolism in one organism should not be extrapolated to another organism or another group of organisms unless data indicate similar metabolic pathways exist (or are very likely to exist) in both organisms. In vitro data may be particularly useful in cross-species extrapolations.

d. Finally, in situations where sufficient data are not available to properly assess the likelihood of significant metabolism in aquatic biota of concern, the chemical should be assumed to undergo little or no metabolism. This assumptions reflects a policy decision by EPA to err on the side of public health protection when sufficient information on metabolism is lacking.

5.4.3 Deriving National BAFs Using Procedure #1

This section contains guidance for calcu lating national BAFs for nonionic organic chemicals using Procedure #1 shown in Figure 5- 1. The types of nonionic organic chemicals for which Procedure #1 is most appropriate are those that are classified as moderately to highly hydrophobic and subject to low (or unknown) rates of metabolism by aquatic biota (see Section 5.4.2 above). Non-aqueous contaminant exposure and subsequent biomagnification in aquatic food webs are of concern for chemicals that are classified in this category. Some examples of nonionic organic chemicals for which Procedure #1 is considered appropriate include:



### C tetra-, penta- & hexachlorobenzenes;



### C PCBs;



### C octachlorostyrene;



### C hexachlorobutadiene;



### C endrin, dieldrin, aldrin;



### C mirex, photomirex;



### C DDT, DDE, DDD; and



### C heptachlor, chlordane, nonachlor.

Under Procedure #1, the following four methods may be used in deriving a national BAF:



### C using a BAF from an acceptable field study (i.e., a field-measured BAF);



### C predicting a BAF from an acceptable field-measured BSAF;5-24 (Equation 5-10) 

### C predicting a BAF from an acceptable laboratory-measured BCF and FCM; and



### C predicting a BAF from an acceptable K
ow and FCM.

As shown in Figure 5-2, once the derivation procedure has been selected, the next steps in deriving a national BAF for a given trophic level include: calculating individual baseline BAF R fd s (step 2), selecting the final baseline BAF R fd(step 3), and calculating the national BAF from the final baseline BAF R fd(step 4). Each of these three steps is discussed separately below.

5.4.3.1 Calculating Individual Baseline BAF R fd s
Calculating an individual baseline BAF R fdinvolves normalizing the field-measured BAF t T (or laboratory-measured BCF t T ) which are based on total concentrations in tissue and water by the lipid content of the study organisms and the freely dissolved concentration in the study water. Both the lipid content in the organism and the freely dissolved concentration (as influenced by organic carbon in water) have been shown to be important factors that influence the bioaccumulation of nonionic organic chemicals (e.g., Mackay, 1982; Connolly and Pederson, 1988; Thomann, 1989, Suffet et al., 1994). Therefore, baseline BAF R fd s (which are expressed on a freely dissolved and lipid-normalized basis) are considered more amenable to extrapolating between different species and bodies of water compared to BAFs expressed using the total concentration in the tissue and water. Because bioaccumulation can be strongly influenced by the trophic position of aquatic organisms (either due to biomagnification or physiological differences), extrapolation of baseline BAF R fd s should not be performed between species of different trophic levels.

1. For each species for which acceptable data are available, calculate all possible baseline BAF R fd s using each of the four methods shown above for Procedure #1.

2. Individual baseline BAF R fd s should be calculated from field-measured BAF t T s, fieldmeasured BSAFs, laboratory BCF t T s, and the K
ow according to the following procedures.

A. Baseline BAF R fd

s from Field-Measured BAFs

A baseline BAF R fdshould be calculated from each field-measured BAF t Tusing information on the lipid fraction in the tissue of concern for the study organism and the fraction of the total chemical that is freely dissolved in the study water.

1. Baseline BAF R fd Equation. For each acceptable field-measured BAF t T , calculate a baseline BAF R fdusing the following equation:

where:5-25 (Equation 5-11) Baseline BAF R fd = BAF expressed on a freely dissolved and lipid-normalized basis Measured BAF t T = BAF based on total concentration in tissue and water f R = Fraction of the tissue that is lipid f fd = Fraction of the total chemical that is freely dissolved in the ambient water

The technical basis of Equation 5-10 is provide d in the Bioaccumulation TSD. Guidance for determining each component of Equation 5-10 is provided below.

2. Determining the Measured BAF t T . The field-measured BAF t Tshown in Equation 5-10 should be calculated based on the total concentration of the chemical in the appropriate tissue of the aquatic organism and the total concentration of the chemical in ambient water at the site of sampling. The equation to derive a measured BAF t Tis:

where:

C t = Total concentration of the chemical in the specified wet tissue C
w = Total concentration of chemical in water

The data used to calculate a field-measured BAF t Tshould be reviewed thoroughly to assess the quality of the data and the overall uncertainty in the BAF value. The following general criteria apply in determining the acceptability of field-measured BAFs that are being considered for deriving national BAFs using Procedure #1.

a. Aquatic organisms used to calculate a field-measured BAF t Tshould be representative of aquatic organisms that are commonly consumed in the United States. An aquatic organism that is not commonly consumed in the United States can be used to calculate an acceptable field-measured BAF t Tprovided that the organism is considered to be a reasonable surrogate for a commonly consumed organism. Information on the ecology, physiology, and biology of the organism should be reviewed when assessing whether an organism is a reasonable surrogate of a commonly consumed organism.

b. The trophic level of the study organism should be determined by taking into account its life stage, diet, size, and the food web structure at the study location. Information from the study site (or similar sites) is preferred when evaluating trophic status. If such information is lacking, general information for assessing trophic status of aquatic organisms can be found in USEPA (2000a,b,c).5-26 c. The percent lipid of the tissue used to determine the field-measured BAF t Tshould be either measured or reliably estimated to permit lipid-normalization of the chemical’s tissue concentration.

d. The study from which the field-measured BAF t Tis derived should contain sufficient supporting information from which to determine that tissue and water samples were collected and analyzed using appropriate, sensitive, accurate, and precise analytical methods.

e. The site of the field study should not be so unique that the BAF cannot be reasonably extrapolated to other locations where the BAF and resulting criteria will apply.

f. The water concentration(s) used to derive the BAF should reflect the average exposure of the aquatic organism that corresponds to the concentration measured in its tissue of concern. For nonionic organic chemicals, greater temporal and spatial averaging of chemical concentrations is required as the K
ow increases. In addition, as variability in water concentrations increase, greater temporal and spatial averaging is also generally required. Greater spatial averaging is also generally required for more mobile organisms.

g. The concentrations of particulate organic carbon and dissolved organic carbon in the study water should be measured or reliably estimated.

EPA is currently developing guidance for designing and conducting field studies for determining field-measured BAF t T s, including recommendations for minimum data requirements. A more detailed discussion of factors that should be considered when determining field-measured BAF t T s is provided in the Bioaccumulation TSD.

3. Determining the Fraction Freely Dissolved (f fd ). As illustrated by Equation 5-10, the fraction of the nonionic organic chemical that is freely dissolved in the study water is required for calculating a baseline BAF R fdfrom a field-measured BAF t T . The freely dissolved fraction is the portion of the nonionic organic chemical that is not bound to particulate organic carbon or dissolved organi c carbon. Together, the concentration of a nonionic organic chemical that is freely dissolved, bound to dissolved organic carbon, and bound to particulate organic carbon constitute its total concentration in water. As discussed further in the Bioaccumulation TSD, the freely dissolved fraction of a chemical is considered to be the best expression of the bioavailable form of nonionic organic chemicals to aquatic organisms (e.g., Suffet et al., 1994; USEPA, 1995b). Because the fraction of a nonionic organic chemical that is freely dissolved may vary among different bodies of water as a result of differences in dissolved and particulate organic carbon in the water, the bioavailability of the total chemical concentration in water is expected to vary from one body of water to another. Therefore, BAFs which are based on the freely dissolved concentration in water (rather than the total concentration in water) are considered to be more reliable for extrapolating and aggregating BAFs among different bodies of water. Currently, availability of BAFs based on measured freely dissolved5-27 (Equation 5-12) concentrations is very limited, partly because of difficulties in analytically measuring the freely dissolved concentration. Thus, if a BAF based on the total water concentration is reported in a given study, the fraction of the chemical that is freely dissolved should be predicted using information on the organic carbon content in the study water.

a. Equation for Determining the Freely Dissolved Fraction. If reliable measured data are unavailable to directly determine the freely dissolved fraction of the chemical in water, the freely dissolved fraction should be estimated using the following equation.

where:

POC = concentration of particulate organic carbon (kg/L) DOC = concentration of dissolved organic carbon (kg/L) K
ow = n-octanol water partition coefficient for the chemical

In Equation 5-12, K
ow is being used to estimate the partition coefficient to POC (i.e., KPOC in L/kg) and 0.08 

### @ K
ow is being used to estimate the partition coefficient to DOC (i.e., the KDOC in L/kg). A discussion of the technical basis, assumptions, and uncertainty associated with the derivation and application of Equation 5-12 is provided in the Bioaccumulation TSD.

b. POC and DOC Values. When converting from the total concentration of a chemical to a freely dissolved concentration using Equation 5-12 above, the POC and DOC concentrations should be obtaine d from the original study from which the field-measured BAF is determined. If POC and DOC concentrations are not reported in the BAF study, reliable estimates of POC and DOC might be obtained from other studies of the same site used in the BAF study or closely related site(s) within the same water body. When using POC/DOC data from other studies of the same water body, care should be taken to ensure that environmental and hydrological conditions that might affect POC or DOC concentrations (i.e., runoff events, proximity to ground water or surface water inputs, sampling season) are reasonably similar to those in the BAF study. Additional information related to selecting POC and DOC values is provided in the Bioaccumulation TSD.

In some cases, BAFs are reported using the concentration of the chemical in filtered or centrifuged water. When converting these BAFs to a freely dissolved basis, the concentration of POC should be set equal to zero when using Equation 5-12. Particulates are removed from water samples by filtering or centrifuging the sample.5-28 (Equation 5-13) c. Selecting K
ow Values. A variety of techniques are available to measure or predict K
ow values. The reliability of these techniques depends to a large extent on the K
ow of the chemical. Because K
ow is an important input parameter for calculating the freely dissolved concentration of nonionic organic chemicals and for deriving BAFs using the other three methods of Procedure #1, care should be taken in selecting the most reliable K
ow value. The value of K
ow for use in estimating the freely dissolved fraction and other procedures used to derive national BAFs should be selected based on the guidance presented in the Bioaccumulation TSD.

4. Determining the Fraction Lipid (f R ). Calculating a baseline BAF R fdfor a nonionic organic chemical using Equation 5-10 also requires that the total chemical concentration measured in the tissue used to determine the field-measured BAF t Tbe normalized by the lipid fraction (f R ) in that same tissue. Lipid normalization of tissue concentrations reflects the assumption that BAFs (and BCFs) for nonionic organic chemicals are directly proportional to the percent lipid in the tissue upon which they are based. This assumption means that an organism with a two percent lipid content would be expected to accumulate twice the amount of a chemical at steady state compared with an organism with one percent lipid content, all else being equal. The assumption that aquatic organisms accumulate nonionic organic chemicals in proportion to their lipid content has been extensively evaluated in the literature (Mackay, 1982; Connell, 1988; Barron, 1990) and is generally accepted. Because the lipid content in aquatic organisms can vary both within and across species, BAFs that are expressed using the lipid-normalized concentration (rather than the total concentration in tissue) are considered to be the most reliable for aggregating multiple BAF values for a given species. Additional discussion of technical basis, assumptions, and uncertainties involved in lipid normalization is provided in the Bioaccumulation TSD.

a. The lipid fraction f R , is routinely reported in bioaccumulation studies involving nonionic organic chemicals. If the lipid fraction is not reported in the BAF study, it can be calculated using the following equation if the appropriate data are reported:

where:

M R = Mass of lipid in specified tissue M t = Mass of specified tissue (wet weight)

b. Because lipid content can vary within an aquatic organism (and among tissues within that organism) due to several factors including the age and sex of the organism, changes in dietary composition, season of sampling and reproductive status, the lipid fraction used to calculate a baseline BAF R fdshould be measured in5-29 the same tissue and organisms used to determine the field-measured BAF t T , unless comparability is demonstrated across organisms.

c. Experience has shown that different solvent systems used to extract lipids for analytical measurement can result in different quantities of lipids being extracted and measured in aquatic organisms (e.g., Randall et al.,1991, 1998). As a result, lipid measurements determined using different solvent systems might lead to apparent differences in lipid-normalized concentrations and lipid-normalized BAFs. The extent to which different solvent systems might affect lipid extractions (and lipid-normalized concentrations) is thought to vary depending on the solvent, chemical of concern, and lipid composition of the tissue being extracted. Guidance on measurement of lipid content, including the choice of solvent system and how different solvent systems may affect lipid content, is provided in the Bioaccumulation TSD.

B. Baseline BAF R fd

Derived from BSAFs

The second method of determining a baseline BAF R fdfor the chemical of concern in Procedure #1 involves the use of BSAFs. Although BSAFs may be used for measuring and predicting bioaccumulation directly from concentrations of chemicals in surface sediment, they may also be used to estimate BAFs (USEPA, 1995b; Cook and Burkhard, 1998). Since BSAFs are based on field data and incorporate effects of chemical bioavailability, food web structure, metabolism, biomagnification, growth, and othe r factors, BAFs estimated from BSAFs will incorporate the net effect of all these factors. The BSAF approach is particularly beneficial for developing water quality criteria for chemicals which are detectable in fish tissues and sediments, but are difficult to detect or measure precisely in the water column.

As shown by Equation 5-14 below, predicting baseline BAF R fd s using BSAFs requires that certain types of data be used for the chemicals of interest (for which BAFs are to be determined)and reference chemicals (for which BAFs are measured) from a common sediment-waterorganism data set. Differences between BSAFs for different organic chemicals are good measures of the relative bioaccumulation potentials of the chemicals. When calculated from a common organism-sediment sample set, chemical-specific differences in BSAFs reflect the net effect of biomagnification, metabolism, food chai n, bioenergetics, and bioavailability factors on the degree of each chemical’s equilibrium/disequilibrium between sediment and biota. At equilibrium, BSAFs are expected to be approximately 1.0. However, deviations from 1.0 (reflecting disequilibrium) are common due to: conditions where water is not at equilibrium with surface sediment; differences in organic carbon content of water and sediment; kinetic limitations for chemical transfer between sediments and water associated with specific biota; biomagnification; or biological processes such as growth or biotransformation. BSAFs are most useful (i.e., most predictable from one site to another) when measured under steady-state (or near steady-state) conditions. The use of non-steady- state BSAFs, such as found with new chemical loadings or rapid increases in loadings, increases uncertainty in this method for the relative degree of disequilibrium between the reference chemicals and the chemicals of interest. In general, the fact that concentrations of hydrophobic chemicals in sediment are less sensitive than concentrations in water to fluctuations in chemical loading and distribution makes the BSAF5-30 (Equation 5-15) method robust for estimating BAFs. Results from validation of the BAF procedure in Lake Ontario, the Fox River and Green Bay, Wisconsin, and the Hudson River, New York, demonstrate good agreement between observed and BSAF-predicted BAFs in the vast majority of comparisons made. Detailed results of the validation studies for the BSAF procedure are provided in the Bioaccumulation TSD.

Baseline BAF R fd s should be calculated using acceptable BSAFs for chemicals of interest and appropriate sediment-to-water fugacity (disequilibrium) ratios ( 

### J

socw )
r /(K
ow )
r for reference chemicals under the following guidelines.

1. Baseline BAF R fd Equation. For each species with an acceptable field measured (BSAF) I , a baseline BAF R fdfor the chemical of interest may be calculated using the following equation with an appropriate value of ( 

### J

socw )
r /(K
ow )
r :

(Equation 5-14)

where:

(Baseline BAF R fd ) I = BAF expressed on a freely dissolved and lipidnormalized basis for chemical of interest “I” (BSAF) I = Biota-sediment accumulation factor for chemical of interest “I” ( 

### J

socw )
r = sediment organic carbon to water freely dissolved concentration ratio of reference chemical “r” (K
ow ) I = octanol-water partition coefficient for chemical of interest “I” (K
ow )
r = octanol-water partition coefficient for the reference chemical “r” D i/r = ratio between 

### J

socw / K
ow for chemicals “I” and “r” (normally chosen so that D i/r = 1)

The technical basis, assumptions, and uncertain ties associated with Equation 5-14 are provided in the Bioaccumulation TSD. Guidance for determining each component of Equation 5-14 is provided below.

2. Determining Field-Measured BSAFs. BSAFs should be determined by relating lipidnormalized concentrations of chemicals in an organism (C R ) to organic carbon-normalized concentrations of the chemicals in surface sediment samples (C
soc ) using the following equation:5-31 (Equation 5-16)

(Equation 5-17) a. Lipid-Normalized Concentration. The lipid-normalized concentration of a chemical in an organism should be determined by:

where:

C t = Concentration of the chemical in the wet tissue (either whole organism or specified tissue) (μg/g) f R = Fraction lipid content in the tissue

b. Organic Carbon-Normalized Concentration. The organic carbon-normalized concentration of a chemical in sediment should be determined by:

where:

C
s = Concentration of chemical in sediment (μg/g sediment) f
oc = Fraction organic carbon in sediment

The organic carbon-normalized concentrations of the chemicals in surface sediment samples should be associated with the average exposure environment of the organism.

3. Sediment-to-Water Partition Coefficient ( 

### J

socw )
r . Sediment-to-water partition coefficients for reference chemicals should be determined by:

(Equation 5-18)

where:

(C
soc )
r = Concentration of a reference chemical in sediment normalized to sediment organic carbon ( C
w f d )
r = Concentration of the reference chemical freely dissolved in water

4. Selecting Reference Chemicals. Reference chemicals with ( 

### J

socw ) / (K
ow ) similar to that of the chemical of interest are preferred fo r this method. Theoretically, knowledge of the5-32 difference between sediment-to-water fugacity ratios for two chemicals, “I” and “r” (D i/r ), could be used when reliable reference chemicals that meet the fugacity equivalence condition are not available. Similarity of ( 

### J

socw ) / (K
ow ) for two chemicals can be indicated on the basis of similar physical-chemical behavior in water (persistence, volatilization), similar mass loading histories, and similar concentration profiles in sediment cores.

Validation studies have demonstrated that choosing reference chemicals with well quantified concentrations in water is important because the uncertainty associated with measurement of barely detected chemicals is large (see the Bioaccumulation TSD). Similarity between K
ow values of the reference and target chemicals is generally desirable, although recent validation studies indicate that the accuracy of the method is not substantially decreased through use of reference chemicals with large differences in K
ow , as long as the chemicals are structurally similar and have similar persistence behavior in water and sediments.

5. The following data, procedural, and quality assurance requirements should be met for predicting baseline BAF R fd s using field-measured BSAFs:

a. Data on the reference chemicals and chemicals of interest should come from a common organism-water-sediment data set at a particular site.

b. The chemicals of interest and reference chemicals should have similar physicochemical properties and persistence in water and sediment.

c. The loadings history of the reference chemicals and chemicals of interest should be similar such that their expected sediment-water disequilibrium ratios ( 

### J

socw /K
ow ) would not be expected to be substantially different (i.e., D i/r ~ 1).

d. The use of multiple reference chemicals is generally preferred for determining the value of ( 

### J

socw )
r so long as the concentrations are well quantified and the aforementioned conditions for selecting reference chemicals are met. In some cases, use of a single reference chemical may be necessary because of limited data.

e. Samples of surface sediments (0-1 cm is ideal) should be from locations in which sediment is regularly deposited and is representative of average surface sediment in the vicinity of the organism.

f. The K
ow value for the target and reference chemicals should be selected as described in the Bioaccumulation TSD.

g. All other data quality and procedural guidelines described earlier for determining field-measured BAFs in Section 5.4.3.1(A) should be met.5-33 (Equation 5-20) Further details on the requirements for predicting BAFs from BSAF measurements, including the data, assumptions, and limitations of this approach are provided in the Bioaccumulation TSD.

C. Baseline BAF R fd

from a Laboratory-Measured BCF t T

and FCM

The third method in Procedure #1 consists of using a laboratory-measured BCF t T(i.e., a BCF based on total concentrations in tissue and water) and FCMs to predict a baseline BAF R fdfor the chemical of concern. The BCF t Tis used in conjunction with an FCM because non-aqueous routes of exposure and subsequent biomagnifica tion is of concern for the types of chemicals applicable to Procedure #1. A laboratory-meas ured BCF inherently accounts for the effects of chemical metabolism that occurs in the organism used to calculate the BCF, but does not account for metabolism which may occur in other organisms of the aquatic food web.

1. Baseline BAF R fd Equation. For each acceptable laboratory-measured BCF t T , calculate a baseline BAF R fdusing the following equation:

(Equation 5-19)

where:

Baseline BAF R fd= BAF expressed on a freely dissolved and lipidnormalized basis Measured BCF t T = BCF based on total concentration in tissue and
water f R = Fraction of the tissue that is lipid f fd = Fraction of the total chemical in the test water that is freely dissolved FCM = The food chain multiplier either obtained from Table 5-1 by linear interpolation for the appropriate trophic level, or from appropriate field data

The technical basis for Equation 5-19 is provided in the Bioaccumulation TSD. Guidance for determining each component of Equation 5-19 is provided below.

2. Determining the Measured BCF t T . The laboratory-measured BCF t Tshown in Equation 5-19 should be calculated using information on the total concentration of the chemical in the tissue of the organism and the total concentration of the chemical in the laboratory test water. The equation to derive a measured BCF t Tis:5-34 where:

C t = Total concentration of the chemical in the specified wet tissue C
w = Total concentration of chemical in the laboratory test water

The data used to calculate a laboratory-measured BCF t Tshould be reviewed thoroughly to assess the quality of the data and the overall uncertainty in the BCF value. The following general criteria apply in determining the acceptability of laboratory-measured BCF t T . a. The test organism should not be diseas ed, unhealthy, or adversely affected by the concentration of the chemical because these attributes may alter accumulation of chemicals compared with healthy organisms.

b. The total concentration of the chemical in the water should be measured and should be relatively constant during the exposure period.

c. The organisms should be exposed to the chemical using a flow-through or renewal procedure.

d. The percent lipid of the tissue used to normalize the BCF t Tshould be either measured or reliably estimated to permit lipid normalization of chemical concentrations.

e. The concentrations of particulate organic carbon and dissolved organic carbon in the study water should be measured or reliably estimated.

f. Aquatic organisms used to calculate a laboratory-measured BCF t Tshould be representative of those aquatic organisms that are commonly consumed in the United States. An aquatic organism which is not commonly consumed in the United States can be used to calculate an acceptable laboratory-measured BCF t T provided that the organism is considered to be a reasonable surrogate for a commonly consumed organism. Information on the ecology, physiology, and biology of the organism should be reviewed when assessing whether an organism is a reasonable surrogate of a commonly consumed organism.

g. BCFs may be based on measurement of radioactivity from radiolabeled parent compounds only when the BCF is intended to include metabolites, when there is confidence that there is no interference due to metabolites of the parent compounds, or when studies are conducted to determine the extent of metabolism, thus allowing for a proper correction.

h. The calculation of the BCF t Tshould appropriately address growth dilution, which can be particularly important in affecting BCF t Tdeterminations for poorly depurated chemicals.5-35 I. Other aspects of the methodology used should be similar to those described by the American Society of Testing and Materials (ASTM, 1999) and USEPA Ecological Effects Test Guidelines (USEPA, 1996).

j. In addition, the magnitude of the K
ow and the availability of corroborating BCF data should be considered. For example, if the steady-state method is used for the BCF t Tdetermination, exposure periods longer than 28 days will generally be required for highly hydrophobic chemicals to reach steady state between the water and the organism.

k. If a baseline BCF R fdderived from a laboratory-measured BCF t Tconsistently increases or decreases as the chemical concentration increases in the test solutions for the test organisms, the BCF t Tshould be selected from the test concentration(s) that would most closely correspond to the 304(a) criterion. Note: a BCF t Tshould not be calculated from a control treatment.

3. Selecting Food Chain Multipliers. An FCM reflects a chemical’s tendency to biomagnify in the aquatic food web. Values of FCMs greater than 1.0 are indicative of biomagnification and typically apply to organic chemicals with log K
ow values between 4.0 and 9.0. For a given chemical, FCMs tend to be greater at higher trophic levels, although FCMs for trophic level three can be higher than those for trophic level four.

Food chain multipliers used to derive baseline BAF R fd s using Procedure #1 can be selected from model-derived or field-derived estimates.

a. Model-Derived FCMs. For nonionic organic chemicals appropriate for Procedure #1, EPA has calculated FCMs for various K
ow values and trophic levels using the bioaccumulation model of Gobas (1993). The FCMs shown in Table 5-1 were calculated using the Gobas model as the ratio of the baseline BAF R fd s for trophic levels 2, 3, and 4 to the baseline BCF R fd

. EPA recommends using the biomagnification model by Gobas (1993) to derive FCMs for nonionic organic chemicals for several reasons. First, the Gobas model includes both benthic and pelagic food chains, thereby incorporating exposure of organisms to chemicals from both the sediment and the water column. Second, the input data needed to run the model can be readily defined. Third, the predicted BAFs using the model are in agreement with field-measured BAFs for chemicals, even those with very high log K
ow s. Finally, the model predicts chemical residues in benthic organisms using equilibrium partitioning theory, which is consistent with EPA’s equilibrium partitioning sediment guidelines (USEPA, 2000d).

The Gobas model requires input of specific data on the structure of the food chain and the water quality characteristics of the water body of interest. For calculating national BAFs, a mixed pelagic/benthic food web structure consisting of four trophic levels is assumed. Trophic level 1 is phytoplankton, trophic level 2 is5-36 zooplankton, trophic level 3 is forage fish (e.g., sculpin and smelt), and trophic level 4 are predatory fish (e.g., salmonids). Additional assumptions are made regarding the composition of the aquatic species’ diets (e.g., salmonids consume 10 percent sculpin, 50 percent alewives, and 40 percent smelt), the physical parameters of the aquatic species (e.g., lipid values), and the water quality characteristics (e.g., water temperature, sediment organic carbon).

A mixed pelagic/benthic food web structure has been assumed for the purpose of calculating FCMs because it is considered to be most representative of the types of food webs that occur in aquatic ecosystems. FCMs derived using the mixed pelagic/benthic structure are also about mid-range in magnitude between a 100% pelagic and 100% benthic driven food web (see the Bioaccumulation TSD). The validity of FCMs derived using the mixed pelagic/benthic food web structure has5-37 Table 5-1 Food-Chain Multipliers for Trophic Levels 2, 3 and 4 (Mixed Pelagic and Benthic Food Web Structure and 

### J

socw / K OW= 23)

Log K OW Trophic Level 2 Trophic Level 3 Trophic Level 4 Log K OW Trophic Level 2 Trophic Level 3 Trophic Level 4

4.0 1.00 1.23 1.07 6.6 1.00 12.9 23.8 4.1 1.00 1.29 1.09 6.7 1.00 13.2 24.4 4.2 1.00 1.36 1.13 6.8 1.00 13.3 24.7 4.3 1.00 1.45 1.17 6.9 1.00 13.3 24.7 4.4 1.00 1.56 1.23 7.0 1.00 13.2 24.3 4.5 1.00 1.70 1.32 7.1 1.00 13.1 23.6 4.6 1.00 1.87 1.44 7.2 1.00 12.8 22.5 4.7 1.00 2.08 1.60 7.3 1.00 12.5 21.2 4.8 1.00 2.33 1.82 7.4 1.00 12.0 19.5 4.9 1.00 2.64 2.12 7.5 1.00 11.5 17.6 5.0 1.00 3.00 2.51 7.6 1.00 10.8 15.5 5.1 1.00 3.43 3.02 7.7 1.00 10.1 13.3 5.2 1.00 3.93 3.68 7.8 1.00 9.31 11.2 5.3 1.00 4.50 4.49 7.9 1.00 8.46 9.11 5.4 1.00 5.14 5.48 8.0 1.00 7.60 7.23 5.5 1.00 5.85 6.65 8.1 1.00 6.73 5.58 5.6 1.00 6.60 8.01 8.2 1.00 5.88 4.19 5.7 1.00 7.40 9.54 8.3 1.00 5.07 3.07 5.8 1.00 8.21 11.2 8.4 1.00 4.33 2.20 5.9 1.00 9.01 13.0 8.5 1.00 3.65 1.54 6.0 1.00 9.79 14.9 8.6 1.00 3.05 1.06 6.1 1.00 10.5 16.7 8.7 1.00 2.52 0.721 6.2 1.00 11.2 18.5 8.8 1.00 2.08 0.483 6.3 1.00 11.7 20.1 8.9 1.00 1.70 0.320 6.4 1.00 12.2 21.6 9.0 1.00 1.38 0.210 6.5 1.00 12.6 22.8

been evaluated in several different ecosystems including Lake Ontario, the tidally influenced Bayou D’Inde in Louisiana, the Fox River and Green Bay, Wisconsin, and the Hudson River in New York. Additional details of the validation of EPA’s national default FCMs and the assumptions, uncertainties, and input parameters for the model are provided in the Bioaccumulation TSD.5-38 Although EPA uses the FCMs in Table 5-1 to derive its national 304(a) criteria, EPA recognizes that food webs of othe r waterbodies might differ from the assumptions used to calculate national BAFs. In these situations, States and authorized Tribes may wish to use alternate food web structures for calculating FCMs for use in setting State or Tribal water quality criteria. Additional guidanceon the use of alternate food web structures for calculating State, Tribal, or sitespecific criteria is provided in the Bioaccumulation TSD.

b. Field-Derived FCMs. In addition to model-derived estimates of FCMs, field data may also be used to derive FCMs. Currently, the use of field-derived FCMs is the only method recommended for estimating FCMs for inorganic and organometalic chemicals because appropriate model-derived estimates are not yet available (see Section 5.6). In contrast to the model-based FCMs described previously, field-derived FCMs account for any metabolism of the chemical of concern by the aquatic organisms used to calculate the FCM.

Field-derived FCMs should be calculated using lipid-normalized concentrations of the nonionic organic chemical in appropriate predator and prey species using the following equations.

FCM TL2 = BMF TL2 (Equation 5-21)

FCM TL3 = (BMF TL3 ) (BMF TL2 ) (Equation 5-22)

FCM TL4 = (BMF TL4 ) (BMF TL3 ) (BMF TL2 ) (Equation 5-23)

where:

FCM = Food chain multiplier for designated trophic level (TL2, TL3, or TL4) BMF = Biomagnification factor for designated trophic level (TL2, TL3, or TL4)

The basic difference between FCMs and BMFs is that FCMs relate back to trophic level one (or trophic level two as assumed by the Gobas (1993) model), whereas BMFs always relate back to the next lowest trophic level. For nonionic organic chemicals, BMFs can be calculated from tissue residue concentrations determined in biota at a site according to the following equations.

BMF TL2 = (C R , TL2 ) / (C R , TL1 ) (Equation 5-24)

BMF TL3 = (C R , TL3 ) / (C R

, TL2 ) (Equation 5-25)

BMF TL4 = (C R , TL4 ) / (C R , TL3 ) (Equation 5-26)

where:5-39 C R = Lipid-normalized concentration of chemical in tissue of appropriate biota that occupy the specified trophic level (TL2, TL3, or TL4)

In addition to the acceptability guidelines pertaining to field-measured BAFs, the following procedural and quality assurance requirements apply to field-measured FCMs.

(1) Information should be available to identify the appropriate trophic levels for the aquatic organisms and appropriate predator-prey relationships for the site from which FCMs are being determined. General information on determining trophic levels of aquatic organisms can be found in USEPA 2000a,b,c.

(2) The aquatic organisms sampled from each trophic level should reflect the most important exposure pathways leading to human exposure via consumption of aquatic organisms. For higher trophic levels (e.g., 3 and 4), aquatic species should also reflect those that are commonly consumed by humans.

(3) The studies from which the FCMs are derived should contain sufficient supporting information from which to determine that tissue samples were collected and analyzed using appropriate, sensitive, accurate, and precise methods.

(4) The percent lipid should be either measured or reliably estimated for the tissue used to determine the FCM.

(5) The tissue concentrations should reflect average exposure over the approximate time required to achieve steady-state in the target species.

D. Baseline BAF R fd

from a K
ow and FCM

The fourth method in Procedure #1 consists of using a K
ow and an appropriate FCM for estimating the baseline BAF R fd . In this method, the K
ow is assumed to be equal to the baseline BCF R fd . Numerous investigations have demonstrated a linear relationship between the logarithm of the BCF and the logarithm of the octanol-water partition coefficient (K
ow ) for organic chemicals for fish and other aquatic organisms. Isnard and Lambert (1988) list various regression equations that illustrate this linear relationship. When the regression equations are constructed using lipid-normalized BCFs, the slope s and intercepts are not significantly different from one and zero, respectively (e.g., de Wolf, et al., 1992). The underlying assumption for the linear relationship between the BCF and K
ow is that the bioconcentration process can be viewed as the partitioning of a chemical between the lipid of the aquatic organisms and water and that the K
ow is a useful surrogate for this partitioning process (Mackay, 1982). To account for biomagnification, Procedure #1 requires the K
ow value be used in conjunction with an appropriate FCM.5-40 (Equation 5-27) 1. Baseline BAF R fd Equation. For each acceptable K
ow value and FCM for the chemical of concern, calculate a baseline BAF R fdusing the following equation.

where:

Baseline BAF R fd= BAF expressed on a freely dissolved and lipid-normalized basis for a given trophic level FCM = The food chain multiplier for the appropriate trophic level obtained from Table 5-1 by linear interpolation or from appropriate field data (used with Procedure #1 only) K
ow = Octanol-water partition coefficient

The BCF-K
ow relationship has been developed primarily for nonionic organic chemicals that are not readily metabolized by aquatic organisms and thus is most appropriate for poorly-metabolized nonionic organic chemicals (i.e., Procedures #1 and #3 as depicted in Figure 5-1). For poorly-metabolized nonionic organic chemicals with large log K
ow s (i.e., > 6), reported log BCFs are often not equal to log K
ow . EPA believes that this nonlinearity is primarily due to not accounting for several factors which affect the BCF determination. These factors include not basing BCFs on the freely dissolved concentration in water, not accounting for growth dilution, not assessing BCFs at steadystate, inaccuracies in measurements of uptake and elimination rate constants, and complications from the use of solvent carriers in the exposure. Application of Equation 5- 27 for predicting BAFs has been conducted in several different ecosystems including Lake Ontario, the tidally influenced Bayou D’Inde in Louisiana, the Fox River and Green Bay, Wisconsin, and the Hudson River in New York. Additional detail on the validation, technical basis, assumptions, and uncertainty associated with Equation 5-27 and is provided in the Bioaccumulation TSD.

2. FCMs and K
ow s . Food chain multipliers and K
ow values should be selected as described previously in Procedure #1.

5.4.3.2 Selecting Final Baseline BAF R fd s
After calculating individual baseline BAF R fd s using as many of the methods in Procedure #1 as possible, the next step is to determine a final baseline BAF R fdfor each trophic level from the individual baseline BAF R fd s (see Figures 5-1 and 5-2). The final baseline BAF R fdwill be used in the last step to determine the national BAF for each trophic level. The final baseline BAF R fdfor each trophic level should be determined from the individual baseline BAF R fd s by considering the data preference hierarchy defined by Procedure #1 and uncertainty in the data. The data preference hierarchy for Procedure #1 is (in order of preference):

1. a baseline BAF R fdfrom an acceptable field-measured BAF (method 1)5-41 2. a baseline BAF R fdpredicted from an acceptable field-measured BSAF (method 2), 3. a baseline BAF R fdpredicted from an acceptable BCF and FCM (method 3), or 4. a baseline BAF R fdpredicted from an acceptable K
ow and FCM (method 4).

This data preference hierarchy reflects EPA’s preference for BAFs based on field-measurements of bioaccumulation (methods 1 and 2) over those based on laboratory-measurements and/or predictions of bioaccumulation (methods 3 and 4). However, this data preference hierarchy should not be considered inflexible. Rather, it should be used as a guide for selecting the final baseline BAF R fd s when the uncertainty is similar among two or more baseline BAF R fd s derived using different methods. The following steps and guidelines should be followed for selecting the final baseline BAF R fd s using Procedure #1.

1. Calculate Species-Mean Baseline BAF R fd s. For each BAF method where more than one acceptable baseline BAF R fdis available for a given species, calculate a species-mean baseline BAF R fdas the geometric mean of all available individual baseline BAF R fd s. When calculating a species-mean baseline BAF R fd , individual baseline BAF R fd s should be reviewed carefully to assess the uncertainty in the BAF values. For highly hydrophobic chemicals applicable to Procedure #1, particular attention should be paid to whether sufficient spatial and temporal averaging of water and tissue concentrations was likely achieved in the BAF, BSAF, or BCF study. Highly uncertain baseline BAF R fd s should not be used. Large differences in individual baseline BAF R fd s for a given species (e.g., greater than a factor of 10) should be investigated further. In such cases, some or all of the baseline BAF R fd s for a given species might not be used. Additional discussion on evaluating acceptability of BAF values is provided in the Bioaccumulation TSD.

2. Calculate Trophic-Level-Mean Baseline BAF R fd s. For each BAF method where more than one acceptable species-mean baseline BAF R fdis available within a given trophic level, calculate a trophic-level-mean baseline BAF R fdas the geometric mean of acceptable species-mean baseline BAF R fd s in that trophic level. Trophic-level-mean baseline BAF R fd s should be calculated for trophic levels two, three, and four because available data on U.S. consumers of fish and shellfish indicate si gnificant consumption of organisms in these trophic levels.

3. Select a Final Baseline BAF R fd for Each Trophic Level. For each trophic level, select the final baseline BAF R fdusing best professional judgment by considering: (1) the datapreference hierarchy shown previously, (2) the relative uncertainty in the trophic-levelmean baseline BAF R fd s derived using different methods, and (3) the weight of evidence among the four methods.

a. In general, when more than one trophic-level-mean baseline BAF R fdis available for a given trophic level, the final trophic-level-mean baseline BAF R fdshould be selected from the most preferred BAF method defined by the data preference hierarchy for Procedure #1.

b. If uncertainty in a trophic-level-mean baseline BAF based on a higher tier (more preferred) method is judged to be substantially greater than a trophic-level-mean5-42 (Equation 5-28) baseline BAF from a lower tier method, and the weight of evidence among the various methods suggests that a BAF value from lower tier method is likely to be more accurate, then the final baseline BAF R fdshould be selected using a trophic level-mean baseline BAF R fdfrom a lower tier method.

c. When considering the weight of evidence among the various BAF methods, greater confidence in the final baseline BAF R fdis generally assigned when BAFs from a greater number of methods are in agreement for a given trophic level. However, lack of agreement among methods does not necessarily indicate less confidence if such disagreements can be adequately explained. For example, if the chemical of concern is metabolized by aquatic organisms represented by a BAF value, one would expect disagreement between a field-measured BAF (the highest priority data) and a predicted BAF using a K
ow and model-derived FCM. Thus, field-measured BAFs should genera lly be given the greatest weight among methods because they reflect direct measures of bioaccumulation and incorporate any metabolism which might occur in the organism and its food web.

d. The above steps should be performed for each trophic level until a final baseline BAF R fdis selected for trophic levels two, three, and four.

5.4.3.3 Calculating National BAFs

The last step in deriving a national BAF for each trophic level is to convert the final baseline BAF R fddetermined in the previous step to a BAF that reflects conditions to which the national 304(a) criteria will apply (Figure 5-2). Since a baseline BAF R fdis by definition normalized by lipid content and expressed on a freely dissolved basis, it needs to be adjusted to reflect the lipid fraction of aquatic organisms commonly consumed in the U.S. and the freely dissolved fraction expected in U.S. bodies of water. Converting a final baseline BAF R fdto a national BAF requires information on: (1) the percent lipid of the aquatic organisms commonly consumed by humans, and (2) the freely dissolved fraction of the chemical of concern that would be expected in the ambient waters of interest. For each trophic level, a national BAF should be determined from a final baseline BAF R fdaccording to the following guidelines.

1. National BAF Equation. For each trophic level, calculate a national BAF using the following equation.

where:

Final Baseline BAF R fd= Final trophic-level-mean baseline BAF expressed on a freely dissolved and lipid-normalized basis for trophic level “n”5-43 f R (TL n) = Lipid fraction of aquatic species consumed at trophic level “n” f fd = Fraction of the total chemical in water that is freely dissolved

The technical basis of Equation 5-28 is provi ded in the Bioaccumulation TSD. Guidance for determining each component of Equation 5-28 is provided below.

2. Determining the Final Baseline BAF R fd

. The final trophic-level-mean baseline BAF R fd s used in this equation are those which have been determined using the guidance presented in Section 5.4.3.2 for selecting the final baseline BAF R fd s.

3. Lipid Content of Commonly Consumed Aquatic Species. As illustrated by Equation 5-28, the percent lipid of the aquatic species consumed by humans is needed to accurately characterize the potential exposure to a chemical from ingestion of aquatic organisms.

a. National Default Lipid Values. For the purposes of calculating a national 304(a) criterion, the following national default values for lipid fraction should be used: 1.9% (for trophic level two organisms), 2.6% (for trophic level three organisms), and 3.0% (for trophic level four organisms).

These national default values for lipid content reflect national per capita average patterns of fish consumption in the United States. Specifically, they were calculated using the consumption-weighted mean lipid content of commonly consumed fish and shellfish as identified by the USDA Continuing Survey of Food Intake by Individuals (CSFII) for 1994 through 1996. This same national survey data was used to derive national default values of fish consumption. To maintain consistency with the fish consumption assumptions, only freshwater and estuarine organisms were included in the derivation of the national default lipid values. Additional details on the technical basis, assumptions, and uncertainty in the national default values of lipid fraction are provided in the Bioaccumulation TSD.

Although national default lipid values are used by EPA to set national 304(a) criteria, EPA encourages States and authorized Tribes to use local or regional data on lipid content of consumed aquatic species when adopting criteria into their water quality standards because local or regional consumption patterns (and lipid content) can differ from national consumption patterns. Additional guidance on developing site-specific values of lipid content, including a database of lipid content for many commonly consumed aquatic organisms, is found in the Bioaccumulation TSD.

4. Freely Dissolved Fraction. The third piece of information required for deriving a national BAF is the freely dissolved fraction of the chemical of concern that is expected5-44 (Equation 5-29) in waters of the United States. As notedpreviously, expressing BAFs on the freely dissolved concentration in water allows a common basis for averaging BAFs from several studies. However, for use in criteria development, these BAFs should be converted back to values based on the total concentration in the water to be consistent with monitored water column and effluent concentrations, which are typically based on total concentrations of chemicals in the water. This should be done by multiplying the freely dissolved baseline BAF R fdby the fraction of the freely dissolved chemical expected in water bodies of the United States where criteria are to be applied, as shown in Equation 5-29.

where:

POC = national default value for the particulate organic carbon concentration (kg/L) DOC = national default value for the dissolved organic carbon concentration (kg/L) K
ow = n-octanol water partition coefficient for the chemical

Equation 5-29 is identical to Equation 5-12, which was used to determine the freely dissolved fraction for deriving baseline BAF R fd s from field-measured BAFs. However, the POC and DOC concentrations used in Equa tion 5-29 reflect those values that are expected in U.S. bodies of water, not the POC and DOC values in the study water used to derive the BAF. Guidance for determining each component of Equation 5-29 follows.

a. National Default Values of POC and DOC. For estimating the freely dissolved fraction of the chemical of concern that is expected in U.S. water bodies, national default values of 0.5 mg/L (5 × 10 -7 kg/L) for POC and 2.9 mg/L (2.9 × 10 -6 kg/L) for DOC should be used. These values are 50 thpercentile values (medians) based on an analysis of over 110,000 DOC values and 85,000 POC values contained in EPA’s STORET database from 1980 through 1999. These default values reflect a combination of values for streams, lakes and estuaries across the United States. Additional details on the technical basis, assumptions, and uncertainty in the derivation and application of the national default values of POC and DOC are provided in the Bioaccumulation TSD.

Although national default values of POC and DOC concentrations are used by EPA to set national 304(a) criteria as described by this document, EPA encourages States and authorized Tribes to use local or regional data on POC and DOC when adopting criteria into their water quality standards. EPA encourages States and Tribes to consider local or regional data on POC and DOC because local or regional conditions may result in differences in POC or DOC5-45 concentrations compared with the values used as national defaults. Additional guidance on developing local or regiona l values of POC and DOC, including a database of POC and DOC values segregated by waterbody type, is found in the Bioaccumulation TSD.

b. K
ow Value. The value selected for the K
ow of the chemical of concern should be the same value used in earlier calculations (e.g., for calculating baseline BAF R fd s and FCMs). Guidance for selecting the K
ow value is found in the Bioaccumulation TSD.

5.4.4 Deriving National BAFs Using Procedure #2

This section provides guidance for calculating national BAFs for nonionic organic chemicals using Procedure #2 shown in Figure 5- 1. The types of nonionic organic chemicals for which Procedure #2 is most appropriate are those that are classified as moderately to highly hydrophobic and subject to high rates of metabolism by aquatic biota (see Section 5.4.2 above). Non-aqueous contaminant exposure and subsequent biomagnification in aquatic food webs are not generally of concern for chemicals that are classified in this category. As a result, FCMs are not used in this procedure. In addition, K
ow -based predictions of bioconcentration are not used in this procedure since the K
ow /BCF relationship is primarily based on poorly metabolized chemicals. Some nonionic organic chemicals for which Procedure #2 is probably appropriate include certain PAHs which are believed to be metabolized substantially by fish (e.g., benzo[a]pyrene, phenanthrene, fluoranthene, pyrene, benzo[a]anthracene and chrysene/triphenylene; USEPA, 1980; Burkhard and Lukasewycz, 2000).

According to Procedure #2, the following three methods can be used in deriving a national BAF:



### C using a BAF from an acceptable field study (i.e., a field-measured BAF) (method 1),



### C predicting a BAF from an acceptable BSAF (method 2), and



### C predicting a BAF from an acceptable BCF (method 3).

Each of these three methods relies on measured data for assessing bioaccumulation and therefore, includes the effects of chemical metabolism by the study organism in the BAF estimate. The field-measured BAF and BSAF methods also incorporate any metabolism which occurs in the aquatic food web.

As shown in Figure 5-2, the next steps in deriving a national BAF after selecting the derivation procedure are: (1) calculating individual baseline BAF R fd s, (2) selecting the final baseline BAF R fd s, and (3) calculating the national BAFs. Each of these three steps is discussed separately below.

5.4.4.1 Calculating Individual Baseline BAF R fd s
As described previously in Procedure #1, calculating individual baseline BAF R fd s involves normalizing the measured BAF t Tor BCF t T(which are based on the total chemical in water and5-46 tissue) by the lipid content of the study organisms and the freely dissolved fraction of the chemical in the study water. Converting measured BAF t T(or BCF t T ) values to baseline BAF R fd(or BCF R fd ) values is designed to account for variation in measured BAF t T s that is caused by differences in lipid content of study organisms a nd differences in the freely dissolved fraction of chemical in study waters. Therefore, baseline BAF R fd s are considered more amenable for extrapolating and averaging BAFs across differe nt species and different study waters compared with total BAF t T s.
1. For each species where acceptable data are available, calculate all possible baseline BAF R fd s using each of the three methods shown above for Procedure #2.

2. Individual baseline BAF R fd s should be calculated from field-measured BAF t T s, fieldmeasured BSAFs, and laboratory BCF t T s according to the following procedures.

A. Baseline BAF R fd

from Field-Measured BAFs

1. Except where noted below, a baseline BAF R fdshould be calculated from a field-measured BAF t Tusing the guidance and equations outlined in Section 5.4.3.1(A) for determining baseline BAF R fd s from field-measured BAFs in Procedure #1.

2. Because nonionic organic chemicals applicable to Procedure #2 have relatively high rates of metabolism in aquatic organisms, they will tend to reach steady state more quickly than nonionic organic chemicals with similar K
ow values but which undergo little or no metabolism. Therefore, less temporal averaging of chemical concentrations would generally be required for determining field-measured BAF t T s with highly metabolizable chemicals compared with chemicals that are poorly metabolized by aquatic biota.5-47 B. Baseline BAF R fd

Derived from Field-measured BSAFs

1. A baseline BAF R fdshould be calculated from a field-measured BSAF using the guidance and equations outlined in Section 5.4.3.1(B) for determining baseline BAF R fd s from fieldmeasured BSAFs in Procedure #1.

C. Baseline BAF R fd

from a Laboratory-Measured BCF

1. Except where noted below, a baseline BAF R fdshould be calculated from a laboratorymeasured BCF t Tusing the guidance and equations outlined in Section 5.4.3.1(c) for determining baseline BAF R fd s from a laboratory-measured BCF and FCM in Procedure #1.

2. Because biomagnification is not an overri ding concern for nonionic organic chemicals applicable to Procedure #2, food chain multipliers are not used in the derivation of a baseline BAF R fdfrom a laboratory-measured BCF t T. 5.4.4.2 Selecting Final Baseline BAF R fd s
After calculating individual, baseline BAF R fd s using as many of the methods in Procedure #2 as possible, the next step is to determine a final baseline BAF R fdfor each trophic level from the individual baseline BAF R fd s. The final baseline BAF R fdwill be used in the last step to determine the national BAF for each trophic level. A final baseline BAF R fdfor each trophic level should be determined from the individual baseline BAF R fd s by considering the data preference hierarchy defined by Procedure #2 and uncertainty in the data. The data preference hierarchy for Procedure #2 is (in order of preference):

1. a baseline BAF R fdfrom an acceptable field-measured BAF (method 1), 2. a baseline BAF R fdfrom an acceptable field-measured BSAF (method 2), or 3. a baseline BAF R fdfrom an acceptable laboratory-measured BCF (method 3).

This data preference hierarchy reflects EPA’s preference for BAFs based on fieldmeasurements of bioaccumulation (methods 1 and 2) over those based on laboratorymeasurements (method 3). However, as explained in Procedure #1, this data preference hierarchy should not be considered inflexible. Rather, it should be used as a guide for selecting the final baseline BAF R fd s when the underlying uncertainty is similar among two or more baseline BAF R fd s derived using different methods. Although bi omagnification is not generally a concern for chemicals subject to Procedure #2, trophic le vel differences in bioaccumulation might be substantial to the extent that the rate of chemical metabolism by organisms in different trophic levels differs. For example, certain PAHs have been shown to be metabolized to a much greater extent by some fish compared with some invertebrate species (James, 1989). Therefore, final baseline BAF R fd s for chemicals applicable to Procedure #2 should be determined on a trophiclevel-specific basis according to the following guidelines.

1. The final baseline BAF R fd s in Procedure #2 should be selected according to the same steps described in Procedure #1 but with the substitution of the data preference hierarchy described above for Procedure #2. Specifically, the species-mean baseline BAF R fd s,5-48 trophic-level-mean baseline BAF R fd s, and the final baseline BAF R fd s should be determined according to the guidelines presented in Procedure #1 (Section 5.4.3.2, Steps 1, 2, and 3).

5.4.4.3 Calculating the National BAFs

As described in Procedure #1, the last step in deriving national BAFs for nonionic organic chemicals is to convert the final baseline BAF R fd s determined in the previous step to BAFs which reflect conditions to which the nati onal 304(a) criteria will apply (Figure 5-2).

1. For trophic levels two, three, and four, national BAFs should be calculated from the final baseline BAF R fd s using the same equation and procedures described previously in Procedure #1 (see Section 5.4.3.3 entitled “Calculating the National BAFs”).

5.4.5 Deriving National BAFs Using Procedure #3

This section provides guidance for calculating national BAFs for nonionic organic chemicals using Procedure #3 shown in Figure 5- 1. The types of nonionic organic chemicals for which Procedure #3 is most appropriate are those that are classified as low in hydrophobicity (i.e., log K
ow values less than 4.0) and subject to low (or unknown) rates of metabolism by aquatic biota (see Section 5.4.2 above). Non-aqueous contaminant exposure and subsequent biomagnification in aquatic food webs are not generally of concern for chemicals that are classified in this category (Fisk et al., 1998; Gobas et al., 1993; Connolly and Pedersen, 1988; Thomann, 1989). As a result, FCMs are not used in this procedure.

According to Procedure #3, the following three methods can be used in deriving a national BAF:



### C using a BAF from an acceptable field study (i.e., a field-measured BAF),



### C predicting a BAF from an acceptable laboratory-measured BCF, and



### C predicting a BAF from an acceptable K
ow . After selecting the derivation procedure, the next steps in deriving a national BAF at a given trophic level for nonionic organic chemicals are: (1) calculating individual baseline BAF R fd s, (2) selecting the final baseline BAF R fd , and (3) calculating the national BAF (Figure 5-2). Each of these three steps is discussed separately below.

5.4.5.1 Calculating Individual Baseline BAF R fd s
Calculating individual baseline BAF R fd s involves normalizing each measured BAF t Tor BCF t T(which are based on the total chemical in water and tissue) by the lipid content of the study organism and the freely dissolved fraction of the chemical in the study water. For additional discussion of the technical basis for calculating baseline BAF R fd s, see Section 5.4.3.1 in Procedure #1.5-49 1. For each species where acceptable data are available, calculate all possible baseline BAF R fd s using each of the three methods shown above for Procedure #3.

2. An individual baseline BAF R fdshould be calculated from field-measured BAF t T s, laboratory-measured BCF t T s, and K
ow values according to the following procedures.

A. Baseline BAF R fd

from Field-Measured BAFs

1. Except where noted below, a baseline BAF R fdshould be calculated from a field-measured BAF t Tusing the guidance and equations outlined in Section 5.4.3.1(A) in Procedure #1.

2. Freely Dissolved Fraction . Due to their low hydrophobicity (i.e., log K
ow < 4.0), nonionic organic chemicals applicable to Procedure #3 are expected to remain almost entirely in the freely dissolved form in natural waters with dissolved and particulate organic carbon concentrations typical of mostfield BAF studies. Therefore, the freely dissolved fraction should be assumed to be equal to 1.0, unless the concentrations of DOC and POC are very high in the field BAF study. For studies with very high DOC or POC concentrations, (e.g., about 100 mg/L or higher for DOC or 10 mg/L or higher for POC), the freely dissolved fraction may be substantially lower than 1.0 and therefore should be calculated using Equation 5-12.

3. Temporal Averaging of Concentrations. Also due to their low hydrophobicity, nonionic organic chemicals appropriate to Procedure #3 will also tend to reach steady state quickly compared with those chemicals to which Procedure #1 applies. Therefore, the extent of temporal averaging of tissue and water concentrations is typically much less than that required for highly hydrophobic chemicals to which Procedure #1 is applied. In addition, field studies used to calculate BAFs for these chemicals should have sampled water and tissue at similar points in time because tissue concentrations respond more rapidly to changes in water concentrations. EPA will be providing additional guidance on appropriate BAF study designs for nonioni c organic chemicals (including those appropriate to Procedure #3) in its forthcoming guidance document on conducting field BAF and BSAF studies.

B. Baseline BAF R fd

from a Laboratory-Measured BCF

1. Except where noted below, a baseline BAF R fdshould be calculated from a laboratorymeasured BCF t Tusing the guidance and equations outlined in Section 5.4.3.1(c) of Procedure #1.

2. Food Chain Multipliers. Because biomagnification is not an overriding concern for the minimally hydrophobic chemicals applicable to Procedure #3, FCMs are not used in the derivation of a baseline BAF R fdfrom a laboratory-measured BCF t T .

3. Freely Dissolved Fraction . Due to their low hydrophobicity (i.e., log K
ow < 4.0), nonionic organic chemicals to which Procedure #3 is applied are expected to remain5-50 almost entirely in the freely dissolved form in waters containing dissolved and particulate organic carbon concentrations typical of labor atory BCF studies. Therefore, the freely dissolved fraction should usually be assumed equal to 1.0. The freely dissolved fraction will be substantially less than 1.0 only in situations where unusually high concentrations of DOC and POC are present in the laboratory BCF study (e.g., above about 100 mg/L for DOC or about 10 mg/L for POC). In this situation, the freely dissolved fraction should be calculated according to Equation 5-12.

C. Baseline BAF R fd

from a K
ow 1. Except where noted below, a baseline BAF R fdshould be calculated from an acceptable K
ow using the guidance and equations outlined in Section 5.4.3.1(D) in Procedure #1.

2. Because biomagnification is not an overri ding concern for nonionic organic chemicals with low hydrophobicity (i.e., log K
ow < 4.0), food chain multipliers are not used in Procedure #3 for deriving the baseline BAF R fdfrom a K
ow . 5.4.5.2 Selecting Final Baseline BAF R fd s
After calculating individual baseline BAF R fd s using as many of the methods in Procedure #3 as possible, the next step is to determine a final baseline BAF R fdfor each trophic level from the individual baseline BAF R fd s (Figure 5-2). The final baseline BAF R fdwill be used in the last step to determine the national BAF for each trophic level. The final baseline BAF R fdfor each trophic level should be determined from the individual baseline BAF R fd s by considering the data preference hierarchy defined by Procedure #3 and uncertainty in the data. The data preference hierarchy for Procedure #3 is (in order of preference):

1. a baseline BAF R fdfrom an acceptable field-measured BAF or laboratory-measured BCF, or 2. a baseline BAF R fdpredicted from an acceptable K
ow value.

This data preference hierarchy reflects EP A’s preference for BAFs that are based on measured data (field-measured BAFs and laboratory-measured BCFs) over BAFs based on predictive methods (K
ow ). This data preference hierarchy should be used as a guide for selecting the final baseline BAF R fd s when the uncertainty is similar among two or more baseline BAF R fd s derived using different methods. Since bio accumulation via dietary uptake and subsequent biomagnification generally are not of concern for chemicals subject to Procedure #3, fieldmeasured BAFs and laboratory-measured BCFs are considered equally in determining the national BAF.

Final baseline BAF R fd s should be selected for each trophic level using the following steps and guidelines.

1. Calculate Species-Mean Baseline BAF R fd s. For each BAF method (i.e., field-measured BAF, BAF from a lab-measured BCF, or BAF from a K
ow ) where more than one5-51 acceptable baseline BAF R fdis available for a given species, calculate a species-mean baseline BAF R fdaccording to the guidance described previously in Procedure #1.

2. Calculate Trophic-Level-Mean Baseline BAF R fd s. For each BAF method where more than one acceptable species-mean baseline BAF R fdis available within a given trophic level, calculate the trophic-level-mean baseline BAF R fdas the geometric mean of acceptable species-mean baseline BAF R fd s in that trophic level.

3. Select a Final Baseline BAF R fd for Each Trophic Level. For each trophic level, select the final baseline BAF R fdusing best professional judgment by considering: (1) the data preference hierarchy, (2) the relative uncertainties among trophic-level-mean baseline BAF R fd s derived using different methods, and (3) the weight of evidence among the three methods.

a. In general, when more than one trophic-level-mean baseline BAF R fdis available within a given trophic level, the final baseline BAF R fdshould be selected from the most preferred BAF method defined by the data preference hierarchy for Procedure #3. Within the first data preference tier, field-measured BAFs and laboratory-measured BCFs are considered equally desirable for deriving a final trophic-level-mean baseline BAF R fdusing Procedure #3. If a trophic-level-mean baseline BAF R fdis available from both a field-measured BAF and a laboratorymeasured BCF, the final baseline BAF R fdshould be selected using the trophiclevel-mean baseline BAF R fdor BCF R fdwith the least overall uncertainty.

b. If uncertainty in a trophic-level-mean baseline BAF R fdbased on a higher tier (more preferred) method is judged to be substantially greater than a trophic-level-mean baseline BAF R fdfrom a lower tier method, then the final baseline BAF R fdshould be selected using a trophic-level-mean baseline BAF R fdfrom a lower tier method.

c. The above steps should be performed for each trophic level until a final baseline BAF R fdis selected for trophic level two, three, and four.

5.4.5.3 Calculating the National BAFs

As described in Procedure #1, the last step in deriving a national BAF for a given trophic level for nonionic organic chemicals is to convert the final baseline BAF R fddetermined in the previous step to a BAF that reflect conditions to which the national 304(a) criterion will apply (Figure 5-2). Each national BAF should be determined from a final baseline BAF R fdaccording to the following guidelines.

1. National BAF Equation. Except where noted below, national BAFs for trophic levels two, three, and four should be calculated from the final, trophic-level-mean baseline BAF R fd s using Equation 5-28 and associated guidance described in Procedure #1 (see Section 5.4.3.3).5-52 2. Freely Dissolved Fraction . Due to their low hydrophobicity (i.e., log K
ow < 4.0), a freely dissolved fraction of 1.0 should be assumed for calculating national BAFs for nonionic organic chemicals using Procedure #3. A freely dissolved fraction of 1.0 should be assumed because at a log K
ow of less than 4.0, nonionic organic chemicals are expected to remain over 99 percent in the freely dissolved form at POC and DOC concentrations corresponding to national default values for U.S. bodies of water (i.e., 0.5 mg/L and 2.9 mg/L, respectively).

5.4.6 Deriving National BAFs Using Procedure #4

This section provides guidance for calculating national BAFs for nonionic organic chemicals using Procedure #4 shown in Figure 5- 1. The types of nonionic organic chemicals for which Procedure #4 is most appropriate are those that are classified as having low hydrophobicity and subject to high rates of metabolism by aquatic biota (see Section 5.4.2 above). Non-aqueous contaminant exposure and subsequent biomagnification in aquatic food webs are not generally of concern for chemicals that are classified in this category. As a result, FCMs are not used in this procedure. In addition, K
ow -based predictions of bioconcentration are not used in this procedure since the K
ow /BCF relationship is primarily based on poorly metabolized chemicals. One example of a nonionic organic chemical for which Procedure #4 appears appropriate is butyl benzyl phthalate in fish. Using radiolabeling techniques with confirmation by chromatographic analysis, Carr et al. (1997) present evidence that indicates butyl benzyl phthalate is extensively metabolized in sunfish. Carr et al. (1997) also report measured BCFs (and subsequently lipid-normalized BCFs) which are substantially below predicted BCFs based on log K
ow. In a study of chlorinated anilines (which would be essentially un-ionized at ambient pH), de Wolf et al. (1992) reported measured BCFs substantially lower than those predicted based on K
ow . The authors suggested that biotransformation (metabolism) involving the amine (NH 2 ) was responsible for the lower measured BCFs.

According to Procedure #4, the following two methods can be used in deriving a national BAF:



### C using a BAF from an acceptable field study (i.e., a field-measured BAF), and



### C predicting a BAF from an acceptable BCF.

After selecting the derivation procedure, the next steps in deriving a national BAF for a given trophic level for nonionic organic chemicals are: (1) calculating individual baseline BAF R fd s, (2) selecting the final baseline BAF R fd , and (3) calculating the national BAF (Figure 5-2). Each of these three steps is discussed separately below.

5.4.6.1 Calculating Individual Baseline BAF R fd s
Calculating individual baseline BAF R fd s involves normalizing the measured BAF t Tor BCF t T (which are based on the total chemical in water and tissue) by the lipid content of the study organism and the freely dissolved fraction of the chemical in the study water. For additional discussion of the technical basis for calculating baseline BAF R fd s, see Section 5.4.3.1 in Procedure #1.5-53 1. For each species where acceptable data are available, calculate all possible baseline BAF R fd s using each of the two methods shown above for Procedure #4.

2. Individual baseline BAF R fd s should be calculated from field-measured BAF t T s and laboratory-measured BCF t T s according to the following procedures.

A. Baseline BAF R fd

from Field-Measured BAFs

1. A baseline BAF R fdshould be calculated from a field-measured BAF t Tusing the guidance and equations outlined in Section 5.4.3.1(A) in Procedure #1.

2. Freely Dissolved Fraction . Due to their low hydrophobicity (i.e., log K
ow < 4.0), nonionic organic chemicals applicable to Procedure #4 are expected to remain almost entirely in the freely dissolved form in natural waters with dissolved and particulate organic carbon concentrations typical of mostfield BAF studies. Therefore, the freely dissolved fraction should be assumed equal to 1.0 unless the concentrations of DOC and POC are very high in the field BAF study. For studies with very high DOC or POC concentrations, (e.g., about 100 mg/L or higher for DOC or 10 mg/L or higher for POC), the freely dissolved fraction may be substantially lower than 1.0 and therefore should be calculated using Equation 5-12.

3. Temporal Averaging of Concentrations. Also due to their low hydrophobicity,nonionic organic chemicals appropriate to Procedure #4 will also tend to reach steadystate quickly compared with those chemicals to which Procedure #1 applies. Therefore, the extent of temporal averaging of tissue and water concentrations is typically much less than that required for highly hydrophobic chemicals to which Procedure #1 is applied. In addition, field studies used to calculate BAFs for these chemicals should have sampled water and tissue at similar points in time because tissue concentrations should respond rapidly to changes in water concentrations. EPA will be providing additional guidance on appropriate BAF study designs for nonioni c organic chemicals (including those appropriate to Procedure #4) in its forthcoming guidance document on conducting field BAF and BSAF studies.

B. Baseline BAF R fd

from a Laboratory-Measured BCF

1. Except where noted below, a baseline BAF R fdshould be calculated from a laboratorymeasured BCF t Tusing the guidance and equations outlined in Section 5.4.3.1(c) of Procedure #1.

2. Food Chain Multipliers. Because biomagnification is not an important concern for the minimally hydrophobic chemicals applicable to Procedure #4, FCMs are not used in the derivation of a baseline BAF R fdfrom a laboratory-measured BCF t T .

3. Freely Dissolved Fraction . Due to their low hydrophobicity (i.e., log K
ow < 4.0), nonionic organic chemicals to which Procedure #4 is applied are expected to remain5-54 almost entirely in the freely dissolved form in waters containing dissolved and particulate organic carbon concentrations typical of labor atory BCF studies. Therefore, the freely dissolved fraction should usually be assumed to be equal to 1.0. The freely dissolved fraction will be substantially less than 1.0 only in situations where unusually high concentrations of DOC and POC are present in the lab BCF study (e.g., above about 100 mg/L for DOC or about 10 mg/L for POC). In this situation, the freely dissolved fraction should be calculated according to Equation 5-12.

5.4.6.2 Selecting Final Baseline BAF R fd s
After calculating individual baseline BAF R fd s using as many of the methods in Procedure #4 as possible, the next step is to determine a final baseline BAF R fdfor a given trophic level from the individual baseline BAF R fd s (Figure 5-2). The final baseline BAF R fdwill be used in the last step to determine the national BAF for each trophic level. A final baseline BAF R fdshould be determined for each trophic level from the individual baseline BAF R fd s by considering the data preference hierarchy defined by Procedure #4 and uncertainty in the data. The data preference hierarchy for Procedure #4 is:

1. a baseline BAF R fdfrom an acceptable field-measured BAF or predicted from an acceptable laboratory-measured BCF.

Since bioaccumulation via dietary uptake and subsequent biomagnification generally are not of concern for chemicals subject to Pro cedure #4, field-measured BAFs and laboratorymeasured BCFs are considered equally in determining the national BAF.

Final baseline BAF R fd s should be selected for each trophic level using the following steps and guidelines.

1. Calculate Species-Mean Baseline BAF R fd s. For each BAF method (i.e., field-measured BAF or a BAF from a lab-measured BCF) where more than one acceptable baseline BAF R fdis available for a given species, calculate a species-mean baseline BAF R fdaccording to the guidance described previously in Procedure #1.

2. Calculate Trophic-Level-Mean Baseline BAF R fd s. For each BAF method where more than one acceptable species-mean baseline BAF R fdis available within a given trophic level, calculate the trophic-level-mean baseline BAF R fdas the geometric mean of acceptable species-mean baseline BAF R fd s for that trophic level.

3. Select a Final Baseline BAF R fd for Each Trophic Level. For each trophic level, select the final baseline BAF R fdusing best professional judgment by considering: (1) the data preference hierarchy, and (2) the relative uncertainties among trophic-level-mean BAFs derived using different methods.

a. As discussed above, field-measured BAFs and laboratory-measured BCFs are considered equally desirable for deriving a final trophic-level-mean baseline5-55 BAF R fdusing Procedure #4. If a trophic-level-mean baseline BAF R fdis available from both a field-measured BAF and a laboratory-measured BCF, the final baseline BAF R fdshould be selected using the trophic-level-mean baseline BAF R fdor BCF R fdwith the least overall uncertainty.

b. The above steps should be performed for each trophic level until a final baseline BAF R fdis selected for trophic levels two, three, and four.

5.4.6.3 Calculating National BAFs

As described in Procedure #1, the last step in deriving a national BAF for a given trophic level for nonionic organic chemicals is to convert the final baseline BAF R fddetermined in the previous step to a BAF that reflects conditions to which the national 304(a) criterion will apply (Figure 5-2). Each national BAF should be determined from a final baseline BAF R fdaccording to the following guidelines.

1. National BAF Equation. Except where noted below, national BAFs for trophic-levels two, three, and four should be calculated from the final, trophic-level-mean baseline BAF R fd s using the same equation and procedures described previously in Procedure #1 (see Section 5.4.3.3 in Procedure #1).

2. Freely Dissolved Fraction . Due to their low hydrophobicity (i.e., log K
ow < 4.0), a freely dissolved fraction of 1.0 should be assumed for calculating national BAFs for nonionic organic chemicals using Procedure #4. A freely dissolved fraction of 1.0 should be assumed because at a log K
ow value of less than 4.0, nonionic organic chemicals are expected to remain over 99 percent in the freely dissolved form at POC and DOC concentrations corresponding to national default values for U.S. bodies of water (i.e., 0.5 mg/L and 2.9 mg/L, respectively).

5.5 NATIONAL BIOACCUMULATION F ACTORS FOR IONIC ORGANIC CHEMICALS

This section contains guidelines for derivi ng national BAFs for ionic organic chemicals (i.e., organic chemicals which undergo significant i onization in water). As defined in Section 5.3.5, ionic organic chemicals contain functional groups which can either readily donate protons (e.g., organic acids with hydroxyl, carboxylic, and sulfonic groups) or readily accept protons (e.g., organic bases with amino and aromatic heterocyclic nitrogen groups). Some examples of ionic organic compounds include:



### C chlorinated phenols (e.g., 2,4,6-trichlorophenol, pentachlorophenol),



### C chlorinated phenoxyalkanoic acids (e.g., 2,4-dichlorophenoxyacetic acid [2,4-D]),



### C nitrophenols (e.g., 2-nitrophenol, 2,4,6-trinitrophenol),



### C cresols (e.g., 2,4-dinitro- o -cresol [DNOC]),



### C pyridines (e.g., 2,4-dimethypyidine),



### C aliphatic and aromatic amines (e.g., trimethylamine, aniline), and5-56 

### C linear alkylbenzenesulfonate (LAS) surfactants.

Ionic organic chemicals are considered separately for deriving national BAFs because the anionic or cationic species of these chemicals behave much differently in the aquatic environment compared with their neutral (un-ioni zed) counterparts. The neutral species of ionic organic chemicals are thought to behave in a similar manner as nonionic organic compounds (e.g., partitioning to lipids and organic carbon as a function of hydrophobicity). However, the ionized (cationic, anionic) species exhibit a considerably more complex behavior involving multiple environmental partitioning mechanisms (e.g., ion exchange, electrostatic, and hydrophobic interactions) and a dependency on pH a nd other factors including ionic strength and ionic composition (Jafvert et al., 1990; Jafv ert 1990; Schwarzenbach, et al., 1993). As a consequence, methods to predict the environmental partitioning of organic cations and anions are less developed and validated compared with methods for nonionic organic chemicals (Spacie, 1994; Suffet et al., 1994).

Given the current limitations in the state of the science for predicting the partitioning and bioaccumulation of the ionized species of ioni c organic chemicals, procedures for deriving national BAFs for these chemicals differ depending on the extent to which the fraction of the total chemical is likely to be represented by the ionized (cationic, anionic) species in U.S. surface waters. When a significant fraction of the total chemical concentration is expected to be present as the ionized species in water, procedures for deriving the national BAF rely on empirical (measured) methods (i.e., Procedures #5 and 6 in Section 5.6). When an insignificant fraction of the total chemical is expected to be present as the ionized species (i.e., the chemical exists essentially in the neutral form), procedures for deriving the national BAF will follow those established for nonionic organic chemicals (e.g., Procedures #1 through #4 in Section 5.4). The following guidelines apply for assessing the occurre nce of cationic and anionic forms at typical environmental pH ranges.

1. For the ionic organic chemical of concern, the dissociation constant, pK
a , should be compared to the range of pH values expected in fresh and estuarine waters of the U.S. At pH equal to the pK
a , 50% of the organic acid or base is expected to be present in the ionized species. The pH values for U.S. fresh and estuarine waters typically range between 6 and 9, although somewhat higher and lower values can occur in some bodies of water (e.g., acidic bogs and lakes, highly alkaline and eutrophic systems, etc.).

2. For organic acids, the chemical will exist almost entirely in its un-ionized form when pH is about 2 or more units below the pK
a . For organic bases, the chemical will exist almost entirely in its un-ionized form when pH is about 2 or more units above the pK
a . In these cases, the aqueous behavior of the chemical would be expected to be similar to nonionic organic chemicals. Therefore, national BA F should usually be derived using Procedures #1 through #4 in Section 5.4.

3. When pH is greater than the pK
a minus 2 for organic acids (or less than the pKa plus 2 for organic bases), the fraction of the total chemical that is expected to exist in its ionized form can become significant (i.e., 

### $ 1% in the ionized). In these cases, the national BAF should usually be derived using Procedures #5 and #6 in Section 5.6.5-57 4. In general, most organic acids (e.g., pentachlorophenol and silvex), exist primarily in the ionized form in ambient waters because their pK
a ’s (4.75 and 3.07, respectively) are much smaller than the pH of the ambient waters. Conversely, most organic bases, (e.g., aniline) exist mostly in the un-ionized form in ambient waters because their pK
a ’s (4.63 for aniline) are much smaller than the pH of the ambient waters.

5. The above guidelines are intended to be a general guide for deriving national BAFs for ionic organic chemicals, not an inflexible rule. Modifications to these guidelines should be considered on a case-by-case basis, particul arly when such modifications are strongly supported by measured bioaccumulation or bioconcentration data. For example, initial models have been developed for predicting the solid and organic-phase partitioning of certain organic acids (e.g., Jafvert 1990, Jafvert et al., 1990). As these or other models become more fully developed and appropriately validated in the future, they should be considered in the development of national BAFs. In addition, since pH is a controlling factor for dissociation and subsequent partitioning of ionic organic chemicals, consideration should be given to expressing BA Fs or BCFs as a function of pH (or other factors) where sufficient data exist to reliably establish such relationships.5-58 5.6 NATIONAL BIOACCUMULATION FACTORS FOR INORGANIC AND ORGANOMETALLIC CHEMICALS

This section contains guidelines for deriving national BAFs for inorganic and organometallic chemicals as defined in Section 5.3.5. The derivation of BAFs for inorganic and organometallic chemicals differs in several ways from procedures for nonionic organic chemicals. First, lipid normalization of chemical concentrations in tissues does not generally apply for inorganic and organometallic chemicals. Thus, BAFs and BCFs cannot be extrapolated from one tissue to another based on lipid-normalized concentrations as is done for nonionic organic chemicals. Second, the bioavailability of inorganics and organometallics in water tends to be chemical-specific and thus, the techni ques for expressing concentrations of nonionic organic chemicals based on the freely dissolved form do not generally apply. Third, at the present time there are no generic bioaccumulation models that can be used to predict BAFs for inorganic and organometallic chemicals as a whole, unlike the existence of K
ow -based models for nonionic organic chemicals. While some chemical-specific bioaccumulation models have been developed for inorganic and organometallic ch emicals (e.g., Mercury Cycling Model by Hudson et. al, 1994), those models currently tend to require site-specific data for input to the model and are restricted to site-specific applications. As the models become more fully developed and validated in the future, they should be considered on a case-by-case basis in conjunction with the following procedures for deriving national BAFs.

5.6.1 Selecting the BAF Derivation Procedure

As shown in Figure 5-1, national BAFs can be derived using two procedures for inorganic and organometallic chemicals (Procedures #5 and #6). The choice of the BAF derivation procedure depends on whether or not the chemical undergoes biomagnification in aquatic food webs.

1. For many inorganic and organometallic chemicals, biomagnification does not occur and the BCF will be equal to the BAF. For these types of chemicals, Procedure #5 should be used to derive the national BAF. Procedure #5 considers BAFs and BCFs to be of equal value in determining the national BAF and does not require the use of FCMs with BCF measurements. Guidance for deriving BAFs using Procedure #5 is provided in Section 5.6.3.

2. For some inorganic and organometallic chemicals (e.g., methylmercury), biomagnification does occur and Procedure #6 should be used to determine the national BAF. Procedure #6 gives general preference to the use of field-measured BAFs over laboratory-measured BCFs and requires FCMs to be used with BCF measurements for predicting BAFs. Guidance for deriving BAFs using Procedure #6 is provided in Section 5.6.4.

3. Determining whether or not biomagnification occurs for inorganic and organometallic chemicals requires chemical-specific data on measured concentrations of the chemical in aquatic organisms and their prey. Concentrations in aquatic organisms that increase substantially at successive trophic levels of a food web suggest that biomagnification is5-59 occurring. Concentrations in aquatic organisms that remain about the same or decrease at successive trophic levels of a food web sugge st that biomagnification is not occurring. When comparing tissue concentrations for assessing biomagnification, care should betaken to ensure that the aquatic organisms chosen actually represent functional predatorprey relationships and that all major prey species are considered in the comparisons.

5.6.2 Bioavailability

The chemical-specific nature of inorganic and organometallic bioavailability is likely due in part to chemical-specific differences in several factors which affect bioavailability and bioaccumulation. These factors include differences in the mechanisms for chemical uptake by aquatic organisms (e.g., passive diffusion, facilitated transport, active transport), differences in sorption affinities to biotic and abiotic ligands, and differences in chemical speciation in water. Some inorganic and organometallic chemicals exist in multiple forms and valence states in aquatic ecosystems that can differ in their bioavailability to aquatic organisms and undergo conversions between forms. For example, selenium can exist in various forms in aquatic ecosystems, including inorganic selenite( +4 ) and selenate( +6 ) oxyanions, elemental selenium ( 0 ) under reducing conditions (primarily in sediments), and organoselenium compounds of selenide ( -2 ). Dominant forms of mercury in natural, oxic waters include inorganic ( +2 ) mercury compounds and methylmercury; the latter is generally considered to be substantially more bioavailable than inorganic mercury compounds to higher trophic level organisms. Although a generic analogue to the “freely dissolved” conversion for nonionic organic chemicals does not presently exist for inorganic and organometallic chemicals as a whole, the occurrence and bioavailability of different forms of these chemicals should be carefully considered when deriving national BAFs.

1. If data indicate that: (1) a particular form (or multiple forms) of the chemical of concern largely governs its bioavailability to target aquatic organisms, and (2) BAFs are more reliable when derived using the bioavailable form(s) compared with using other form(s) of the chemical of concern, then BAFs and BCFs should be based on the appropriate bioavailable form(s).

2. Because different forms of many inorganic and organometallic chemicals may interconvert once released to the aquatic environment, regulatory and mass balance considerations typically require an accounting of the total concentration in water. In these cases, sufficient data should be available to enable conversion between total concentrations and the other (presumably more bioavailable) forms in water.

5.6.3 Deriving BAFs Using Procedure #5

This section contains guidance for calculating national BAFs for inorganic and organometallic chemicals using Procedure #5 as shown in Figure 5-1. The types of inorganic and organometallic chemicals for which Procedure #5 is appropriate are those that are not likely to biomagnify in aquatic food webs (see Section 5.1 above). In Procedure #5, two methods are available to derive the national BAF for a given trophic level:5-60 

### C using a BAF from an acceptable field study (i.e., field-measured BAF), or



### C predicting a BAF from an acceptable laboratory-measured BCF.

Individual BAFs should be determined from fiel d-measured BAFs or laboratory-measured BCFs according to the following guidelines.

5.6.3.1 Determining Field-Measured BAFs

1. Except where noted below, field-measured BAFs should be determined using the guidance provided in Section 5.4.3.1(A) of Procedure #1.

2. As described previously, conversion of field-measured BAFs to baseline BAF R fd s based on lipid-normalized and freely-dissolved concen trations does not apply for inorganic and organometallic chemicals. Therefore, the guidance and equations provided in Procedure #1 which pertain to converting field-measured BAFs to baseline BAF R fd s and subsequently to national BAFs do not generally apply to inorganic chemicals. As discussed in Section 5.6.2 above, an analogous procedure in concept might be required for converting total BAFs to BAFs based on the most bioavailable form(s) for some inorganic andorganometallic chemicals of concern. Such procedures should be applied on a chemicalspecific basis.

3. BAFs should be expressed on a wet-weight basis; BAFs reported on a dry-weight basis can be used only if they are converted to a wet-weight basis using a conversion factor that is measured or reliably estimated for the tissue used in the determination of the BAF.

4. BAFs should be based on concentrations in the edible tissue(s) of the biota unless it is demonstrated that whole-body BAFs are similar to edible tissue BAFs. For some finfish and shellfish species, whole body is considered to be the edible tissue.

5. The concentrations of an inorganic or organometallic chemical in a bioaccumulation study should be greater than normal background levels and greater than levels required for normal nutrition of the test species if the chemical is a micronutrient, but below levels that adversely affect the species. Bioaccumulation of an inorganic or organometallic chemical that is essential to the nutrition of aquatic organisms might be overestimated if concentrations are at or below normal background levels due to selective accumulation by the organisms to meet their nutritional requirements.5-61 5.6.3.2 Determining Laboratory-Measured BCFs

1. Except where noted below, BAFs should be predicted from laboratory-measured BCFs using the guidance provided in Section 5.4.3.1(c) of Procedure #1.

2. As described previously, conversion of laboratory-measured BCFs to baseline BCF R fd s based on lipid-normalized and freely dissolved concentrations does not apply for inorganic and organometallic chemicals. Th erefore, the guidance and equations provided in Procedure #1 which pertain to converting laboratory-measured BCFs to baseline BCF R fd s and subsequently to national BCFs do not generally apply to inorganic and organometallic chemicals. As discussed in Section 5.6.2 above, an analogous procedure in concept might be required for converting total BCFs to BCFs based on the most bioavailable form(s) of some inorganic and organometallic chemicals of concern. Such procedures should be applied on a chemical-specific basis. In addition, the use of FCMs with BCFs does not apply to chemicals applicable to Procedure #5.

3. BCFs should be expressed on a wet-weight basis; BCFs reported on a dry-weight basis can be used only if they are converted to a wet-weight basis using a conversion factor that is measured or reliably estimated for the tissue used in the determination of the BCF.

4. BCFs should be based on concentrations in the edible tissue(s) of the biota unless it is demonstrated that whole-body BCFs are similar to edible tissue BCFs. For some finfish and shellfish species, whole body is considered to be the edible tissue.

5. The concentrations of an inorganic or organometallic chemical in a bioconcentration test should be greater than normal background levels and greater than levels required for normal nutrition of the test species if the chemical is a micronutrient, but below levels that adversely affect the species. Bioaccumulation of an inorganic or organometallic chemical that is essential to the nutrition of aquatic organisms might be overestimated if concentrations are at or below normal background levels due to selective accumulation by the organisms to meet their nutritional requirements.

5.6.3.3 Determining the National BAFs

After calculating individual BAFs using as many of the methods in Procedure #5 as possible, the next step is to determine national BAFs for each trophic level from the individual BAFs. The national BAFs will be used to determine the national 304(a) criteria. The national BAFs should be determined from the individual BAFs by considering the data preference hierarchy defined for Procedure #5 and uncertainty in the data. The data preference hierarchy for Procedure #5 is:

1. a BAF from an acceptable field-measured BAF or predicted from an acceptable laboratory-measured BCF.

Since bioaccumulation via dietary uptake a nd subsequent biomagnification are not of concern for chemicals subject to Procedure #5, field-measured BAFs and laboratory-measured5-62 BCFs are considered equally in determining the national BAFs. The national BAFs should be selected for each trophic level using the following steps and guidelines.

1. Calculate Species-Mean BAFs. For each BAF method where more than one acceptable field-measured BAF (or a BAF predicted from a BCF) is available for a given species, calculate the species-mean BAF as the geometric mean of all acceptable individual measured or BCF-predicted BAFs. When calculating species-mean BAFs, individual measured or BCF-predicted BAFs should be reviewed carefully to assess uncertainties in the BAF values. Highly uncertain BAFs should not be used. Large differences in individual BAFs for a given species (e.g., greater than a factor of 10) should be investigated further and in such cases, some or all of the BAFs for a given species might not be used. Additional discussion on evaluating the acceptability of BAF and BCF values is provided in the Bioaccumulation TSD.

2. Calculate Trophic-Level-Mean BAFs. For each BAF method where more than one acceptable species-mean BAF is available within a given trophic level, calculate the trophic-level-mean BAF as the geometric mean of acceptable species-mean BAFs in that trophic level. Trophic-level-mean BAFs should be calculated for trophic levels two, three and four because available data on U.S. consumers of fish and shellfish indicate significant consumption of organisms in these trophic levels.

3. Select a Final National BAF for Each Trophic Level. For each trophic level, select the final national BAF using best professional judgment by considering: (1) the data preference hierarchy in Procedure #5, and (2) the relative uncertainties among trophic level-mean BAFs derived using different methods.

a. As discussed above, field-measured BAFs and laboratory-measured BCFs are considered equally desirable for deriving a final national BAF using Procedure #5. If a trophic-level-mean BAF is available from both a field-measured BAF and a laboratory-measured BCF, the final national BAF should be selected using the trophic-level-mean BAF with the least overall uncertainty.

b. The above steps should be performed for each trophic level until a national BAF is selected for trophic levels two, three, and four.

5.6.4 Deriving BAFs Using Procedure #6

This section contains guidance for calculating national BAFs for inorganic and organometallic chemicals using Procedure #6 as shown in Figure 5-1. The types of inorganic and organometallic chemicals for which Procedure #6 is appropriate are those that are considered likely to biomagnify in aquatic food webs (see Section 5.6.1 above). Methylmercury is an example of an organometallic chemical to which Procedure #6 applies. In Procedure #6, two methods are available to derive the national BAF:



### C using a BAF from an acceptable field study (i.e., field-measured BAF), or5-63 

### C predicting a BAF from an acceptable laboratory-measured BCF and a FCM.

Individual BAFs should be determined from fiel d-measured BAFs or laboratory-measured BCFs and FCMs according to the following guidelines.

5.6.4.1 Determining Field-Measured BAFs

1. Field-measured BAFs should be determined using the guidance provided in Section 5.6.3.1 of Procedure #5.

5.6.4.2 Determining Laboratory-Measured BCFs

1. Except where noted below, BAFs should be predicted from laboratory-measured BCFs using the guidance provided in Section 5.6.3.2 of Procedure #5.

2. Because biomagnification is of concern for chemicals applicable to Procedure #6, BAFs should be predicted from laboratory-measuredBCF using FCMs. Currently, there are no generic models from which to predict FCMs for inorganic or organometallic chemicals. Therefore, FCMs should be determined using field data as described in the section entitled: “Field-Derived FCMs” in Section 5.4.3.1(c) of Procedure #1. Unlike nonionic organic chemicals, field-derived FCMs for inorganic and organometallic chemicals are not based on lipid-normalized concentrations in tissues. For calculating FCMs for inorganic and organometallic chemicals, concentrations in tissues should be based on the consistent use of either wet-weight or dry-we ight concentrations in edible tissues. FCMs should be derived for trophic levels two, three, and four.

5.6.4.3 Determining the National BAF

After calculating individual BAFs using as many of the methods in Procedure #6 as possible, the next step is to determine national BAFs for each trophic level from the individual BAFs. The national BAFs will be used to determine the national 304(a) criteria. The national BAFs should be determined from the individual BAFs by considering the data preference hierarchy defined for Procedure #6 and uncertainty in the data. The data preference hierarchy for Procedure #6 is (in order of preference):

1. a BAF from an acceptable field-measured BAF, or 2. a predicted BAF from an acceptable laboratory-measured BCF and FCM.

This data preference hierarchy reflects EPA’ s preference for field-measured BAFs over BAFs predicted from a laboratory-measured BCFand FCM, because field-measured BAFs are direct measures of bioaccumulation and biomagni fication in aquatic food webs. BAFs predicted from laboratory-measured BCFs and FCMs indir ectly account for biomagnification through the use of the FCM. For each trophic level, the national BAFs should be determined using the following steps and guidelines.5-64 1. Calculate Species-Mean BAFs. For each BAF method where more than one acceptable field-measured BAF or BAF predicted using a BCF and FCM is available, calculate a species-mean BAF according to the guidance described previously in Procedure #5.

2. Calculate Trophic Level-Mean BAFs. For each BAF method where more than one acceptable species-mean BAF is available within a given trophic level, calculate the trophic level-mean BAF according to guidance described previously in Procedure #5.

3. Select a Final National BAF for Each Trophic Level. For each trophic level, select the final national BAF using best professional judgment by considering: (1) the data preference hierarchy in Procedure #6, and (2) the relative uncertainties among trophic level-mean BAFs derived using different methods.

a. When a trophic-level mean BAF is available using both methods for a given trophic level (i.e., a field-measured BAF and a BAF predicted from a BCF and FCM), the national BAF should usually be selected using the field-measured BAF which is the preferred BAF method in the data preference hierarchy in Procedure #6.

b. If uncertainty in the trophic-level mean BAF derived using field-measured BAFs is considered to be substantially greater than a trophic-level mean BAF derived using a BCF and FCM, the national BAF for that trophic level should be selected from the second tier (BCF 

### @ FCM) method.

c. The above steps should be performed for each trophic level until a national BAF is selected for trophic levels two, three, and four.

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