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- 1 O f f i c e o f A i r Q u a l i t y P l a n n i n g & S t a n d a r d s a n d O f f i c e o f R e s e a r c h a n d D e ve l o p m e n t



# V o l u m e I V : A n A s s e s s m e n t o f E x p o s u r e t o M e r c u r y i n t h e U n i t e d S t a t e s

### MERCURY STUDY REPORT TO CONGRESS VOLUME IV: AN ASSESSMENT OF EXPOSURE TO MERCURY IN THE UNITED STATES

December 1997



### Office of Air Quality Planning and Standards and Office of Research and Development U.S. Environmental Protection Agencyi 

### TABLE OF CONTENTS Page
U.S. EPA AUTHORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv SCIENTIFIC PEER REVIEWERS ...................................................... v WORK GROUP AND U.S. EPA/ORD REVIEWERS

...................................... viii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv LIST OF SYMBOLS, UNITS AND ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv EXECUTIVE SUMMARY

......................................................... ES-1
1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1
2. APPROACH TO EXPOSURE ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 Modeling Exposures near Mercury Emissions Sources . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1.1 Description of Computer Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1.2 Estimates of Background Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 2.2 Description of Hypothetical Exposure Scenarios for Humans . . . . . . . . . . . . . . . . . . . 2-4 2.2.1 Hypothetical Location Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.2.2 Description of Hypothetical Human Exposure Scenarios . . . . . . . . . . . . . . . . 2-5 2.3 Summary of Exposure Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 2.4 Emissions Sources

.................................................... 2-12 2.5 Predicted Concentrations in Environmental Media

........................... 2-12
3. PREDICTED INDIVIDUAL EXPOSURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1 Illustration of Ex p osure Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1.1 Concentrations in Environmental Media and Biota . . . . . . . . . . . . . . . . . . . . . 3-2 3.1.2. Results for H yp othetical Ex p osure Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 3-6 3.2 Results of Combinin g Local and Re g ional Models - Predicted Human Ex p osure . . . 3-12 3.2.1 Inhalation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12 3.2.2 A g ricultural Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12 3.2.3 Urban Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-37 3.2.4 Fish In g estion Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-37 3.3 Issues Related to Predicted Mercury Ex p osure Estimates . . . . . . . . . . . . . . . . . . . . . . 3-38 3.4 Summar y Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-39
4. POPULATION EXPOSURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1 Fish Consump tion amon g the General U.S. Po p ulation . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1.1 Patterns of Fish Consump tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1.2 Fre q uenc y of Consum p tion of Fish Based on Surve y s of Individuals . . . . . 4-13 4.1.3 Sub p o p ulations with Potentiall y Hi g her Consump tion Rates . . . . . . . . . . . . 4-22 4.1.4 Summar y of Hawaiian Island Fish Consump tion Data . . . . . . . . . . . . . . . . . 4-42 4.1.5 Summar y of Alaskan Fish Consump tion Data . . . . . . . . . . . . . . . . . . . . . . . . 4-43 4.1.6 Summar y of Canadian Data on Mercury Intake from Fish and Marine Mammals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-48 4.2 Trends in Fish and Shellfish Consump tion in the United States . . . . . . . . . . . . . . . . . 4-50 4.2.1 Fish and Shellfish Consump tion: United States, 1975 to 1995 . . . . . . . . . . . 4-50

### TABLE OF CONTENTS (continued) Page

ii 4.2.2 Current Market Trends, 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-53 4.2.3 Patterns in Fish and Shellfish Consum p tion: United States, 1996 . . . . . . . . . 4-53 4.2.4 Production Patterns and Mercur y Concentrations for S p ecific Fish and Shellfish S p ecies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57 4.3 Mercur y Concentrations In Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-59 4.3.1 National Marine Fisheries Service Data Base . . . . . . . . . . . . . . . . . . . . . . . . 4-59 4.3.2 Mercur y Concentrations in Marine Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-66 4.3.3 Freshwater Fish Mercur y Data Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70 4.3.4 Mercur y Concentrations In Freshwater Fish . . . . . . . . . . . . . . . . . . . . . . . . . 4-70 4.3.5 Calculation of Mercury Concentrations in Fish Dishes . . . . . . . . . . . . . . . . . 4-73 4.4 Intake of Meth y lmercur y from Fish/fish Dishes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75 4.4.1 Intakes " p er User" and " p er Cap ita" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75 4.4.2 Meth y lmercur y Intake from Fish and Shellfish amon g Women of Child-bearin g A g e and Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-78 4.4.3 Month-Lon g Estimates for Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-82 4.4.4 Habitat of Fish Consumed and Mercury Ex p osure from Fish of Marine, Estuarine and Freshwater Ori g in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-86 4.4.5 Meth y lmercur y Consump tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-87 4.5 Conclusions on Meth y lmercur y Intake from Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-87
5. POPULATION EXPOSURES - NON-DIETARY SOURCES . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.1 Dental Amal g ams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.2 Occu p ational Ex p osures to Mercur y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.3 Miscellaneous Sources of Mercury Ex p osure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3 5.4 Cases of Mercur y Poisonin g . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3
6. COMPARISON OF ESTIMATED EXPOSURE WITH BIOMONITORING . . . . . . . . . . . . . 6-1 6.1 Biomarkers of Ex p osure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1 6.2 Biomarkers of Ex p osure Predictive of Intake of Meth y lmercur y . . . . . . . . . . . . . . . . . 6-1 6.3 Sam p le Handlin g and Anal y sis of Blood Samp les for Mercur y . . . . . . . . . . . . . . . . . . 6-2 6.4 Association of Blood Mercury with Fish Consump tion . . . . . . . . . . . . . . . . . . . . . . . . 6-2 6.4.1 Half-Times of Meth y lmercur y in Blood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2 6.4.2 Fraction of Total Blood Mercur y that Is Or g anic or Meth y lmercur y . . . . . . . 6-3 6.4.3 Meth y lmercur y Consump tion from Fish and Blood Mercur y Values . . . . . . . 6-3 6.4.4 North American Re p orts on Blood Mercur y Concentrations . . . . . . . . . . . . . . 6-4 6.5 Hair Mercur y as a Biomarker of Meth y lmercur y Ex p osure . . . . . . . . . . . . . . . . . . . . . 6-8 6.5.1 Hair Comp osition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8 6.5.2 Hair Mercur y Concentrations in North America . . . . . . . . . . . . . . . . . . . . . . 6-10 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15 6.6.1 Blood Mercur y Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15 6.6.2 Hair Mercur y Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15
7. CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1
8. RESEARCH NEEDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

### TABLE OF CONTENTS (continued) Page

iii 9. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 APPENDIX A EXPOSURE PARAMETER JUSTIFICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1
APPENDIX B ESTIMATED NATIONAL AND REGIONAL POPULATIONS OF WOMEN OF CHILD-BEARING AGE: UNITED STATES, 1990 . . . . . . . . . . . . . B-1
APPENDIX C ANALYSIS OF MERCURY LEVELS IN FISH AND SHELLFISH REPORTED IN NATIONAL MARINE FISHERIES SERVICE SURVEY OF TRACE ELEMENTS IN THE FISHERY RESERVE . . . . . . . . . . . . . . . . . . . . . C-1
APPENDIX D HUMAN FISH CONSUMPTION AND MERCURY INGESTION DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1iv 

### U.S. EPA AUTHORS

Principal Authors Kathryn R. Mahaffey, Ph.D. Glenn E. Rice National Center for Environmental National Center for Environmental Assessment - Washington Assessment - Cincinnati Office of Research and Development Office of Research and Development Washington, DC Cincinnati, Ohio

Contributing Author
Jeff Swartout National Center for Environmental Assessment - Cincinnati Office of Research and Development Cincinnati, Ohiov 

### SCIENTIFIC PEER REVIEWERS

Dr. William J. Adams* Dr. Joan Daisey* Kennecott Utah Corporation Lawrence Berkeley National Laboratory Dr. Brian J. Allee Dr. John A. Dellinger* Harza Northwest, Incorporated Medical College of Wisconsin
Dr. Thomas D. Atkeson Dr. Kim N. Dietrich* Florida Department of Environmental University of Cincinnati Protection
Dr. Donald G. Barnes* Great Lakes Natural Resource Center U.S. EPA Science Advisory Board National Wildlife Federation for the
Dr. Steven M. Bartell SENES Oak Ridge, Inc. Dr. Lawrence J. Fischer* Dr. David Bellinger* Children’s Hospital, Boston Dr. William F. Fitzgerald Dr. Nicolas Bloom* Avery Point Frontier Geosciences, Inc. Dr. Mike Bolger U.S. EPA Science Advisory Board U.S. Food and Drug Administration
Dr. Dallas Burtraw* National Center for Health Statistics Resources for the Future Dr. Thomas Burbacher* University of Maryland at Baltimore University of Washington Seattle Dr. Steven G. Gilbert*
Dr. James P. Butler University of Chicago Dr. Cynthia C. Gilmour* Argonne National Laboratory The Academy of Natural Sciences Dr. Rick Canady Dr. Robert Goyer Agency for Toxic Substances and Disease National Institute of Environmental Health Registry Sciences Dr. Rufus Chaney Dr. George Gray U.S. Department of Agriculture Harvard School of Public Health Dr. Tim Eder

States of Michigan and Ohio

Michigan State University

University of Connecticut

A. Robert Flaak*

Dr. Katharine Flegal

Dr. Bruce A. Fowler*

Biosupport, Inc.

### SCIENTIFIC PEER REVIEWERS (continued)

vi Dr. Terry Haines Berkeley National Biological Service Dr. Gary Heinz* National Oceanic and Atmospheric Patuxent Wildlife Research Center Administration
Joann L. Held New Jersey Department of Environmental Dr. Michael W. Meyer* Protection & Energy Wisconsin Department of Natural Resources
Dr. Robert E. Hueter* Dr. Maria Morandi* Mote Marine Laboratory University of Texas Science Center at Houston Dr. Harold E. B. Humphrey* Dr. Paul Mushak Michigan Department of Community Health PB Associates Dr. James P. Hurley* Dr. Christopher Newland* University of Wisconsin Auburn University Madison Dr. Joseph L. Jacobson* The University of Michigan Wayne State University Ann Arbor
Dr. Gerald J. Keeler Dr. W. Steven Otwell* University of Michigan University of Florida Ann Arbor Gainesville Dr. Ronald J. Kendall* Dr. Jozef M. Pacyna Clemson University Norwegian Institute for Air Research Dr. Lynda P. Knobeloch* Dr. Ruth Patterson Wisconsin Division of Health Cancer Prevention Research Program
Dr. Leonard Levin Electric Power Research Institute Dr. Donald Porcella Dr. Steven E. Lindberg* Oak Ridge National Laboratory Dr. Deborah C. Rice*
Dr. Genevieve M. Matanoski* The Johns Hopkins University Samuel R. Rondberg* Dr. Margaret McDowell National Center for Health Statistics Charles Schmidt
Dr. Thomas McKone* University of California Dr. Pamela Shubat Dr. Malcolm Meaburn

U.S. Department of Commerce

Dr. Jerome O. Nriagu*

Fred Gutchinson Cancer Research Center

Electric Power Research Institute

Toxicology Research Center

U.S. EPA Science Advisory Board

U.S. Department of Energy

### SCIENTIFIC PEER REVIEWERS (continued)

vii Minnesota Department of Health Dr. Valerie Thomas* Dr. Ellen K. Silbergeld* University of Maryland Dr. M. Anthony Verity Baltimore University of California
Dr. Howard A. Simonin* NYSDEC Aquatic Toxicant Research Unit Dr. Ann Spacie* Purdue University
Dr. Alan H. Stern New Jersey Department of Environmental Protection & Energy
Dr. David G. Strimaitis* Earth Tech
Dr. Edward B. Swain Minnesota Pollution Control Agency Princeton University

Los Angeles

*With EPA’s Science Advisory Board, Mercury Review Subcommitteviii 

### WORK GROUP AND U.S. EPA/ORD REVIEWERS

Core Work Group Reviewers : U.S. EPA/ORD Reviewers : Dan Axelrad, U.S. EPA Robert Beliles, Ph.D., D.A.B.T. Office of Policy, Planning and Evaluation National Center for Environmental Assessment Angela Bandemehr, U.S. EPA Region 5 Eletha Brady-Roberts Jim Darr, U.S. EPA Cincinnati, OH Office of Pollution Prevention and Toxic Substances Annie M. Jarabek Thomas Gentile, State of New York Research Triangle Park, NC Department of Environmental Conservation Arnie Kuzmack, U.S. EPA National Center for Environmental Assessment Office of Water Washington, DC David Layland, U.S. EPA Susan Braen Norton Office of Solid Waste and Emergency Response National Center for Environmental Assessment Karen Levy, U.S. EPA Office of Policy Analysis and Review Terry Harvey, D.V.M. Steve Levy, U.S. EPA Cincinnati, OH Office of Solid Waste and Emergency Response Lorraine Randecker, U.S. EPA Office of Pollution Prevention and Toxic Substances Joy Taylor, State of Michigan Department of Natural Resources Washington, DC

National Center for Environmental Assessment

National Center for Environmental Assessment

Matthew Lorber

Washington, DC

National Center for Environmental Assessmentix 

### LIST OF TABLES Page 2-1 Models Used to Predict Mercur y Air Concentrations, De p osition Fluxes and Environmental Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 2-2 Percentiles of the Meth y lmercur y Bioaccumulation Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 2-3 In p uts to IEM-2M Model for the Two Time Periods Modeled . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2-4 Summar y of Human Ex p osure Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6 2-5 Fish Consump tion Rates for Columbia River Tribes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8 2-6 Dail y Fish Consump tion Rates Amon g Adults Fish Consump tion b y Columbia River Tribes . 2-8 2-7 Fish Consump tion Rates used in this Stud y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 2-8 Potential De p endenc y of Ex p osure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 2-9 Default Values of Scenario-Inde p endent Ex p osure Parameters . . . . . . . . . . . . . . . . . . . . . . . . 2-10 2-10 Values for Scenario-De p endent Ex p osure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11 2-11 Process Parameters for the Model Plants Considered in the Local Im p act Anal y sis . . . . . . . . 2-13 2-12 Predicted Mercur y Values for Environmental Media at Eastern Site (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 2-13 Predicted Mercur y Values for Environmental Media at Eastern Site (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16 2-14 Predicted Mercur y Values in Water Column and Biota for Eastern Site (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-17 2-15 Predicted Mercur y Values in Water Column and Biota for Eastern Site (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-18 2-16 Predicted Mercur y Values for Environmental Media at Western Site (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-19 2-17 Predicted Mercur y Values for Environmental Media at Western Site (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-21 2-18 Predicted Mercur y Values in Water Column and Biota for Western Site (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-22 2-19 Predicted Mercur y Values in Water Column and Biota for Western Site (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-23 3-1 Predicted Mercur y Concentrations after Pre-facility Simulations Performed for Eastern Site . 3-2 3-2 Modeled results for Lar g e Hos p ital HMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 3-3 Predicted Mercur y Ex p osure for Subsistence Farmer Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 3-8 3-4 Predicted Mercur y Ex p osure for Rural Home Gardener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 3-5 Predicted Mercur y Ex p osure for Urban Avera g e Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10 3-6 Predicted Mercur y Ex p osure for Urban Hi g h-end Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10 3-7 Predicted Mercur y Ex p osure for Hi g h-end Fish Consump tion Scenario . . . . . . . . . . . . . . . . . 3-11 3-8 Predicted Mercur y Ex p osure for Recreational An g ler Scenario . . . . . . . . . . . . . . . . . . . . . . . 3-12 3-9 Eastern Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Subsistence Farmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13 3-10 Eastern Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Rural Home Gardner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15 3-11 Eastern Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Urban Avera g e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17 3-12 Eastern Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Urban Hi g h End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19 3-13 Eastern Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Subsistence Fisher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-21

### LIST OF TABLES (continued) Page

x 3-14 Eastern Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Recreational An g ler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23 3-15 Western Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Subsistence Farmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24 3-16 Western Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Rural Home Gardner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-26 3-17 Western Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Urban Avera g e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28 3-18 Western Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Urban Hi g h End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30 3-19 Western Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Subsistence Fisher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-32 3-20 Western Site — RELMAP 50th and 90th Percentiles Predicted In g estion (m g /k g /da y ) for Recreational An g ler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-34 3-21 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Inhalation . . . . . . . . . . . . . . . 3-35 3-22 Western Site — RELMAP 50th and 90th Percentiles Predicted Inhalation . . . . . . . . . . . . . . 3-36
4-1 Avera g e Servin g Size ( g ms) for Seafood from USDA Handbook # 11 Used to Calculate Fish Intake b y FDA (1978) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5 4-2 Fish S p ecies and Number of Persons Usin g the S p ecies of Fish. (Adap ted from Ru pp et al., 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6 4-3 Fish Consump tion from the NPD 1973-1974 Surve y (Modified from Ru pp et al., 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6 4-4 Distribution of Fish Consump tion for Females b y A g e* Consump tion Cate g or y ( g ms/da y ) (from SRI, 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 4-5 CSFII 89-91 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8 4-6 CSFII 1994 Data — Da y s 1 and 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9 4-7 CSFII 1995 Data — Da y s 1 and 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10 4-8 Fish Consump tion ( g ms) b y Season for Res p ondents Re p ortin g Seafood Consump tion CFSII 1994 — Da y 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10 4-9 All A g e Grou p s NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12 4-10 NHANES III Adult Res p ondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12 4-11 NHANES III Child Res p ondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13 4-12 Consum p tion of Fish and Shellfish ( g ms/da y ), and Self-Re p orted Bod y Wei g ht (k g ) in Res p ondents of the 1989-1991 CSFII Surve y . "Per Cap ita" Data for All Surve y Res p ondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15 4-13 Consum p tion of Fish and Shellfish ( g ms/da y ), and Self-Re p orted Bod y Wei g ht (k g ) in Res p ondents of the 1989-1991 CSFII Surve y . . . . . . . . 4-15 4-14 Fre q uenc y of Fish/Shellfish In g estion and Percent of Res p ondents . . . . . . . . . . . . . . . . . . . . 4-17 4-15a Fre q uenc y of Fish and Shellfish Consump tion b y Percent amon g All Adults, Both Genders, Wei g hted Data, NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18 4-15b Fre q uenc y of Fish and Shellfish Consump tion b y Race/Ethnicit y , Women A g ed 15-45 Years, Wei g hted Data, NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18 4-16a Distribution of the Fre q uenc y of Fish and Shellfish Consump tion b y Race/Ethnicit y All Adults, Both Genders, Wei g hted Data, NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19 4-16b Distribution of the Fre q uenc y of Fish and Shellfish Consump tion b y Race/Ethnicit y Amon g Adult Women A g es 15-45 Years, Wei g hted Data, NHANES III . . . . . . . . . . . . . . . . 4-19

### LIST OF TABLES (continued) Page

xi 4-17 Classification of Fish S p ecies b y Habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20 4-18 Wei g hted Estimates of Fish and Shellfish Consumed ( g ms) for Females and Males A g ed 15-44 Years Re p orted in NHANES III (Per User) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21 4-19 Wei g hted Estimates for Fish and Shellfish Consumed ( g ms) b y Female and Male Res p ondents A g ed 15 — 44 Years Re p orted in the NHANES III Surve y b y Habitat of S p ecies Consumed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21 4-20 Consum p tion of Fish and Shellfish ( g ms/da y ) amon g Ethnicall y Diverse Grou p s . . . . . . . . . 4-24 4-21 Fish Consump tion of an Urban “Subsistent” Grou p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26 4-22 Hi g h Fish Consump tion amon g Urban Sub j ects: Case Re p ort . . . . . . . . . . . . . . . . . . . . . . . . . 4-26 4-23 Com p ilation of the An g ler Consump tion Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-28 4-24 Median Recreationall y Cau g ht Fish Consump tion Rate Estimates b y Ethnic Grou p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-31 4-25 Freshwater Fish Consump tion Estimates of Turcotte (1983) . . . . . . . . . . . . . . . . . . . . . . . . . . 4-31 4-26 Dail y Intake of S p ortfish and Total Fish for the Fish-consumin g Portion of the Po p ulation Studied b y Fiore et al. (1989) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-32 4-27 Fish Consump tion Rate Data for Grou p s Identified in Hovin g a et al. (1992) as Eaters and Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-33 4-28 Fish Consump tion Rates for Maine An g lers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-34 4-29 Fish Consump tion Rates of Florida An g lers Who Receive Food Stam p s . . . . . . . . . . . . . . . . 4-34 4-30 Fish Consump tion b y Native U.S. Po p ulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-35 4-31 Fish Consum p tion b y Columbia River Tribes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-39 4-32 Dail y Fish Consump tion Rates b y Adults of Columbia River Tribes . . . . . . . . . . . . . . . . . . . 4-39 4-33 Fish Consump tion ( g ms/k g bw/da y ) b y the Tulali p and S q uaxin Island Tribes . . . . . . . . . . . 4-40 4-34 Local Fish Meals Consumed B y Time Period for the Mohawk and Comp arison Nursin g Mothers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-41 4-35 S p ecies Com p osition of Hawaii's Retail Seafood Trade — 1981 Purchases . . . . . . . . . . . . . . 4-43 4-36 Mean Per Cap ita Harvest of Fish and Marine Mammals ( g /da y ) . . . . . . . . . . . . . . . . . . . . . . . 4-46 4-37 Estimated Dail y Intake of Food and Mercur y for Arctic Inuit . . . . . . . . . . . . . . . . . . . . . . . . . 4-47 4-38 Mercur y Concentrations ( μg H g / g wet wei g ht) in Traditional Foods Consumed b y Canadian Abori g inal Peo p les . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-49 4-39 Estimated Dail y Intake of Mercur y Usin g Contaminant Data Base and Dietar y Information from Dene and Inuit Communities in Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-49 4-40 Percent of Fish/Shellfish b y Processin g T yp e between 1910 and 1995 . . . . . . . . . . . . . . . . . . 4-50 4-41 U.S. Su pp l y of Edible Commercial Fishery Products: 1990 and 1995 . . . . . . . . . . . . . . . . . . 4-51 4-42 U.S. Annual Per Ca p ita Consum p tion of Canned Fisher y Products: 1990 and 1995 (Pounds Per Ca p ita) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-52 4-43 U.S. Annual Per Ca p ita Consum p tion (in p ounds*) of Certain Fisher y Items: 1990 and 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-52 4-44 Ten Most Commonl y Re p orted Fish/Shellfish/Mixed Dishes b y Season CSFII 1994 and CSFII 1995 — Da y 1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-54 4-45 Re g ional Po p ularit y of Fish and Shellfish S p ecies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-56 4-46 Po p ularit y of Fish/Shellfish S p ecies in Restaurants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-56 4-47 Fre q uencies of Various Fish and Shellfish Food T yp es for Children A g es 1 to 5 and 6 to 11 Years b y Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57 4-48 Summar y of Mercur y Concentrations in Fish S p ecies ( μg H g / g fresh wei g ht) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-60 4-49 Mercur y Concentrations in Marine Finfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-67

### LIST OF TABLES (continued) Page

xii 4-50 Mercur y Concentrations in Marine Shellfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-68 4-51 Mercur y Concentrations in Marine Molluscan Ce p halo p ods . . . . . . . . . . . . . . . . . . . . . . . . . . 4-69 4-52 Anal y ses of Mercur y Standard Reference Materials Used b y Lowe et al. (1985) in Su pp ort of Anal y ses of Freshwater Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-69 4-53 Freshwater Fish Mercur y Concentrations from Lowe et al., (1985) . . . . . . . . . . . . . . . . . . . . 4-71 4-54 Mercur y Concentrations in Freshwater Fish U.S. EPA (1992) and Bahnick et al. (1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-73 4-55 CSFII 89-91 Number of Res p ondents - All A g e Grou p s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75 4-56 CSFII 89-91 Adult Res p ondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-76 4-57 Contemp orar y Dietar y Surve y s — 1990s General U.S. Po p ulation . . . . . . . . . . . . . . . . . . . . . 4-76 4-58 Per Ca p ita Fish/Shellfish Consum p tion ( g ms/da y ) and Mercur y Ex p osure ( μg /k g bod y wei g ht/da y ) From CSFII 89-91 Based on Avera g e of Three 24-Hour Recalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-77 4-59 Per Ca p ita Fish/Shellfish Consum p tion Based on Individual Da y s of 24-Hour Recall Data General U.S. Po p ulation Surve y s — 1990s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-77 4-60 Per User Fish/Shellfish Consump tion ( g rams p er da y ) and Mercur y Ex p osure ( μg /k g bw/da y ) Based on Avera g e of Three 24-Hour Recalls — CSFII 89-91 . . . . . . . . . . . . . . . . . . . . . . . . . 4-78 4-61 “Per User” Intake of Fish and Shellfish ( g ms/da y ) and Ex p osure to Mercur y ( μg H g /k g bw/da y ) amon g Individuals Re p ortin g Consump tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-78 4-62 “Per Cap ita” Fish/Shellfish Consum p tion ( g rams/da y ) and Mercur y Ex p osure ( μg /k g bw/da y ) Based on Avera g e of Three 24-Hour Dietar y Recalls — CSFII 89-91 . . . . . . . . . . . . . . . . . . 4-79 4-63 “Per User” Fish/Shellfish Consump tion ( g rams/da y ) and Mercur y Ex p osure ( μg /k g bw/da y ) Based on Avera g e of Three 24-Hour Dietar y Recalls — CSFII 89-91 . . . . . . . . . . . . . . . . . . 4-79 4-64 Consump tion of Fish and Shellfish ( g rams/da y ) and Mercur y Ex p osure ( μg H g /k g bw/da y ) amon g Different A g e Cate g ories of Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-80 4-65 Fish and Shellfish Consump tion ( g rams/da y ) and Mercur y Ex p osure ( μg /k g bod y wei g ht/da y ) for Children A g ed 14 y ears and Youn g er — CSFII 89-91 . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-80 4-66 “Per User” Fish and/or Shellfish Consump tion ( g rams/da y ) and Mercur y Ex p osure ( μg H g /k g bw/da y ) b y Children a g es 14 and Youn g er . . . . . . . . . . . . . . . 4-81 4-67 Consum p tion of Fish and Shellfish ( g rams/da y ) and Mercur y Ex p osure ( μg H g /k g bw/da y ) Amon g Ethnicall y Diverse Grou p s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-81 4-68a Month-Lon g Estimates of Fish and Shellfish Consum p tion ( g ms/da y ) General Po p ulation b y Ethnic/Racial Grou p National Estimates Based on NHANES III Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-83 4-68b Month-Lon g Estimates of Mercur y Ex p osure ( μg /k g bw/da y ) Po p ulation b y Ethnic/Racial Grou p National Estimates Based on NHANES III Data . . . . . . 4-83 4-69 Month-Lon g Estimates of Ex p osure to Fish and Shellfish ( g ms/da y ) for Women A g es 15 throu g h 44 Years Combined Distributions Based on NHANES III Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-84 4-70 Month-Lon g Estimates of Mercur y Ex p osure ( μg /k g bw /da y ) for Women A g es 15 throu g h 44 All Sub p o p ulations Combined National Estimates Based on NHANES III Data . . . . . . . . . . 4-84 4-71 Month-Lon g Estimates of Fish/Shellfish Consum p tion ( g ms/da y ) amon g Children A g es 3 throu g h 6 Years. National Estimates Based on NHANES III Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-85 4-72 Month-Lon g Estimates of Ex p osure to Fish and Shellfish ( g ms/da y ) and Mercur y ( μg /k g bw/day ) amon g Children A g es 3 throu g h 6 Years. National Estimates for Individual Ethnic/Racial Grou p s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-85

### LIST OF TABLES (continued) Page

xiii 4-73 Ex p osure of Men A g es 15 to 44 Years to Mercur y ( μg H g /k g bw/da y ) from Fish and Shellfish of Marine, Estuarine, and Freshwater Ori g in . . . . . . . . . . . . . . . . . . 4-86 4-74 Ex p osure of Women A g ed 15-44 Years to Mercur y ( μg H g /k g bw/da y ) from Fish and Shellfish of Marine, Estuarine, and Freshwater Ori g in . . . . . . . . . . . . . . . . . . . . . . . 4-87
5-1 Occu p ational Standards for Airborne Mercur y Ex p osure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2 6-1 Literature Derived Values for Total Mercur y Concentrations in Whole Blood . . . . . . . . . . . . . 6-3 6-2 Blood Mercur y Concentrations Values Re p orted for the United States . . . . . . . . . . . . . . . . . . . 6-5 6-3 Hair Mercur y Concentrations ( μg H g / g ram hair or pp m) from Residents of Various Communities in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11 6-4 Association of Hair Mercur y Concentrations ( μg H g / g ram hair) with Fre q uenc y of Fish In g estion b y Adult Men and Women Livin g in 32 Locations within 13 Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-14xiv 

### LIST OF FIGURES Page 2-1 Configuration of Hypothetical Water Body and Watershed Relative to Local Source . . . . . . . 2-5 4-1 Distribution of Fish Consumption Rates of Various Populations

...................... 4-23xv 

### LIST OF SYMBOLS, UNITS AND ACRONYMS

AC Activated carbon APCD Air pollution control device ASME American Society of Mechanical Engineers CAA Clean Air Act as Amended in 1990 CaS Calcium sulfide cf Cubic feet CFB Circulating fluidized bed cm Cubic meter CRF Capital recovery factor dscf Dry standard cubic feet dscm Dry standard cubic meter ESP Electrostatic precipitator DSI Dry sorbent injection EPRI Electric Power Research Institute FFDCA Federal Food, Drug, Cosmetic Act FFs Fabric filters FGD Flue gas desulfurization FIFRA Federal Insecticide, Fungicide, Rodenticide Act FWS U.S. Fish and Wildlife Service GACT Generally available control technology GLFCATF Great Lakes Fish Consumption Advisory Task Force GLNPO Great Lakes National Program Office g Gram gr Grains HAPs Hazardous air pollutants HCl Hydrochloric acid Hg Mercury HgCl Mercuric chloride HgI Mercuric iodide HgO Mercuric oxide HgS Mercuric sulfide HgSe Mercuric selenite HMTA Hazardous Materials Transportation Act HVAC Heating, ventilating and air conditioning IDLH Immediately dangerous to life and health INGAA Interstate Natural Gas Association Of America kg Kilogram kW Kilowatt MACT Maximum achievable control technology MB Mass burn MCL Maximum contaminant level Mg Megagram MSW Municipal solid waste MW Megawatt MWCs Municipal waste combustors

### LIST OF SYMBOLS, UNITS AND ACRONYMS (continued)

xvi MWIs Medical waste incinerators NaCl Sodium chloride NaOH Sodium hydroxide ng Nanogram NIOSH National Institute for Occupational Safety and Health Nm Normal cubic meter 3 NOAA National Oceanic and Atmospheric Administration NPDES National Pollutant Discharge Elimination System NSP Northern States Power NSPS New source performance standard OAQPS Office of Air Quality Planning and Standards (U.S. EPA) OECD Organization for Economic Co-operation and Development O&M Operation and maintenance OSHA Occupational Safety and Health Administration PCBs Polychlorinated biphenyls PELs Permissible exposure limits PM Particulate matter ppm parts per million ppmv parts per million by volume RQ Reportable quantity SARA Superfund Amendments and Reauthorization Act scf Standard cubic feet scm Standard cubic meter SD Spray dryer SDAs Spray dryer absorbers TCC Total capital cost TCLP Toxicity characteristic leaching procedure TMT Trimercapto-s-triazine tpd Tons per day TRI Toxic Release Inventory μg Microgram UNDEERC University of North Dakota Energy and Environmental Research Center WS Wet scrubber WW WaterwallES-1 

### EXECUTIVE SUMMARY

Section 112(n)(1)(B) of the Clean Air Act (CAA), as amended in 1990, requires the U.S. Environmental Protection Agency (U.S. EPA) to submit a study on atmospheric mercury emissions to Congress. The sources of emissions that must be studied include electric utility steam generating units, municipal waste combustion units and other sources, including area sources. Congress directed that the Mercury Study evaluate many aspects of mercury emissions, including the rate and mass of emissions, health and environmental effects, technologies to control such emissions, and the costs of such controls. In response to this mandate, U.S. EPA has prepared an eight-volume Mercury Study Report to Congress. This document is the exposure assessment (Volume IV) of the Mercury Study Report to Congress. The exposure assessment is one component of the risk assessment of U.S. anthropogenic mercury emissions. The analysis in this volume builds on the fate and transport data compiled in Volume III of the study. This exposure assessment considers both inhalation and ingestion exposure routes. For mercury emitted to the atmosphere, ingestion is an indirect route of exposure that results from mercury deposition onto soil, water bodies and plants and uptake through the food chain. The analyses in this volume are integrated with information relating to human and wildlife health impacts of mercury in the Risk Characterization Volume (Volume VII) of the Report. National Assessment of Mercury Exposure from Fish Consumption A current assessment of U.S. general population methylmercury exposure through the consumption of fish is provided in this volume. This assessment was conducted to provide an estimateof mercury exposure through the consumption of fish to the general U.S. population. It is not a sitespecific assessment but rather a national assessment. This assessment utilizes data from the Continuing Surveys of Food Intake by Individuals (CSFII 89-91, CSFII 1994, CSFII 1995) and the third National Heath and Nutrition Examination Survey (NHANES III) to estimate a range of fish consumption rates among U.S. fish eaters. Both per capita and per user (only individuals who reported fish consumption) were considered. For each fish-eater, the number of fish meals, the quantities and species of fish consumed and the self-reported body weights were used to estimate mercury exposure on a body weight basis. The constitution of the survey population was weighted to reflect the actual U.S. population. Results of smaller surveys on  high-end fish consumers are also included. These estimates of fish consumption rates were combined with fish species-specific mean values for measured mercury concentrations. The fish mercury concentration data were obtained from the National Marine Fisheries Service, Bahnick et al., (1994), and Lowe et al., (1985). Through the application of specific fish preparation factors (USDA, 1995), estimates of the range of methylmercury exposure from the consumption of fish were prepared for the fish-consuming segment of the U.S. population. Per kilogram body weight estimates of methylmercury exposure were determined by dividing the total daily methylmercury exposure from this pathway by the self-reported body weights. Estimates of month-long patterns of fish and shellfish consumption were based on the data reporting frequency of fish/shellfish consumption obtained in the third National Health and Nutrition Examination Survey (NHANES III) conducted between 1988 and 1994. Combining these frequency data with other information on respondents in NHANES III (i.e., 24-hour recall data and self-reported body weight of subjects), and mean mercury concentrations in fish/shellfish, these projected month-long estimates of fish/shellfish consumption describe moderate-term mercury exposures for the general United States population.ES-2 Conclusions The following conclusions are presented in approximate order of degree of certainty in the conclusion, based on the quality of the underlying database. The conclusions progress from those with greater certainty to those with lesser certainty.

 Consumption of fish is the dominant pathway of exposure to methylmercury for fishconsuming humans. There is a great deal of variability among individuals in these populations with respect to food sources and fish consumption rates. As a result, there is a great deal of variability in exposure to methylmercury in these populations. The anthropogenic contribution to the total amount of methylmercury in fish is, in part, the result of anthropogenic mercury releases from industrial and combustion sources increasing mercury body burdens in fish. As a consequence of human consumption of the affected fish, there is an incremental increase in exposure to methylmercury.

 The critical variables contributing to these different outcomes in measuring exposures are these: a) the fish consumption rate; b) the body weight of the individual in relation to the fish consumption rate; c) the level of methylmercury found in different fish species consumed; and d) the frequency of fish consumption.

 The results of the current exposure of the U.S. population from fish consumption indicate that most of the population consumes fish and is exposed to methylmercury as a result. Approximately 85% of adults in the United States consume fish and shellfish at least once a month with about 40% of adults selecting fish and shellfish as part of their diets at least once a week (based on food frequency data collected among more than 19,000 adult respondents in the NHANES III conducted between 1988 and 1994). This same survey identified 1-2% of adults who indicated they consume fish and shellfish almost daily.

 In the nationally-based dietary surveys, the types of fish most frequently reported to be eaten by consumers are tuna, shrimp, and Alaskan pollock. The importance of these species is corroborated by U.S. National Marine Fisheries Service data on per capita consumption rates of commercial fish species.

 National surveys indicate that Asian/Pacific Islander-American and Black-American subpopulations report more frequent consumption of fish and shellfish than other survey participants.

 Superimposed on this general pattern of fish and shellfish consumption is freshwater fish consumption, which may pose a significant source of methylmercury exposure to consumers of such fish. The magnitude of methylmercury exposure from freshwater fish varies with local consumption rates and methylmercury concentrations in the fish. The modeling exercise indicated that some of these methylmercury concentrations in freshwater fish may be elevated as a result of mercury emissions from anthropogenicES-3 sources. Exposures may be elevated among some members of this subpopulation; these may be evidenced by analyses of blood mercury showing concentrations in excess of 10micrograms per liter (μg/L) that have been reported among multiple freshwater fishconsumer subpopulations.

 The results of the assessment of current exposure of the U.S. population from fish consumption as described in this volume. Exposure to methylmercury from contaminated fish results in an incremental increase in mercury exposure for most U.S.fish-consumers. Methylmercury exposure rates on a per body weight basis among fishconsuming children are predicted to be higher than for fish-consuming adults. The 50th percentile exposure rate among fish-consuming children under the age of 10 and younger is approximately 0.3 μg/kg of body weight per day. The 90th percentile predicted exposures are approximately three times greater or 0.8-1.0 μg/kg body weight/day. The predicted average exposure among males and females fish consumers of reproductive age is 0.1 μg of methylmercury/ kg body weight/day. Given that these are one-day estimates, it would be inappropriate to compare these values to the RfD except for subpopulations that eat fish/shellfish almost every day. Fish consumption rates by adult men and women vary from zero to more than 300 grams per day. These predictions are consistent across the three major contemporary national food consumption surveys.

 Estimated month-long patterns of fish/shellfish intake and mercury exposures indicate that fish/shellfish consumption is lowest among “White/NonHispanics” (73 grams/day), second highest among “Black/NonHispanics” (97 grams/day) and highest among the category designated as “Other” (123 grams/day). The category “Other” includes persons of Asian/Pacific Islander ethnicity, NonMexican Hispanics (typically persons of Caribbean ethnicity), Native American tribal members and Native Alaskans, and additional persons. Based on these estimates of month-long fish/shellfish consumption as the basis for determining methylmercury exposure, an estimated 9% of the general population exceeds the RfD. Among women of childbearing age, 7% exceeded the RfD based on month-long projections of fish/shellfish intake. Approximately 1% of women have methylmercury exposures three-to-four times the RfD. Children in the age group 3-to-6-years have higher intakes of methylmercury than do adults relative to body weight. Approximately 25% of children exceed the RfD, and 5% of children have methylmercury exposures from fish/shellfish two-to-three times the RfD (i.e., 0.29 μg/kg body weight/day).

 Blood mercury concentrations and hair mercury levels are biomarkers used to indicate exposure to mercury. Inorganic mercury exposure occur occupationally and for some individuals through ritualistic/hobby exposures to inorganic mercury. Dental restorations with silver/mercury amalgams can also contribute to inorganic mercury exposures. Methylmercury exposure is almost exclusively through consumption of fish, shellfish, and marine mammals. Occupational exposures to methylmercury are rare. Normative data describing blood and/or hair mercury for a population representative of the United States do not exist, however, some data are available. Blood mercury concentrations in the United States are usually less than 10 μg/L; however, blood mercury concentrations in excess of 30 μg/L have been reported and are attributed to fish consumption. Hair mercury concentrations in the United States are typically less than 1μg/g, however, hair mercury concentration greater than 10μ/g have been reported forES-4 women of childbearing age living in the United States. U.S. EPA’s RfD is associated with a blood mercury concentration of 4-5 μg/L and a hair mercury concentration of approximately 1μg/g. The “benchmark” dose is associated with mercury concentrations of 44 μg/L in blood and 11.1 μg/g in hair. The “benchmark” dose for methylmercury is based on neurotoxic effects observed in Iraqi children exposed in utero to methylmercury.

 Specialized smaller surveys of subpopulations including anglers and Native American Tribal members indicate high fish consumption rates and elevated blood/hair mercury concentrations occur.1-1 

### 1. INTRODUCTION Section 112(n)(1)(B) of the Clean Air Act (CAA), as amended in 1990, requires the U.S. Environmental Protection Agency (EPA) to submit a study on atmospheric mercury emissions to Congress. The sources of emissions that must be studied include electric utility steam generating units, municipal waste combustion units, and other sources, including area sources. Congress directed that the Mercury Study evaluate many aspects of mercury emissions, including the rate and mass of emissions, health and environmental effects, technologies to control such emissions, and the costs of such controls. In response to this mandate, EPA has prepared an eight-volume Mercury Study Report to Congress. The eight volumes are as follows: I. Executive Summary II. An Inventory of Anthropogenic Mercury Emissions in the United States III. Fate and Transport of Mercury in the Environment IV. An Assessment of Exposure to Mercury in the United States V. Health Effects of Mercury and Mercury Compounds VI. An Ecological Assessment for Anthropogenic Mercury Emissions in the United States VII. Characterization of Human Health and Wildlife Risks from Mercury Exposure in the United States VIII. An Evaluation of Mercury Control Technologies and Costs This document is the exposure assessment (Volume IV) of U.S. EPA's Report to Congress on Mercury. The exposure assessment is one element of the human health and ecological risk assessment of U.S. anthropogenic mercury (Hg) emissions. The exposure assessment considers both inhalation and ingestion exposure routes. For atmospheric mercury emissions, ingestion is an indirect route of exposure that results from mercury deposition onto soil, water bodies and plants and uptake through the food chain. The information in this document is integrated with information relating to human and wildlife health impacts of mercury in Volume VII of the report. Using deposition values obtained from fate and transport models in Volume III, this assessment addresses the exposures that result from selected, major anthropogenic combustion and manufacturing sources. This volume also estimates current exposures to the general U.S. population that result from mercury concentrations in freshwater and marine fish. This volume does not address all anthropogenic emission sources, nor does it address emissions from natural sources. Volume IV is composed of nine chapters and three appendices. The Introduction is followed by Chapter 2, which describes the approach utilized to calculate mercury exposures to humans and wildlife. Chapter 3 presents estimates of mercury exposure to individuals in the human population and wildlife. Chapter 4 describes current U.S. exposures through consumption of fish. The fish methylmercury concentrations and the human fish consumption rates were developed using measured data. Exposures through other routes such as dental amalgams and occupational scenarios are summarized in Chapter 5. The predicted human exposures are compared to biomonitoring data in Chapter 6. Chapter 7 presents the conclusions of this Volume. Information needed for better assessment of exposure to emitted mercury and to current concentrations in media and biota is listed in Chapter 8. Finally, Chapter 9 lists all references cited in this volume.1-2 There are four appendices to Volume IV: Exposure Parameter Justifications (Appendix A); Estimated National and Regional Populations of Women of Child-Bearing Age (Appendix B); Analysis of Mercury Levels in Fish and Shellfish (Appendix C); and Human Fish Consumption and Mercury Ingestion Distributions (Appendix D). The assessment of human mercury exposure through the consumption of fish as described in Chapter 4 utilizes data from the continuing surveys of food intake by individuals (CSFII 89-91, CSFII 1994, CSFII 1995) and the third National Health and Nutrition Examination Survey (NHANES III). Both per capita and per user (only individuals who reported fish consumption) were considered. Foreach fish-eater, the number of fish meals, the quantities and species of fish consumed and the selfreported body weights were used to estimate mercury exposure on a body weight basis. The constitution of the survey population was weighted to reflect the actual U.S. population. Results of smaller surveys on  high-end fish consumers are also included. Continuing Surveys of Food Intake by Individuals (CSFII 89-91) to estimate a range of fish consumption rates among fish eaters. For each fish-eater, the 3- day CSFII 89-91 study identified the number of fish meals, the quantities and species of fish consumed and the self-reported body weights of the consumers. The constitution of the survey population was weighted to reflect the actual U.S. population. These estimates of fish consumption rates were combined with fish species-specific mean values for measured methylmercury concentrations. The fish methylmercury concentration data were obtained from the National Marine Fisheries Service, Bahnick et al., (1994), Lowe et al., (1985), and FDA (1995). Through the application of specific fish preparation factors (USDA, 1995), estimates of the range of methylmercury exposure from the consumption of fish were prepared for the fish-consuming segment of the U.S. population. Per body weight estimates of methylmercury exposure were determined by dividing the total daily methylmercury exposure from this pathway by the self-reported.2-1 

### 2. APPROACH TO EXPOSURE ASSESSMENT This chapter summarizes the methods employed to calculate exposures of humans to anthropogenic mercury emissions. These methods utilize the predictions of the environmental fate modeling presented in Volume III. The models used for the human exposure assessment are identical to those used for the wildlife exposure assessment (Volume VI of this Report). For the human exposure modeling analysis, two hypothetical sites in the eastern and western U.S. were developed. The proximity of these sites to the source was varied to examine the effect of distance on model predictions. To account for the long-range transport of emitted mercury, the 50th and 90th percentile RELMAP atmospheric concentrations and deposition rates were included in the estimates from the local air dispersion model. To account for other sources of mercury, estimates of background concentrations of mercury were also included in this exposure assessment. Human exposure estimates were developed through the use of mathematical models and a series of assumptions about human dietary behaviors and ingestion rates. Three separate exposure sceanrios pertaining to the types and sources of foods consumed were developed. Parameters that affected hypothetical human exposure are identified in Sections 2.2 and 2.3; some of these parameters have the potential to change across scenarios. Appendix A describes the specific human exposure factors utilized in this volume. 2.1 Modeling Exposures near Mercury Emissions Sources This section summarizes the computer models used to assess mercury exposure resulting from hypothetical local source emissions; this includes a description of the environmental fate models selected. Modeling assumptions related to the presence of  background mercury as well as mercury transported from other regions of the U.S. are also presented. These models and modeling assumptions are used to predict exposures of hypothetical humans residing in areas around mercury emission sources. 2.1.1 Description of Computer Models Atmospheric transport models were used to simulate the deposition of mercury at two different geographical scales (Table 2-1). A regional-scale analysis was conducted using the Regional Lagrangian Model of Air Pollution (RELMAP). RELMAP calculates annual mean air concentrations and annual mean deposition rates for each cell in a 40 km grid. This analysis covered the 48 contiguous states and was based upon a recent inventory of mercury emissions sources (presented in Volume II of this Report). The results of the RELMAP model accounted for the long-range transport of mercury emitted from anthropogenic sources. The local-scale exposure analysis was conducted by using both RELMAP and a local air transport model, GAS-ISC3, to generate hypothetical exposure scenarios for four mercury emission source classes. GAS-ISC3 uses hourly meteorological data to estimate hourly air concentrations and deposition fluxes within 50 km of a point source. For each hour, general plume characteristics are estimated based on the source parameters (gas exit velocity, temperature, stack diameter, stack height, wind speed at stack top, atmospheric stability conditions) for that hour. GAS-ISC3 was run using one year of actual meteorological data (1989, the same meteorologic year as was utilized in the RELMAP modeling). The average annual predicted values for air concentration and deposition rates were then used as inputs for to IEM-2M model for 30 years, the assumed typical lifetime of a facility.2-2 Table 2-1 Models Used to Predict Mercury Air Concentrations, Deposition Fluxes and Environmental Concentrations

Model Description

RELMAP Predicts average annual atmospheric mercury concentration and wet and dry deposition flux for each 40 km grid in the U.S. due to all 2 anthropocentric sources of mercury in the U.S. and a natural background atmospheric mercury concentration.

GAS-ISC3 Predicts average concentration and deposition fluxes within 50 km of emission source.

IEM-2M Predicts environmental concentrations based on air concentrations and deposition rates to watershed and water body.

The IEM-2M model was used to estimate mercury levels in soil, water and biota based on both regional and local-scale estimates of atmospheric concentrations of mercury and mercury deposition. IEM-2M is composed of two integrated modules that simulate mercury fate using mass balance equations describing watershed soils and a shallow lake. IEM-2M simulates three chemical components — elemental mercury, Hg , divalent mercury, HgII, and methylmercury, MHg. Mass balances are performed 0 for each mercury component, with internal transformation rates linking Hg , HgII, and MHg. Sources 0 include wetfall and dryfall loadings of each component to watershed soils and to the water body. An additional source is diffusion of atmospheric Hg vapor to watershed soils and the water body. Sinks 0 include leaching of each component from watershed soils, burial of each component from lake sediments, volatilization of Hg and MHg from the soil and water column, and advection of each component out of 0 the lake. At the core of IEM-2M are nine differential equations describing the mass balance of each mercury component in the surficial soil layer, in the water column, and in the surficial benthic sediments. The equations are solved for a specified interval of time, and predicted concentrations output at fixed intervals. For each calculational time step, IEM-2M first performs a terrestrial mass balance to obtain mercury concentrations in watershed soils. Soil concentrations are used along with vapor concentrations and deposition rates to calculate concentrations in various food plants. These are used, in turn, to calculate concentrations in animals. IEM-2M simultaneously performs an aquatic mass balance driven by direct atmospheric deposition along with runoff and erosion loads from watershed soils. Human exposures through inhalation and ingestion of other contaminated food items (as well as soils) were also evaluated. Levels of atmospheric mercury were estimated by summing the predicted concentrations of the RELMAP and GAS-ISC3 models. Soil concentrations were derived directly from estimates of the IEM-2M model. Concentrations in green plants were estimated using soil-to-plant and air-to-plant biotransfer factors; mercury in these plants was derived from the local and regional scale air modeling as well as estimates of background mercury (Section 2.1.2). Estimates of the mercury concentrations in animal tissues and animal products are generally the product of predicted mercury concentrations in green plants and soils, animal consumption rates, and specific biotransfer factors. Mercury in these animals was derived from the local and regional scale air modeling as well as estimates of background mercury.2-3 Mercury residues in fish were estimated by making the simplifying assumption that aquatic food chains can be adequately represented using four trophic levels. Respectively, these trophic levels are the following: level 1 - phytoplankton (algal producers); level 2 - zooplankton (primary herbivorous consumers); level 3 - small forage fish (secondary consumers); and level 4 - larger, piscivorous fish (tertiary consumers), which are eaten by humans. This type of food chain typifies the pelagic assemblages found in large freshwater lakes, and has been used extensively to model bioaccumulation of hydrophobic organic compounds (see for example Thomann, 1989; Clark et al., 1990; Gobas, 1993). It is recognized, however, that food chain structure can vary considerably among aquatic systems resulting in large differences in bioaccumulation in a given species of fish (Futter, 1994; Cabana et al., 1994a,b). The second simplifying assumption utilized in this effort was that methylmercury concentrations in fish are directly proportional to dissolved methylmercury concentrations in the water column. It is recognized that this relationship can vary widely among both physically similar and dissimilar water bodies. Methylmercury concentrations in fish were derived from predicted water column concentrations of dissolved methylmercury by using BAFs for trophic level 4 fish (Table 2-2). The BAFs selected for these calculations were estimated from existing field data. The BAF (dissolved methylmercury basis) for trophic level 4 fish is 1.6 x 10 . Methylmercury was estimated to constitute 7.8% of the total dissolved 6 mercury in the water column, and 65% of this was assumed to be freely dissolved. The technical basis for these estimates is presented in Volume III, Appendix D. The potential variability around these predicted fish residue values is highlighted in Table 2-2. Percentile information for the BAF estimates are presented.

Table 2-2 Percentiles of the Methylmercury Bioaccumulation Factor

Parameter Percentile of Distribution 5th 25th 50th 75th 95th Trophic 4 BAF 3.3x10 5.0x10 6.8x10 9.2x10 1.4x 10 66 6 6 7

2.1.2 Estimates of Background Mercury In Volume III of this Report it was noted that mercury was a constituent of the environment and has always been present on the planet. Estimates of atmospheric mercury concentrations and deposition rates from periods pre-dating large-scale anthropogenic emissions (“pre-anthropogenic”) and from current data were presented for hypothetical eastern and western sites. These estimates were used as inputs to the IEM-2M model. The equilibrium results of the IEM-2M model were calculated for both the eastern and western sites and for both the pre-anthropogenic and current time periods. (Chemical equilibrium is defined here as “a steady state, in which opposing chemical reactions occur at equal rates." (Pauling, 1963)). When modeling the pre-anthropogenic period, the initial conditions of all model compartments except the atmosphere were set to a mercury concentration of zero. The results of running the pre-anthropogenic conditions to equilibrium in IEM-2M were used as the initial conditions for estimating the current mercury concentrations. Table 2-3 lists the estimated mercury air concentrations and deposition rates used at both hypothetical sites and for both time periods.2-4 Table 2-3 Inputs to IEM-2M Model for the Two Time Periods Modeled
Time Period Eastern Site Western Site Air Concentration Annual Air Concentration Annual ng/m Deposition Rate ng/m Deposition Rate 3 μg/m /yr μg/m /yr 2 3 2

Pre- 0.5 3 0.5 1 Anthropogenic Current* 1.6 10 1.6 2 * This time period does not reflect the potential contributions of local sources.
2.2 Description of Hypothetical Exposure Scenarios for Humans In general, exposure scenarios are real or hypothetical situations that define the source of contamination, the potential receptor populations, the potential pathway(s) of exposure and the variables that affect the exposure pathways. Mercury exposure in this analysis was assessed for humans residing at hypothetical locations in the eastern and western United States. The fate of deposited mercury was examined in three types of settings: rural (agricultural); lacustrine (or water body); and urban. These three settings were selected because of the variety they encompass and because each is expected to provide a potentially elevated mercury concentration in environmental media of concern for human exposure; for example, elevated mercury concentrations are expected in the waters of lakes near mercury emission sources. These exposure scenarios included the total amount of food derived from affected areas and the extent of mercury contamination of these food sources. For an exposure assessment which is meant to represent a broad base of potential exposures, it is not practical to model many different types of farms, gardens, etc. As for the rest of the study, a limited number of representative, generalized types of activities have been modeled. 2.2.1 Hypothetical Location Descriptions Mercury exposure is assessed for humans hypothetically located at two generic sites: a humid site east of 90 degrees west longitude, and a more arid site west of 90 degrees west longitude (these are described in Volume III). Both sites were assumed to be located in relatively flat terrain. Exposure at each site was assessed for humans residing at 2.5, 10, or 25 km from the emissions source, as shown in Figure 2-1. The primary physical differences between the two hypothetical sites as parameterized included the assumed average annual precipitation rate, the assumed erosion characteristics for the watershed, and the amount of dilution flow from the water body. The eastern site had generally steeper terrain in the watershed than was assumed for the western site. The atmospheric mercury concentration over the hypothetical western site was the sum of the 50th or 90th percentile of the RELMAP output for the entire contiguous United States west of 90 degrees west longitude and the GAS-ISC3 prediction resulting from the local source mercury emissions. Similarly, the mercury concentration over the hypothetical eastern site was the sum of the 50th or 90th percentile of the2-5 

### Figure 2-1 Configuration of Hypothetical Water Body and Watershed Relative to Local Source

RELMAP output for the entire contiguous United States east of 90 degrees west longitude and the GASISC3 prediction resulting from the local source mercury emissions. Deposition to both sites were, similarly, the sum of the predicted depositions for GAS-ISC3 and the 50th or 90th percentile RELMAP result. 2.2.2 Description of Hypothetical Human Exposure Scenarios Human exposure to environmental mercury is the result of mercury concentrations at specific human exposure points (e.g., ingested fish). For each location and setting, mercury exposure was estimated for individuals representing several specific subpopulations expected to have both typical and higher exposure levels. The individuals representing the subpopulations were defined to model average and high-end exposures in the three settings: rural, urban, and lacustrine. In this section each subpopulation is discussed. A more detailed description of the values chosen for parameters of the exposure assessment is given in Appendix A. Table 2-4 summarizes the hypothetical scenarios considered as well as the exposure pathways considered in each scenario.2-6Table 2-4Summary of Human Exposure ScenariosLocationRural Urban Lacustrine Remote LakesaSubsistence Farmer Gardener Resident Worker/High-end High End Fisherman angler High End Fisherman anglerHomeRec. Rec.Adult Child Adult Adult Adult Child Adult Child Adult Adult Child AdultPicaAirinhalationX X X X XX X X X X X XSoilingestionX X X X XX X X X X XAnimalingestionX XVegetableingestionX X X XX X XLocal fishingestionX XX X XXLocal wateringestionXX X XXNotes:Lakes locatedgreater than 50 km from a mercuryemission sourceaBlank = Pathwaynot considered.X = Pathwayconsidered.2-7 2.2.2.1 Rural Exposure Scenarios Both a high-end and average rural scenario were evaluated. The high-end scenario consisted of a subsistence farmer and child who consumed elevated levels of locally grown food products. It was assumed that each farm was located on a square plot of land with an area 40,000 m (approximately 10 2 acres). The subsistence farmer was assumed to raise livestock and to consume home-grown animal tissue and animal products, including chickens and eggs as well as beef and dairy cattle. All chicken feed was assumed to be derived from non-local sources. For cattle, 100% of the hay and corn used for feed was assumed to be from the local area. It was also assumed that the subsistence farmer collected rainwater in cisterns for drinking. The typical rural dweller was assumed to raise a small garden and derive some of his food from that source. 2.2.2.2 Urban Exposure Scenarios In the urban high end scenario, it was assumed that the person had a small garden similar in size to that of the average rural scenario. To address the fact that home-grown fruits and vegetables generally make up a smaller portion of the diet in urban areas, the contact fractions were based on weight ratios of home-grown to total fruits and vegetables consumed for city households. These fractions can be up to 10 times smaller than the values for rural households, depending on food plant type (see Table 2-4 and Appendix A). Exposure duration for inhalation was 24 hours per day. The high-end urban scenario included a pica child. An average urban scenario consisted of an adult who worked outside of local area. The exposure duration for inhalation, therefore, was only 16 hours a day compared to the 24 hours a day for the rural and high-end urban scenarios. The only other pathway considered for this scenario was ingestion of average levels of soil. 2.2.2.3 Description of Hypothetical Human Exposure Scenarios for Individuals Using Water Bodies The fish ingestion pathway was the dominant source of methylmercury intake in exposure scenarios wherein the fish ingestion pathway was considered appropriate. For this assessment, three human fish consumption scenarios were considered for the hypothetical lakes: (1) an adult high-end fish consumer scenario, in which an individual was assumed to ingest large amounts of locally-caught fish as well as home-grown garden produce (plant ingestion parameters identical to the rural home gardenerscenario), consume drinking water from the affected water body and inhale the air; (2) a child of a highend local fish consumer, assumed to ingest local fish, garden produce, and soil as well as inhale the affected air; and (3) a recreational angler scenario, in which the exposure pathways evaluated were fish ingestion, inhalation, and soil ingestion. These consumption scenarios were thought to represent identified fish-consuming subpopulations in the United States. Fish for human consumption from local water bodies can be derived from many sources including self-caught, gifts, and grocery and restaurant purchases. For the purposes of this study, all fish consumed were assumed to originate from the hypothetical lakes, which were considered to represent several small lakes that might be present in the type of hypothetical locations considered. No commercial distribution of locally caught fish was assumed; exposure to locally-caught fish was modeled for the three fish-consuming subpopulations described above. Fish consumption rates for the three fish-consuming subpopulations were derived from the Columbia River Inter-Tribal Fish Commission Report (1994). Other estimates of human fish consumption rates are reported later in this volume; these estimates highlight the broad variability in2-8 consumption rates. The Columbia River Inter-Tribal Fish Commission Report (1994) estimated fish consumption rates for members of four tribes inhabiting the Columbia River Basin. The estimated fish consumption rates were based on interviews with 513 adult tribe members who lived on or near the reservation. The participants had been selected from patient registrations lists provided by the Indian Health Service. Adults interviewed provided information on fish consumption for themselves and for 204 children under 5 years of age. Fish consumption rates for tribal members are shown in Tables 2-5 and 2-6. The values used in this study are shown in Table 2-7. The values listed below reflect an annual average, but monthly variations were also reported. For example, the average daily consumption rate during the two highest intake months was 107.8 grams/day, and the daily consumption rate during the two lowest consumption months was 30.7 grams/day. Fish were consumed by over 90% of the surveyed population with only 9% of the respondents reporting no fish consumption. The maximum daily consumption rate for fish reported by one member of this group was 972 grams/day. Since most of the population consisted of fish consumers (“users”), utilization of per capita estimates was considered appropriate.

Table 2-5 Fish Consumption Rates for Columbia River Tribes a Subpopulation Mean Daily Fish Consumption (g/day) Total Adult Population, aged 18 years and older 59 Children, aged 5 years and younger 20 Adult Females 56 Adult Males 63
Columbia River Inter-Tribal Commission, 1994. a

Table 2-6 Daily Fish Consumption Rates Among Adults Fish Consumption by Columbia River Tribes a Percentile grams/day 50th 29-32 90th 97-130 95th 170 99th 389 Columbia River Inter-Tribal Commission, 1994. a2-9 Table 2-7 Fish Consumption Rates used in this Study Subpopulation Fish Consumption Rate (g/day) a High-end Adult 60 High-end Child 20 Recreational Angler 30

Columbia River Inter-Tribal Commission, 1994. a
The fish consumed by humans in both the hypothetical eastern and western sites were obtained from lakes. The drainage lakes were assumed to be circular with a diameter of 1.78 km and average depth of 5 m. A 2 cm benthic sediment depth was assumed for the lakes. The watershed area associated with each lake was 37.3 km . 2 2.3 Summary of Exposure Parameter Values To a large degree, there are only a few parameters that vary across these scenarios. Table 2-8 categorizes exposure parameters as invariant or variant with each scenario. A complete list of the values used and rationale for these values is given in Appendix A.

Table 2-8 Potential Dependency of Exposure Parameters
Parameters Constant Across Scenarios Parameters that Potentially Change Across Scenarios Body weight Fish ingestion rates Exposure duration Contact fractions for vegetables, animal products, and water Inhalation rate Contact time for inhalation Animal and vegetable consumption rates Child soil ingestion rates Adult soil ingestion rates Drinking water ingestion rates

Table 2-9 shows the default values for the scenario-independent parameters for both the child and adult receptors, and Table 2-10 shows the default values for the scenario-dependent exposure parameters. The technical bases for these values are in Appendix A. The hypothetical scenarios are discussed in more detail in the following sections.2-10 Table 2-9 Default Values of Scenario-Independent Exposure Parameters

Parameter Adult Child Default Value a

Body weight (kg) 70 17 Inhalation rate (m /day) 20 16 3 Vegetable consumption rates (g dry weight/kg body weight/day) b Leafy vegetables 0.028 0.008 Grains and cereals 1.87 3.77 Legumes 0.381 0.666 Potatoes 0.17 0.274 Root vegetables 0.024 0.036 Fruits 0.57 0.223 Fruiting vegetables 0.064 0.12 Animal product consumption rates (g dry weight/kg body weight/day) Beef (excluding liver) 0.341 0.553 Beef liver 0.066 0.025 Dairy 0.599 2.04 Pork 0.169 0.236 Poultry 0.111 0.214 Eggs 0.073 0.093 Lamb 0.057 0.061Soil Ingestion rates (g/day) 0.1 Scenariodependent Water ingestion rate (L/day) 2 1

See A pp endix A for details re g ardin g these p arameter values. a DW= dr y wei g ht; BW = bod y wei g ht. b2-11Table 2-10Values for Scenario-Dependent Exposure ParametersaParameter Adult Child Adult Resident Gardener Child Adult Child AdultRural Subsistence Rural HomeRecreationalFarmer Gardener Urban Scenarios High End Fisher AnglerAdult Home PicaFish Ingestion rates (g/day) NANA NA NA NA NA 60 20 30cSoil Ingestion Rate (g/day) 0.1 0.2 0.1 0.1 0.1 7.5 0.1 0.2 0.1Contact time for inhalation (hr/day)24 24 24 16 24 24 24 24 24Contact fractions (unitless)Animal products 1 1 NA NA NA NA NA NA NALeafy vegetables 1 1 0.058 NA 0.026 NA 0.058 0.058 NAGrains and cereals 1 1 0.667 NA 0.195 NA 0.667 0.667 NALegumes 1 1 0.8 NA 0.5 NA 0.8 0.8 NAPotatoes 1 1 0.225 NA 0.031 NA 0.225 0.225 NAFruits 1 1 0.233 NA 0.076 NA 0.233 0.233 NAFruiting vegetables 1 1 0.623 NA 0.317 NA 0.623 0.623 NARoot vegetables 1 1 0.268 NA 0.073 NA 0.268 0.268 NADrinking water 1 1 NA NA NA NA 1 1 1bSee Appendix A for more details regarding these values.aThe source of the contaminated drinking water is different for the subsistence farmer and high end fisher scenarios.bNA - Not Considered to be Applicable to this assessment. For example, urban residents were assumed to eat no locally caught fish. Any fish ingested by this subpopulation wascconsidered to be contaminated by mercury from outside the modeling domain and, thus, not considered.2-12 Consumption rates, bioconcentration factors, and biotransfer factors may be derived based on tissue (plant, animal, and dairy) weights on either a wet or dry basis. Wet weight and dry weight are related by this formula: Dry Weight = Wet Weight / (1 - moisture content) It is critical that parameters used together are consistent based on either dry weight or wet weight. Manyplants are nearly 90% water, and a mix of wet and dry weight modeling parameters can result in a tenfold error. The fish BAF and fish consumption rates in this Report were calculated using wet weight values. Consumption rates, plant bioaccumulation factors, and animal biotransfer factors were all based upon dry weights of tissues. Animal and plant consumption rates as well as inhalation rates are constant across exposure scenarios. The contact fraction changes generally across the exposure scenarios. The contact fraction represents the fraction of locally-grown or affected food consumed. Typically, in exposure assessments the higher the contact fraction the greater the exposure. 2.4 Emissions Sources Model plants (hypothetical anthropogenic mercury emissions sources) representing four source classes were developed to represent a range of mercury emissions sources. The source categories were selected for the indirect exposure analysis based on their estimated annual mercury emissions or their potential to be localized point sources of concern. The categories selected were these: municipal waste combustors (MWCs), medical waste incinerators (MWIs), utility boilers, and chlor-alkali plants. Table 2-11 shows the process parameters assumed for each of these facilities. The characteristics of the facilities were derived based on typical rather than extreme representations; the facilities are known as model plants (See Volume II). 2.5 Predicted Concentrations in Environmental Media High rates of mercury deposition were associated with proximity to industrial sources emitting substantial levels of divalent mercury (Tables 2-12 and 2-15). Additional factors that contributed to high local deposition rates include low stack height and slow stack exit gas velocities. In general, predicted mercury concentrations in environmental media at 2.5 km were higher than levels predicted at 10 or 25 km. This was due primarily to the dilution of the mercury emissions in the atmosphere. Mercury concentrations in biota also typically demonstrated the same pattern. When the two hypothetical locations were compared (western and eastern), higher mercury concentrations were predicted to occur in the environmental media and biota at the eastern location. This was due primarily to higher levels of precipitation at the eastern site, which tends to remove mercury from the atmosphere. Also, the assumptions of background mercury are higher for the eastern than the western site. This is also attributed to the generally higher precipitation rates in the eastern United States.2-13Table 2-11Process Parameters for the Model Plants Considered in the Local Impact AnalysisModel PlantPlant (% of Height Diameter Rate (Hg/HgVelocityTemp.Sizeyear) (ft) (ft) (kg/yr) /Hg)(m/sec) (°F)CapacityStack Stack Emission Percent Exit ExitHgSpeciation0 2+PLarge Municipal 2,250 90% 230 9.5 220 21.9 285Waste tons/day60/30/10CombustorsSmall Municipal 200 90% 140 5 20 60/30/10 21.9 375Waste tons/dayCombustorsLarge 1500 88% 40 2.7 4.58 33/50/17 9.4 175Commercial lb/hrHMI capacityWaste (1000Incinerator lb/hr(Wetscrubber) actual)Large Hospital 1000 39% 40 2.3 23.9 2/73/25 16 1500HMI Waste lb/hrIncinerators capacity(Good (667Combustion) lb/hractual)Small Hospital 100 lb/hr 27% 40 0.9 1.34 2/73/27 10.4 1500HMI Waste capacityIncinerators (67 lb/hr(1/4 sec. actual)Combustion)Large Hospital 1000 39% 40 2.3 0.84 33/50/17 9.0 175HMI Waste lb/hrIncinerators capacity(Wet Scrubber) (667lb/hractual)Table 2-11 (continued)Process Parameters for the Model Plants Considered in the Local Impact AnalysisModel PlantPlant (% of Height Diameter Rate (Hg/HgVelocityTemp.Sizeyear) (ft) (ft) (kg/yr) /Hg)(m/sec) (°F)CapacityStack Stack Emission Percent Exit ExitHgSpeciation0 2+P2-14Small Hospital100 lb/hr 27% 40 0.9 0.05 33/50/17 5.6 175HMI Waste capacityIncinerators (67 lb/hr(Wet Scrubber) actual)Large Coal-fired 975 65% 732 27 230 50/30/20 31.1 273UtilityBoiler MegawattsMedium 375 65% 465 18 90 50/30/20 26.7 275Coal-fired MegawatUtilityBoiler tsSmall Coal-fired 100 65% 266 12 10 50/30/20 6.6 295UtilityBoiler MegawattsMedium 285 65% 290 14 2 50/30/20 20.7 322Oil-fired UtilityMegawatBoiler tsChlor-alkali 300 tons 90% 10 0.5 380 70/30/0 0.1 Ambieplant chlorine/ntdayHg = Elemental Mercurya 0Hg = Divalent Vapor Phase Mercuryb 2+Hg = Particle-Bound MercurycP2-15Table 2-12Predicted Mercury Values for Environmental Media at Eastern Site (ISC3 + RELMAP 50th)50th PercentilePlant Distance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total %Bac %Rel %ISC(ng/m3) (ug/m2/yr) Hg Soil kgrou MapConcent ndration inUntilledSoil(ng/g)Variant b:Large Municipal Waste Combustor 2.5 km 1.7E+00 97% 3% 4.2E+01 34% 66% 1.0E+02 46% 8% 47%10 km 1.7E+00 98% 2% 2.6E+01 57% 43% 7.4E+01 63% 11% 26%25 km 1.7E+00 99% 1% 1.9E+01 78% 22% 6.1E+01 76% 13% 11%Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 1.9E+01 74% 26% 6.3E+01 74% 12% 14%10 km 1.7E+00 100% 0% 1.6E+01 90% 10% 5.7E+01 82% 14% 5%25 km 1.7E+00 100% 0% 1.5E+01 97% 3% 5.5E+01 85% 14% 1%Large Commercial HMI 2.5 km 1.7E+00 99% 1% 1.9E+01 76% 24% 6.2E+01 75% 12% 13%10 km 1.7E+00 100% 0% 1.5E+01 95% 5% 5.6E+01 84% 14% 2%25 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 85% 14% 1%Large Hospital HMI 2.5 km 1.7E+00 97% 3% 4.4E+01 33% 67% 1.1E+02 44% 7% 48%10 km 1.7E+00 99% 1% 2.0E+01 74% 26% 6.3E+01 74% 12% 14%25 km 1.7E+00 100% 0% 1.6E+01 92% 8% 5.7E+01 82% 14% 4%Small Hospital HMI 2.5 km 1.7E+00 100% 0% 1.6E+01 88% 12% 5.8E+01 81% 13% 6%10 km 1.7E+00 100% 0% 1.5E+01 98% 2% 5.5E+01 85% 14% 1%25 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 86% 14% 0%Large Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 1.5E+01 94% 6% 5.6E+01 84% 14% 3%10 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 85% 14% 0%25 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 86% 14% 0%Small Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 86% 14% 0%10 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.4E+01 86% 14% 0%25 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.4E+01 86% 14% 0%Large Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 3.0E+01 48% 52% 8.1E+01 58% 10% 33%10 km 1.7E+00 100% 0% 1.7E+01 83% 17% 5.9E+01 79% 13% 8%25 km 1.7E+00 100% 0% 1.6E+01 93% 7% 5.6E+01 83% 14% 3%Medium Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 2.1E+01 68% 32% 6.6E+01 71% 12% 18%10 km 1.7E+00 100% 0% 1.6E+01 89% 11% 5.8E+01 81% 13% 5%25 km 1.7E+00 100% 0% 1.5E+01 94% 6% 5.6E+01 84% 14% 3%Small Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 1.6E+01 90% 10% 5.7E+01 82% 14% 5%10 km 1.7E+00 100% 0% 1.5E+01 96% 4% 5.5E+01 84% 14% 2%25 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 85% 14% 1%Medium OIl-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 85% 14% 1%10 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 86% 14% 0%25 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 86% 14% 0%Chlor-alkali plant 2.5 km 4.0E+00 42% 58% 2.5E+02 6% 94% 4.5E+02 10% 2% 88%10 km 2.1E+00 79% 21% 4.6E+01 32% 68% 1.1E+02 43% 7% 50%25 km 1.8E+00 92% 8% 2.2E+01 65% 35% 6.8E+01 69% 11% 20%Table 2-132-16Predicted Mercury Values for Environmental Media at Eastern Site (ISC3 + RELMAP 90th)90th PercentilePlantDistance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total Hg Soil %Backgro %Rel %ISC(ng/m3) (ug/m2/yr) Concentration und Mapin Untilled Soil(ng/g)Variant b:Large Municipal Waste Combustor2.5 km 1.8E+00 97% 3% 5.5E+01 50% 50% 1.2E+02 38% 24% 38%10 km 1.8E+00 98% 2% 3.8E+01 71% 29% 9.5E+01 49% 31% 20%25 km 1.7E+00 99% 1% 3.1E+01 87% 13% 8.3E+01 56% 35% 8%Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 3.2E+01 85% 15% 8.5E+01 55% 35% 10%10 km 1.7E+00 100% 0% 2.9E+01 95% 5% 7.9E+01 59% 37% 3%25 km 1.7E+00 100% 0% 2.8E+01 98% 2% 7.7E+01 61% 38% 1%Large Commercial HMI 2.5 km 1.7E+00 99% 1% 3.2E+01 85% 15% 8.4E+01 55% 35% 9%10 km 1.7E+00 100% 0% 2.8E+01 98% 2% 7.7E+01 60% 38% 2%25 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 61% 39% 0%Large Hospital HMI 2.5 km 1.8E+00 97% 3% 5.7E+01 48% 52% 1.3E+02 37% 23% 40%10 km 1.7E+00 99% 1% 3.2E+01 84% 16% 8.5E+01 55% 35% 10%25 km 1.7E+00 100% 0% 2.8E+01 96% 4% 7.8E+01 60% 38% 3%Small Hospital HMI 2.5 km 1.7E+00 100% 0% 2.9E+01 93% 7% 8.0E+01 59% 37% 4%10 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 61% 38% 1%25 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%Large Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 2.8E+01 97% 3% 7.8E+01 60% 38% 2%10 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%25 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%Small Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%10 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%25 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%Large Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 4.3E+01 64% 36% 1.0E+02 45% 29% 26%10 km 1.7E+00 100% 0% 3.0E+01 90% 10% 8.1E+01 57% 36% 6%25 km 1.7E+00 100% 0% 2.8E+01 96% 4% 7.8E+01 60% 38% 2%Medium Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 3.4E+01 80% 20% 8.8E+01 53% 34% 13%10 km 1.7E+00 100% 0% 2.9E+01 94% 6% 7.9E+01 59% 37% 4%25 km 1.7E+00 100% 0% 2.8E+01 97% 3% 7.8E+01 60% 38% 2%Small Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 2.9E+01 94% 6% 7.9E+01 59% 37% 3%10 km 1.7E+00 100% 0% 2.8E+01 98% 2% 7.7E+01 60% 38% 1%25 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 61% 39% 0%Medium OIl-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 61% 39% 0%10 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%25 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 61% 39% 0%Chlor-alkali plant 2.5 km 4.0E+00 43% 57% 2.6E+02 10% 90% 4.8E+02 10% 6% 84%10 km 2.2E+00 79% 21% 5.9E+01 46% 54% 1.3E+02 36% 23% 41%25 km 1.9E+00 92% 8% 3.5E+01 77% 23% 9.0E+01 52% 33% 15%2-17 Table 2-14 Predicted Mercury Values in Water Column and Biota for Eastern Site (ISC3 + RELMAP 50th)
50th Percentile MHg Tier 4 %Backgro %Rel %ISC Total Hg Grain %Bac %Rel %ISC Dissolv Fish und Map Concentration kgrou Map

ed MHg (ng/g) nd Water Concent Conc.( ration ng/l) (ug/g) Variant b:Large Municipal Waste 2.5 km 1.7E-01 1.1E+00 38% 7% 54% 2.1E+00 93% 3% 4% Combustor 10 km 1.1E-01 7.6E-01 58% 11% 31% 2.1E+00 94% 3% 2% 25 km 8.9E-02 6.0E-01 73% 14% 13% 2.1E+00 95% 3% 1% Variant b:Small Municipal Waste 2.5 km 9.5E-02 6.4E-01 68% 13% 18% 2.1E+00 96% 3% 1% Combustor 10 km 8.2E-02 5.6E-01 79% 15% 6% 2.1E+00 96% 3% 0% 25 km 7.9E-02 5.3E-01 83% 16% 2% 2.1E+00 96% 3% 0% Large Commercial HMI 2.5 km 9.6E-02 6.5E-01 68% 13% 19% 2.1E+00 96% 3% 1% 10 km 8.0E-02 5.4E-01 82% 16% 3% 2.1E+00 96% 3% 0% 25 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0% Large Hospital HMI 2.5 km 1.9E-01 1.3E+00 34% 6% 60% 2.1E+00 93% 3% 4% 10 km 9.4E-02 6.4E-01 69% 13% 18% 2.1E+00 96% 3% 1% 25 km 8.1E-02 5.5E-01 80% 15% 5% 2.1E+00 96% 3% 0% Small Hospital HMI 2.5 km 8.5E-02 5.8E-01 76% 15% 9% 2.1E+00 96% 3% 0% 10 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0% 25 km 7.8E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% Large Hospital HMI (wet scrubber) 2.5 km 8.1E-02 5.5E-01 80% 15% 4% 2.1E+00 96% 3% 0% 10 km 7.8E-02 5.3E-01 84% 16% 1% 2.1E+00 97% 3% 0% 25 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% Small Hospital HMI (wet scrubber) 2.5 km 7.8E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% 10 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% 25 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% Large Coal-fired Utility Boiler 2.5 km 1.3E-01 9.1E-01 48% 9% 42% 2.1E+00 96% 3% 1% 10 km 8.6E-02 5.9E-01 75% 14% 10% 2.1E+00 96% 3% 0% 25 km 8.0E-02 5.5E-01 81% 15% 4% 2.1E+00 96% 3% 0% Medium Coal-fired Utility Boiler 2.5 km 1.0E-01 6.9E-01 64% 12% 24% 2.1E+00 96% 3% 0% 10 km 8.3E-02 5.6E-01 78% 15% 7% 2.1E+00 96% 3% 0% 25 km 8.0E-02 5.4E-01 81% 16% 3% 2.1E+00 96% 3% 0% Small Coal-fired Utility Boiler 2.5 km 8.3E-02 5.6E-01 79% 15% 6% 2.1E+00 96% 3% 0% 10 km 7.9E-02 5.4E-01 82% 16% 2% 2.1E+00 96% 3% 0% 25 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0% Medium OIl-fired Utility Boiler 2.5 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0% 10 km 7.8E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% 25 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0% Chlor-alkali plant 2.5 km 1.0E+0 6.8E+00 6% 1% 92% 4.5E+00 44% 2% 54% 0 10 km 1.8E-01 1.2E+00 37% 7% 56% 2.5E+00 79% 3% 18% 25 km 1.0E-01 6.8E-01 65% 12% 23% 2.2E+00 90% 3% 7%2-18 Table 2-15 Predicted Mercury Values in Water Column and Biota for Eastern Site (ISC3 + RELMAP 90th)

MHg Tier 4 %Backgro %Rel %ISC Total Hg Grain %Bac %Rel %ISC Dissolv Fish und Map Concentration kgrou Map

ed MHg (ng/g) nd Water Concent Conc.( ration ng/l) (ug/g) Variant b:Large Municipal Waste 2 . 5 2.0E-01 1.4E+00 32% 23% 45% 2.2E+00 90% 6% 4% Combustor km 10 km 1.5E-01 9.9E-01 44% 32% 23% 2.2E+00 91% 6% 2% 25 km 1.2E-01 8.4E-01 52% 38% 9% 2.1E+00 93% 6% 1% Variant b:Small Municipal Waste 2 . 5 1.3E-01 8.8E-01 50% 36% 13% 2.1E+00 93% 6% 1% Combustor km 10 km 1.2E-01 8.0E-01 55% 40% 4% 2.1E+00 93% 6% 0% 25 km 1.1E-01 7.7E-01 57% 42% 1% 2.1E+00 94% 6% 0% Large Commercial HMI 2 . 5 1.3E-01 8.9E-01 50% 36% 14% 2.1E+00 93% 6% 1% km 10 km 1.1E-01 7.8E-01 57% 41% 2% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.7E-01 58% 42% 0% 2.1E+00 94% 6% 0% Large Hospital HMI 2 . 5 2.3E-01 1.5E+00 29% 21% 51% 2.2E+00 90% 6% 4% km 10 km 1.3E-01 8.8E-01 50% 37% 13% 2.1E+00 93% 6% 1% 25 km 1.2E-01 7.9E-01 56% 41% 3% 2.1E+00 93% 6% 0% Small Hospital HMI 2 . 5 1.2E-01 8.2E-01 54% 39% 7% 2.1E+00 93% 6% 0% km 10 km 1.1E-01 7.7E-01 57% 42% 1% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% Large Hospital HMI (wet scrubber) 2 . 5 1.2E-01 7.9E-01 56% 41% 3% 2.1E+00 94% 6% 0% km 10 km 1.1E-01 7.7E-01 58% 42% 0% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% Small Hospital HMI (wet scrubber) 2 . 5 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% km 10 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% Large Coal-fired Utility Boiler 2 . 5 1.7E-01 1.1E+00 38% 28% 34% 2.1E+00 93% 6% 1% km 10 km 1.2E-01 8.2E-01 54% 39% 7% 2.1E+00 93% 6% 0% 25 km 1.2E-01 7.8E-01 56% 41% 3% 2.1E+00 94% 6% 0% Medium Coal-fired Utility Boiler 2 . 5 1.4E-01 9.3E-01 48% 35% 18% 2.1E+00 93% 6% 0% km 10 km 1.2E-01 8.0E-01 55% 40% 5% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.8E-01 57% 41% 2% 2.1E+00 94% 6% 0% Small Coal-fired Utility Boiler 2 . 5 1.2E-01 8.0E-01 55% 40% 5% 2.1E+00 94% 6% 0% km 10 km 1.1E-01 7.8E-01 57% 41% 2% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.7E-01 58% 42% 1% 2.1E+00 94% 6% 0% Medium OIl-fired Utility Boiler 2 . 5 1.1E-01 7.7E-01 58% 42% 1% 2.1E+00 94% 6% 0% km 10 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% 25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0% Chlor-alkali plant 2 . 5 1.0E+0 7.1E+00 6% 5% 89% 4.6E+00 43% 3% 54% km 0 10 km 2.1E-01 1.4E+00 31% 22% 47% 2.6E+00 77% 5% 18% 25 km 1.4E-01 9.2E-01 48% 35% 17% 2.3E+00 88% 6% 6%Table 2-16 (continued)Predicted MercuryValues for Environmental Media at Western Site (ISC3 + RELMAP 50th)2-19Table 2-16Predicted Mercury Values for Environmental Media at Western Site (ISC3 + RELMAP 50th)50th PercentilePlantDistance Air Concentration %RelMap%ISC Total Deposition %RelMap%ISC Total %Bac %Rel %ISC(ng/m3) (ug/m2/yr) HgSoil kgrou MapConcent ndration inUntilledSoil(ng/g)Variant b:Large Municipal Waste Combustor2.5 km 1.7E+00 98% 2% 2.0E+01 11% 89% 3.8E+01 20% 1% 79%10 km 1.6E+00 98% 2% 1.1E+01 20% 80% 2.3E+01 33% 2% 65%25 km 1.6E+00 99% 1% 5.6E+00 41% 59% 1.3E+01 56% 4% 40%Variant b:Small Municipal Waste Combustor 2.5 km 1.6E+00 99% 1% 6.2E+00 38% 62% 1.4E+01 53% 4% 44%10 km 1.6E+00 100% 0% 3.4E+00 68% 32% 9.9E+00 76% 5% 18%25 km 1.6E+00 100% 0% 2.7E+00 87% 13% 8.6E+00 87% 6% 6%Large Commercial HMI 2.5 km 1.6E+00 99% 1% 6.0E+00 38% 62% 1.4E+01 53% 4% 43%10 km 1.6E+00 100% 0% 2.8E+00 83% 17% 8.9E+00 85% 6% 9%25 km 1.6E+00 100% 0% 2.4E+00 95% 5% 8.3E+00 91% 6% 2%Large Hospital HMI 2.5 km 1.7E+00 98% 2% 2.7E+01 9% 91% 4.8E+01 16% 1% 83%10 km 1.6E+00 99% 1% 5.9E+00 39% 61% 1.4E+01 54% 4% 42%25 km 1.6E+00 100% 0% 3.3E+00 71% 29% 9.6E+00 79% 5% 16%Small Hospital HMI 2.5 km 1.6E+00 100% 0% 3.9E+00 59% 41% 1.1E+01 71% 5% 24%10 km 1.6E+00 100% 0% 2.5E+00 92% 8% 8.4E+00 90% 6% 4%25 km 1.6E+00 100% 0% 2.4E+00 98% 2% 8.2E+00 93% 6% 1%Large Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 3.0E+00 77% 23% 9.2E+00 82% 6% 12%10 km 1.6E+00 100% 0% 2.4E+00 96% 4% 8.2E+00 92% 6% 2%25 km 1.6E+00 100% 0% 2.3E+00 99% 1% 8.1E+00 93% 6% 0%Small Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 2.4E+00 98% 2% 8.2E+00 93% 6% 1%10 km 1.6E+00 100% 0% 2.3E+00 100% 0% 8.1E+00 93% 6% 0%25 km 1.6E+00 100% 0% 2.3E+00 100% 0% 8.1E+00 94% 6% 0%Large Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 5.8E+00 40% 60% 1.4E+01 55% 4% 42%10 km 1.6E+00 100% 0% 3.5E+00 67% 33% 9.9E+00 76% 5% 19%25 km 1.6E+00 100% 0% 3.3E+00 69% 31% 9.8E+00 78% 5% 17%Medium Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 4.3E+00 53% 47% 1.1E+01 66% 5% 29%10 km 1.6E+00 100% 0% 3.7E+00 63% 37% 1.0E+01 73% 5% 22%25 km 1.6E+00 100% 0% 3.2E+00 73% 27% 9.5E+00 79% 5% 15%Small Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 3.4E+00 69% 31% 9.8E+00 77% 5% 18%Table 2-16 (continued)Predicted MercuryValues for Environmental Media at Western Site (ISC3 + RELMAP 50th)50th PercentilePlantDistance Air Concentration %RelMap%ISC Total Deposition %RelMap%ISC Total %Bac %Rel %ISC(ng/m3) (ug/m2/yr) HgSoil kgrou MapConcent ndration inUntilledSoil(ng/g)2-2010 km1.6E+00 100% 0% 2.8E+00 84% 16% 8.8E+00 86% 6% 8%25 km 1.6E+00 100% 0% 2.5E+00 94% 6% 8.3E+00 91% 6% 3%Medium OIl-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 2.4E+00 96% 4% 8.2E+00 92% 6% 2%10 km 1.6E+00 100% 0% 2.4E+00 97% 3% 8.2E+00 92% 6% 1%25 km 1.6E+00 100% 0% 2.3E+00 99% 1% 8.1E+00 93% 6% 1%Chlor-alkali plant 2.5 km 3.5E+00 46% 54% 1.9E+02 1% 99% 3.2E+02 2% 0% 97%10 km 1.9E+00 84% 16% 2.5E+01 9% 91% 4.5E+01 17% 1% 82%25 km 1.7E+00 94% 6% 8.1E+00 28% 72% 1.8E+01 43% 3% 54%2-21Table 2-17Predicted MercuryValues for Environmental Media at Western Site (ISC3 + RELMAP 90th)90th PercentilePlantDistance Air Concentration %RelMap%ISC Total Deposition %RelMap%ISC Total %Bac %Rel %ISC(ng/m3) (ug/m2/yr) HgSoil kgrou MapConcent ndration inUntilledSoil(ng/g)Variant b:Large Municipal Waste Combustor2.5 km 1.7E+00 98% 2% 2.6E+01 31% 69% 4.7E+01 16% 21% 63%10 km 1.7E+00 98% 2% 1.7E+01 47% 53% 3.2E+01 24% 31% 46%25 km 1.7E+00 99% 1% 1.1E+01 71% 29% 2.3E+01 33% 43% 24%Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 1.2E+01 67% 33% 2.4E+01 32% 41% 27%10 km 1.6E+00 100% 0% 9.1E+00 88% 12% 1.9E+01 39% 51% 9%25 km 1.6E+00 100% 0% 8.3E+00 96% 4% 1.8E+01 42% 55% 3%Large Commercial HMI 2.5 km 1.7E+00 99% 1% 1.2E+01 68% 32% 2.3E+01 32% 42% 26%10 km 1.6E+00 100% 0% 8.5E+00 94% 6% 1.8E+01 42% 54% 4%25 km 1.6E+00 100% 0% 8.1E+00 98% 2% 1.8E+01 43% 56% 1%Large Hospital HMI 2.5 km 1.7E+00 98% 2% 3.2E+01 25% 75% 5.7E+01 13% 17% 70%10 km 1.7E+00 99% 1% 1.2E+01 69% 31% 2.3E+01 32% 42% 25%25 km 1.6E+00 100% 0% 8.9E+00 90% 10% 1.9E+01 40% 52% 8%Small Hospital HMI 2.5 km 1.6E+00 100% 0% 9.6E+00 83% 17% 2.0E+01 38% 49% 13%10 km 1.6E+00 100% 0% 8.2E+00 98% 2% 1.8E+01 43% 55% 2%25 km 1.6E+00 100% 0% 8.0E+00 99% 1% 1.7E+01 43% 56% 0%Large Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 8.7E+00 92% 8% 1.9E+01 41% 53% 6%10 km 1.6E+00 100% 0% 8.1E+00 99% 1% 1.8E+01 43% 56% 1%25 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 43% 56% 0%Small Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 8.0E+00 99% 1% 1.7E+01 43% 56% 0%10 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 44% 56% 0%25 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 44% 56% 0%Large Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 1.2E+01 69% 31% 2.3E+01 33% 42% 25%10 km 1.6E+00 100% 0% 9.1E+00 88% 12% 1.9E+01 39% 51% 10%25 km 1.6E+00 100% 0% 9.0E+00 89% 11% 1.9E+01 40% 52% 9%Medium Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 1.0E+01 80% 20% 2.1E+01 37% 47% 16%10 km 1.6E+00 100% 0% 9.4E+00 85% 15% 2.0E+01 39% 50% 11%25 km 1.6E+00 100% 0% 8.9E+00 90% 10% 1.9E+01 40% 52% 8%Small Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 9.1E+00 88% 12% 1.9E+01 40% 51% 9%10 km 1.6E+00 100% 0% 8.5E+00 95% 5% 1.8E+01 42% 54% 4%25 km 1.6E+00 100% 0% 8.2E+00 98% 2% 1.8E+01 43% 56% 1%Medium OIl-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 8.1E+00 99% 1% 1.8E+01 43% 56% 1%10 km 1.6E+00 100% 0% 8.1E+00 99% 1% 1.7E+01 43% 56% 1%25 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 43% 56% 0%Chlor-alkali plant 2.5 km 3.6E+00 46% 54% 2.0E+02 4% 96% 3.3E+02 2% 3% 95%10 km 1.9E+00 84% 16% 3.0E+01 26% 74% 5.4E+01 14% 18% 68%25 km 1.7E+00 94% 6% 1.4E+01 58% 42% 2.7E+01 28% 36% 35%2-22 Table 2-18 Predicted Mercury Values in Water Column and Biota for Western Site (ISC3 + RELMAP 50th)
50th Percentile
MH g Tier 4 %Back g ro %Rel %ISC Total H g Grain %Bac %Rel %ISC Dissolv Fish und Ma p Concentration k g rou Ma p

ed MH g (n g / g ) nd Water Concent Conc.( ration n g /l) (u g / g ) Variant b:Large Municipal Waste 2 . 5 8.8E-02 6.0E-01 15% 1% 84% 1.7E+00 96% 1% 3% Combustor km 10 km 5.5E-02 3.7E-01 24% 2% 74% 1.7E+00 97% 1% 2% 25 km 2.7E-02 1.9E-01 48% 4% 48% 1.7E+00 98% 1% 1% Variant b:Small Municipal Waste 2 . 5 3.3E-02 2.3E-01 40% 3% 57% 1.6E+00 98% 1% 1% Combustor km 10 km 1.9E-02 1.3E-01 68% 6% 26% 1.6E+00 99% 1% 0% 25 km 1.6E-02 1.1E-01 84% 7% 9% 1.6E+00 99% 1% 0% Large Commercial HMI 2 . 5 3.4E-02 2.3E-01 39% 3% 58% 1.6E+00 98% 1% 1% km 10 km 1.7E-02 1.1E-01 80% 7% 14% 1.6E+00 99% 1% 0% 25 km 1.5E-02 1.0E-01 89% 8% 3% 1.6E+00 99% 1% 0% Large Hospital HMI 2 . 5 1.4E-01 9.6E-01 9% 1% 90% 1.7E+00 95% 1% 4% km 10 km 3.1E-02 2.1E-01 42% 4% 54% 1.6E+00 98% 1% 1% 25 km 1.8E-02 1.2E-01 73% 6% 20% 1.6E+00 99% 1% 0% Small Hospital HMI 2 . 5 2.3E-02 1.5E-01 58% 5% 37% 1.6E+00 99% 1% 0% km 10 km 1.5E-02 1.0E-01 87% 7% 6% 1.6E+00 99% 1% 0% 25 km 1.4E-02 9.8E-02 91% 8% 1% 1.6E+00 99% 1% 0% Large Hospital HMI (wet scrubber) 2 . 5 1.8E-02 1.2E-01 73% 6% 20% 1.6E+00 99% 1% 0% km 10 km 1.5E-02 1.0E-01 90% 8% 3% 1.6E+00 99% 1% 0% 25 km 1.4E-02 9.8E-02 92% 8% 1% 1.6E+00 99% 1% 0% Small Hospital HMI (wet scrubber) 2 . 5 1.5E-02 9.9E-02 91% 8% 2% 1.6E+00 99% 1% 0% km 10 km 1.4E-02 9.7E-02 92% 8% 0% 1.6E+00 99% 1% 0% 25 km 1.4E-02 9.7E-02 92% 8% 0% 1.6E+00 99% 1% 0% Large Coal-fired Utility Boiler 2 . 5 3.1E-02 2.1E-01 43% 4% 53% 1.6E+00 99% 1% 0% km 10 km 1.9E-02 1.3E-01 70% 6% 24% 1.6E+00 99% 1% 0% 25 km 1.8E-02 1.2E-01 73% 6% 21% 1.6E+00 99% 1% 0% Medium Coal-fired Utility Boiler 2 . 5 2.3E-02 1.5E-01 58% 5% 37% 1.6E+00 99% 1% 0% km 10 km 2.0E-02 1.4E-01 66% 6% 28% 1.6E+00 99% 1% 0% 25 km 1.8E-02 1.2E-01 74% 6% 19% 1.6E+00 99% 1% 0% Small Coal-fired Utility Boiler 2 . 5 1.9E-02 1.3E-01 70% 6% 24% 1.6E+00 99% 1% 0% km 10 km 1.6E-02 1.1E-01 81% 7% 13% 1.6E+00 99% 1% 0% 25 km 1.5E-02 1.0E-01 88% 7% 4% 1.6E+00 99% 1% 0% Medium OIl-fired Utility Boiler 2 . 5 1.5E-02 1.0E-01 90% 8% 2% 1.6E+00 99% 1% 0% km 10 km 1.5E-02 9.9E-02 91% 8% 2% 1.6E+00 99% 1% 0% 25 km 1.4E-02 9.8E-02 92% 8% 1% 1.6E+00 99% 1% 0% Chlor-alkali plant 2 . 5 1.0E+0 6.9E+00 1% 0% 99% 3.7E+00 44% 0% 56% km 0 10 km 1.2E-01 8.0E-01 11% 1% 88% 1.9E+00 83% 1% 16% 25 km 3.7E-02 2.5E-01 36% 3% 61% 1.7E+00 93% 1% 6%2-23 Table 2-19 Predicted Mercury Values in Water Column and Biota for Western Site (ISC3 + RELMAP 90th)
90th Percentile
MH g Tier 4 %Back g ro %Rel %ISC Total H g Grain %Bac %Rel %ISC Dissolv Fish und Ma p Concentration k g rou Ma p

ed MH g (n g / g ) nd Water Concent Conc.( ration n g /l) (u g / g ) Variant b:Large Municipal Waste 2 . 5 1.1E-01 7.3E-01 12% 19% 68% 1.7E+00 94% 3% 3% Combustor km 10 km 7.5E-02 5.1E-01 18% 28% 54% 1.7E+00 95% 3% 2% 25 km 4.7E-02 3.2E-01 28% 45% 28% 1.7E+00 96% 3% 1% Variant b:Small Municipal Waste 2 . 5 5.3E-02 3.6E-01 25% 39% 36% 1.7E+00 96% 3% 1% Combustor km 10 km 3.9E-02 2.7E-01 34% 53% 13% 1.7E+00 97% 3% 0% 25 km 3.5E-02 2.4E-01 37% 59% 4% 1.7E+00 97% 3% 0% Large Commercial HMI 2 . 5 5.4E-02 3.6E-01 25% 39% 36% 1.7E+00 96% 3% 1% km 10 km 3.6E-02 2.5E-01 36% 58% 6% 1.7E+00 97% 3% 0% 25 km 3.5E-02 2.4E-01 38% 61% 1% 1.7E+00 97% 3% 0% Large Hospital HMI 2 . 5 1.6E-01 1.1E+00 8% 13% 79% 1.7E+00 94% 3% 4% km 10 km 5.1E-02 3.5E-01 26% 41% 33% 1.7E+00 96% 3% 1% 25 km 3.8E-02 2.6E-01 35% 55% 10% 1.7E+00 97% 3% 0% Small Hospital HMI 2 . 5 4.3E-02 2.9E-01 31% 49% 20% 1.7E+00 97% 3% 0% km 10 km 3.5E-02 2.4E-01 38% 60% 3% 1.7E+00 97% 3% 0% 25 km 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0% Large Hospital HMI (wet 2 . 5 3.8E-02 2.6E-01 35% 55% 10% 1.7E+00 97% 3% 0% scrubber) km 10 km 3.5E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0% 25 km 3.4E-02 2.3E-01 39% 61% 0% 1.7E+00 97% 3% 0% Small Hospital HMI (wet 2 . 5 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0% scrubber) km 10 km 3.4E-02 2.3E-01 39% 61% 0% 1.7E+00 97% 3% 0% 25 km 3.4E-02 2.3E-01 39% 61% 0% 1.7E+00 97% 3% 0% Large Coal-fired Utility Boiler 2 . 5 5.0E-02 3.4E-01 26% 42% 32% 1.7E+00 97% 3% 0% km 10 km 3.9E-02 2.6E-01 34% 54% 12% 1.7E+00 97% 3% 0% 25 km 3.8E-02 2.6E-01 35% 55% 10% 1.7E+00 97% 3% 0% Medium Coal-fired Utility Boiler 2 . 5 4.3E-02 2.9E-01 31% 49% 20% 1.7E+00 97% 3% 0% km 10 km 4.0E-02 2.7E-01 33% 53% 14% 1.7E+00 97% 3% 0% 25 km 3.8E-02 2.6E-01 35% 56% 9% 1.7E+00 97% 3% 0% Small Coal-fired Utility Boiler 2 . 5 3.9E-02 2.6E-01 34% 54% 12% 1.7E+00 97% 3% 0% km 10 km 3.6E-02 2.5E-01 36% 58% 6% 1.7E+00 97% 3% 0% 25 km 3.5E-02 2.4E-01 38% 60% 2% 1.7E+00 97% 3% 0% Medium OIl-fired Utility Boiler 2 . 5 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0% km 10 km 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0% 25 km 3.4E-02 2.3E-01 38% 61% 0% 1.7E+00 97% 3% 0% Chlor-alkali plant 2 . 5 1.0E+0 7.1E+00 1% 2% 97% 3.7E+00 43% 1% 55% km 10 km 1.4E-01 9.4E-01 10% 15% 75% 2.0E+00 82% 2% 16% 25 km 5.7E-02 3.9E-01 23% 37% 40% 1.8E+00 92% 3% 6%3-1 

### 3. PREDICTED INDIVIDUAL EXPOSURE Using the three models, RELMAP, ISC3, and IEM-2M as well as the hypothetical exposure scenarios described in Chapter 2 of this Volume, estimates of exposure to individuals residing around local emissions sources were developed. This exposure assessment incorperated many variables including types of emissions sources, activity patterns of exposed individuals, climate and impact of regional atmospheric mercury. Different combinations of these variables provide for a number of potential outputs. This chapter initially presents a description of the results for one such combination; this is presented to illustrate how the other combinations presented were developed. This section is followed by a presentation of the results of the modeling. 3.1 Illustration of Exposure Results The purpose of this section is to illustrate the results of the exposure modeling by discussing the results for one facility, distance and site. For the purpose of illustration, the large hospital HMI without a wet scrubber is selected in the eastern site, and the RELMAP 50th percentile is used as as an example of the contribution of regional anthropogenic mercury sources. It is noted that a complete discussion is not practical for all facilities; there are 144 possible combinations: 12 model plants, 2 sites, 3 distances, and two possible RELMAP values (50th percentile or 90th percentile). These results demonstrate the impacts of the exposure assessment assumptions used for the hypothetical populations inhabiting the watershed and water body. It also provides a forum to discuss the more general features and implications of the exposure assumptions. The hospital HMI model plant is assumed to emit a total of 24 kg of mercury a year. Of these mercury emissions, 73% is divalent mercury vapor, 25 is divalent mercury attached to particulates, and 2% is elemental mercury vapor. At 2.5 km from the source, the total area-averaged air concentration is predicted to be 1.7 ng/m , of which approximately 3% is predicted to be due to the facility and the rest to 3 regional sources addressed with the RELMAP. The total mercury deposition rate on the watershed is predicted to be 44 μg/m /yr, with about 70% (30 μg/m /yr) due to the facility; the total deposition rate is 2 2 the sum of the predictions of RELMAP (50th percentile) and ISC3 at 2.5 km from the facility in the prevailing downwind direction. The predicted area-averaged deposition rate onto the waterbody, which is located on the side closest to the facility, is 88 μg/m /yr. 2 The air concentration and deposition rates predicted for the facility are combined with the 50th percentile of the results for the RELMAP model and used as inputs for the IEM-2M model. The initial conditions assumed are the steady-state results after modeling two different periods of constant deposition and air concentration. The first period reflects pre-industrial conditions, in which case a mercury air concentration of 0.5 ng/m and deposition rate of 3 μg/m /yr are assumed. The second period 3 2 represents conditions that exist after the pre-industrial period but before the facility is in operation. The assumed air concentration was 1.6 ng/m and the deposition rate was 10 μg/m /yr. Table 3-1 shows some 3 2 of the media concentrations predicted after these two simulations.3-2 Table 3-1 Predicted Mercury Concentrations after Pre-facility Simulations Performed for Eastern Site (these results are used as initial conditions in IEM-2M model for this site) %Hg0 %Hg2 %MHg Watershed soil (ng/g) 47 0.02 98 2 Dissolved in water column 0.9 8 85 7 (ng/L)

3.1.1 Concentrations in Environmental Media and Biota The predicted concentrations of the three mercury species considered are summarized for various environmental media and biota in the Table 3-2.

Table 3-2 Modeled results for Large Hospital HMI (Humid Site, 2.5 km ) Using ISC3 + RELMAP (East 50th percentile) Total %RelMap %ISC Waterbody Deposition Rate 8.8E+01 16% 84% (μg/m2/yr) Watershed Air Concentration 1.7E+00 97% 3% (ng/m3) Watershed Deposition Rate 4.4E+01 33% 67% (μg/m2/yr) %Relmap %Backgroun %ISC %Hg2 %MHg d Total Mercury Dissolved Surface 2.9 Water Concentration (ng/L) Dissolved Methylmercury 0.19 6% 34% 60% 0% 100% concentration in water body (ng/L) Tier 3 Fish 3.1E-01 6% 34% 60% 0% 100% Tier 4 Fish 1.3E+00 6% 34% 60% 0% 100% Tilled Soil (ng/g) 5.0E+01 1% 93% 6% 98% 2% Notill soil (ng/g) 1.1E+02 7% 44% 48% 98% 2% Produce (μg/g dry weight) Grain 2.1E-03 3% 93% 4% 92% 8% Root Uptake 22% Direct Deposition 0% Air-to-plant 78% Legumes 2.5E-03 3% 91% 6% 93% 7% Root Uptake 31% Direct Deposition 3% Air-to-plant 66% Potatoes 5.1E-03 1% 93% 6% 96% 4% Root Uptake 100% Direct Deposition 0%Total %RelMap %ISC Waterbody Deposition Rate 8.8E+01 16% 84% (μg/m2/yr) Watershed Air Concentration 1.7E+00 97% 3% (ng/m3) Watershed Deposition Rate 4.4E+01 33% 67% (μg/m2/yr) %Relmap %Backgroun %ISC %Hg2 %MHg d

3-3 Air-to-plant 0%Table 3-2 (continued) Modeled results for Large Hospital HMI (Humid Site, 2.5 km) Using ISC3 + RELMAP (East 50th percentile) Total %RelMap %ISC Waterbody Deposition Rate 8.8E+01 16% 84% (μg/m2/yr) Watershed Air Concentration 1.7E+00 97% 3% (ng/m3) Watershed Deposition Rate 4.4E+01 33% 67% (μg/m2/yr) %Relmap %Backgroun %ISC %Hg2 %MHg d

3-4 Root Vegetables 1.9E-03 1% 93% 6% 95% 5% Root Uptake 100% Direct Deposition 0% Air-to-plant 0% Fruits 3.5E-02 4% 92% 4% 95% 5% Root Uptake 3% Direct Deposition 1% Air-to-plant 96% Fruiting Vegetables 3.5E-02 4% 92% 4% 95% 5% Root Uptake 3% Direct Deposition 1% Air-to-plant 96% Leafy Vegetables 3.4E-02 4% 91% 5% 79% 21% Root Uptake 0% Direct Deposition 2% Air-to-plant 98% Animal Products (μg/g dry weight) Beef 8.6E-03 4% 86% 10% 81% 19% from grain 0% from Forage 71% from Silage 20%

from Soil 9% Beef Liver 2.2E-02 4% 86% 10% 81% 19% from grain 0% from Forage 71% from Silage 20%

from Soil 9% Dairy 1.1E-02 4% 87% 9% 81% 19% from grain 1% from Forage 70% from Silage 21%

from Soil 8% Pork 7.0E-06 4% 89% 7% 82% 18% from grain 12% from Silage 81%

from Soil 7% Poultry 1.2E-04 7% 52% 41% 97% 3% from grain 15%

from Soil 85% Eggs 1.2E-04 7% 52% 41% 97% 3% from grain 15%Table 3-2 (continued) Modeled results for Large Hospital HMI (Humid Site, 2.5 km) Using ISC3 + RELMAP (East 50th percentile) Total %RelMap %ISC Waterbody Deposition Rate 8.8E+01 16% 84% (μg/m2/yr) Watershed Air Concentration 1.7E+00 97% 3% (ng/m3) Watershed Deposition Rate 4.4E+01 33% 67% (μg/m2/yr) %Relmap %Backgroun %ISC %Hg2 %MHg d

3-5 from Soil 85% Lamb 3.9E-03 4% 84% 11% 81% 19% from forage 88%

from Soil 12% Other Produce (μg/g dry weight) Forage 3.5E-02 4% 90% 6% 79% 21% Root Uptake 0% Direct Deposition 4% Air-to-plant 96% Silage 3.4E-02 4% 92% 4% 79% 21% Root Uptake 0% Direct Deposition 1% Air-to-plant 99%

3.1.1.1 Methylmercury Concentrations in Fish The methylmercury concentration in the fish is determined by multiplying the dissolved methylmercury concentration in water by a BAF (derivation is described in Volume 3 Appendix D). The facility is predicted to account for more than half of the methylmercury in the fish for the waterbody located 2.5 km from the source. This is not via the deposition of methylmercury itself; rather, it is due to the deposition of elemental and divalent mercury which is either predicted to be methylated after direct deposition in the water body, or is methylated in the watershed soil and subsequently flows into the waterbody via runoff or erosion. The “background” is predicted to account for approximately one third of the methylmercury concentration in fish. This background represents the steady-state conditions that are predicted to exist prior to both the facility and the sources represented in the RELMAP modeling, and are used as initial conditions in the IEM-2M modeling to predict biota concentrations and human exposure. In the four-tier trophic food chain model used in this Report, fish were assumed to feed at two levels. Trophic level 3 fish were assumed to feed on plankton which are predicted to be contaminated with comparatively low levels of methylmercury. Trophic level 4 fish were assumed to feed on trophic level 3 fish, which have higher methylmercury concentrations than the plankton. The median BAF of 1.6e6 L/kg for trophic level 3 fish was estimated using several sets of data on measured mercury concentrations in fish and water. The media BAF for trophic level 4 of 6.8e6 L/kg) was estimated by applying a predator-prey factor (of approximately 5) to the bioaccumulation factor estimated for trophic level 3 fish.3-6 3.1.1.2 Concentrations in Other Biota Plant Concentrations Three routes by which plants can take up mercury are addressed here: root uptake, whereby the plant is assumed to take up mercury from the soil; direct deposition, whereby the mercury deposited on the plantshoot from atmospheric deposition transfers to the plant; and air-to-plant transfer, whereby the mercury in the air is transported through the stomata into the plant. In all cases, at least 79% of the mercury in the plant products is predicted to be of the divalent form, with the rest being methylmercury. The mercury in potatoes and root vegetables results solely from root uptake since no air uptake was assumed to occur for these plants (Appendix B of Volume III). For leafy vegetables, all the mercury is predicted to be from air uptake since no root uptake was assumed to occur. For grains, legumes, fruits and fruiting vegetables the bulk of mercury is also modeled to come from air uptake of elemental mercury and transformation to other species; note, however, that the air and soil biotransfer factors were calculated based on a conservative premise that air and soil uptake should be of comparable strength. This was done because the soil concentrations used for this demonstration are several times lower than the soil concentrations from the Cappon (1981 and 1987) studies from which the soil BCFs were derived. For more details pertaining to the plant-soil BCF please see Appendix B of Volume III. Generally, the facility is predicted to contribute less than 10% to the total mercury plant concentration. For the plant types for which air-uptake is assumed to be the primary source of mercury, the facility contribution is similar to the contribution of the facility to the local air concentrations. For the plant types that uptake mercury primarily from the soil, it is due to the predicted dynamics of the tilled soil in which the plants are assumed to be grown. Hanson et al. (1994) stated that "dry foliar surfaces in terrestrial forest landscapes may not be a net sink for atmospheric Hg , but rather as a dynamic exchange surface that can function as a source or 0 sink dependent on current Hg vapor concentrations, leaf temperatures, surface condition (wet versus dry) and level of atmospheric oxidants." Similarly, Mosbaek et al. (1988) showed that most of the mercury in leafy plants is attributable to air-leaf transfer, but that for a given period of time the amount of elemental mercury released from the plant-soil system greatly exceeds the amount collected from the air by the plants. It is also likely that many plants accumulate airborne mercury to certain concentrations, after which net deposition of elemental mercury does not occur. This is also a function of the large area of uncertainty in deriving soil-to-plant and air-to-plant BCFs for mercury due to the wide variation in values among different studies. This is described in Appendix B of Volume III, Section B.1.2.2, B.1.2.2.1, and B.1.2.2.2.In general, the plant uptake of mercury is predicted to be dominated by either root uptake or airto-plant transfer. For facilities in which the deposition rate is significantly higher, direct deposition may be a more important pathway. Similarly, the root uptake pathway may be more important in areas with higher soil concentrations. 3.1.1.3 Mercury Concentrations in Animal Products The concentrations in animal products were calculated by multiplying the total daily intake of a particular species of mercury by a transfer factor that can depend on the animal species and tissue. The animals considered may be exposed to mercury via four possible pathways: ingestion of contaminated3-7 grain, forage, silage, or soil. The contribution from these pathways depends on both the predicted concentration in the plant or soil and the ingestion rate for a particular pathway. For beef and dairy products, most of the intake of mercury is from forage and silage because these plants are assumed to make up over 80% of their total diet (see Appendix A). The predicted concentration for beef liver is slightly higher than that for beef due to a higher transfer factor for beef liver. For poultry products, most of the mercury exposure is predicted to occur through the ingestion of soil (N.B. the untilled soil is assumed to be consumed). 3.1.2. Results for Hypothetical Exposure Scenarios In this section the predicted biota concentrations are used in conjunction with the hypothetical exposure scenarios to estimate exposure to the human receptors. Based on the predicted concentrations in biota and using the hypothetical exposure scenarios described in the previous sections, the predicted human intake rates for each scenario are shown in Tables 3-3 through Table 3-8. In general, exposure to mercury is dominated by indirect exposure for any scenario that includes an ingestion pathway other than soil. Furthermore, exposure tends to be dominated by either divalent or methylmercury species rather than elemental mercury. For the agricultural and urban scenarios, divalent mercury is the dominant form. For the scenarios that include fish ingestion, methylmercury dominates predicted exposure.
3.1.2.1 Rural Scenarios For the rural scenarios considered, exposure to divalent mercury accounted for over 90% of the total mercury exposure. The primary exposure pathway is from plant products which account for 50- 70% of the total mercury exposure. Most of the exposure through plant products is predicted to occur from consumption of fruits and grains. The rural subsistence farmer receptors are predicted to have about four times as much exposure to mercury as the rural home gardener. Exposure to mercury from milk (dairy) dominates exposure from animal products for the high end rural scenario considered (total of seven types of animal products are assumed to be consumed). These individuals were assumed not to consume fish; as a consequence, predicted methylmercury exposures are low. The local source is predicted to account for less than 10% of the total mercury exposure for the rural scenarios.
3.1.2.2 Urban Scenarios For the urban average scenario, the only exposure pathways considered are inhalation and ingestion of soil. For the urban high end scenario, the ingestion of home grown produce is considered as well, although with lower contact fractions than for the rural home gardener scenario. For the urban average scenarios, exposure to mercury from the inhalation route was equal to or exceeded indirect exposure. The urban high-end scenario included a small garden to the urban average3-8 scenario, with the result that similar contributions to the total divalent mercury and methylmercury exposures occurred as for the rural home gardeners. The urban high-end adult receptor had a predicted mercury exposure of about one-half that of the rural home gardener. The high end urban child scenario consisted of a pica child assumed to ingest 7.5 grams of soil per day. The exposure rate is then proportional to the assumed untilled soil concentration, which in this case is 100 ng/g. 3.1.2.3 Fish Ingestion Scenarios It was assumed that the high-end fish consumer eats fish from the affected freshwater lake on a daily basis; that is, seasonal consumption rate variation was not addressed. This individual is the most exposed adult to methylmercury in this assessment, and was predicted to be exposed to approximately twice the level of methylmercury that the recreational angler is exposed. Fish consumption is predicted to be the primary source of methylmercury in the diet. The high-end fisher was assumed to consume two times as much fish as the recreational angler (60 g/day vs. 30 g/day). On a gram per bodyweight basis, the high-end fish-consuming child was the maximally exposed subpopulation. This is based on the hypothetical child’s fish consumption rate and the bodyweight, and is consistent with the data presented in the Chapter 4 of this Volume. For the fish ingestion scenarios, intake of mercury was mainly the methylmercury species. Although intake of methylmercury via plants and soil is considered in the high-end fish consumption scenario, it accounts for less than 1% of the total methylmercury intake. The recreational angler was assumed to be exposed to mercury via fish, soil and water consumption. Exposure via soil and water however, accounted for less than 0.1% of the total mercury intake.3-9 Table 3-3 Predicted Mercury Exposure for Subsistence Farmer Scenario
ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Subsistence Farmer mg/kg/day Adult Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 4.9E-07 4% 93% 3% 0% 0% Ingestion Total 4.1E-05 0% 4% 90% 6% 90% 10% Fish Ingestion 0.0E+00 0% NA NA NA NA NA Water Ingestion 5.0E-07 1% 25% 56% 18% 97% 2% Produce Ingestion 2.9E-05 71% 4% 92% 4% 94% 6% Grains 4.0E-06 10% 3% 93% 4% 92% 8% Legumes 9.5E-07 2% 3% 91% 6% 93% 7% Potatoes 8.7E-07 2% 1% 93% 6% 96% 4% Root vegetables 4.5E-08 0% 1% 93% 6% 95% 5% Fruits 2.0E-05 49% 4% 92% 4% 95% 5% Fruiting vegetables 2.2E-06 5% 4% 92% 4% 95% 5%

Leafy vegetables 9.6E-07 2% 4% 91% 5% 79% 21% Animal Ingestion 1.1E-05 27% 4% 86% 9% 81% 19% Beef 2.9E-06 7% 4% 86% 10% 81% 19% Beef liver 1.4E-06 4% 4% 86% 10% 81% 19% Dairy 6.5E-06 16% 4% 87% 9% 81% 19% Pork 1.2E-09 0% 4% 89% 7% 82% 18% Poultry 1.4E-08 0% 7% 52% 41% 97% 3%

Eggs 9.0E-09 0% 7% 52% 41% 97% 3% Lamb 2.2E-07 1% 4% 84% 11% 81% 19% Soil Ingestion 1.5E-07 0% 7% 44% 48% 98% 2% ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Subsistence Farmer mg/kg/day Child Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 1.6E-06 4% 93% 3% 0% 0% Total Ingestion 5.3E-05 0% 4% 87% 8% 87% 13% Fish Ingestion 0.0E+00 0% NA NA NA NA NA Water Ingestion 1.0E-06 2% 25% 56% 18% 97% 2% Produce Ingestion 2.3E-05 44% 3% 92% 4% 94% 6% Grains 8.1E-06 15% 3% 93% 4% 92% 8% Legumes 1.7E-06 3% 3% 91% 6% 93% 7% Potatoes 1.4E-06 3% 1% 93% 6% 96% 4% Root vegetables 6.7E-08 0% 1% 93% 6% 95% 5% Fruits 7.8E-06 15% 4% 92% 4% 95% 5% Fruiting vegetables 4.2E-06 8% 4% 92% 4% 95% 5%

Leafy vegetables 2.7E-07 1% 4% 91% 5% 79% 21% Animal Ingestion 2.8E-05 52% 4% 86% 9% 81% 19% Beef 4.8E-06 9% 4% 86% 10% 81% 19% Beef liver 5.4E-07 1% 4% 86% 10% 81% 19% Dairy 2.2E-05 41% 4% 87% 9% 81% 19% Pork 1.7E-09 0% 4% 89% 7% 82% 18% Poultry 2.6E-08 0% 7% 52% 41% 97% 3%

Eggs 1.1E-08 0% 7% 52% 41% 97% 3% Lamb 2.4E-07 0% 4% 84% 11% 81% 19% Soil Ingestion 1.2E-06 2% 7% 44% 48% 98% 2%3-10 Table 3-4 Predicted Mercury Exposure for Rural Home Gardener
ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Rural Home Gardener mg/kg/day Adult Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 4.9E-07 4% 93% 3% 0% 0% Ingestion Total 9.9E-06 0% 4% 91% 5% 94% 6% Fish Ingestion 0.0E+00 0% NA NA NA NA NA Water Ingestion 0.0E+00 0% NA NA NA NA NA Produce Ingestion 9.7E-06 98% 4% 92% 4% 94% 6% Grains 2.7E-06 27% 3% 93% 4% 92% 8% Legumes 7.6E-07 8% 3% 91% 6% 93% 7% Potatoes 2.0E-07 2% 1% 93% 6% 96% 4% Root vegetables 1.2E-08 0% 1% 93% 6% 95% 5% Fruits 4.6E-06 47% 4% 92% 4% 95% 5% Fruiting vegetables 1.4E-06 14% 4% 92% 4% 95% 5%

Leafy vegetables 5.5E-08 1% 4% 91% 5% 79% 21% Animal Ingestion 0.0E+00 0% NA NA NA NA NA Beef 0.0E+00 0% NA NA NA NA NA Beef liver 0.0E+00 0% NA NA NA NA NA Dairy 0.0E+00 0% NA NA NA NA NA Pork 0.0E+00 0% NA NA NA NA NA Poultry 0.0E+00 0% NA NA NA NA NA

Eggs 0.0E+00 0% NA NA NA NA NA Lamb 0.0E+00 0% NA NA NA NA NA Soil Ingestion 1.5E-07 2% 7% 44% 48% 98% 2% ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Rural Home Gardener mg/kg/day Child Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 1.6E-06 4% 93% 3% 0% 0% Total Ingestion 1.3E-05 0% 4% 88% 9% 94% 6% Fish Ingestion 0.0E+00 0% NA NA NA NA NA Water Ingestion 0.0E+00 0% NA NA NA NA NA Produce Ingestion 1.1E-05 90% 3% 92% 4% 94% 6% Grains 5.4E-06 42% 3% 93% 4% 92% 8% Legumes 1.3E-06 10% 3% 91% 6% 93% 7% Potatoes 3.2E-07 2% 1% 93% 6% 96% 4% Root vegetables 1.8E-08 0% 1% 93% 6% 95% 5% Fruits 1.8E-06 14% 4% 92% 4% 95% 5% Fruiting vegetables 2.6E-06 20% 4% 92% 4% 95% 5%

Leafy vegetables 1.6E-08 0% 4% 91% 5% 79% 21% Animal Ingestion 0.0E+00 0% NA NA NA NA NA Beef 0.0E+00 0% NA NA NA NA NA Beef liver 0.0E+00 0% NA NA NA NA NA Dairy 0.0E+00 0% NA NA NA NA NA Pork 0.0E+00 0% NA NA NA NA NA Poultry 0.0E+00 0% NA NA NA NA NA

Eggs 0.0E+00 0% NA NA NA NA NA Lamb 0.0E+00 0% NA NA NA NA NA Soil Ingestion 1.2E-06 10% 7% 44% 48% 98% 2%3-11 Table 3-5 Predicted Mercury Exposure for Urban Average Scenario
ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Urban Average mg/kg/day Adult Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 3.3E-07 4% 93% 3% 0% 0% Ingestion Total 2.0E-07 100% 6% 54% 40% 98% 2% Soil Ingestion 2.0E-07 100% 6% 54% 40% 98% 2% ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Urban Average mg/kg/day Child Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 1.6E-06 4% 93% 3% 0% 0% Total Ingestion 1.6E-06 100% 6% 54% 40% 98% 2% Soil Ingestion 1.6E-06 100% 6% 54% 40% 98% 2%

Table 3-6 Predicted Mercury Exposure for Urban High-end Scenarios
ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Urban High End mg/kg/day Adult Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 4.9E-07 4% 93% 3% 0% 0% Ingestion Total 4.0E-06 100% 4% 91% 6% 94% 6% Fish Ingestion 0.0E+00 0% NA NA NA NA NA Water Ingestion 0.0E+00 0% NA NA NA NA NA Produce Ingestion 3.8E-06 95% 3% 93% 4% 94% 6% Grains 8.8E-07 22% 3% 94% 3% 93% 7% Legumes 5.6E-07 14% 3% 92% 5% 93% 7% Potatoes 4.2E-08 1% 1% 95% 4% 96% 4% Root vegetables 5.1E-09 0% 1% 95% 4% 95% 5% Fruits 1.5E-06 39% 4% 92% 4% 95% 5% Fruiting vegetables 7.2E-07 18% 4% 92% 4% 95% 5%

Leafy vegetables 2.5E-08 1% 4% 91% 5% 79% 21% Soil Ingestion 2.0E-07 5% 6% 54% 40% 98% 2% ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Urban High End mg/kg/day Child Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 1.6E-06 4% 93% 3% 0% 0% Total Ingestion 6.1E-05 100% 6% 54% 40% 98% 2% Soil Ingestion 6.1E-05 100% 6% 54% 40% 98% 2%3-12 Table 3-7 Predicted Mercury Exposure for High-end Fish Consumption Scenario
ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Subsistence Fisher mg/kg/day Adult Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 4.9E-07 4% 93% 3% 0% 0% Ingestion Total 1.1E-03 0% 6% 34% 59% 1% 99% Fish Ingestion 1.1E-03 99% 6% 34% 60% 0% 100% Water Ingestion 1.0E-07 0% 6% 32% 62% 87% 7% Produce Ingestion 9.7E-06 1% 4% 92% 4% 94% 6% Grains 2.7E-06 0% 3% 93% 4% 92% 8% Legumes 7.6E-07 0% 3% 91% 6% 93% 7% Potatoes 2.0E-07 0% 1% 93% 6% 96% 4% Root vegetables 1.2E-08 0% 1% 93% 6% 95% 5% Fruits 4.6E-06 0% 4% 92% 4% 95% 5% Fruiting vegetables 1.4E-06 0% 4% 92% 4% 95% 5%

Leafy vegetables 5.5E-08 0% 4% 91% 5% 79% 21% Animal Ingestion 0.0E+00 0% NA NA NA NA NA Beef 0.0E+00 0% NA NA NA NA NA Beef liver 0.0E+00 0% NA NA NA NA NA Dairy 0.0E+00 0% NA NA NA NA NA Pork 0.0E+00 0% NA NA NA NA NA Poultry 0.0E+00 0% NA NA NA NA NA

Eggs 0.0E+00 0% NA NA NA NA NA Lamb 0.0E+00 0% NA NA NA NA NA Soil Ingestion 1.5E-07 0% 7% 44% 48% 98% 2% ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) High end Fish Consumer mg/kg/day Child Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 1.6E-06 4% 93% 3% 0% 0% Total Ingestion 1.6E-03 0% 6% 34% 59% 1% 99% Fish Ingestion 1.5E-03 99% 6% 34% 60% 0% 100% Water Ingestion 2.2E-07 0% 6% 32% 62% 87% 7% Produce Ingestion 1.1E-05 1% 3% 92% 4% 94% 6% Grains 5.4E-06 0% 3% 93% 4% 92% 8% Legumes 1.3E-06 0% 3% 91% 6% 93% 7% Potatoes 3.2E-07 0% 1% 93% 6% 96% 4% Root vegetables 1.8E-08 0% 1% 93% 6% 95% 5% Fruits 1.8E-06 0% 4% 92% 4% 95% 5% Fruiting vegetables 2.6E-06 0% 4% 92% 4% 95% 5%

Leafy vegetables 1.6E-08 0% 4% 91% 5% 79% 21% Soil Ingestion 1.2E-06 0% 7% 44% 48% 98% 2%3-13 Table 3-8 Predicted Mercury Exposure for Recreational Angler Scenario
ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile) Recreational Angler mg/kg/day Adult Total %Relmap %Background %ISC %Hg2 %MHg Inhalation 4.9E-07 4% 93% 3% 0% 0% Ingestion Total 5.6E-04 0% 6% 34% 60% 0% 100% Fish Ingestion 5.6E-04 100% 6% 34% 60% 0% 100% Water Ingestion 1.0E-07 0% 6% 32% 62% 87% 7% Soil Ingestion 1.5E-07 0% 7% 45% 48% 98% 2%
3.2 Results of Combining Local and Regional Models - Predicted Human Exposure In this section the results are presented for combining the local and regional impacts of anthropogenic sources. For both the eastern and western sites, the 50th and 90th percentile of the predicted air concentrations and deposition rates by the regional air model are used in conjunction with the air concentrations and deposition rates predicted by the local scale model for each plant to obtain estimates of environmental concentrations and possible exposure for humans. Background mercury concentrations in environmental media are also included. Tables 3-9 through 3-22 show the predicted human intake for each exposure scenario and site. The results include receptors located at three distances from the facility (2.5km, 10km, and 25km). In all cases, the predicted impact of the local source decreases as the distance from the local source increases.
3.2.1 Inhalation Only for the chlor-alkali plant is the local source predicted to account for more than 50% of total mercury exposure due to inhalation, and then only for the closest receptor considered (2.5km). The primary form of mercury that constitutes this exposure is elemental mercury. Further, the inhalation route is rarely predicted to be the dominant pathway of total mercury exposure when compared to indirect exposure. The exception is the “urban average” exposure, in which an adult is assumed to ingest average amounts of soil in the impacted area. The insignificance of exposure through the inhalation route when compared to ingestion routes was described previously by the WHO (WHO, 1990). 3.2.2 Agricultural Scenarios In general, the local source is predicted to account for less than 10% of the total mercury exposure for the agricultural scenarios, compared to the contribution of the regional sources (RELMAP) and background. This is because for these scenarios ingestion of plants is the dominant pathway for mercury exposure, and the plant concentrations are predicted to accumulate mercury from the air more than via soil uptake. The contribution of the local source is then roughly equivalent to the impact of the local source on the air concentration. It is only for the chlor-alkali plant that this contribution is more than 20% (at 2.5km and 10km). The mercury in potatoes and root vegetables results solely from root uptake since no air uptake was assumed to occur for these plants (Appendix A). For leafy vegetables, all the mercury is predicted to be from air uptake since no root uptake was assumed to occur. For grains, legumes, fruits and3-14Table 3-9Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FarmerEastern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Subsistence Farmer2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 5.4E-05 9% 4.1E-05 6% 5.1E-05 4% 4.0E-05 4% 5.0E-05 2% 3.9E-05 2%Municipal WasteCombustorVariant b:Small 5.0E-05 2% 3.9E-05 1% 4.9E-05 1% 3.9E-05 1% 4.9E-05 0% 3.8E-05 0%Municipal WasteCombustorLarge Commercial HMI 5.0E-05 2% 3.9E-05 1% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%Large Hospital HMI 5.3E-05 8% 4.1E-05 6% 5.0E-05 2% 3.9E-05 1% 4.9E-05 1% 3.8E-05 0%Small Hospital HMI 4.9E-05 1% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%Large Hospital HMI 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%(wet scrubber)Small Hospital HMI 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%(wet scrubber)Large Coal-fired Utility 5.1E-05 4% 3.9E-05 3% 5.0E-05 1% 3.9E-05 1% 4.9E-05 0% 3.8E-05 0%BoilerMedium Coal-fired 5.0E-05 2% 3.9E-05 1% 4.9E-05 1% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%Utility BoilerSmall Coal-fired Utility 4.9E-05 1% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%BoilerMedium OIl-fired 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0%Utility BoilerChlor-alkali plant 1.3E-04 62% 9.6E-05 60% 6.3E-05 23% 4.9E-05 22% 5.3E-05 8% 4.2E-05 8%3-15Table 3-9 (continued)Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FarmerEastern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Subsistence Farmer2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 5.7E-05 8% 4.3E-05 6% 5.4E-05 4% 4.2E-05 3% 5.3E-05 2% 4.1E-05 2%Municipal WasteCombustorVariant b:Small 5.3E-05 2% 4.1E-05 1% 5.3E-05 1% 4.0E-05 1% 5.2E-05 0% 4.0E-05 0%Municipal WasteCombustorLarge Commercial HMI 5.3E-05 2% 4.1E-05 1% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%Large Hospital HMI 5.7E-05 8% 4.3E-05 6% 5.3E-05 2% 4.1E-05 1% 5.2E-05 0% 4.0E-05 0%Small Hospital HMI 5.3E-05 1% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%Large Hospital HMI 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%(wet scrubber)Small Hospital HMI 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%(wet scrubber)Large Coal-fired Utility 5.4E-05 4% 4.1E-05 2% 5.3E-05 1% 4.0E-05 1% 5.2E-05 0% 4.0E-05 0%BoilerMedium Coal-fired 5.3E-05 2% 4.1E-05 1% 5.2E-05 1% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%Utility BoilerSmall Coal-fired Utility 5.2E-05 1% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%BoilerMedium OIl-fired 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0%Utility BoilerChlor-alkali plant 1.3E-04 61% 9.8E-05 59% 6.7E-05 22% 5.1E-05 21% 5.7E-05 8% 4.3E-05 8%3-16Table 3-10Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Rural Home GardnerEastern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Rural Home Gardener2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.3E-05 8% 9.9E-06 5% 1.2E-05 4% 9.7E-06 3% 1.2E-05 2% 9.5E-06 1%Municipal WasteCombustorVariant b:Small 1.2E-05 2% 9.5E-06 1% 1.2E-05 1% 9.4E-06 1% 1.2E-05 0% 9.4E-06 0%Municipal WasteCombustorLarge Commercial HMI 1.2E-05 2% 9.5E-06 1% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%Large Hospital HMI 1.3E-05 9% 9.9E-06 5% 1.2E-05 2% 9.5E-06 1% 1.2E-05 1% 9.4E-06 0%Small Hospital HMI 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%Large Hospital HMI 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%(wet scrubber)Small Hospital HMI 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%(wet scrubber)Large Coal-fired Utility 1.2E-05 3% 9.5E-06 1% 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%BoilerMedium Coal-fired 1.2E-05 2% 9.5E-06 1% 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%Utility BoilerSmall Coal-fired Utility 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%BoilerMedium OIl-fired 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0%Utility BoilerChlor-alkali plant 3.0E-05 62% 2.2E-05 58% 1.5E-05 22% 1.2E-05 20% 1.3E-05 8% 1.0E-05 7%3-17Table 3-10 (continued)Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Rural Home GardnerEastern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Rural Home Gardener2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.3E-05 8% 1.0E-05 5% 1.3E-05 4% 1.0E-05 3% 1.2E-05 2% 9.9E-06 1%Municipal WasteCombustorVariant b:Small 1.2E-05 2% 9.8E-06 1% 1.2E-05 1% 9.8E-06 1% 1.2E-05 0% 9.8E-06 0%Municipal WasteCombustorLarge Commercial HMI 1.2E-05 2% 9.8E-06 1% 1.2E-05 0% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0%Large Hospital HMI 1.3E-05 8% 1.0E-05 5% 1.2E-05 2% 9.8E-06 1% 1.2E-05 1% 9.8E-06 0%Small Hospital HMI 1.2E-05 1% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0%Large Hospital HMI 1.2E-05 0% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0%(wet scrubber)Small Hospital HMI 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0%(wet scrubber)Large Coal-fired Utility 1.3E-05 3% 9.8E-06 1% 1.2E-05 1% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0%BoilerMedium Coal-fired 1.2E-05 2% 9.8E-06 1% 1.2E-05 1% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0%Utility BoilerSmall Coal-fired Utility 1.2E-05 0% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0%BoilerMedium OIl-fired 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0%Utility BoilerChlor-alkali plant 3.1E-05 60% 2.3E-05 57% 1.6E-05 21% 1.2E-05 20% 1.3E-05 7% 1.0E-05 7%3-18Table 3-11Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban AverageEastern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Urban Average2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.6E-06 38% 1.9E-07 38% 1.2E-06 20% 1.5E-07 20% 1.1E-06 8% 1.3E-07 8%Municipal WasteCombustorVariant b:Small 1.1E-06 10% 1.3E-07 10% 1.0E-06 3% 1.2E-07 3% 1.0E-06 1% 1.2E-07 1%Municipal WasteCombustorLarge Commercial 1.1E-06 9% 1.3E-07 9% 1.0E-06 2% 1.2E-07 2% 1.0E-06 0% 1.2E-07 0%HMILarge Hospital HMI 1.6E-06 40% 2.0E-07 40% 1.1E-06 10% 1.3E-07 10% 1.0E-06 3% 1.2E-07 3%Small Hospital HMI 1.0E-06 4% 1.3E-07 4% 1.0E-06 1% 1.2E-07 1% 9.9E-07 0% 1.2E-07 0%Large Hospital HMI 1.0E-06 2% 1.2E-07 2% 1.0E-06 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0%(wet scrubber)Small Hospital HMI 9.9E-07 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0%(wet scrubber)Large Coal-fired 1.3E-06 26% 1.6E-07 26% 1.1E-06 6% 1.3E-07 6% 1.0E-06 2% 1.2E-07 2%Utility BoilerMedium Coal-fired 1.1E-06 13% 1.4E-07 13% 1.0E-06 4% 1.3E-07 4% 1.0E-06 2% 1.2E-07 2%Utility BoilerSmall Coal-fired 1.0E-06 3% 1.2E-07 3% 1.0E-06 1% 1.2E-07 1% 1.0E-06 0% 1.2E-07 0%Utility BoilerMedium OIl-fired 1.0E-06 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0%Utility BoilerChlor-alkali plant 6.1E-06 84% 7.4E-07 84% 1.7E-06 41% 2.0E-07 41% 1.2E-06 15% 1.4E-07 15%3-19Table 3-11 (continued)Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban AverageEastern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Urban Average2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.9E-06 32% 2.3E-07 32% 1.5E-06 16% 1.8E-07 16% 1.4E-06 7% 1.7E-07 7%Municipal WasteCombustorVariant b:Small 1.4E-06 8% 1.7E-07 8% 1.3E-06 3% 1.6E-07 3% 1.3E-06 1% 1.6E-07 1%Municipal WasteCombustorLarge Commercial 1.4E-06 7% 1.7E-07 7% 1.3E-06 1% 1.6E-07 1% 1.3E-06 0% 1.5E-07 0%HMILarge Hospital HMI 1.9E-06 34% 2.3E-07 34% 1.4E-06 8% 1.7E-07 8% 1.3E-06 2% 1.6E-07 2%Small Hospital HMI 1.3E-06 3% 1.6E-07 3% 1.3E-06 0% 1.6E-07 0% 1.3E-06 0% 1.5E-07 0%Large Hospital HMI 1.3E-06 1% 1.6E-07 1% 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0%(wet scrubber)Small Hospital HMI 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0%(wet scrubber)Large Coal-fired 1.6E-06 21% 2.0E-07 21% 1.3E-06 5% 1.6E-07 5% 1.3E-06 2% 1.6E-07 2%Utility BoilerMedium Coal-fired 1.4E-06 10% 1.7E-07 10% 1.3E-06 3% 1.6E-07 3% 1.3E-06 1% 1.6E-07 1%Utility BoilerSmall Coal-fired 1.3E-06 3% 1.6E-07 3% 1.3E-06 1% 1.6E-07 1% 1.3E-06 0% 1.5E-07 0%Utility BoilerMedium OIl-fired 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0%Utility BoilerChlor-alkali plant 6.4E-06 80% 7.7E-07 80% 2.0E-06 35% 2.4E-07 35% 1.4E-06 12% 1.8E-07 12%3-20Table 3-12Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban High EndEastern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Urban High End2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 5.9E-05 38% 3.9E-06 6% 4.6E-05 20% 3.9E-06 3% 4.0E-05 8% 3.8E-06 2%Municipal WasteCombustorVariant b:Small 4.1E-05 10% 3.8E-06 1% 3.8E-05 3% 3.7E-06 1% 3.7E-05 1% 3.7E-06 0%Municipal WasteCombustorLarge Commercial 4.0E-05 9% 3.8E-06 1% 3.7E-05 2% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0%HMILarge Hospital HMI 6.1E-05 40% 4.0E-06 6% 4.1E-05 10% 3.8E-06 1% 3.8E-05 3% 3.7E-06 0%Small Hospital HMI 3.8E-05 4% 3.7E-06 0% 3.7E-05 1% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0%Large Hospital HMI 3.7E-05 2% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0%(wet scrubber)Small Hospital HMI 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0%(wet scrubber)Large Coal-fired 4.9E-05 26% 3.8E-06 2% 3.9E-05 6% 3.7E-06 0% 3.8E-05 2% 3.7E-06 0%Utility BoilerMedium Coal-fired 4.2E-05 13% 3.8E-06 1% 3.8E-05 4% 3.7E-06 0% 3.7E-05 2% 3.7E-06 0%Utility BoilerSmall Coal-fired 3.8E-05 3% 3.7E-06 0% 3.7E-05 1% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0%Utility BoilerMedium OIl-fired 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0%Utility BoilerChlor-alkali plant 2.3E-04 84% 8.9E-06 58% 6.2E-05 41% 4.7E-06 20% 4.3E-05 15% 4.0E-06 7%3-21Table 3-12 (continued)Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban High EndEastern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Urban High End2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 7.0E-05 33% 4.1E-06 5% 5.6E-05 16% 4.0E-06 3% 5.0E-05 7% 3.9E-06 2%Municipal WasteCombustorVariant b:Small 5.1E-05 8% 3.9E-06 1% 4.8E-05 3% 3.9E-06 1% 4.7E-05 1% 3.9E-06 0%Municipal WasteCombustorLarge Commercial 5.1E-05 7% 3.9E-06 1% 4.8E-05 1% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0%HMILarge Hospital HMI 7.1E-05 34% 4.1E-06 6% 5.1E-05 8% 3.9E-06 1% 4.8E-05 2% 3.9E-06 0%Small Hospital HMI 4.9E-05 3% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0%Large Hospital HMI 4.8E-05 1% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0%(wet scrubber)Small Hospital HMI 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0%(wet scrubber)Large Coal-fired 6.0E-05 21% 3.9E-06 2% 4.9E-05 5% 3.9E-06 0% 4.8E-05 2% 3.9E-06 0%Utility BoilerMedium Coal-fired 5.3E-05 11% 3.9E-06 1% 4.9E-05 3% 3.9E-06 0% 4.8E-05 1% 3.9E-06 0%Utility BoilerSmall Coal-fired 4.8E-05 3% 3.9E-06 0% 4.8E-05 1% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0%Utility BoilerMedium OIl-fired 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0%Utility BoilerChlor-alkali plant 2.4E-04 80% 9.1E-06 57% 7.3E-05 35% 4.8E-06 20% 5.4E-05 12% 4.2E-06 7%3-22Table 3-13Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FisherEastern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Subsistence Fisher2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.4E-03 54% 1.0E-03 54% 9.0E-04 30% 6.6E-04 30% 7.2E-04 13% 5.3E-04 13%Municipal WasteCombustorVariant b:Small 7.7E-04 18% 5.6E-04 18% 6.7E-04 6% 4.9E-04 6% 6.4E-04 2% 4.7E-04 2%Municipal WasteCombustorLarge Commercial 7.8E-04 19% 5.7E-04 19% 6.5E-04 3% 4.7E-04 3% 6.3E-04 1% 4.6E-04 1%HMILarge Hospital HMI 1.6E-03 59% 1.1E-03 59% 7.6E-04 17% 5.6E-04 17% 6.6E-04 4% 4.8E-04 4%Small Hospital HMI 6.9E-04 9% 5.1E-04 9% 6.4E-04 1% 4.7E-04 1% 6.3E-04 0% 4.6E-04 0%Large Hospital HMI 6.6E-04 4% 4.8E-04 4% 6.3E-04 1% 4.6E-04 1% 6.3E-04 0% 4.6E-04 0%(wet scrubber)Small Hospital HMI 6.3E-04 0% 4.6E-04 0% 6.3E-04 0% 4.6E-04 0% 6.3E-04 0% 4.6E-04 0%(wet scrubber)Large Coal-fired 1.1E-03 42% 7.9E-04 42% 7.0E-04 10% 5.1E-04 10% 6.5E-04 4% 4.8E-04 4%Utility BoilerMedium Coal-fired 8.2E-04 23% 6.0E-04 23% 6.7E-04 7% 4.9E-04 7% 6.5E-04 3% 4.7E-04 3%Utility BoilerSmall Coal-fired 6.7E-04 6% 4.9E-04 6% 6.5E-04 2% 4.7E-04 2% 6.3E-04 1% 4.6E-04 1%Utility BoilerMedium OIl-fired 6.4E-04 1% 4.6E-04 1% 6.3E-04 0% 4.6E-04 0% 6.3E-04 0% 4.6E-04 0%Utility BoilerChlor-alkali plant 8.0E-03 92% 5.9E-03 92% 1.4E-03 56% 1.0E-03 56% 8.2E-04 23% 5.9E-04 23%3-23Table 3-13 (continued)Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FisherEastern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Subsistence Fisher2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.6E-03 45% 1.2E-03 45% 1.2E-03 23% 8.6E-04 23% 1.0E-03 9% 7.3E-04 9%Municipal WasteCombustorVariant b:Small 1.1E-03 13% 7.7E-04 13% 9.5E-04 4% 6.9E-04 4% 9.2E-04 1% 6.7E-04 1%Municipal WasteCombustorLarge Commercial 1.1E-03 14% 7.7E-04 14% 9.3E-04 2% 6.8E-04 2% 9.1E-04 0% 6.7E-04 0%HMILarge Hospital HMI 1.8E-03 50% 1.3E-03 50% 1.0E-03 13% 7.6E-04 13% 9.4E-04 3% 6.9E-04 3%Small Hospital HMI 9.7E-04 6% 7.1E-04 6% 9.2E-04 1% 6.7E-04 1% 9.1E-04 0% 6.7E-04 0%Large Hospital HMI 9.4E-04 3% 6.8E-04 3% 9.1E-04 0% 6.7E-04 0% 9.1E-04 0% 6.6E-04 0%(wet scrubber)Small Hospital HMI 9.1E-04 0% 6.7E-04 0% 9.1E-04 0% 6.6E-04 0% 9.1E-04 0% 6.6E-04 0%(wet scrubber)Large Coal-fired 1.4E-03 33% 1.0E-03 33% 9.8E-04 7% 7.2E-04 7% 9.3E-04 3% 6.8E-04 3%Utility BoilerMedium Coal-fired 1.1E-03 17% 8.0E-04 17% 9.5E-04 5% 7.0E-04 5% 9.3E-04 2% 6.8E-04 2%Utility BoilerSmall Coal-fired 9.5E-04 4% 7.0E-04 4% 9.3E-04 2% 6.7E-04 2% 9.1E-04 1% 6.7E-04 1%Utility BoilerMedium OIl-fired 9.2E-04 1% 6.7E-04 1% 9.1E-04 0% 6.7E-04 0% 9.1E-04 0% 6.6E-04 0%Utility BoilerChlor-alkali plant 8.3E-03 89% 6.1E-03 89% 1.7E-03 47% 1.3E-03 47% 1.1E-03 17% 8.0E-04 17%3-24Table 3-14Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Recreational AnglerEastern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Recreational Angler2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 5.0E-04 54% 3.3E-04 30% 2.6E-04 13%Variant b:Small Municipal Waste Combustor 2.8E-04 18% 2.4E-04 6% 2.3E-04 2%Large Commercial HMI 2.8E-04 19% 2.3E-04 3% 2.3E-04 1%Large Hospital HMI 5.6E-04 60% 2.8E-04 18% 2.4E-04 5%Small Hospital HMI 2.5E-04 9% 2.3E-04 1% 2.3E-04 0%Large Hospital HMI (wet scrubber) 2.4E-04 4% 2.3E-04 1% 2.3E-04 0%Small Hospital HMI (wet scrubber) 2.3E-04 0% 2.3E-04 0% 2.3E-04 0%Large Coal-fired Utility Boiler 3.9E-04 42% 2.5E-04 10% 2.4E-04 4%Medium Coal-fired Utility Boiler 3.0E-04 24% 2.4E-04 7% 2.3E-04 3%Small Coal-fired Utility Boiler 2.4E-04 6% 2.3E-04 2% 2.3E-04 1%Medium OIl-fired Utility Boiler 2.3E-04 1% 2.3E-04 0% 2.3E-04 0%Chlor-alkali plant 2.9E-03 92% 5.2E-04 56% 2.9E-04 23%Eastern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Recreational Angler2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 6.0E-04 45% 4.3E-04 23% 3.6E-04 9%Variant b:Small Municipal Waste Combustor 3.8E-04 13% 3.4E-04 4% 3.3E-04 1%Large Commercial HMI 3.8E-04 14% 3.4E-04 2% 3.3E-04 0%Large Hospital HMI 6.7E-04 51% 3.8E-04 13% 3.4E-04 3%Small Hospital HMI 3.5E-04 7% 3.3E-04 1% 3.3E-04 0%Large Hospital HMI (wet scrubber) 3.4E-04 3% 3.3E-04 0% 3.3E-04 0%Small Hospital HMI (wet scrubber) 3.3E-04 0% 3.3E-04 0% 3.3E-04 0%Large Coal-fired Utility Boiler 4.9E-04 33% 3.6E-04 7% 3.4E-04 3%Medium Coal-fired Utility Boiler 4.0E-04 18% 3.5E-04 5% 3.4E-04 2%Small Coal-fired Utility Boiler 3.4E-04 5% 3.3E-04 2% 3.3E-04 1%Medium OIl-fired Utility Boiler 3.3E-04 1% 3.3E-04 0% 3.3E-04 0%Chlor-alkali plant 3.0E-03 89% 6.2E-04 47% 4.0E-04 17%3-253-26Table 3-15Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FarmerWestern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Subsistence Farmer2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 4.6E-05 8% 3.6E-05 6% 4.4E-05 4% 3.5E-05 3% 4.3E-05 2% 3.5E-05 1%Municipal WasteCombustorVariant b:Small 4.3E-05 2% 3.5E-05 1% 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% 3.4E-05 0%Municipal WasteCombustorLarge Commercial HMI 4.3E-05 2% 3.5E-05 1% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0%Large Hospital HMI 4.6E-05 8% 3.6E-05 5% 4.3E-05 1% 3.5E-05 1% 4.3E-05 0% 3.4E-05 0%Small Hospital HMI 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0%Large Hospital HMI 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0%(wet scrubber)Small Hospital HMI 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0%(wet scrubber)Large Coal-fired Utility 4.4E-05 3% 3.5E-05 2% 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% 3.4E-05 0%BoilerMedium Coal-fired 4.3E-05 1% 3.5E-05 1% 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% 3.4E-05 0%Utility BoilerSmall Coal-fired Utility 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0%BoilerMedium OIl-fired 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0%Utility BoilerChlor-alkali plant 1.1E-04 61% 8.3E-05 58% 5.2E-05 19% 4.2E-05 17% 4.6E-05 7% 3.7E-05 6%3-27Table 3-15 (continued)Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FarmerWestern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Subsistence Farmer2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 4.9E-05 7% 3.8E-05 5% 4.7E-05 4% 3.7E-05 3% 4.6E-05 2% 3.6E-05 1%Municipal WasteCombustorVariant b:Small 4.6E-05 2% 3.6E-05 1% 4.5E-05 1% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%Municipal WasteCombustorLarge Commercial HMI 4.6E-05 2% 3.6E-05 1% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%Large Hospital HMI 4.9E-05 7% 3.8E-05 5% 4.6E-05 1% 3.6E-05 1% 4.5E-05 0% 3.6E-05 0%Small Hospital HMI 4.5E-05 1% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%Large Hospital HMI 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%(wet scrubber)Small Hospital HMI 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%(wet scrubber)Large Coal-fired Utility 4.6E-05 3% 3.6E-05 2% 4.6E-05 1% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%BoilerMedium Coal-fired 4.6E-05 1% 3.6E-05 1% 4.5E-05 1% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%Utility BoilerSmall Coal-fired Utility 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%BoilerMedium OIl-fired 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0%Utility BoilerChlor-alkali plant 1.1E-04 60% 8.4E-05 57% 5.5E-05 18% 4.3E-05 17% 4.8E-05 6% 3.8E-05 6%3-28Table 3-16Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Rural Home GardnerWestern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Rural Home Gardener2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 9.9E-06 7% 8.4E-06 4% 9.6E-06 4% 8.3E-06 2% 9.3E-06 2% 8.2E-06 1%Municipal WasteCombustorVariant b:Small 9.3E-06 2% 8.2E-06 1% 9.2E-06 1% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%Municipal WasteCombustorLarge Commercial HMI 9.3E-06 2% 8.2E-06 1% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%Large Hospital HMI 1.0E-05 8% 8.5E-06 4% 9.3E-06 2% 8.2E-06 1% 9.2E-06 0% 8.1E-06 0%Small Hospital HMI 9.2E-06 1% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%Large Hospital HMI 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%(wet scrubber)Small Hospital HMI 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%(wet scrubber)Large Coal-fired Utility 9.3E-06 1% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%BoilerMedium Coal-fired 9.2E-06 1% 8.1E-06 0% 9.2E-06 1% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%Utility BoilerSmall Coal-fired Utility 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%BoilerMedium OIl-fired 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0%Utility BoilerChlor-alkali plant 2.5E-05 63% 1.9E-05 57% 1.1E-05 20% 9.8E-06 17% 9.9E-06 7% 8.6E-06 6%3-29Table 3-16 (continued)Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Rural Home GardnerWestern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Rural Home Gardener2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.0E-05 7% 8.6E-06 4% 9.8E-06 4% 8.5E-06 2% 9.6E-06 2% 8.4E-06 1%Municipal WasteCombustorVariant b:Small 9.6E-06 2% 8.4E-06 1% 9.5E-06 1% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%Municipal WasteCombustorLarge Commercial HMI 9.6E-06 2% 8.4E-06 1% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%Large Hospital HMI 1.0E-05 8% 8.6E-06 4% 9.6E-06 2% 8.3E-06 1% 9.5E-06 0% 8.3E-06 0%Small Hospital HMI 9.5E-06 1% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%Large Hospital HMI 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%(wet scrubber)Small Hospital HMI 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%(wet scrubber)Large Coal-fired Utility 9.6E-06 1% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%BoilerMedium Coal-fired 9.5E-06 1% 8.3E-06 0% 9.5E-06 1% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%Utility BoilerSmall Coal-fired Utility 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%BoilerMedium OIl-fired 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0%Utility BoilerChlor-alkali plant 2.5E-05 62% 1.9E-05 57% 1.2E-05 19% 9.9E-06 17% 1.0E-05 7% 8.8E-06 6%3-30Table 3-17Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban AverageWestern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Urban Average2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 4.9E-07 75% 6.0E-08 75% 3.1E-07 60% 3.7E-08 60% 1.9E-07 35% 2.3E-08 35%Municipal WasteCombustorVariant b:Small 2.0E-07 39% 2.5E-08 39% 1.5E-07 15% 1.8E-08 15% 1.3E-07 5% 1.6E-08 5%Municipal WasteCombustorLarge Commercial HMI 2.0E-07 38% 2.4E-08 38% 1.3E-07 7% 1.6E-08 7% 1.3E-07 2% 1.5E-08 2%Large Hospital HMI 6.2E-07 80% 7.5E-08 80% 2.0E-07 38% 2.4E-08 38% 1.4E-07 13% 1.7E-08 13%Small Hospital HMI 1.6E-07 21% 1.9E-08 21% 1.3E-07 3% 1.5E-08 3% 1.2E-07 1% 1.5E-08 1%Large Hospital HMI 1.4E-07 10% 1.7E-08 10% 1.2E-07 1% 1.5E-08 1% 1.2E-07 0% 1.5E-08 0%(wet scrubber)Small Hospital HMI 1.2E-07 1% 1.5E-08 1% 1.2E-07 0% 1.5E-08 0% 1.2E-07 0% 1.5E-08 0%(wet scrubber)Large Coal-fired Utility 2.0E-07 37% 2.4E-08 37% 1.5E-07 16% 1.8E-08 16% 1.4E-07 14% 1.7E-08 14%BoilerMedium Coal-fired 1.6E-07 25% 2.0E-08 25% 1.5E-07 19% 1.8E-08 19% 1.4E-07 13% 1.7E-08 13%Utility BoilerSmall Coal-fired Utility 1.4E-07 15% 1.8E-08 15% 1.3E-07 7% 1.6E-08 7% 1.3E-07 3% 1.5E-08 3%BoilerMedium OIl-fired 1.3E-07 2% 1.5E-08 2% 1.2E-07 1% 1.5E-08 1% 1.2E-07 0% 1.5E-08 0%Utility BoilerChlor-alkali plant 4.0E-06 97% 4.9E-07 97% 5.8E-07 79% 7.0E-08 79% 2.4E-07 49% 2.9E-08 49%3-31Table 3-17 (continued)Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban AverageWestern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Urban Average2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 6.1E-07 61% 7.4E-08 61% 4.2E-07 44% 5.2E-08 44% 3.1E-07 22% 3.7E-08 22%Municipal WasteCombustorVariant b:Small 3.2E-07 25% 3.9E-08 25% 2.6E-07 9% 3.2E-08 9% 2.5E-07 3% 3.0E-08 3%Municipal WasteCombustorLarge Commercial HMI 3.2E-07 24% 3.8E-08 24% 2.5E-07 4% 3.0E-08 4% 2.4E-07 1% 2.9E-08 1%Large Hospital HMI 7.4E-07 67% 8.9E-08 67% 3.1E-07 24% 3.8E-08 24% 2.6E-07 7% 3.1E-08 7%Small Hospital HMI 2.7E-07 12% 3.3E-08 12% 2.4E-07 2% 3.0E-08 2% 2.4E-07 0% 2.9E-08 0%Large Hospital HMI 2.5E-07 6% 3.1E-08 6% 2.4E-07 1% 2.9E-08 1% 2.4E-07 0% 2.9E-08 0%(wet scrubber)Small Hospital HMI 2.4E-07 0% 2.9E-08 0% 2.4E-07 0% 2.9E-08 0% 2.4E-07 0% 2.9E-08 0%(wet scrubber)Large Coal-fired Utility 3.1E-07 23% 3.8E-08 23% 2.6E-07 9% 3.2E-08 9% 2.6E-07 8% 3.2E-08 8%BoilerMedium Coal-fired 2.8E-07 15% 3.4E-08 15% 2.7E-07 10% 3.2E-08 10% 2.6E-07 7% 3.1E-08 7%Utility BoilerSmall Coal-fired Utility 2.6E-07 8% 3.2E-08 8% 2.5E-07 4% 3.0E-08 4% 2.4E-07 1% 2.9E-08 1%BoilerMedium OIl-fired 2.4E-07 1% 2.9E-08 1% 2.4E-07 1% 2.9E-08 1% 2.4E-07 0% 2.9E-08 0%Utility BoilerChlor-alkali plant 4.1E-06 94% 5.0E-07 94% 7.0E-07 66% 8.5E-08 66% 3.6E-07 33% 4.4E-08 33%3-32Table 3-18Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban High EndWestern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Urban High End2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 1.8E-05 76% 3.1E-06 5% 1.1E-05 61% 3.1E-06 3% 6.9E-06 36% 3.0E-06 1%Municipal WasteCombustorVariant b:Small 7.3E-06 40% 3.0E-06 1% 5.2E-06 16% 3.0E-06 0% 4.7E-06 6% 3.0E-06 0%Municipal WasteCombustorLarge Commercial HMI 7.2E-06 39% 3.0E-06 1% 4.8E-06 8% 3.0E-06 0% 4.5E-06 2% 3.0E-06 0%Large Hospital HMI 2.3E-05 81% 3.2E-06 6% 7.2E-06 38% 3.0E-06 1% 5.1E-06 14% 3.0E-06 0%Small Hospital HMI 5.6E-06 22% 3.0E-06 0% 4.6E-06 3% 3.0E-06 0% 4.4E-06 1% 3.0E-06 0%Large Hospital HMI 4.9E-06 11% 3.0E-06 0% 4.5E-06 2% 3.0E-06 0% 4.4E-06 0% 3.0E-06 0%(wet scrubber)Small Hospital HMI 4.4E-06 1% 3.0E-06 0% 4.4E-06 0% 3.0E-06 0% 4.4E-06 0% 3.0E-06 0%(wet scrubber)Large Coal-fired Utility 7.1E-06 38% 3.0E-06 1% 5.3E-06 16% 3.0E-06 0% 5.2E-06 15% 3.0E-06 0%BoilerMedium Coal-fired 6.0E-06 26% 3.0E-06 0% 5.4E-06 19% 3.0E-06 0% 5.1E-06 13% 3.0E-06 0%Utility BoilerSmall Coal-fired Utility 5.2E-06 15% 3.0E-06 0% 4.8E-06 7% 3.0E-06 0% 4.5E-06 3% 3.0E-06 0%BoilerMedium OIl-fired 4.5E-06 2% 3.0E-06 0% 4.5E-06 1% 3.0E-06 0% 4.4E-06 0% 3.0E-06 0%Utility BoilerChlor-alkali plant 1.5E-04 97% 7.3E-06 59% 2.1E-05 79% 3.6E-06 18% 8.8E-06 50% 3.2E-06 6%3-33Table 3-18 (continued)Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban High EndWestern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Urban High End2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 2.3E-05 61% 3.2E-06 5% 1.6E-05 44% 3.1E-06 3% 1.1E-05 22% 3.1E-06 1%Municipal WasteCombustorVariant b:Small 1.2E-05 25% 3.1E-06 1% 9.6E-06 9% 3.1E-06 0% 9.0E-06 3% 3.1E-06 0%Municipal WasteCombustorLarge Commercial HMI 1.2E-05 24% 3.1E-06 1% 9.1E-06 4% 3.1E-06 0% 8.8E-06 1% 3.1E-06 0%Large Hospital HMI 2.7E-05 68% 3.2E-06 6% 1.1E-05 24% 3.1E-06 1% 9.4E-06 8% 3.1E-06 0%Small Hospital HMI 9.9E-06 12% 3.1E-06 0% 8.9E-06 2% 3.1E-06 0% 8.8E-06 0% 3.1E-06 0%Large Hospital HMI 9.3E-06 6% 3.1E-06 0% 8.8E-06 1% 3.1E-06 0% 8.7E-06 0% 3.1E-06 0%(wet scrubber)Small Hospital HMI 8.8E-06 0% 3.1E-06 0% 8.7E-06 0% 3.1E-06 0% 8.7E-06 0% 3.1E-06 0%(wet scrubber)Large Coal-fired Utility 1.1E-05 23% 3.1E-06 1% 9.6E-06 9% 3.1E-06 0% 9.5E-06 8% 3.1E-06 0%BoilerMedium Coal-fired 1.0E-05 15% 3.1E-06 0% 9.8E-06 11% 3.1E-06 0% 9.4E-06 7% 3.1E-06 0%Utility BoilerSmall Coal-fired Utility 9.5E-06 8% 3.1E-06 0% 9.1E-06 4% 3.1E-06 0% 8.8E-06 1% 3.1E-06 0%BoilerMedium OIl-fired 8.8E-06 1% 3.1E-06 0% 8.8E-06 1% 3.1E-06 0% 8.7E-06 0% 3.1E-06 0%Utility BoilerChlor-alkali plant 1.5E-04 94% 7.4E-06 58% 2.6E-05 66% 3.7E-06 17% 1.3E-05 34% 3.3E-06 6%3-34Table 3-19Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FisherWestern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Subsistence Fisher2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 7.2E-04 83% 5.2E-04 83% 4.5E-04 72% 3.3E-04 72% 2.3E-04 46% 1.7E-04 45%Municipal WasteCombustorVariant b:Small 2.8E-04 55% 2.0E-04 55% 1.6E-04 25% 1.2E-04 24% 1.3E-04 8% 9.9E-05 8%Municipal WasteCombustorLarge Commercial HMI 2.8E-04 56% 2.1E-04 55% 1.4E-04 13% 1.0E-04 13% 1.3E-04 3% 9.4E-05 3%Large Hospital HMI 1.1E-03 89% 8.3E-04 89% 2.6E-04 52% 1.9E-04 52% 1.5E-04 19% 1.1E-04 19%Small Hospital HMI 1.9E-04 35% 1.4E-04 35% 1.3E-04 6% 9.7E-05 6% 1.3E-04 1% 9.3E-05 1%Large Hospital HMI 1.5E-04 19% 1.1E-04 19% 1.3E-04 3% 9.4E-05 3% 1.2E-04 1% 9.2E-05 1%(wet scrubber)Small Hospital HMI 1.3E-04 1% 9.3E-05 1% 1.2E-04 0% 9.2E-05 0% 1.2E-04 0% 9.1E-05 0%(wet scrubber)Large Coal-fired Utility 2.5E-04 51% 1.9E-04 51% 1.6E-04 23% 1.2E-04 23% 1.5E-04 20% 1.1E-04 20%BoilerMedium Coal-fired 1.9E-04 35% 1.4E-04 35% 1.7E-04 27% 1.2E-04 26% 1.5E-04 18% 1.1E-04 18%Utility BoilerSmall Coal-fired Utility 1.6E-04 23% 1.2E-04 23% 1.4E-04 12% 1.0E-04 12% 1.3E-04 4% 9.5E-05 4%BoilerMedium OIl-fired 1.3E-04 2% 9.3E-05 2% 1.3E-04 2% 9.3E-05 2% 1.2E-04 1% 9.2E-05 1%Utility BoilerChlor-alkali plant 8.2E-03 98% 6.0E-03 98% 9.6E-04 87% 7.0E-04 87% 3.1E-04 60% 2.2E-04 59%3-35Table 3-19 (continued)Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Subsistence FisherWestern SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Subsistence Fisher2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large 8.7E-04 68% 6.4E-04 68% 6.1E-04 53% 4.4E-04 53% 3.9E-04 27% 2.8E-04 27%Municipal WasteCombustorVariant b:Small 4.3E-04 35% 3.2E-04 35% 3.2E-04 13% 2.4E-04 12% 2.9E-04 4% 2.2E-04 4%Municipal WasteCombustorLarge Commercial HMI 4.4E-04 36% 3.2E-04 35% 3.0E-04 6% 2.2E-04 6% 2.9E-04 1% 2.1E-04 1%Large Hospital HMI 1.3E-03 78% 9.5E-04 78% 4.2E-04 32% 3.1E-04 32% 3.1E-04 9% 2.3E-04 9%Small Hospital HMI 3.5E-04 19% 2.6E-04 19% 2.9E-04 3% 2.1E-04 3% 2.8E-04 1% 2.1E-04 1%Large Hospital HMI 3.1E-04 9% 2.3E-04 9% 2.9E-04 1% 2.1E-04 1% 2.8E-04 0% 2.1E-04 0%(wet scrubber)Small Hospital HMI 2.8E-04 1% 2.1E-04 1% 2.8E-04 0% 2.1E-04 0% 2.8E-04 0% 2.1E-04 0%(wet scrubber)Large Coal-fired Utility 4.1E-04 32% 3.0E-04 31% 3.2E-04 12% 2.3E-04 11% 3.1E-04 10% 2.3E-04 10%BoilerMedium Coal-fired 3.5E-04 19% 2.6E-04 19% 3.3E-04 14% 2.4E-04 14% 3.1E-04 9% 2.3E-04 9%Utility BoilerSmall Coal-fired Utility 3.2E-04 12% 2.3E-04 11% 3.0E-04 6% 2.2E-04 6% 2.9E-04 2% 2.1E-04 2%BoilerMedium OIl-fired 2.9E-04 1% 2.1E-04 1% 2.8E-04 1% 2.1E-04 1% 2.8E-04 0% 2.1E-04 0%Utility BoilerChlor-alkali plant 8.3E-03 97% 6.1E-03 97% 1.1E-03 75% 8.2E-04 75% 4.7E-04 39% 3.4E-04 39%3-36Table 3-20Western Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Recreational AnglerWestern SiteRELMAP 50th percentilePredicted Ingestion (mg/kg/day) for Recreational Angler2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 2.6E-04 83% 1.6E-04 73% 8.1E-05 47%Variant b:Small Municipal Waste Combustor 9.9E-05 57% 5.8E-05 26% 4.7E-05 8%Large Commercial HMI 1.0E-04 57% 4.9E-05 13% 4.4E-05 3%Large Hospital HMI 4.1E-04 90% 9.2E-05 54% 5.4E-05 20%Small Hospital HMI 6.8E-05 37% 4.6E-05 6% 4.3E-05 1%Large Hospital HMI (wet scrubber) 5.3E-05 20% 4.4E-05 3% 4.3E-05 1%Small Hospital HMI (wet scrubber) 4.4E-05 2% 4.3E-05 0% 4.3E-05 0%Large Coal-fired Utility Boiler 9.0E-05 53% 5.6E-05 24% 5.4E-05 21%Medium Coal-fired Utility Boiler 6.7E-05 36% 5.9E-05 28% 5.3E-05 19%Small Coal-fired Utility Boiler 5.6E-05 24% 4.9E-05 12% 4.5E-05 4%Medium OIl-fired Utility Boiler 4.4E-05 2% 4.4E-05 2% 4.3E-05 1%Chlor-alkali plant 3.0E-03 99% 3.5E-04 88% 1.1E-04 61%Western SiteRELMAP 90th percentilePredicted Ingestion (mg/kg/day) for Recreational Angler2.5km 10 km 25kmChild Adult Child Adult Child AdultValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 3.2E-04 68% 2.2E-04 54% 1.4E-04 27%Variant b:Small Municipal Waste Combustor 1.6E-04 36% 1.2E-04 13% 1.0E-04 4%Large Commercial HMI 1.6E-04 36% 1.1E-04 6% 1.0E-04 1%Large Hospital HMI 4.7E-04 79% 1.5E-04 33% 1.1E-04 10%Small Hospital HMI 1.3E-04 20% 1.0E-04 3% 1.0E-04 1%Large Hospital HMI (wet scrubber) 1.1E-04 10% 1.0E-04 1% 1.0E-04 0%Small Hospital HMI (wet scrubber) 1.0E-04 1% 1.0E-04 0% 1.0E-04 0%Large Coal-fired Utility Boiler 1.5E-04 32% 1.1E-04 12% 1.1E-04 10%Medium Coal-fired Utility Boiler 1.3E-04 20% 1.2E-04 14% 1.1E-04 9%Small Coal-fired Utility Boiler 1.1E-04 12% 1.1E-04 6% 1.0E-04 2%Medium OIl-fired Utility Boiler 1.0E-04 1% 1.0E-04 1% 1.0E-04 0%Chlor-alkali plant 3.0E-03 97% 4.1E-04 75% 1.7E-04 40%3-373-38Table 3-21Eastern Site — RELMAP 50th and 90th PercentilesPredicted InhalationEastern SitePredicted Inhalation for Eastern SiteRELMAP 50th percentile 2.5km 10km 25kmChild Adult Full time A d u l t Child Adult Full time A d u l t Child Adult Full time A d u l tPart time Part time Part timeValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 1.6E-06 3% 4.9E-07 3% 3.3E-07 3% 1.6E-06 2% 4.9E-07 2% 3.3E-07 2% 1.6E-06 1% 4.8E-07 1% 3.2E-07 1%Variant b:Small Municipal Waste Combustor 1.6E-06 1% 4.8E-07 1% 3.2E-07 1% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Large Commercial HMI 1.6E-06 1% 4.8E-07 1% 3.2E-07 1% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Large Hospital HMI 1.6E-06 3% 4.9E-07 3% 3.3E-07 3% 1.6E-06 1% 4.8E-07 1% 3.2E-07 1% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Small Hospital HMI 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Large Hospital HMI (wet scrubber) 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Small Hospital HMI (wet scrubber) 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Large Coal-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Medium Coal-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Small Coal-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Medium OIl-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0%Chlor-alkali plant 3.7E-06 58% 1.1E-06 58% 7.6E-07 58% 2.0E-06 21% 6.0E-07 21% 4.0E-07 21% 1.7E-06 8% 5.2E-07 8% 3.4E-07 8%Eastern SitePredicted Inhalation for Eastern SiteRELMAP 90th percentile 2.5km 10km 25kmChild Adult Full time A d u l t Child Adult Full time A d u l t Child Adult Full time A d u l tPart time Part time Part timeValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 1.7E-06 3% 5.1E-07 3% 3.4E-07 3% 1.7E-06 2% 5.0E-07 2% 3.4E-07 2% 1.6E-06 1% 5.0E-07 1% 3.3E-07 1%Variant b:Small Municipal Waste Combustor 1.6E-06 1% 5.0E-07 1% 3.3E-07 1% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Large Commercial HMI 1.6E-06 1% 5.0E-07 1% 3.3E-07 1% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Large Hospital HMI 1.7E-06 3% 5.1E-07 3% 3.4E-07 3% 1.6E-06 1% 5.0E-07 1% 3.3E-07 1% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Small Hospital HMI 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Large Hospital HMI (wet scrubber) 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Small Hospital HMI (wet scrubber) 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Large Coal-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Medium Coal-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Small Coal-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Medium OIl-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0%Chlor-alkali plant 3.8E-06 57% 1.1E-06 57% 7.7E-07 57% 2.0E-06 21% 6.2E-07 21% 4.1E-07 21% 1.8E-06 8% 5.3E-07 8% 3.5E-07 8%3-39Table 3-22Western Site — RELMAP 50th and 90th PercentilesPredicted InhalationWestern SitePredicted Inhalation for Western SIteRELMAP 50th percentile 2.5km 10km 25kmChild Adult Full time Adult Part time Child Adult Full time Adult Part time Child Adult Full time Adult Part timeValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 1.6E-06 2% 4.7E-07 2% 3.2E-07 2% 1.5E-06 2% 4.7E-07 2% 3.1E-07 2% 1.5E-06 1% 4.7E-07 1% 3.1E-07 1%Variant b:Small Municipal Waste Combustor 1.5E-06 1% 4.6E-07 1% 3.1E-07 1% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Large Commercial HMI 1.5E-06 1% 4.7E-07 1% 3.1E-07 1% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Large Hospital HMI 1.6E-06 2% 4.7E-07 2% 3.2E-07 2% 1.5E-06 1% 4.6E-07 1% 3.1E-07 1% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Small Hospital HMI 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Large Hospital HMI (wet scrubber) 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Small Hospital HMI (wet scrubber) 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Large Coal-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Medium Coal-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Small Coal-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Medium OIl-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0%Chlor-alkali plant 3.3E-06 54% 1.0E-06 54% 6.8E-07 54% 1.8E-06 16% 5.5E-07 16% 3.7E-07 16% 1.6E-06 6% 4.9E-07 6% 3.3E-07 6%Western SItePredicted Inhalation for Western SIteRELMAP 90th percentile 2.5km 10km 25kmChild Adult Full time Adult Part time Child Adult Full time Adult Part time Child Adult Full time Adult Part timeValue %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISCVariant b:Large Municipal Waste Combustor 1.6E-06 2% 4.8E-07 2% 3.2E-07 2% 1.6E-06 2% 4.8E-07 2% 3.2E-07 2% 1.6E-06 1% 4.7E-07 1% 3.2E-07 1%Variant b:Small Municipal Waste Combustor 1.6E-06 1% 4.7E-07 1% 3.1E-07 1% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Large Commercial HMI 1.6E-06 1% 4.7E-07 1% 3.2E-07 1% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Large Hospital HMI 1.6E-06 2% 4.8E-07 2% 3.2E-07 2% 1.6E-06 1% 4.7E-07 1% 3.1E-07 1% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Small Hospital HMI 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Large Hospital HMI (wet scrubber) 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Small Hospital HMI (wet scrubber) 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Large Coal-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Medium Coal-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Small Coal-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Medium OIl-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0%Chlor-alkali plant 3.4E-06 54% 1.0E-06 54% 6.8E-07 54% 1.8E-06 16% 5.6E-07 16% 3.7E-07 16% 1.6E-06 6% 5.0E-07 6% 3.3E-07 6%3-40 fruiting vegetables the bulk of mercury is also modeled to be the result of uptake of mercury from the atmosphere into the plant. Although not shown in the tables below, divalent mercury accounts for approximately 90% of the total mercury intake for the agricultural scenarios, with the rest being methylmercury. This partitioning is reflective of the predicted speciation of mercury in the ingested plant and animal products. The differences between facilities are due to differences in parameters that affect effective stack height, and the assumed total mercury emission rate. The speciation of mercury emissions is not an important factor because the speciation only affects the predicted deposition rates, not the total mercury air concentrations.
3.2.3 Urban Scenarios With the exception of the child exhibbiting pica behavior in this scenario (urban high end child), the predicted mercury exposures in the urban scenarios are generally an order of magnitude lower than those for the agricultural scenarios. This reflects the lower ingestion rates assumed for locally grown plant products. As for the agricultural scenarios, divalent mercury is the primary form of mercury to which they receptors are exposed. The larger contribution of the local sources in these scenarios reflects the fact that only for the urban high end is consumption of plant products assumed: for the other urban scenarios exposure to mercury from the local source is assumed to be solely through ingestion of soil. The contributions of the local source shown for the urban scenarios thus reflect the contribution of the local source on the soil concentrations, which themselves are driven by the mercury deposition rates. The mercury deposition rates are generally driven by the assumed speciation of mercury emissions. The contribution of the local source when pica behaviour is exhibbited (urban high end child) reflects the contribution of the local source to the soil concentration. 3.2.4 Fish Ingestion Scenarios The predicted mercury exposure in the fish ingestion scenarios (high-end fisher and recreational angler) is dominated by exposure through ingestion of fish, even though some exposure through ingestion of plant products is also assumed. Methylmercury is the primary form of mercury to which these receptors are exposed. The fish concentrations are driven by the predicted dissolved methylmercury concentrations in the surface water, which themselves are driven by the watershed soil concentrations and the waterbody atmospheric mercury deposition rate. For several of the facilities at both the eastern and western sites, the majority of the exposure to mercury is predicted to be due to the local source for the waterbody located 2.5 km from the facility. This is also true for some facilities at both 10 km and 25 km. These results reflect the contribution of the local source to total mercury deposition onto the waterbody and the watershed soils. The contribution of the local source is larger at the western site because both the regional and pre-industrial deposition rates are lower than at the eastern site, while the results for the local source (using ISC) are more similar. However, the total mercury exposure is approximately twice as low at the drier western site compared to the eastern site due primarily to differences in meteorology.3-41 3.3 Issues Related to Predicted Mercury Exposure Estimates In the modeling effort exposure for six different hypothetical adult humans was modeled. Atmospheric emissions of anthropogenic origin, local background and regional atmospheric mercury may not be the only sources of mercury exposure. Individuals can be exposed to mercury from other sources such as occupation and consumption of non-local (e.g., marine) fish. Quantitative estimates of these sources are presented in the following chapters of this Volume. This section considers the logic of adding exposure from these additional sources in an assessment. Occupational mercury may be an important source of exposure. This source may apply to any hypothetical adult modeled here with the exception of the subsistence farmer. For a given area with a relevant industrial base, it may be appropriate to consider these exposures for members of the population, when assessing mercury exposures. In the modeling effort several hypothetical humans were assumed not to consume locally-caught fish. These hypothetical individuals include: a subsistence farmer and child, a rural home gardener, and the urban dwellers. For these hypothetical individuals, it is reasonable to assume that some fraction of the individuals they represent will consume marine fish. For this marine fish consuming subset, the ranges of methylmercury exposure from marine fish consumption that are estimated later in this Volume are applicable. Methylmercury from marine fish consumption, if considered, is an incremental increase over the estimated intakes. In the modeling effort several hypothetical individuals were assumed to consume high levels of locally caught fish. These individuals include: an angler, who is assumed to consume 60 grams of local fish/day, a child, who is assumed to consume 20 grams of local fish/day and a recreational angler who is assumed to consume 30 grams fish/day. Since these hypothetical individuals consume high levels of local fish, it is probably inappropriate to consider exposure through an additional fish consumption pathway. Although it is reasonable to assume that some individuals consume both local and other fish; for example, Fiore et al. (1989) documented the consumption of both self-caught and purchased fish in U.S. anglers, these data are not combined in this assessment. The initial conditions assumed before the facility is modeled (referred to here as “background”) are potentially critical to the total mercury exposure. This is particularly important because the magnitude of the contribution of a local source to the total may be used to assess its impact. A delicate balance is required when including such a “background” in the analysis. This is because it is not just a matter of a local source’s contribution to this background, but the total impact of background plus the local source that is ultimately the primary concern. Overestimating the background will result in a concurrent decrease in the contribution of a given local source, but may result in exceeding thresholds that would not be exceeded if lower estimates of background are assumed. Resolution of this issue is not within the objectives of the current report; it is noted, however, that there is no available guidance on how to incorporate background in exposure assessment. For a local scale mercury exposure assessment it is important to measure mercury concentrations in various media. The impact of the uncertainty in the predicted air concentrations and deposition rates for each facility is most important for the fish ingestion and pica child scenarios. This is because, in general, the local source does not contribute significantly to the mercury exposure for the agricultural and urban scenarios. The exception to this pattern is the chlor-alkali model plant. In this case, the low assumed mercury release height results in the facility having a substantial impact on the mercury air concentrations close to the facility.3-42 3.4 Summary Conclusions

 The contribution of the local source, compared to background and the regional contribution, is larger at the western, drier site than at the eastern site. This is because both the regional impact and background values are much lower at the western site than is prdicted to occur for the local source. However, the magnitude of the total exposure at the western site is about half that at the eastern site due to the drier meteorology at the western site.

 For the agricultural scenarios, it is generally the background or regional sources that account for the majority of total mercury exposure. This is because the dominant pathway of mercury exposure in these scenarios is the ingestion of plants, which accumulate most of their mercury from the air, and most of the local sources are predicted to have little impact on the local average air concentrations compared to the regional sources.

 Most of the mercury to which the hypothetical receptor is exposed in the agricultural and urban scenarios is divalent mercury. This is because most of the mercury in plants and soil is predicted to be of this form. In contrast, in the fish ingestion scenairos methylmercury is the primary form of mercury to which the receptor is exposed.

 For the fish ingestion scenarios, the local sources are predicted to account for the majority of the total mercury exposure for waterbodies close to the facility. This is particularly true for the western site, where the background and regional contribution tothe total mercury deposition are lower.4-1 

### 4. POPULATION EXPOSURE — FISH CONSUMPTION 4.1 Fish Consumption among the General U.S. Population
Fish bioaccumulate meth y lmercur y throu g h the freshwater a q uatic and marine food-chains. Mercur y -contaminated p h y to p lankton and zoo p lankton are consumed b y p lanktivorous fish (referred to in other p arts of this Volume at tro p hic level 3 fish). Methy lmercury is thou g ht to bioaccumulate in this g rou p as well as in the p iscivorous fish. Both marine and freshwater fish bioaccumulate methy lmercur y in their muscle tissue. Consum p tion of these meth y lmercur y -contaminated fish results in ex p osures to human p o p ulations. Additional data have become available between 1995 and 1997 that p ermit estimates of mercur y consump tion from marine mammals and birds b y p o p ulations livin g in the far Northern latitudes.
Consum p tion of fish is hi g hl y variable across the U.S. p o p ulation unlike consum p tion of other dietar y comp onents, such as bread or starch, that are almost ubi q uitousl y consumed. This cha p ter p resents an estimate of the ma g nitude of fish consump tion in both the g eneral U.S. p o p ulation and in s p ecific sub p o p ulations (e. g ., children and women of child-bearin g a g e). This estimate identified the p ortion of the p o p ulation that consumes fish and shellfish. It also p rovides estimates of s p ecies of fish consumed and the q uantit y of fish consumed based on cross-sectional surve y data. Use of a national data base differentiates data in this Cha p ter from site-s p ecific assessments. Data p resented in this Cha p ter differ from site-s p ecific assessments in which consum p tion of contaminated local freshwater fish are included.
Inclusion of fish in the diet varies with g eo g rap hic location, seasons of the y ear, ethnicit y , and p ersonal food p references. Data on fish consum p tion have been calculated t yp icall y as either on “ p er ca p ita” or “ p er user” basis. The former term is obtained b y dividin g the su pp l y of fish across an entire p o p ulation to establish a “ p er ca p ita” consump tion rate. The latter term divides the su pp l y of fish across onl y the p ortion of the p o p ulation that consumes fish, p rovidin g “ p er user” rates of consump tion.
Identif y in g differences in fish consump tion rates for p o p ulation g rou p s can be achieved throu g h anal y sis of dietar y surve y data for the g eneral United States p o p ulation and s p ecified sub p o p ulations; e. g ., some Native American tribes, recreational an g lers, women of childbearin g a g e, and children. The United States De p artment of A g riculture (USDA) has conducted a series of nationall y -based dietar y surve y s, includin g the 1977-1978 Nationwide Food Consum p tion Surve y and the Continuin g Surve y s of Food Intake b y Individuals (CFSII) over the p eriod 1989 throu g h 1995 (CFSII 89-91; CSFII, 1994; CFSII, 1995). In addition, data from the third National Health and Nutrition Examination Surve y (NHANES III), conducted between 1988 and 1994, p rovide estimates of fish consum p tion p atterns in the earl y 1990s. Anal y ses of fish consum p tion p atterns amon g the g eneral U.S. p o p ulation and selected a g e/ g ender g rou p in g s are described below. Fish consump tion rate data from s p ecific Native American tribes and an g lin g p o p ulations are identified and used to corroborate the nationwide fish consum p tion data.
4.1.1 Patterns of Fish Consump tion
Althou g h the consump tion fre q uenc y of fish is low com p ared with stap le foods such as g rain p roducts, dietar y intake of fish can be estimated from surve y data. The initial issue of how to estimate fish consum p tion de p ends to a g reat extent on the choice of dietar y assessment method. Available techni q ues include lon g -term dietar y histories, q uestionnaires to identif y t yp ical food intake or short-term dietar y recall techni q ues and q uestionnaires on food fre q uenc y . The first consideration is to obtain dietar y information that reflects t yp ical fish consump tion. A true estimate of meth y lmercur y intake from fish is com p licated b y chan g es in fish intake over time, differences in s p ecies of fish consumed, variation in the meth y lmercur y concentration in a s p ecies of fish, and broad chan g es in the sources of fish enterin g the U.S. market p lace. For exam p le, increases in a q uaculture or fishfarmin g and increased reliance on im p orted fish for domestic consum p tion ma y affect consump tion estimates. Tem p oral variation in dietar y p atterns is an issue to4-2 consider in the evaluation of short-term recall/record data. For e p idemiolo g ical studies that seek to understand the relationshi p of lon g -term dietar y p atterns to chronic disease, t yp ical food intake is the relevant p arameter to evaluate (Willett, 1990).
Because meth y lmercur y is a develo p mental toxin that ma y p roduce adverse effects followin g a com p arativel y brief ex p osure p eriod (i.e., a few months rather than decades), com p arativel y short-term dietar y p atterns can have im p ortance. Conse q uentl y , estimation of recent p atterns of meth y lmercur y consum p tion from fish is the relevant ex p osure for the health end p oint of concern. Because it is not p ossible to p recisel y identif y the p eriod of develo p ment durin g which mercur y is likel y to dama g e the nervous s y stem of the develo p in g fetus or g rowin g child, ex p osure of women of childbearin g a g e or y our children to mercury via consump tion of fish is a cause for concern.
This chap ter describes the distribution of fish intakes for the g eneral p o p ulation and for sub p o p ulations defined b y a g e or g ender; e. g ., women of child-bearin g a g e. Estimates of the number of women who are p re g nant in an y g iven y ear are based on methods shown in A pp endix B. The anal y sis is not intended to estimate fish consum p tion b y an individual and relate it to an individual’s health outcomes. Dietar y q uestionnaires or dietary histories ma y identif y broad p atterns of fish consump tion, but these techni q ues p rovide less s p ecific recollection of foods consumed such as the s p ecies of fish eaten. Likewise estimates of the q uantit y of fish consumed become less p recise as the eatin g event becomes more remote in time. The selection of a dietary surve y method to describe fish intakes b y the sub p o p ulation of interest re q uires a balancin g the s p ecificit y of information collected with the g eneralization of short-term dietary p atterns to lon g er-term food intakes.
After the app ro p riate p eriod of fish intake is selected, the second area of concern becomes the variation in the meth y lmercur y concentrations of the fish consumed. A central feature of food intake amon g sub j ects with a free choice of foods is the da y -to-da y variabilit y in foods consumed su p erim p osed on an underl y in g food intake p attern (Willett, 1990). In e p idemiolo gy studies, an individual's true intake of a food such as fish could be considered as the mean intake for a lar g e number of da y s. Collectivel y , the true intakes b y these individuals define a fre q uenc y distribution for the stud y p o p ulation as a whole (Willett, 1990). It is rarel y p ossible to measure a lar g e number of da y s of dietar y intake for individual sub j ects; conse q uentl y , a sam p le of one or several da y s is used to re p resent the true intake (Willett, 1990). The effect of this sam p lin g is to increase artificall y the standard deviation, i.e., to broaden the tails of the distribution (Willett, 1990). This results in estimates of intake that are both lar g er and smaller than the true lon g -term avera g es for an y sub j ect. Overall, authorities in nutritional e p idemiolo gy (amon g others see Willett, 1990) conclude that "measurements of dietar y intake based on a sin g le or small number of 24-hour recalls p er sub j ect ma y p rovide a reasonable (unbiased) estimate of the mean of a g rou p , but the standard deviation will be g reatl y overestimated."
Assessment of recent dietar y intakes can be achieved throu g h dietar y records for various p eriods (typ icall y 7-da y records or 3-da y records) or dietar y recall (t yp icall y 24-hour recalls or 3-da y recalls) (amon g others see Witschi, 1990). Questions on food fre q uenc y in dietar y histories can be used to estimate how often a p o p ulation consumes fish and shellfish. Research is currentl y in p ro g ress to estimate usual intake distributions that account for intake data of foods that are not consumed on a dail y basis (amon g others see Nusser et al. 1996). In 1996, Nusser et al. p ublished a statistical app roach to estimatin g moderate-term (e. g ., months) p atterns of food consump tion based on multi p le 24-hour dietar y recalls obtained from the same individual.
Sources of error in short-term recalls and records affect all dietar y surve y methodolo g ies. These include errors made b y the res p ondent or recorder of dietar y information as well as the interviewer or reviewer. Information used to calculate the intake of the chemical of interest is another source of error. The detection limit of the anal y te, the fre q uenc y of zero and trace values, and how such values are mana g ed can statisticall y influence the accurac y of the mean mercur y concentration for a fish s p ecies. The third source of error in dietar y assessments is the data base used to calculate intakes of the chemical from the food consumed, for examp le the data ma y no lon g er reflect current concentrations of the chemical in foods.4-3 The abilit y of the sub j ect to remember the food consumed and in what q uantities it was consumed is central to these methods (amon g man y others see Witschi, 1990). In an anal y sis of data from the National Health and Nutrition Evaluation Surve y (NHANES), the lar g est source of error was uncertaint y of sub j ects about foods consumed on the recall da y (Youland and En g le, 1976). Fish consum p tion app ears to be more accuratel y remembered than most other food g rou p s. Karvetti and Knuts (1985) observed the actual intake of 140 sub j ects and later interviewed them b y 24-hour recall. The y found that fish was omitted from the dietar y recall less than 5% of the time and erroneousl y recalled a pp roximatel y 7% of the time. The validit y of 24-hour recalls for fish consum p tion was g reater than all other food g rou p s. Interviewer and reviewer errors can be reasonabl y p redicted to be consistent for a g iven surve y and unlikel y to affect re p ortin g of fish consump tion selectivel y . 4.1.1.1 Estimates of Fish Intake for Po p ulations
Data on fish consum p tion have been calculated t yp icall y as either " p er ca p ita" or " p er user". The former term is obtained b y dividin g the su pp l y of fish across an entire p o p ulation to establish a " p er cap ita" consum p tion rate. The latter term divides the su pp l y of fish across onl y the p ortion of the p o p ulation that consumes fish; i.e., " p er user" rates of consump tion.
Surve y methods can broadl y be classified into lon g itudinal methods or cross-sectional surve y s. T yp icall y lon g -term or lon g itudinal estimates of intake can be used to reflect p atterns for individuals (e. g ., dietar y histories); or lon g itudinal estimates of moderate duration (e. g ., month-lon g p eriods) for individuals or g rou p s. Cross-sectional data are used to g ive a "sna p shot" in time and are typ icall y used to p rovide information on the distribution of intakes for g rou p s within the p o p ulation of interest. Cross-sectional data t yp icall y are for 24-hour or 3-da y samp lin g p eriods and consist of recall of foods consumed in res p onse to q uestionin g b y a trained interviewer, or the y ma y be taken from written records of foods consumed.
Durin g the p ast decade, reviewers of dietary surve y methodolo gy (for exam p le, the Food and Nutrition Board of the National Research Council/National Academy of Sciences; the Life Sciences Research Office of the Federation of American Societies of Ex p erimental Biolo gy ) have evaluated various techni q ues with re g ard to their suitabilit y for estimatin g ex p osure to contaminants and intake of nutrients. The Food and Nutrition Board of the National Research Council/National Academ y of Sciences in their 1986 p ublication on Nutrient Adequacy Assessment Using Food Consumption Surveys noted that dietar y intake of an individual is not constant from da y to da y , but varies on a dail y basis both in amount and in typ e of foods eaten (intraindividual variation). Variations between p ersons in their usual food intake avera g ed over time is referred to as interindividual variation. Amon g North American p o p ulations, the intraindividual variation is usuall y considered to be as lar g e as or g reater than the interindividual variation. Havin g evaluated a number of data sets, the Academ y 's Subcommittee concluded that three da y s of observation may be more than is re q uired for the derivation of the distribution of usual intakes.
Ma j or sources of data on dietar y intake of fish used in p re p arin g this Re p ort to Con g ress are the cross-sectional data from the USDA CSFII conducted from 1989 throu g h 1995 (CSFII 89-91; CSFII 1994; and CSFII 1995); on cross-sectional data from the NHANES III conducted between 1988 and 1994; and the lon g er-term data on fish consum p tion based on recorded fish consum p tion for various numbers of one-month p eriods of time durin g the y ears 1973-1974 b y the National Purchase Diar y (NPD 73-74) conducted b y the Market Research Corp oration. Lon g er-term data on fish consum p tion has also been obtained from q uestions on fre quenc y of fish consum p tion that were included in the NHANES III survey and in CSFII 1994 and CSFII 1995.
Identif y in g differences in fish consump tion rates for p o p ulation g rou p s can be achieved throu g h anal y sis of dietar y surve y data for the g eneral U.S. p o p ulation and s p ecified sub p o p ulations; e. g ., some tribes of Native Americans includin g Alaskan tribes, and recreational an g lers. The USDA has conducted a series of nationall y -based dietar y surve y s includin g the 1977-1978 Nationwide Food Consum p tion Surve y and the Continuin g Surve y s of Food Intake b y Individuals over the p eriod 1989 throu g h 1991 (CSFII 89-91, CSFII4-4 1994, and CSFII 1995), as well as the National Center for Health Statistics stratified p o p ulation based examination surve y conducted between 1988 and 1994 (NHANES III). Anal y ses of fish consump tion p atterns amon g the g eneral U.S. p o p ulation are described below.
4.1.1.2 Estimates of Month-Lon g Fish and Shellfish Consump tion from Cross-sectional Data
The adverse develo p mental effects of meth y lmercur y in g estion are closel y associated with the cumulative q uantit y of meth y lmercur y consumed. The p eriod of develo p ment that is critical to the ex p ression of adverse develo p mental effects is not known with p recision. In humans, the critical ex p osure p eriod is thou g ht to be comp arativel y short-term based on the methy lmercur y p oisonin g outbreak in Ira qand various case re p orts of in utero meth y lmercur y p oisonin g (see the Human Health and Risk Characterization Volumes for additional information). Conse q uentl y , it is im p ortant to be able to p redict moderate-term ex p osures from cross-sectional data on meth y lmercur y ex p osure. Estimates of a sin g le da y ’s ex p osure to meth y lmercur y can be calculated from 24-hour recall data. The q uantit y of fish/shellfish ( p ortion size) and s p ecies of fish/shellfish consumed b y an individual over a da y can be used to calculate dail y intake of fish/shellfish. The 24-hour recall data describe p ortion size and s p ecies of fish consumed. B y includin g the amount of mercur y p resent in this amount of fish, an estimate of mercur y in g estion can be made. This p rovides the distribution of mercur y intakes for a 24-hour or 1-da y p eriod. Dividin g total mercur y intake p er da y b y the p erson’s bod y wei g ht p ermits calculation of μ g H g /k g bw /da y . Rankin g these estimates b y increasin g q uantit y p ermits identification of various p ercentiles; e. g ., 50th, 90th, 95th, etc. These rankin g s are the basis for “ p er user” p ercentiles.
The p ro j ection of dail y dietar y ex p osure to meth y lmercur y (i.e., μg /k g bw /da y ) to ex p osure for a moderate p eriod of time (e. g ., months) has been a well-reco g nized com p lication of usin g dietar y data. If multi p le 24-hour recall data for an individual are available, Nusser et al. (1996) have described a statistical method for p ro j ectin g moderate-term dietar y intakes. Publication of this methodolo gy is com p arativel y recent and the com p uter software/hardware re q uirements for these statistical anal y ses are somewhat com p lex. Conse q uentl y , another app roach for p ro j ectin g month-lon g fish/shellfish consump tion and meth y lmercur y ex p osures was needed.
The number of da y s p er month that an individual consumes meth y lmercur y from diet can be estimated from data on fre q uenc y of fish/shellfish consump tion. The NHANES III included q uestions on how often p er da y /week/month, over the p ast 12-months, an individual consumed fish and shellfish. These data are described below (Section 4.1.2.2) for p ersons 12 y ears of a g e and older. Children under 12 y ears-ofa g e were not p art of the res p ondents in NHANES III who were asked about fre q uenc y of fish and shellfish consum p tion. Accordin g l y , the authors of this re p ort have made the sim p lif y in g assump tion that the fre q uenc y of fish consum p tion for adults from the same ethnic, racial, and economic g rou p s can be app lied to estimates of fish and shellfish intake for children. Estimates of mercur y ex p osure based on a sin g le da y ’s intake ( μg /k g bw/da y ) s p ecific for individual child surve y p artici p ants were available from the 24-hour recall data in NHANES III. These data and the adult’s fre q uenc y of fish consump tion data were used to estimate month-lon g p ro j ections of meth y lmercur y ex p osures for children. 4.1.1.3 1973 and 1974 National Purchase Diary Data The National Purchase Diary 1973-74 (NPD 73-74) data are based on a sam p le of 7,662 families (25,165 individuals) out of 9,590 families samp led between Se p tember 1973 and Au g ust 1974 (SRI International Contract Re p ort to U.S. EPA, 1980; Ru pp et al., 1980). Available rep orts are not entirel y clear on how the subsam p le of 7,662 was chosen. Fish consump tion was based on q uestionnaires comp leted b y4-5 the female head of the household in which she recorded the date of an y meal containin g fish, the t yp e of fish (s p ecies), the p acka g in g of the fish (canned, frozen, fresh, dried, or smoked, or eaten out), whether fresh fish was recreationall y cau g ht or commerciall y p urchased, the amount of fish p re p ared for the meal, the number of servin g s consumed b y each famil y member and an y g uests, and the amount of fish not consumed durin g the meal. Meals eaten both at home and awa y from home were recorded. Ninety -four p ercent of the res p ondents re p orted consumin g seafood durin g the sam p lin g p eriod.
Use of these data to estimate intake of fish or mercur y on a bod y wei g ht basis is limited b y the followin g data g ap s: 1. This surve y did not include data on the q uantit y of fish re p resented b y a servin g and information to calculate actual fish consum p tion from entries described as breaded fish or fish mixed with other in g redients. Portion size was estimated by usin g avera g e p ortion size for seafood from the USDA Handbook # 11, Table 10, p a g e 40-41. The avera g e servin g sizes from this USDA source are shown in Table 4-1. Table 4-1 Average Serving Size (gms) for Seafood from USDA Handbook # 11 Used to Calculate Fish Intake by FDA (1978) Age Group Male Female (years) Subjects Subjects (gms) (gms) 0-1 20 20 1-5 66 66 6-11 95 95 12-17 131 100 18-54 158 125 55-75 159 130 Over 75 180 139

2. There ma y have been s y stematic under-recordin g of fish intake as it was noted that typ ical intakes declined 30% between the first surve y p eriod and the last surve y p eriod amon g p ersons who comp leted four surve y diaries (Cris p in-Smith et al., 1985).
3. There have been chan g es in the q uantities and t yp es of fish consumed between 1973-1974 and p resent. The USDA (Putnam, 1991) indicated that, on avera g e, fish consump tion increased 27% between 1970 to 1974 and 1990. This increase is also noted b y the National Academ y of Sciences in Seafood Safety (1991). Whether or not this increase a pp lies to the hi g hest p ercentiles of fish consump tion (e. g ., 95th or 99th p ercentile) was not described in the USDA p ublication.
Chan g es in the t yp es of fish consumed have been noted. For exam p le, Heuter et al. (1995) noted that there is currentl y a much g reater U.S. consump tion of shark comp ared to p ast decades.4-6 4. Althou g h the NPD data with the sam p le wei g hts were used to p ro j ect these data to the g eneral U.S. p o p ulation (SRI International under U.S. EPA Contract 68-01-3887), in 1980, U.S. EPA was subse q uentl y informed that the samp le wei g hts were not lon g er available. Conse q uentl y , additional anal y ses with these data, in a manner than can be p ro j ected to the g eneral p o p ulation, no lon g er app ear to be p ossible.
5. Bod y wei g hts of the individuals surve y ed do not app ear in p ublished materials. If bod y wei g hts of the individuals p artici p atin g in this surve y were recorded these data do not a pp ear to have been used in subse q uent anal y ses. Data on fish consump tion from the NPD 73-74 surve y have been p ublished b y Ru pp et al. (1980) and anal y zed b y U.S. EPA's contractor SRI International (1980). These data indicate that when a month-lon g surve y p eriod is used, 94% of the surve y ed p o p ulation consumed fish. The s p ecies of fish most commonl y consumed are shown in Table 4-2.

Table 4-2 Fish Species and Number of Persons Using the Species of Fish. (Adapted from Rupp et al., 1980)
Category Number of Individuals Consuming Fish Based on 24,652 Replies* Tuna, li g ht 16,817 Shrim p 5,808 Flounders 3,327 Not re p orted (or identified) 3,117 Perch (Marine) 2,519 Salmon 2,454 Clams 2,242 Cod 1,492 Pollock 1,466 * More than one s p ecies of fish ma y be eaten b y an individual. Ru pp et al. (1980) also estimated q uantities of fish and shellfish consumed b y teena g ers a g ed 12-18 y ears and b y adults a g ed 18 to 98 y ears. These data are shown in Table 4-3. The distribution of fish consump tion for a g e g rou p s that included women of child-bearin g a g es are shown in Table 4-4. Table 4-3 Fish Consumption from the NPD 1973-1974 Survey (Modified from Rupp et al., 1980)
Age Group 50th Percentile 90th Percentile 99th Percentile Maximum Teena g ers A g ed 1.88 k g /y ear 8.66 k g /y ear 25.03 k g /y ear 62.12 k g /y ear 12-18 Years or 69 g rams/da y Adults A g ed 18 2.66 k g /y ear 14.53 k g /y ear 40.93 k g /y ear 167.20 k g /y ear to 98 Years or 112 g rams/da y4-7 Table 4-4 Distribution of Fish Consumption for Females by Age*

Consumption Category (gms/day) (from SRI, 1980)
Age (years) 47.6-60.0 60.1-122.5 Over 122.5 10-19 0.2 0.4 0.0 20-29 0.9 0.9 0.0 30-39 1.9 1.7 0.1 40-49 3.4 2.1 0.2

* The p ercenta g e of females in an a g e bracket who consume, on avera g e, a s p ecified amount ( g rams) of fish p er da y . The calculations in this table were based u p on the res p ondents to the NPD surve y who consumed fish in the month of the surve y . The NPD Research estimates that these res p ondents re p resent, on a wei g hted basis, 94.0% of the p o p ulation of U.S. residents (from Table 6, SRI Re p ort, 1980).

4.1.1.4 Nationwide Food Consump tion Surve y of 1977-78
Fish consump tion is not evenl y divided across the U.S. p o p ulation. Anal y sis of p atterns of fish consum p tion have been p erformed on data obtained from dietary surve y s of nationall y re p resentative p o p ulations. For examp le, Crochetti and Guthrie (1982) anal y zed the food consump tion p atterns of p ersons who p artici p ated in the Nationwide Food Consum p tion Surve y of 1977-78. Po p ulations s p ecificall y excluded from this anal y sis were children under four y ears of a g e, p re g nant and nursin g women, ve g etarians, individuals cate g orized b y race as "other" (i.e., not "white" and not "black"), individuals not related to other members of the household in which the y lived, and individuals with incom p lete records. After these exclusions, the stud y p o p ulation consisted on 24,085 individual dietar y records for a 3-da y p eriod.
Persons re p ortin g consump tion of fish, shellfish, and seafood at least once in their 3-day dietar y record were cate g orized as fish consumers. Combinations of fish, shellfish, or seafood with ve g etables and/or starches (e. g ., rice, p asta) or fish sandwiches were cate g orized as consumers of fish "combinations". Amon g the overall p o p ulation, 25.0% of res p ondents re p orted consump tion of fish with an additional 9.6% re p ortin g consum p tion of fish "combinations" in the 3-da y p eriod for a total of 34.6% re p ortin g consum p tion of fish and/or fish combinations. Fre q uenc y of consump tion was comp arable for male and female res p ondents with 24.1% of men and 25.7% of women rep ortin g consump tion of fish in their 3-da y dietar y records. Fish "combinations" were re p orted as dietar y items b y 11.2% of women and 9.9% of men. Both these food cate g ories were consumed t yp icall y as mid-da y and evenin g meals, rather than as breakfast or as snacks. For p ersons who listed fish in their 3-da y dietar y records, 89.7% listed fish in one meal onl y with 10.1% of res p ondents consumin g fish in two meals and 0.1% consumin g fish in three meals. For dishes that combined fish and other foods (i.e., fish "combinations"), amon g p ersons who re p orted eatin g fish combinations, 93.4% re p orted this food in one meal onl y with 6.5% of individuals consumin g two meals containin g fish "combinations."
There app ears to be little difference between men and women in their likelihood of consumin g fish based on p atterns observed in this national surve y (Crochetti and Guthrie, 1982). Based on this anal y sis, allocation of fish consum p tion on a " p er ca p ita" basis does not ade q uatel y reflect the fish consump tion p atterns of the g eneral p o p ulation of the United States. While " p er ca p ita" estimates resulted in an overestimate of fish consum p tion for the app roximatel y 65% of the U.S. p o p ulation who did not re p ort consumin g fish, these t yp es of estimates b y their nature substantiall y underestimated fish consump tion rates b y p ersons who consume fish. This p attern of underestimation is im p ortant in an assessment of im p act of infre q uentl y consumed foods such as fish.4-8 4.1.1.5 CSFII 1989-1991 The second set of nation-wide data (CSFII 89-91) are p resented in Table 4-5, includin g an a g e/g ender anal y sis of the fish-consumin g p o p ulation. Based on anal y sis of 11,706 res p ondents who su pp lied 3-da y s of dietar y record in the CSFII of 1989-1991, the fre q uenc y of fish consum p tion within the 3-da y p eriod was determined. Anal y ses of these dietar y records indicate that 30.9% of res p ondents consumed fish, either alone or as p art of a dish that contained fish. Most resp ondents eatin g fish consumed one fish meal within the 3- da y p eriod. Two p ercent (2%) of res p ondents re p orted consumin g fish two or more times durin g the 3-da y p eriod, and 0.5% of these fish-eatin g res p ondents re p orted fish consump tion three or more times durin g the 3-da y stud y p eriod. Amon g p ersons who re p orted eatin g fish within the 3-da y p eriod of the surve y , 44.1% re p orted eatin g marine finfish (other than or in addition to tuna, shark, barracuda, and swordfish). Marine finfish were more fre q uentl y consumed than freshwater fish. Of the 1593 p eo p le who re p orted eatin g finfish, 492 (30.9%) identified these as freshwater fish.

Table 4-5 CSFII 89-91 Data Gender Aged 14 Years Aged 15 through Aged 45 Years Total for All Age or Younger 44 Years or Older Groups Number of Individuals With 3 Days of Dietary Records Males 1497 (51.7%) 2131 (42.9%) 1537 (40.0%) 5,165 (44.1%) Females 1396 (48.3%) 2837 (57.1%) 2308 (60.0%) 6,541 (55.9%) Total 2893 (24.7%) 4968 (42.4%) 3845 (32.8%) 11,706 Respondents Reporting Consumption of All Fish and Shellfish

(Data weighted to be representative of the U.S. population.) Males 380 (52.8%) 646 (42.8%) 556 (39.3%) 1582 (43.8%) Females 340 (47.2%) 864 (57.2%) 828 (58.5%) 2032 (56.2%) Total 720 (19.9%) 1510 (41.8%) 1415 (39.2%) 3614 (30.9%)

4.1.1.6 CSFII 1994 and CSFII 1995
Anal y ses in 1994 were based on 5296 res p ondents on da y 1 and 5293 res p ondents on da y 2. A chan g e in surve y methods resulted in food consump tion data bein g collected for two da y s rather than for three da y s as in the 1989-91 surve y . Dietar y records included fish or shellfish for 598 individuals on da y 1 and 596 individuals for da y 2. These da y s were not necessaril y se q uential. Fish/shellfish consum p tion b y a g e and g ender cate g ories for CSFII 1994 and CSFII 1995 are shown in Tables 4-6 and 4-7, res p ectivel y . Overall, 11.3% of res p ondents re p orted fish or shellfish consum p tion. The rate was lower amon g children under 15 y ears of a g e and hi g her amon g adults a g ed 45 y ears and older.4-9 Table 4-6 CSFII 1994 Data — Days 1 and 2 Gender Aged 14 Years Aged 15 Aged 15 and Total for All or Younger through 44 Older Age Groups Number of Individuals with Dietary Recalls — Day 1 Males 932 852 869 2653 Females 942 842 859 2643 Total 1874 1694 1728 5296 % consumption fish 7.9 10.9 15.4 11.3 Respondents Reporting Consumption of All Fish and Shellfish — Day 1 Males 65 90 138 293 Females 83 94 128 305 Total 148 184 266 598 Number of Individuals with Dietary Recalls — Day 2* Males 993 852 868 2653 Females 941 840 859 2640 Total 1874 1692 1727 5293 % consumption fish 8.6 10.2 15.1 11.3 Respondents Reporting Consumption of All Fish and Shellfish — Day 2 Males 74 86 132 292 Females 88 87 129 304 Total 162 173 261 596
* Methodology changes based on two 24-hour recalls, not necessarily sequential.

To assess whether or not there were seasonal differences in fish and shellfish consum p tion, the y ear was divided into six two-month intervals. Fish intake data was anal y zed b y season. These values are shown in Table 4-8.4-10 Table 4-7 CSFII 1995 Data — Days 1 and 2 Gender Aged 14 Years Aged 15 through Aged 15 and Total for All Age or Younger 44 Older Groups Number of Individuals with Dietary Recalls — Day 1 Males 863 649 1,067 2,579 Females 808 635 1,041 2,484 Total 1,671 1,284 2,108 5,063 % Consuming Fish 7.5 11.7 15.4 11.9 Respondents Reporting Consumption of All Fish and Shellfish — Day 1 Males 63 77 170 310 Females 63 73 155 291 Total 126 150 325 601 Number of Individuals with Dietary Recalls — Day 2 Males 862 648 1,067 2,577 Females 809 634 1,042 2,485 Total 1,671 1,282 2,109 5,062 % Consuming Fish 8.8 12.9 14.5 12.2 Respondents Reporting Consumption of All Fish and Shellfish — Day 2 Males 81 82 168 331 Females 67 84 138 289 Total 148 166 306 620

Table 4-8 Fish Consumption (gms) by Season for Respondents Reporting Seafood Consumption CFSII 1994 — Day 1

Statistics Season Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec Mean 102 92 92 107 100 105 Std. Dev* 74 74 82 87 77 77 Minimum 212122 Maximum 373 488 960 903 413 517Table 4-8 (continued) Fish Consumption (grams) by Season for Respondents Reporting Seafood Consumption CFS II 1994 — Day 1

Statistics Season Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec

Althou g h children are oversamp led in the surve y desi g n, not all assessmsents were carried out amon g 1 y oun g children. For examp le, 24-hour dietar y recall data were obtained for children, however, fre q uenc y of fish consum p tion information was not obtained. 4-11

Percentiles 5th 14 10 22 21 12 14 10th 28 19 28 28 23 24 25th 50 51 42 53 49 48 Median 86 73 57 85 79 85 75th 114 123 118 139 129 165 90th 202 173 190 196 204 189 95th 293 227 295 272 253 235 Observations 183 219 210 242 191 163 Sum of Weights (000s) 10,197 11,383 11,817 11,506 9,573 9,113 * The values in these cells are the wei g hted standard deviations of the individual observations. Estimates of the standard errors of the means were not calculated.

4.1.1.7 NHANES III General Descri p tion The NHANES III, conducted between 1988 and 1994, used a multista g e p robabilit y desi g n that involved selection of p rimar y samp lin g units, se g ments (clusters of households) within these units, households, eli g ible p ersons, and finall y samp le p ersons. Primar y samp lin g units t yp icall y were com p osed of a count y or g rou p of conti g uous counties. Certain sub g rou p s in the p o p ulation that were of s p ecial interest for nutritional assessment were oversam p led: p reschool children (six months throu g h five y ears old) , p ersons 60 throu g h 74 y ears old, and the p oor ( p ersons livin g in areas defined as p oor b y the 1 United States Bureau of the Census for the 1990 census). The U.S. Bureau of the Census selected the NHANES III samp le accordin g to ri g orous s p ecifications from the National Center for Health Statistics so that the p robabilit y of selection for each p erson in the samp le could be determined.
The statistics p resented in the re p ort are p o p ulation estimates. The findin g s for each p erson in the sam p le were inflated b y the reci p rocal of selection p robabilities, ad j usted to account for p ersons who were not examined, and stratified afterward accordin g to race, sex and a g e, so that the final wei g hted p o p ulation estimates closel y app roximated the civilian noninstitutionalized p o p ulation of the United States as estimated inde p endentl y b y the U.S. Bureau of the Census at the midp oint of the surve y , March 1, 1990.4-12 Althou g h NHANES III was conducted between 1988 and 1994, data on food consump tion onl y became available in 1996. The surve y includes one 24-hour recall obtained b y a trained interviewer. This data base contains 29,973 dietar y records includin g 3864 individuals who consumed fish and shellfish (Table 4-9). Consump tion of fish differed b y a g e. Overall 12.9% of res p ondents included fish or shellfish in their 24-hour dietar y recall. As observed in CSFII 1994, the data amon g children a g ed 14 y ears and y oun g er was about half the p ercenta g es of fish consump tion for a g es 45 and older (Tables 4-10 and 4-11). There were q uestions on fre q uenc y of fish/shellfish consump tion in the CSFII 1994 and CSFII 1995 data bases; however, the s p ecific information obtained excluded canned fish. Conse q uentl y , these data were not used to estimate month-lon g fish consump tion. The 24-hour recall data were anal y zed for both children and adults.

Table 4-9 All Age Groups NHANES III Ages 14 and Ages 15 Ages 45 and Total

Younger through 44 Older Years Total 12,048 10,041 7,884 29,973 Fish Consumption 1060 1527 1274 3861 % Consumption Fish 8.8 15.2 16.2 12.9

Table 4-10 NHANES III Adult Respondents Gender Ages 15 to 44 Age 45 Years Total for All Age Years and Older Groups Total Respondents Males 4,620 3,783 8,403 Females 5,421 4,101 9,522

Total 10,041 7,884 29,989 Respondents Reporting Fish Consumption Males 664 605 1269 Females 883 645 1528

Total 1527 1274 28014-13 Table 4-11 NHANES III Child Respondents Age Group Total Fish Consumers % Reporting Fish 1-5 Years 7595 626 8.2 6-11 Years 3217 323 10.0 12-14 Years Female 660 58 8.8 12-14 Years Male 576 53 9.2 Total 12,048 1060 8.8

4.1.2 Fre q uenc y of Consum p tion of Fish Based on Surve y s of Individuals
4.1.2.1 CSFII 1989-1991 In the USDA 1989 throu g h 1991 Continuin g Surve y s of Food Intake b y Individuals (CSFII 89- 91), food consum p tion data were obtained from nationall y re p resentative sam p les of individuals. These surve y s included women of child-bearin g a g e — 15 throu g h 44 y ears of a g e. Data from the CSFII for the p eriod includin g 1989 and 1991 were used to calculate fish intake b y the g eneral p o p ulation and women of child-bearin g a g e. This sub p o p ulation included p re g nant women, which are a sub p o p ulation of interest in the Mercury Stud y : Re p ort to Con g ress, because of the p otential develo p mental toxicit y to the fetus accomp an y in g in g estion of meth y lmercur y . Anal y sis of Vital and Health Statistics data from 1990 indicated that 9.5% of women in this a g e g rou p can be p redicted to be p re g nant in a g iven y ear. The size of this p o p ulation has been estimated usin g the methodolo gy described in the Addendum to this cha p ter, entitled "Estimated National and Re g ional Po p ulations of United States Women of Child-Bearin g A g e." The data described in this section were obtained from nationall y re p resentative sam p les of individuals and were wei g hted to reflect the U.S. p o p ulation usin g the sam p lin g wei g hts p rovided b y USDA. The basic surve y was desi g ned to p rovide a multista g e stratified area p robabilit y samp le re p resentative of the 48 conterminous states. Wei g htin g for the 1989, 1990 and 1991 data sets was done in two sta g es. In the first p hase a fundamental sam p lin g wei g ht (the inverse of the p robabilit y of selection) was comp uted and the res p ondin g wei g ht (the inverse of the p robabilit y of selection) was com p uted for each res p ondin g household. This fundamental sam p lin g wei g ht was then ad j usted to account for non-res p onse at the area se g ment level. The second p hase of comp utations used the wei g hts p roduced in the first p hase as the startin g p oint of a rewei g hin g p rocess that used re g ression techni q ues to calibrate the sam p le to match characteristics thou g ht to be correlated with eatin g behavior.
The wei g hts used in this anal y sis reflect CSFII individuals p rovidin g intakes for three da y s. Wei g hts for the 3-da y individual intake sam p le were constructed se p aratel y for each of the three g endera g e g rou p s: males a g es 20 and over, females a g es 20 and over and p ersons a g ed less than 20 y ears. Characteristics used in wei g ht construction included day of the week, month of the y ear, re g ion, urbanization, income as a p ercent of p overt y , food stamp use, home ownershi p , household comp osition, race, ethnicit y and a g e of the individual. The individual's em p lo y ment status for the p revious week was used for p ersons a g es 20 and older, and the em p lo y ment status of the female head of household was used for individuals less than 20 y ears of a g e. The end result of this dual wei g htin g p rocess was to p rovide consump tion estimates which are re p resentative of the U.S. p o p ulation.
Res p ondents were drawn from stratified area p robabilit y samp les of noninstitutionalized U.S. households. Surve y res p ondents were surve y ed across all four seasons of the y ear, and data were4-14 obtained across all seven day s of the week. The dietary assessment methodolo gy consisted of assessment of three consecutive da y s of food intake, measured throu g h one 24-hour-recall and two 1-da y food records. For this anal y sis, the samp le was limited to those individuals who p rovided records or recalls of three da y s of dietar y intake.
For p urp oses of interp retabilit y , it should be noted that assessment of fish consum p tion p atterns b y recall/record assessment methods will p robabl y differ from assessments based on food fre q uenc y methods (See Section 4.1.2.3, below). In order to be desi g nated a consumer or "user" of fish for p urp oses of the p resent anal y sis, an individual would need to have re p orted consump tion of one or more fish/shellfish p roducts at some time durin g the three da y s when dietar y intake was assessed. Since fish is not a fre q uentl y consumed food for the ma j orit y of individuals, this dietary assessment method will likel y underestimate the extent of fish consum p tion, because some individuals who normall y consume fish will be missed if the y did not consume fish durin g the three da y s of assessment. In contrast, such users would be p icked u p b y a food fre q uenc y q uestionnaire. The recall/record dietar y assessment method does have the advanta g e, however, of p rovidin g more p recise estimates of the q uantities of fish consumed that would be obtained with a food fre q uenc y record. The information that follows comes from the CSFII 1989-1991 and was p rovided under contract to U.S. EPA b y Dr. Pamela Haines of the De p artment of Nutrition of the Universit y of North Carolina School of Public Health. Data are p resented for followin g g rou p s of individuals surve y ed b y USDA in the CSFII: data for the total p o p ulation, data g rou p ed b y g ender, and for data g rou p ed b y a g e- g ender cate g ories for the a g e g rou p s 14 y ears or y oun g er, 15 throu g h 44 y ears, and 45 y ears and older (Table 4- 5).
Fish consump tion was defined to reflect consum p tion of app roximatel y 250 individual "Fish onl y " food codes and app roximatel y 165 "Mixed dish-fish" food codes p resent in the 1994 version of the USDA food com p osition tables. The USDA maintains a data base (called the "Recip e File") that describes all food in g redients that are p art of a p articular food. Throu g h consultation with Dr. Betty Perloff, an USDA ex p ert in the USDA reci p e file, and Dr. Jacob Exler, an USDA ex p ert in food com p osition, the USDA reci p e file was searched for food codes containin g fish or shellfish. The reci p e was then scanned to determine fish codes that were p resent in the reci p e re p orted as consumed b y the surve y res p ondent. The p ercent of the reci p e that was fish b y wei g ht was determined b y dividin g the wei g ht of the fish/shellfish in the dish b y the total wei g ht of the dish.
As with most dietar y assessment studies, multi p le da y s of intake were avera g ed to reflect usual dietar y intake better. Intakes re p orted over the three-da y p eriod were summed and then divided b y three to p rovide consump tion estimates on a p er p erson, p er da y basis.
Fish consump tion was defined within the followin g cate g ories.
1. Fish and Shellfish , all t yp es reflected consump tion of an y fish food code. 2. Marine Finfish , included fish not further s p ecified (e. g ., tuna) and p rocessed fish sticks, as well as anchov y , cod, croaker, eel, flounder, haddock, hake, herrin g , mackerel, mullet, ocean p erch, p om p ano, p or gy , ra y , salmon, sardines, sea bass, skate, smelt, stur g eon, whitin g . 3. Marine Shellfish included abalone, clams, crab, cray fish, lobster, mussels, o y sters, scallo p s, shrim p and snails. 4. Tuna , contained onl y tuna. 5. Shark, Barracuda, and Swordfish contained j ust these three s p ecies of fish. 6. Freshwater Fish contained car p , catfish, p erch, p ike, trout and bass.
The anal y sis was stratified to reflect " p er cap ita" (Table 4-12), as well as " p er user" (Table 4-13), consump tion p atterns. A "consumer" of Fish and Shellfish , all t yp es was one who consumed an y of the4-15 included fish onl y or mixed-fish dish foods. A Marine Finfish consumer was one who consumed an y of the s p ecies of fish included within the marine finfish cate g or y , and so on for each cate g or y . The p ercent of the p o p ulation or sub p o p ulation consumin g fish was listed for the entire p o p ulation, as well as g ender s p ecific values, and a g e- g ender cate g or y s p ecific values.

Table 4-12 Consumption of Fish and Shellfish (gms/day), and Self-Reported Body Weight (kg) in Respondents of the 1989-1991 CSFII Survey. "Per Capita" Data for All Survey Respondents (Data are wei g hted to be re p resentative of the U.S. p o p ulation.) Gender Aged 14 Years or Aged 15 through Aged 45 Years or Total Younger 44 Years Older Mean SD kg Mean SD kg Mean SD Kg Mean SD kg bw bw bw bw Males 9 20 26 19 35 73 20 36 90 17 33 68 Females 8 18 24 14 28 63 18 30 67 14 27 58

Table 4-13 Consumption of Fish and Shellfish (gms/day), and Self-Reported Body Weight (kg) in Respondents of the 1989-1991 CSFII Survey (Data for "Users" Onl y . Data are wei g hted to be re p resentative of the U.S. p o p ulation.) Gender Aged 14 Years or Aged 15 through Aged 45 Years or Total Younger 44 Years Older Mean SD kg Mean SD kg Mean SD Kg Mean SD kg bw bw bw bw Males 32 27 28 54 39 80 51 42 83 49 39 59 Females 29 24 24 41 35 63 42 34 68 40 33 54

Consump tion of fish-onl y and mixed-fish-dishes was summed across the three available da y s of dietar y intake data. This sum was then divided b y three to create avera g e p er da y fish consump tion fi g ures. In the tables that describe fish intake, information is p resented on samp le size, p ercent of the p o p ulation who consumed an y p roduct within the s p ecified fish cate g or y , the mean g rams consumed p er da y and the mean g rams consumed p er kilo g ram bod y wei g ht (based on self-re p orted bod y wei g hts), standard deviation, minimum, maximum, and the p o p ulation intake levels at the 5th, 25th, 50th (median), 75th, and 95th p ercentiles of the intake distribution for each a g e- g ender cate g or y . The means and standard deviations were determined usin g a SAS p ro g ram. Surve y samp le wei g hts were app lied. Anal y sis with SAS does not take desi g n effects into account, so the estimates of variance ma y differ from those obtained if SUDAAN or such p acka g es had been used. It should be noted, however, that the p oint estimates of consum p tion ( g rams p er consumer p er da y , g rams p er consumer p er kilo g ram of bod y wei g ht) will be exactl y the same between the two statistical anal y sis p acka g es. Thus, the p oint estimates re p orted are accurate and a pp ro p riate for interp retation on a national level. Data were obtained for 11,706 individuals re p ortin g 3-da y s of diet in the 1989-1991 CSFII surve y . Anal y ses were based on data wei g hted throu g h statistical p rocedures (as described p reviousl y ) to be re p resentative of the U.S. p o p ulation. The total g rou p of res p ondents re p ortin g consump tion of finfish and/or shellfish durin g the 3-da y p eriod were g rou p ed as a sub p o p ulation who consumed fish, as can be observed in Table 4-13. Fish and shellfish (total fish consum p tion) were re p orted to be eaten b y 36144-16 p ersons (30.9%) of the 11,706 of the surve y res p ondents (see Tables 4-12 and 4-13). The sub p o p ulation considered to be of g reatest interest in this Mercury Stud y : Re p ort to Con g ress were women of childbearin g a g e (15 throu g h 44 y ear-old females). Amon g this g rou p of women a g es 15 throu g h 44 y ears, 864 women of the 2837 surve y ed (30.5%) re p orted consumin g fish (see Tables 4-12 and 4-13). Within this g rou p , 334 women re p orted consump tion of finfish durin g the 3-da y surve y p eriod.
Consump tion of fish and shellfish varied b y s p ecies of fish. Overall, marine finfish (not includin g tuna, swordfish, barracuda, and shark) and tuna were consumed b y more individuals and in g reater q uantit y than were shellfish. Tuna fish was the most fre q uentl y consumed fish p roduct, and se p arate tables are p rovided that identif y q uantit y of tuna fish consumed. Two other cate g ories of finfish were identified: freshwater fish and a cate g or y comp rised of swordfish, barracuda, and shark. Freshwater fish were of interest because U.S. EPA's anal y sis of the fate and trans p ort of ambient, anthro p o g enic mercur y emissions from sources of concern in this re p ort indicates that fish ma y bioaccumulate emitted mercur y . Swordfish, barracuda, and shark were also identified as a se p arate cate g or y . These are p redator y , hi g hl y mi g rator y s p ecies that s p end much of their lives at the hi g h end of marine food web. These fish are lar g e and accumulate hi g her concentrations of mercur y than do lower tro p hic level, smaller fish.
4.1.2.2 Estimated Fre q uenc y of Fish/shellfish Consump tion Based on Food Fre q uenc y Questions in CSFII 1994 and NHANES III
Both surve y s included q uestions on fre q uenc y of consum p tion of fish and shellfish. The s p ecific wordin g of the q uestions are shown in the box. The wordin g of CSFII 1994 se p arated canned fish from fish makin g it difficult to p rovide an overall estimate of fish consum p tion because no se p arate q uestion addressed fre q uenc y of consum p tion of canned fish. The CSFII surve y also p rovided a se p arate q uestion on whether of not an y of the fish the res p ondent ate was cau g ht b y the res p ondent or someone known to the res p ondent. Amon g those res p ondents who ate non-canned fish durin g the p ast 12-month p eriod (84.1% of res p ondents), 37.5% indicated that the y had consumed fish cau g ht b y themselves or a p erson known to them. Shellfish were re p orted to have been consumed b y 62.2% of res p ondents durin g the p ast 12-month p eriod.4-17 Fish Consumption Survey Questions CFSII 1994 During the past 12 months, that is, since last (NAME OF MONTH), (have you/has NAME) eaten any (FOOD) in any form? Yes No Shellfish . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 1 2 Fish, other than shellfish or canned fish . . . . . . . . . . . 1 2 IF YES: Was any of the fish you ate caught by you or someone you know? . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 NHANES III N2. MAIN DISHES, MEAT, FISH, CHICKEN, AND EGGS Times Day Week Month Never or DK g. Shrimp, clams, oysters, crabs, and lobster ____ per 1  D 2  W 3  M 4  N or 9  DK h. Fish including fillets, fish sticks fish sandwiches, and tuna fish ___ per 1  D 2  W 3  M 4  N or 9  DK

In the CSFII 1994 surve y , sub j ects who consumed fish other than shellfish or canned fish were to select the answer “ y es.” Because canned fish (e. g ., tuna, sardines) re p resent ma j or food items, a p ortion of the fish consumers would indicate the y were nonconsumers if the y ate canned fish onl y . Conse q uentl y , usin g the results from the CSFII 1994 q uestion would underestimate the fre q uenc y of consump tion of fish.
NHANES III included two q uestions on fish and shellfish consum p tion as p art of the household interview p ortion of the surve y . The s p ecific format and wordin g are shown below. Questions N2 g and N2h addressed shrimp /shellfish and fish se p aratel y . Res p ondents were asked to indicate their fre q uenc y of consum p tion: never, or how often dail y , weekl y , or monthl y the y consumed shrimp /shellfish ( g ) or fish (h). Anal y ses of data from these q uestions p rovided the estimates of fre q uenc y of fish and shellfish consump tion shown in Table 4-14.
Table 4-14 Frequency of Fish/Shellfish Ingestion and Percent of Respondents*

(NHANES III, Food Frequency Questionnaire, Weighted Data) Number of times All Adults Women Aged Men Aged Women Aged 45 Men Aged 45 per month 15 — 44 Years 15 —44 Years Years and Older Years and Older 0 12 14 11 11 9 1 or more 88 86 89 89 91 2 or more 79 78 81 80 83 4 or more 58 56 58 61 63 8 or more 23 25 29 30 31 12 or more 13 12 14 15 14 24 or more 33 3 2 3 30 or more 12 2 1 2 *Adult subjects only. Food frequency data were not collected for children ages 11 and younger.4-18 Fre q uenc y of fish and shellfish consump tion data have also been calculated b y ethnic/racial g rou p in g . The g rou p s were: Non-His p anic whites (“Whites”), Non-His p anic blacks (“Blacks”) and p ersons desi g nated as “Other” who included p ersons of Asian/Pacific Islander ethinicity , Native Americans, Non-Mexican His p anics ( p redominatel y p ersons from Puerto Rica and other Carribean Islands), and additional g rou p s not in the cate g ories “Whites” or “Blacks”. Food fre q uenc y data for these g rou p s is shown in Tables 4-15 and 4-16. Table 4-15a Frequency of Fish and Shellfish Consumption by Percent among All Adults, Both Genders, Weighted Data, NHANES III* (Estimated Frequency Per Month) Frequency per Month White Black Other Zero 11.8 11.3 15.1 Once a Month or More 88.2 88.7 84.9 Once a Week or More 57.1 63.5 60.3 Twice a Week or More 25.9 31.9 31.2 Three-Times a Week or More 11.6 15.0 22.9 A pp roximatel y Dail y (6 Times 1.9 3.3 8.9 Per Week) * Adult subjects only. Food frequency data were not collected for children aged 11 years and younger.

Table 4-15b Frequency of Fish and Shellfish Consumption by Race/Ethnicity, Women Aged 15-44 Years, Weighted Data, NHANES III (Estimated Frequency Per Month) Frequency per Month White Black Other Zero 13.2 10.1 19.1 Once a Month or More 86.8 89.9 80.9 Once a Week or More 54.5 62.8 59.3 Twice a Week or More 22.0 31.7 35.6 Three-Times a Week or More 9.5 15.9 22.7 A pp roximatel y Dail y (6 Times 1.7 3.2 9.2 Per Week)4-19 Table 4-16a Distribution of the Frequency of Fish and Shellfish Consumption by Race/Ethnicity All Adults, Both Genders, Weighted Data, NHANES III
Percentile Whites Blacks Other 50th 4 4 5 75th 8 8 10 90th 13 13 22 95th 17 19 32

Table 4-16b Distribution of the Frequency of Fish and Shellfish Consumption By Race/Ethnicity Among Adult Women Aged 15-44, Weighted Data, NHANES III
Percentile Whites Blacks Other 50th 4 4 5 75th 7 8 10 90th 11 14 23 95th 15 20 31

Overall 88% of all adults consume fish and shellfish at least once a month with 58% of adults consumin g fish at least once a week. Between 13% and 23% consume fish/shellfish two or three times p er week. An estimated 3% indicate the y consume fish and shellfish six times a week with 1% of all res p ondents indicatin g the y eat fish and shellfish dail y . Comp arativel y small differences exist based on a g e and g ender of adults. Two p ercent of women of re p roductive a g e and 2% of men in the a g e ran g e 15 throu g h 44 y ears indicate the y consume fish/shellfish dail y . Amon g diverse sub p o p ulations those desi g nated as “Other” consume fish and shellfish more fre q uentl y than do individuals in g rou p s identified as “White” and “Black”. In the “Other” cate g or y 5% of individuals consume fish and shellfish dail y (95th p ercentile value). A pp roximatel y 10% of the sub p o p ulation of “Whites” consume fish and shellfish three-times or more p er week with app roximatel y 23% of p ersons in the “Other” classification consumin g fish and shellfish three-times a week or more.
4.1.2.3 Fre q uenc y of Consum p tion of Various Fish S p ecies b y Res p ondents in NHANES III
Grou p in g of fish and shellfish s p ecies b y habitat (i.e., freshwater, estuarine, and marine) was done based on an or g anization develo p ed b y US EPA’s Office of Water. Table 4-17 shows which s p ecies were g rou p ed into these three habitat cate g ories.4-20 Table 4-17 Classification of Fish Species by Habitat*

Marine Estuarine Freshwater Abalone Anchov y Carp Barracuda Clams (8%) Catfish Clams (92%) Crab (46%) Pike Cod Croaker Salmon (1%) Crab (54%) Flatfish (29%) Trout Flatfish (71%) Flounder Haddock Herrin g Halibut Mullet Lobster O y ster Mackerel Perch Mussels Scallo p (1%) Ocean Perch Scu p Octo p us Shrim p Pollock Smelts Pomp ano Stur g eon Por gy Salmon (99%) Sardine Scallo p (99%) Sea Bass Seafood (e. g ., fish sauce) Shark Snapp er Swordfish Sole S q uid Tuna Whitefish Whitin g *Unprocessed fish (Food Codes 2815061 and 2815065) were not classified by habitat.

Mean consump tion rates for onl y males and females who re p orted consumin g fish/shellfish in the NHANES III data set are shown in Table 4-18. Consump tion rates for s p ecies g rou p ed as marine, estuarine, and freshwater are shown in Table 4-19. Marine fish are the most fre q uentl y consumed followed b y estuarine and freshwater fish. However, when freshwater fish are consumed the p ortion size is lar g er than for marine or estuarine fish. Males consumed lar g er p ortions of an y of the fish g rou p s than did female sub j ects.4-21 Table 4-18 Weighted Estimates of Fish and Shellfish Consumed (gms) for Females and Males Aged 15 - 44 Years Reported in NHANES III (Per User)
Statistic Females Males

Mean 103 146 Standard Deviation 116 149 Minimum 1 1 Maximum 117 1097 Percentiles 5th 12 14 10th 20 28 25th 37 51 Median 73 97

75th 131 185 90th 228 345 95th 288 435 Observations 883 645 Sum of Weights (000s) 1,162 9,223

Table 4-19 Weighted Estimates for Fish and Shellfish Consumed (gms) by Female and Male Respondents Aged 15 - 44 Years Reported in the NHANES III Survey by Habitat of Species Consumed
Statistic Marine Fish Estuarine Fish Freshwater Fish Females Males Females Males Females Males Mean 86 113 69 122 158 274 Std. Dev 86 122 64 131 138 268 Minimum 0 0 0 0 7 14 Maximum 957 1004 517 981 740 1097 Percentiles 5th 8 1 8 5 13 42 10th 14 12 9 8 26 42 25th 37 44 22 29 50 123 Median 55 84 47 64 127 185 75th 109 153 101 175 235 313 90th 209 204 168 355 330 617 95th 247 351 202 357 330 929Table 4-19 (continued) Weighted Estimates for Fish and Shellfish Consumed (gms) by Female and Male Respondents Aged 15 - 44 Years Reported in the NHANES III Survey by Habitat of Species Consumed
Statistic Marine Fish Estuarine Fish Freshwater Fish Females Males Females Males Females Males

4-22 Observations 519 387 221 198 82 60 Sum of Weights (000s) 6,457 5,999 2,653 2,477 516 588

4.1.3 Sub p o p ulations with Potentiall y Hi g her Consump tion Rates
The p urp ose of this section is to document fish consum p tion rates amon g U.S. sub p o p ulations thou g ht to have hi g her rates of fish consump tion. These sub p o p ulations include residents of the States of Alaska and Hawaii, Native American Tribes, Asian/Pacific Island ethnic g rou p s, an g lers, and children; these g rou p s were selected for anal y sis because of p otentiall y elevated fish consum p tion rates rather than because the y were thou g ht to have a hi g h innate sensitivit y to meth y lmercur y . The p resented estimates are the results of fish consump tion surve y s conducted on the s p ecific p o p ulations. The surve y s use several different techni q ues and illustrate a broad ran g e of consump tion rates amon g these sub p o p ulations. In several studies the fish consum p tion rates of the sub p o p ulations corroborate the hi g hend (90th p ercentile and above) fish consum p tion estimates of the the nationwide food consum p tion surve y s. Man y of the surve y s of fish consump tion conducted on hi g h-end fish consumers also included anal y ses for mercur y in hair and blood of the p eo p le who were sub j ects. These data on biolo g ical monitorin g p rovide an additional bases to estimate mercur y ex p osure. 4.1.3.1 Sub p o p ulations Included in Nationall y Re p resentative Food Consump tion Surve y s Contem p orar y food consum p tion surve y s desi g ned to be re p resentative of the U.S. p o p ulation as a whole included identifiers for ethnicall y diverse sub p o p ulations. Publicl y available data from the NHANES III combined three sub p o p ulations of interest with re g ard to level of fish consump tion: Asian/Pacific ori g in, Native American ori g in, and others. B y contrast, the CSFII 1994 and CSFII 1995 surve y s p rovided se p arate estimates for identified ethnic sub p o p ulations: white, black, Asian and Pacific Islander, Native American and Alaskan Native, and other (see Fi g ure 4-1). The 50th, 90th and 95th p ercentiles for all surve y p artici p ants in CSFII 1994 and CSFII 1995 for “Da y 1" and “Da y 2" recall data are shown in Table 4-20. The number of 24-hour recall food consump tion re p orts for each g rou p is noted in the table food note. Data are p resented for both “ p er cap ita” and “ p er user.” The sub p o p ulation self-desi g nated as “white” has the smallest intake of fish/shellfish and mercury at the 50th p ercentile. “Blacks” have hi g her levels of intake and Asian and Pacific Islanders have the hi g hest intake of fish/shellfish. Similar p atterns are observed at the 90th and 95th p ercentile.
If the data are calculated for onl y those p ersons who re p orted consumin g fish and shellfish, a somewhat different p attern emer g es. A median intake of fish/shellfish is the lowest amon g Asian and Pacific Islanders, intermediate amon g “whites” and hi g hest amon g “blacks.” The number of observations amon g Native Americans and Alaska Natives are too small to p roduce reliable estimates.7

. 5 1 4 1 8 2 6 2 7

. 7 3 0

. 8 3 2 3 4 3 7 4 3 5 9

6 3 1 0 0 1 0 9 1 1 2

1 5 0 1 5 6 1 7 0

2 0 0 2 2 9 2 5 0 3 0 0 3 5 0 3 9 0 4 0 0 4 5 2 W o l f e & W a l k e r ' 8 7 H i g h e s t R e s p o n s e G r o u p M e a n i n A K

C R I T F C ' 9 4 9 9 t h % i l e A d u l t

P u f f e r ' 8 9 9 0 t h % i l e L E G E N D

- P O P U L A T I O N S G E N E R A L U

. S

. P O P U L A T I O N N P D 7 3 /7 4 C S F I I
R E C R E A T I O N A L A N G L E R S P U F F E R F I O R E C O N N E L

Y S U B S I S T E N C E F I S H E R S W O L F E & W A L K E R

N A T I V E A M E R I C A N S C R I T F C T O Y , T U L A L I P N O B M A N E P A ' 9 2 W I T R I B E S

C R I T F C ' 9 4 9 5 t h % i l e T o y ' 9 5 T u l a l i p T r i b e 9 0 t h % i l e

N P D 7 3 / 7 4 A d u l t 9 9 t h % i l e N o b m a n n ' 9 2 A K T r i b e s M e a n F i o r e ' 8 9 9 5 t h % i l e W I A n g l e r s C R I T F C ' 9 4 A d u l t M e a n T o y ' 9 5 T u l a l i p T r i b e M e d i a n P u f f e r ' 8 1 M e d i a n

F i o r e ' 8 9 7 5 t h % i l e W I A n g l e r s E P A ' 9 2 W I T r i b e s N P D 7 3 / 7 4 A d u l t 9 0 t h % i l e C o n n o l y ' 9 0 N Y A n g l e r s M e a n F i o r e ' 8 9 W I A n g l e r s C S F I I A g e 1 5

- 4 4 M e a n a n d N P D 7 3 / 7 4 A d u l t 5 0 t h % i l e



### F i g u r e 4
- 1 D i s t r i b u ti o n o f F i s h C o n s u m p t i o n R a t e s o f V a r i o u s P o p u l a t i o n s

0

4

- 2 34-24 Table 4-20 Consumption of Fish and Shellfish (gms/day) among Ethnically Diverse Groups (Source: CSFII 1994 and CSFII 1995) Ethnic Group Fish Consumption (grams/day) Per Capita Per User 1 2 White

50th Percentile 90th Percentile 95th Percentile 80 243 Zero 72

24 192

Black

50th Percentile 90th Percentile 95th Percentile 104 302 Zero 82

48 228

Asian and Pacific Islander

50th Percentile 90th Percentile 95th Percentile 127 292 Zero 62

80 189

Native American and Alaska Native 50th Percentile 90th Percentile Zero of small numbers of 95th Percentile Zero Estimate not made because
56 respondents

. Other

50th Percentile 90th Percentile 95th Percentile Zero 83 Zero 294

62 327 Total number of 24-hour food consumption recall reports: White (16,241); Black (2,580); Asian and 1 Pacific Islander (532); Native American and Alaska Native (166): and Other (1,195).

Number of 24-hour food consumption recall reports: White (1,821); Black (329); Asian and Pacific 2 Islander (155); Native American and Alaska Native (12); and Other (98).

4.1.3.2 S p ecialized Surve y s Durin g the p ast decade, data describin g the q uantities of fish consumed b y an g ler, economicall y subsistent, and North American Tribal g rou p s have been p ublished (Tables 4-23 and 4-30). Sub p o p ulations of p articular concern because of ex p osure p atterns are Native Americans, s p ort an g lers, the urban p oor, and children. Data on fish consump tion for these g rou p s indicate that ex p osures for these sub g rou p s exceed those of the g eneral p o p ulation of adults. If North American data, includin g those from Canada, are considered, mercury ex p osures from the marine food web (es p eciall y if marine mammals are consumed) exceed limits such as the Tolerable Dail y Intake established b y Health Canada (Chan, 1997) and the Acce p table Dail y Intake established b y the U.S. Food and Dru g Administration.
The data cited below on s p ecific sub p o p ulations are not utilized in this Re p ort as the basis of a site-s p ecific assessment. In a site-s p ecific assessment the fish consum p tion rates amon g a surve y ed p o p ulation would be combined with s p ecific measurements of meth y lmercur y concentrations in the local fish actuall y consumed to estimate the human contact rate. Ideall y , some follow-u p anal y sis such as concentrations of mercury in human blood or hair would ensue.4-25 Anal y tic and surve y methods to estimate the fish consum p tion rates of the res p ondents are described for each p o p ulation. This chap ter does not constitute an exhaustive review of the methods em p lo y ed. An attemp t was made to characterize the p o p ulation surve y ed. Additionall y , to characterize the entire ran g e of fish consump tion rates in the surve y ed p o p ulations, the consum p tion rates of both avera g e and hi g h-end consumers as well as other s p ecific an g ler sub p o p ulations (e. g ., fish consump tion b y an g ler race or a g e) are p resented.
The sources of consumed fish are also identified in the summaries. Fish consumed b y humans can be derived from man y sources; these include self-cau g ht, g ift, as well as g rocer y and restaurant p urchases. Some studies describe onl y the consump tion rates for self-cau g ht fish or freshwater fish, others estimate total fish consum p tion, and some delineate each source of fish. Humans also consume fish from man y different t yp es of water bodies. When described b y the re p ortin g authors, these are also identified.
Assum p tions concernin g fish consump tion made b y the stud y authors are also identified. Humans g enerall y do not eat the entire fish; however, the s p ecies and bod y p arts of fish which are consumed ma y be hi g hl y variable amon g an g ler p o p ulations (for examp le, see To y et al. 1995). An g lers do not eat their entire catch, and, some s p ecies of fish are t yp icall y not eaten b y s p ecific an g lin g sub p o p ulations. For examp le, Ebert et al. (1993) noted that some typ es and p arts of harvested fish are used as bait, fed to p ets or simp l y discarded. Stud y authors account for the differences between catch wei g ht and number in a variety of different wa y s. T yp icall y , a consump tion factor was app lied. These assump tions im p act the author's consum p tion rate estimates.
Data from an g ler and indi g enous p o p ulations are useful in that the y corroborate the ran g es identified in the 3-da y fish consump tion data. The data are not utilized in this Re p ort as the basis of a site-s p ecific assessment. In a site-s p ecific assessment the fish consum p tion rates amon g a surve y ed p o p ulation would be combined with s p ecific measurements of meth y lmercur y concentrations in the local fish actuall y consumed to estimate the human contact rate. Ideall y , some follow-u p anal y sis such as concentrations in human blood or hair would ensue.
4.1.3.3 U.S. Subsistent Po p ulations
Lar g e urban p o p ulations include individuals who obtain some of their food b y catchin g and eatin g fish from local urban waters. For examp le, Waller et al. (1996) identified p o p ulations livin g alon g the lake shore of Chica g o who have read y access to fishin g waters of Lake Michi g an alon g the break waters, the harbors, and in the p ark la g oons ad j acent to Lake Michi g an (Table 4-21). Similar situations occur for man y water bodies in urban areas throu g hout the United States.4-26 Table 4-21 Fish Consumption of an Urban “Subsistent” Group Study Description of Fish Consumption Pattern Notes Group Waller et al., 484 p re g nant African- 45 of 444 ate no fish; 46 of 444 T yp es of fish eaten most fre q uentl y 1996 American, urban p oor consumed s p ort-cau g ht fish; 34 in descendin g order: catfish, p erch, women of the women who consumed buffalo, silver bass, and whitin g . s p ort-cau g ht fish also consumed Others included: bull heads, store-bou g ht fish. sunfish, blue g ills, and crapp ie.Most catfish consumed was storebou g ht. Generall y fisheaters did not consume onl y one t yp e of fish. Most of the individuals eatin g s p ort-cau g ht fish also ate wild fowl and other g ame (duck, raccoon, o p ossum, s q uirrel, turke y , g oose, and other fowl.

Another g rou p of urban consumers who subsist on fish are p ersons who are not limited in income, but individuals who choose to consume a lar g e p ro p ortion of their dietar y p rotein from fish because of taste p reference or p ursuit of health benefits attributed to fish. For an undetermined number of these individuals, a p articular s p ecies of fish ma y be p referred (e. g ., swordfish, sea bass, etc.) and consumed extensivel y . De p endin g on the mercur y concentration of the p referred fish, the result of consumin g diets hi g h in fish from one source can be substantiall y increased ex p osure to mercur y . For exam p le, Knobeloch et al. (1996) p rovide cases re p orts of a famil y whose blood mercur y concentrations increased about ten-fold followin g lon g -term consump tion of a p articular commercial source of im p orted fish (Table 4-22). Likewise, investi g ation b y state authorities in Maine of elevated blood mercur y concentrations thou g ht to result from occu p ational ex p osures to mercur y , in fact, resulted from fre q uent consump tion of fish (Dr. Allison Hawkes, 1997). After followin g p h y sician’s advise to reduce fish consump tion the blood mercur y levels decreased. Table 4-22 High Fish Consumption among Urban Subjects: Case Report Study Description of Fish Consumption Pattern Notes Group
Knobeloch et Famil y consumin g Wisconsin famil y consumed two Famil y members had blood mercury al., 1995 commerciall y available meals/week of seabass im p orted levels elevated to 37 and 58 μg /L fish. from Chile and obtained and hair mercur y values of 10 and 12 commerciall y which had a mercur y μg / g . Cessation of fish consump tion concentration between 0.5 and 0.7 for 200 da y s reduced blood mercur y μg / g . Other fish havin g low mercur y levels to 3 and 5 μg /L. concentrations (<0.05 μg / g ) were also consumed. The father consumed an avera g e of 113 g of fish/da y , the mother and son consumed app roximatel y 75 and 37 g rams of fish/da y , res p ectivel y . Calculated mercur y intakes ran g ed from 9 μg /da y ( y oun g child) to 52 μg /da y for the father in the household.4-27 4.1.3.4 U.S. Immi g rant Po p ulations
Sub p o p ulations of recent immi g rants to the United States retain food p atterns characteristic of their cultures with ada p tations based on the available food su pp l y . In the 1980s and 1990s, the p ro p ortion of the U.S. p o p ulation whose ancestry was Southeast Asian or Caribbean ori g in increased. The p eo p le of rural Cambodia, Laos, and Vietnam su pp lemented their a g ricultural resources b y huntin g and fishin g (Shubat et al., 1996) and man y continue to do so in the United States. Puffer (1981) found that Oriental/Samoan recreational an g lers had fish consump tion rates twice the mean value for all an g lers in the surve y . S p ecialized fish advisories for chemical contaminants and outreach p ro g rams for Southeast Asian communities have been develo p ed (Shubat et al., 1996). Increased consump tion of p urchased frozen fish, as well as self-cau g ht fish, amon g Southeast Asians has been re p orted (Shatenstein et al., 1997). Overall, these sub p o p ulations have hi g her fish consump tion than does the g eneral U.S. p o p ulation.
4.1.3.5 U.S. An g lin g Po p ulation Size Estimate and Behaviors
Man y citizens catch and consume fish from U.S. waters. The U.S. Fish and Wildlife Service (U.S. FWS, 1988) re p orted that in 1985, 26% of the U.S. p o p ulation fished; over 46 million p eo p le in the U.S. s p ent time fishin g durin g 1985. Within the U.S. p o p ulation fishin g rates ran g ed from a low of 17% for the p o p ulation in the Middle Atlantic states u p to 36% in the West North Central States. These an g lin g sub p o p ulations included both licensed and non-licensed fishers, hook and line an g lers as well as those who utilized s p ecial an g lin g techni q ues (e. g ., bow and arrows, s p ears or ice-fishin g ). U.S. FWS (1988) also noted the harvest and consump tion of fish from water bodies where fishin g is p rohibited. This disre g ard or i g norance of fish advisories is corroborated in other U.S. an g ler surve y s. For exam p le, Fiore et al. (1989) noted that 72% of the res p ondents in a Wisconsin an g ler surve y were familiar with the State of Wisconsin Fish Consum p tion Health Advisor y , and 57% of the res p ondents re p orted chan g in g their fishin g or fish consum p tion habits based on the advisory . West et al. (1989) noted that 87.3% of res p ondents were "aware or g enerall y aware" of Michi g an State's fish consump tion advisories. Finall y , Connell y et al. (1990) re p orted that 82% of res p ondents knew about the New York State fish health advisories. The y also noted a s p ecific exam p le in which an g ler consump tion exceeded an advisory . The State of New York State recommends the consum p tion of no more than 12 fish meals/y ear of contaminated Lake Ontario fish s p ecies; y et, 15% of the an g lers, who fished this lake, re p orted eatin g more than 12 fish meals of the contaminated s p ecies from the lake in that y ear. The extent of the an g lin g p o p ulation can also be j ud g ed from a q uestion included in the USDA’s CSFII for the y ears 1994 and 1995. In res p onse to a q uestion of whether or not the y had eaten fish within the p ast 12 months, 84% of individuals indicated the y had. Of those who had eaten fish, 38% indicated that the fish the y had eaten was cau g ht b y themselves or someone known to the res p ondent.
4.1.3.6 U.S. An g ler Surve y s Summary of Angler Surveys
The results of the fish consump tion surve y s are comp iled in Table 4-23. These results illustrate the ran g e of fish consump tion rates identified in an g ler consump tion surve y s. There is a broad ran g e of fish consump tion rates re p orted for an g lin g p o p ulations. The ran g e extends from 2 g /da y to g reater than 200 g /da y . The variabilit y is the result of differences in the stud y desi g ns and p urp oses as well as differences in the p o p ulations surve y ed.4-28 Table 4-23 Compilation of the Angler Consumption Studies Source Population Percentile Daily Fish Notes Consumption g/day Soldat, 1970 Columbia Mean 2 Estimate of avera g e finfish River consump tion from river. An g lers Puffer, 1981; Los An g eles Median 37 Estimates for an g lers and as cited in U.S. area coastal 90th Percentile 225 famil y members who consume EPA, 1990 an g lers their catch. Consump tion rate Ethnic Sub p o p ulation includes in g estion of both Medians finfish and shellfish. African-American 24 Caucasian 46 Mexican-American 33 Oriental/Samoan 71 Pierce et al., Commence- 50th Percentile 23 Finfish onl y 1981; as cited in ment Bay in 90th Percentile 54 EPA, 1990 Tacoma, WA Maximum Re p orted 381 Fiore et al., 1989 Licensed WI Mean 12 Fish-Eaters, Dail y S p ortfish An g lers 75th Percentile 16 Intake 95th Percentile 37 Mean 26 Fish-Eaters, Total Fish Intake 75th Percentile 34 95th Percentile 63 West et al., 1989 Licensed MI Mean 19 Dail y S p ortfish Intake An g lers Mean for Minorities 22 Maximum Re p orted >200 West et al., 1993 Licensed MI Mean 15 Dail y s p ortfish intake An g lers 43 Turcotte, 1983 GA an g lers Child 10 Estimates of Freshwater Fish Teena g er 23 Intake from the Savannah River Avera g e An g ler 31 Maximum An g ler 58 Hovin g a et al., Caucasians Maximum Re p orted 132 Re-examination of Previousl y 1992 and 1993 livin g alon g Identified Hi g h-End Fish Lake Consumin g Po p ulation Michi g an Ebert et al., 1993 ME an g lers Mean 6 S p ortfish Intake licensed to 50th Percentile 2 fish inland 75th Percentile 6 waters 90th Percentile 13 95th Percentile 26Table 4-23 (continued) Compilation of the Angler Consumption Studies

Source Population Percentile Daily Fish Notes Consumption g/day

4-29 Courval et al., Data on 46% of A pp roximatel y 30% of female 1996 1,950 res p ondents res p ondents consumed no q uestion- re p orted eatin g s p ort-cau g ht fish - about double naires from s p ort-cau g ht fish that of male res p ondents. In Michi g an 1-12 times: 20% the 1 to 12 meal/month ran g e an g lers a g ed re p orted eatin g males and females about 18-34 y ears. no s p ort-cau g ht e q uall y re p resented. More than fish; 20% 13 meals/month ex p osure consumed 13 to cate g or y had a hi g her 24 meals. p ro p ortion of males. A pp roximatel y 10% consumed 25 to more than 49 meals/month. Meredith and 29 locations Com p ared Surve y to determine Malvestuto, 1996 in Alabama. harvest method consum p tion rates of an g lers Seasonal and servin g -size y ielded com p arable estimates estimates of methods of of g rams/da y consumed. freshwater estimatin g However, servin g size method fish consum p tion. y ielded four-times as man y consum p tion consumers. Harvest method y ielded estimates of 43 g rams/da y fish consumed from all sites in Alabama (number = 563).
Servin g -size method estimates 46 g rams/da y from all sites in Alabama (number = 1311)
Consum p tion lowest in the S p rin gTable 4-23 (continued) Compilation of the Angler Consumption Studies

Source Population Percentile Daily Fish Notes Consumption g/day

4-30 Shubat et al., 30 Hmon g Res p ondents ate Consump tion of cau g ht fish 1996 an g lers an avera g e of onl y . No information about (residents of 3.3±3.0 fish size of meals. S p ecies most St. Paul and meals p er month fre q uentl y cau g ht: cra pp ie, Minneap olis) (ran g e 0.5 to white bass and walle y e, other fishin g St. 12). Median 2 bass (lar g emouth and Croix or meals p er month smallmouth), northern p ike, Mississi pp i and 8.8 meals at trout, blue g ill and catfish. Rivers. A g es 90th p ercentile. 17-88. Sekerke et al., FL residents Male Mean 60 Total Home Fish Consum p tion 1994 receivin g Female Mean 40 foodstamp s

Anglers of the Columbia River, Washington Soldat (1970) measured fishin g activit y alon g the Columbia River durin g the da y li g ht hours of one calendar y ear (1967-68). The avera g e an g ler in the samp led p o p ulation made 4.7 fishin g tri p s p er y ear and cau g ht an avera g e of 1 fish p er tri p . Assumin g 200 g of fish consumed p er meal, Soldat estimated an avera g e of 0.7 fish meals were harvested p er tri p ; this results in an avera g e of 3.3 Columbia River fish meals/ y ear. The p roduct of 3.3 meals/y ear and 200 g /meal is 660 g /y ear; an estimate of 1.8 g /da y results. While not re p ortin g the hi g h-end harvestin g or consum p tion rates, Soldat re p orted that app roximatel y 15% of the 1400 an g lers interviewed cau g ht 90% of the fish. Los Angeles, California Anglers The results of studies from Puffer (1981) and Pierce et al. (1981) are described in U.S. EPA (1989). Puffer (1981) conducted 1,059 interviews with an g lers in the coastal Los An g eles area for an entire y ear. Consump tion rates were estimated for an g lers who ate their catch. These estimates were based on an g lin g fre q uenc y and the assump tion of e q ual fish consump tion amon g all fish-eatin g famil y members. The median consum p tion rate for fish and shellfish was 37 g /da y . The 90th p ercentile was 224.8 g /da y . Table 4-24 notes the hi g her consump tion rate estimates amon g Orientals and Samoans.4-31 Table 4-24 Median Recreationally Caught Fish Consumption Rate Estimates by Ethnic Group (Puffer, 1981) Ethnic Group Median Consumption Rate (g/day) African-American 24 Caucasian 46 Mexican-American 33 Oriental/Samoan 71 Total 37

Anglers of the Commencement Bay Area in Tacoma, Washington Pierce et al. (1981), as re p orted in the U.S. EPA 1990 Ex p osure Factors Handbook, conducted a total of 509 interviews in the summer and fall around Commencement Ba y in Tacoma, Washin g ton. The y assumed that 49% of the live fish wei g ht was edible and that 98% of the total catch was eaten. The estimated 50th p ercentile consum p tion rate was 23 g /da y and the estimated 90th p ercentile consum p tion rate 54 g /da y . The maximum estimated consum p tion rate was 381 g /da y based on dail y an g lin g . Anglers of the Savannah River in Georgia Turcotte (1983) estimated fish consum p tion from the Savannah River based on total harvest, p o p ulation studies and a Geor g ia fisher y surve y (Table 4-25). The an g ler surve y data, which included the number of fishin g tri p s p er y ear as well as the number and wei g hts of fish harvested p er tri p , were used to estimate the avera g e consump tion rate in the an g ler p o p ulation. Several techni q ues includin g the use of the an g ler surve y data were used to estimate the maximum fish consum p tion in the an g ler p o p ulation. Estimates of avera g e fish consump tion for children and teens was also p rovided. Table 4-25 Freshwater Fish Consumption Estimates of Turcotte (1983) Georgia Estimated Freshwater Fish Subpopulation Consumption Rate (g/day) Child 10 Teen-a g er 23 Avera g e An g ler 31 Maximum An g ler 58

Alabama Anglers Meredith and Malestuto (1996) studied an g lers in 29 locations in Alabama to estimate freshwater fish consump tion (Table 4-23). The p urp ose of their stud y had been to comp are two methods of4-32 estimatin g fish consump tion: The harvest or krill surve y comp ared with the servin g -size method of estimatin g fish consump tion. These two techni q ues y ielded com p arable estimates of mean fish intake (43 and 46 g ms/ p erson/da y , res p ectivel y ). The servin g size method identified 1311 consumers while the harvest method identified onl y 563 consumers. Wisconsin Anglers Fiore et al. (1989) surve y ed the fishin g and fish consump tion habits of 801 licensed Wisconsin an g lers. The res p ondents were divided into 2 g rou p s: fish eaters and non-eaters. The fish eaters g rou p was further subdivided into four g rou p s: those who consumed 0-1.8 k g fish/y r, 1.9-4.5 k g fish/y r, 4.6- 10.9 k g fish/y r and 10.9 < k g fish/y r. Usin g an assump tion of 8 oz. (227 g rams) fish consumed/meal, the authors estimated that the mean number of s p ort fish meals/y ear for all res p ondents (includin g noneaters) was 18. The mean number of other fish meals/ y ear includin g non-eaters was 24. The total number of fish meals/y ear was 41 for fish eaters and non-eaters combined and 42 for fish eaters onl y . Recreational an g lers were found to consume both commercial fish as well as s p ort fish. The estimated dail y consump tion rates of the eaters-onl y are p resented in Table 4-26. Table 4-26 Daily Intake of Sportfish and Total Fish for the Fish-consuming Portion of the Population Studied by Fiore et al. (1989) Percentile Daily Sport-Fish Intake Daily Total Fish Intake Mean 12 g /da y 26 g /da y 75th 16 g /da y 34 g /da y 95th 37 g /da y 63 g /da y

Michigan Anglers West et al. (1989) used a mail surve y to conduct a 7-da y fish consump tion recall stud y for licensed Michi g an an g lers. The res p ondents numbered 1104, and the res p onse rate was 47.3%. The mean fish consump tion rate for an g lers and other fish-eatin g members of their households was 18.3 g /da y , and the standard deviation was 26.8 g . Because the stud y was conducted from Januar y throu g h June, an off-season for some forms of an g lin g in Michi g an, hi g her rates of fish consump tion would be ex p ected durin g the summer and fall months. A full- y ear's mean fish consump tion rate of 19.2 g /da y was estimated from seasonal data. The mean fish consum p tion rate for minorities was estimated to be 21.7 g /da y . The hi g hest consump tion rates re p orted were over 200 g /da y ; this occurred in 0.1% of the p o p ulation surve y ed. Overall, fish consump tion rates increased with an g ler a g e and lower education levels. Lower income and education level g rou p s were found to be the onl y g rou p which consumed bottom-feeders. New York State Anglers
Connell y et al. (1990) re p orted the results of a statewide surve y of New York an g lers. The 10,314 res p ondents (62.4% res p onse rate) re p orted a mean of 20.5 da y s s p ent fishin g /y ear. Of the res p ondents, 84% fished the inland waters of New York State, and 42% re p orted fishin g in the Great Lakes. An overall mean of 45.2 fish meals p er y ear was determined for New York an g lers. The authors assumed an avera g e meal size of 8 oz. (227 g ) of fish and estimated a y earl y consump tion rate of 10.1 k g4-33 fish (27.7 g fish/da y ). Unlike the Michi g an an g ler stud y (West et al., 1989), the overall mean number of fish meals consumed increased with education level of the an g ler. Fish consump tion also increased with increasin g income; res p ondents earnin g more than $50,000/y ear consumed a mean of 54.3 meals p er y ear, and those with some p ost- g raduate education consumed a mean of 56.2 meals p er y ear. The hi g hest re p orted re g ional mean consump tion rates (58.8 meals/ y ear) occurred in the Suffolk and Nassau Counties of New York State. Anglers of Lake Michigan
As p art of a lar g er effort, Hovin g a et al. (1992 and 1993) re-examined 115 eaters of Great Lakes fish and 127 controls, who consumed smaller q uantities of fish, ori g inall y identified in a 1982 effort. Both more recent (1989) as well as 1982 consum p tion rates of Great Lakes s p ortfish were estimated. All of the p artici p ants in the stud y were Caucasian and resided in 11 communities alon g Lake Michi g an. The p o p ulation was divided into eaters (defined as individuals consumin g 10.9 k g (30 g /da y ) or g reater) and controls (defined as individuals consumin g no more than 2.72 k g /y r). The consum p tion rates for the g rou p s are re p orted in Table 4-27. Table 4-27 Fish Consumption Rate Data for Groups Identified in Hovinga et al. (1992) as Eaters and Controls Groups 1982 1982 Consumption 1989 1989 Consumption Meals/Year Rates (kg/yr) Meals/Year Rates (kg/yr) Mean (Range) Mean (Range) Mean (Range) Mean (Range) Eaters 54 (24-132) 18 (11-53) 38 (0-108) 10 (0-48) Controls -- -- 4.1 (0-52) 0.73 (0-8.8)

Anglers of Inland Waters in the State of Maine Ebert et al. (1993) examined freshwater fish consump tion rates of 1,612 an g lers licensed to fish the inland (fresh) waters of Maine. The y onl y anal y zed fish cau g ht and eaten b y the an g lers. An g lers were asked to recall the number, s p ecies and avera g e len g th of fish eaten in the p revious y ear; the actual fish consump tion rates were estimated based on an estimate of edible p ortion of the fish. The 78% of res p ondents who fished in the p revious y ear and 7% who did not fish but did consume freshwater fish were combined for the anal y sis. An g lers who p racticed ice-fishin g as well as fish cau g ht in both standin g and flowin g waters were included. Twent y -three p ercent of the an g lers consumed no freshwater fish. If the authors assumed that the fish were shared evenl y amon g all fish consumers in the an g ler's famil y , a mean consump tion rate of 3.7 g /da y was estimated for each consumer. Table 4-28 p rovides the fish consump tion rates for Maine an g lers.4-34 Table 4-28 Fish Consumption Rates for Maine Anglers Percentile All Anglers Fish-consuming Anglers Mean 5.0 6.4 50th (median) 1.1 2.0 75th 4.2 5.8 90th 11 13 95th 21 26

Florida Anglers Who Receive Food Stamps
As p art of a lar g er effort the Florida De p artment of Environmental Re g ulation attem p ted to identif y fish consump tion rates of an g lers who were thou g ht to consume hi g her rates of fish. Face-toface interviews were conducted at five Florida food stam p distribution centers. The selected food stam p distribution centers were located in counties either thou g ht to have a hi g h likelihood of subsistence an g lers or where p ollutant concentrations in fish were known. Interviews with twent y -five household's p rimar y seafood p re p arer were conducted at each center p er q uarter for an entire y ear. A total of 500 interviews was collected. The interviewed were asked to recall fish consum p tion within the last 7 day s. S p ecificall y , the res p ondents were asked to recall the s p ecies, sources and q uantities of fish consumed. Note that the res p ondents were onl y asked to recall fish meals p re p ared at home (actual consum p tion rates ma y have been hi g her if the res p ondents consumed seafood elsewhere) and that the sources of fish were from both salt and freshwater. The results of the surve y conducted b y Sekerke et al. (1994) are in Table 4-29.

Table 4-29 Fish Consumption Rates of Florida Anglers Who Receive Food Stamps Respondent No. Average Finfish Average Shellfish Consumption Consumption Adult Males 366 60 g /da y 50 g /da y Adult Females 596 40 g /da y 30 g /da y

4.1.3.7 Indi g enous Po p ulations of the United States
The tribes and ethnic g rou p s who comp rise the indi g enous p o p ulations of the United States show wide variabilit y in fish consump tion p atterns. Althou g h some tribes, such as the Nava j o, consume minimal amounts of fish as p art of their traditional culture, other native g rou p s — such as the Eskimos, Indians, and Aleuts of Alaska, or the tribes of Pu g et Sound — traditionall y consume hi g h q uantities of fish and fish p roducts. The U.S. indi g enous p o p ulations are widel y distributed g eo g rap hicall y . For exam p le, a U.S. EPA re p ort (1992b) identified 281 Federal Indian reservations that cover 54 million acres in the United States. Treaty ri g hts to g raze livestock, hunt, and fish are held b y native p eo p les for an additional 100 to 125 million acres. There are an estimated two million American Indians in the4-35 United States (U.S. EPA, 1992b). Fort y -five p ercent of these two million native p eo p le live on or near reservations and trust lands. Hi g h-end fish consumin g g rou p s include Alaska natives who number between 85,000 and 86,000 p eo p le (Nobmann et al., 1992).
Fish p roducts consumed b y indi g enous p o p ulations ma y rel y on p re p aration methods that differ from ones t yp icall y encountered in the diet of the g eneral U.S. p o p ulation. B y wa y of illustration, food intake data obtained from Alaskan natives were used to calculate nutrient intakes usin g a com p uter and software p ro g ram. These comp uterized databases had been develo p ed b y the U.S. Veterans Administration (VA) for p atients in the national Veteran's Administration hos p ital s y stem. Nobmann et al. (1992) found the y needed to add data for 210 dietary items consumed b y Alaskan Natives to the 2400 food items in the VA files. In the mid-1990s data on fish consump tion b y indi g enous p o p ulations of the United States were re p orted for Alaska Natives (Nobmann et al., 1992), Wisconsin Tribes (U.S. EPA, 1992), the Columbia River Tribes (Columbia River Inter-Tribal Fish Commission, 1994) and selected Pu g et Sound Tribes (To y et al. 1995). Findin g s from these studies can be used to assess differences in fish consum p tion between these indi g enous g rou p s and the g eneral U.S. p o p ulation. Summary of Native American Angler Surveys
Table 4-30 summarizes the re p orted consump tion rates of Native Americans detailed here. Althou g h not all Native American tribal g rou p s traditionall y include fish as p art of their diets, g rou p s livin g near rivers, lakes, and coastal areas consume a vide variet y of fish and shellfish. The hi g hest levels of fish and shellfish consum p tion are thou g ht to occur amon g tribal g rou p s livin g alon g the Pacific Coast and in Alaska. Tribal g rou p s in the Great Lakes re g ion also include fish as p art of their t yp ical diet. The data base to estimate q uantities of fish consumed has been g reatl y enhanced over the p ast five y ears with the p ublication of a number of dietary assessments conducted as p art of activities to determine ex p osure to chemical contaminants in fish.
Surve y s of Native American an g lers in the United States indicate an avera g e fish/shellfish consump tion in the ra g e of 30 to 80 g rams p er da y (U.S. EPA, 1992b; Harnl y et al., 1997; To y et al., 1995) with 90th p ercentile consum p tion of about 150 g rams/da y or hi g her (To y et al., 1995). Inclusion of data on Alaskan Native Americans results in still hi g her levels of fish and shellfish intake. For exam p le, Nobmann et al. (1992) re p orted mean fish consump tion estimates in excess of 100 g rams/da y .

Table 4-30 Fish Consumption by Native U.S. Populations Source Population Percentile Fish-Meals Notes Consumed or Fish Consumption (gms) Nobmann 351 Alaska Native Mean 109 g ms of fish and et al., 1992 adults (Eskimos, shellfish p er da y . Indians, Aleuts) U.S. EPA, Wisconsin Tribes 11 Mean 32 g ms of fish p er da y 1992b Native American Indian TribesTable 4-30 (continued) Fish Consumption by Native U.S. Populations Source Population Percentile Fish-Meals Notes Consumed or Fish Consumption (gms)

4-36 Peterson et 323 Chi pp ewa adults Mean = 1.7 fish al., 1995 > 18 y ears of a g e. meals/week. (1.9 and 1.5 fish meals/week for male and for female res p ondents, res p ectivel y ). 0.26% of males and 0.15% of females re p orted eatin g 3 or more fish-meals p er week. 50% of res p ondents ate one or less fish meals p er week. 21% of res p ondents ate three or more fish meals p er week. 2% of res p ondents ate fish-meals each da y . To y et al., Tulali p and S q uaxin 50th percentile: Re p ort contains 1995 Island Tribes. 263 Finfish, 22 g ms/da y ; data for adult sub j ects. total fish consumed, anadromous fish, 43 g ms/da y . p ela g ic, bottom 90th percentile: Data are based on Finfish, 88 g ms/da y ; an avera g e bod y total fish, 156 wei g ht of 70 g ms/da y . k g /da y . and shell fish.

Fitz g erald 97 nursin g Mohawk 24.7% ate 1-9 local Stud y conducted et al., 1995 women fish meals/ y ear durin g from 1986-1992 p re g nanc y ; in area where fish 10.3% ate >9 local are contaminated fish meals/y ear durin g with PCB p re g nanc y ; 41.2% ate 1-9 local fish meals/y ear one y ear p rior to p re g nanc y ; 15.4% ate >9 local fish meals/y ear one y ear p rior to p re g nanc y ;Table 4-30 (continued) Fish Consumption by Native U.S. Populations Source Population Percentile Fish-Meals Notes Consumed or Fish Consumption (gms)

4-37 Centers for Miccouskee Indian Local fish: 31% (58 Blue g ill most Disease Tribes of South p ersons) re p orted common s p ecies Control, Florida (1993), 2 eatin g fish from of local fish 1993 children and 183 Ever g lades durin g consumed. adults comp leted p revious 6 months. Lar g emouth bass dietar y q uestionaires Maximum dail y consumed in consum p tion: 168 g reatest q uantit y g rams Median dail y consum p tion: 3.5 g rams Marine fish: 57% (105 commonl y p ersons) consumed consumed (b y all marine fish durin g 105 of marine p revious 6 months. consumers) and Nonlocal freshwater amounts (7.0 fish: 1 individual, 25 g rams/da y g rams/da y median level) Local wildlife: 65% consumed: deer (120 p artici p ants) (57% of consumed local g ame. p artici p ants), Canned tuna most

in the lar g est

Local g ame

wildboar (10%), redbell y turtle (10%), fro g (5%) and alli g ator (3%) Gerstenber 89 O j ibwa Tribal 35% of res p ondents ate Most fre q uentl y g er et al., members from the Lake Su p erior fish consumed fish 1997 Great Lakes Re g ion 1x/week. 6.7% ate from Lake Lake Su p erior fish Su p erior: lake 2x/week. trout (37%),
Consum p tion of fish whitefish (27%). from other lakes: 12.5% ate these lakes: Walle y e. 1x/week 5.7% ate these 2x/week Hi g hest fish
89 res p ondents A p ril, Ma y , and avera g ed 29 fish June meals/y ear (ran g e zero to 150 fish meals/ y ear) walle y e (27%),

From inland

consum p tion inTable 4-30 (continued) Fish Consumption by Native U.S. Populations Source Population Percentile Fish-Meals Notes Consumed or Fish Consumption (gms)

4-38 Harnl y et Native Americans Fish-consumin g S p ortfish s p ecies: al., 1997 livin g near Clear Lake p artici p ants avera g ed catfish, p erch, California 60 g /da y of s p ortfish hitch, bass, car p and 24 g /da y of commercial fish. Commercial fish: 10% of adults salmon, crab, consumed H g intakes > shrimp . 30 μg /da y snapp er, tuna,

Wisconsin Tribes
An U.S. EPA re p ort entitled Tribes at Risk (The Wisconsin Tribes Comp arative Risk Pro j ect) (US EPA, 1992) re p orted an avera g e total dail y fish intake for Native Americans livin g in Wisconsin of 35 g ms/da y . The avera g e dail y intake of locall y harvested fish was 31.5 g rams. Peterson et al. (1995) surve y ed 323 Chi pp ewa adults over 18 y ears of a g e livin g on the Chi pp ewa reservation in Wisconsin. The surve y was conducted b y interview and included q uestions about season, s p ecies and source of fish consumed. The surve y was carried out in Ma y . Fish consump tion was found to be seasonal with the hi g hest fish consump tion occurrin g in A p ril and Ma y . Fish s p ecies t yp icall y consumed were walle y e and northern p ike, muskellun g e and bass. Durin g the months in which the Chi pp ewa ate the most fish, 50% of res p ondents re p orted eatin g one or fewer fish meals p er week, 21% re p orted eatin g three or more fish meals p er week, and 2% re p orted dail y fish consump tion. The mean number of fish meals p er week durin g the p eak consump tion p eriod was 1.7 meals; this is a pp roximatel y 42% hi g her than the 1.2 fish meals p er week that res p ondents re p orted as their usual fish consum p tion. Hi g her levels of fish consump tion were re p orted b y males (1.9 meals p er week) than b y females (1.5 meals p er week). Amon g male res p ondents 0.26% ate 3 or more fish meals p er week, whereas 0.15% of female res p ondents ate 3 or more meals of fish p er week. Unemp lo y ed p ersons t yp icall y had hi g her fish consump tion rates. Columbia River Tribes The Columbia River Inter-Tribal Fish Commission (1994) estimated fish consum p tion rates based on interviews with 513 adult tribal members of four tribes inhabitin g the Columbia River Basin (see Tables 4-31 and 4-32). The p artici p ants had been selected from p atient re g istration lists p rovided b y the Indian Health Service. Data on fish consum p tion b y 204 children under 5 y ears of a g e were obtained b y interviewin g the adults.
Fish were consumed b y over 90% of the p o p ulation with onl y 9% of the res p ondents re p ortin g no fish consump tion. The avera g e dail y consump tion rate durin g the two hi g hest intake months was 108 g rams/da y , and the dail y consump tion rate durin g the two hi g hest and lowest intake months were 108 g /da y and 31 g /da y , res p ectivel y . Members who were a g ed 60 y ears and older had an avera g e dail y consump tion rate of 74 g rams/da y . Durin g the p ast two decades, a decrease in fish consum p tion was4-39 g enerall y noted amon g res p ondents in this surve y . The maximum dail y consump tion rate for fish re p orted for this g rou p was app roximatel y 970 g rams/da y .

Table 4-31 Fish Consumption by Columbia River Tribes (Columbia River Inter-Tribal Commission, 1994) Subpopulation Mean Daily Fish Consumption (g/day)
Total Adult Po p ulation, a g ed 18 y ears and older 59 Children, a g ed 5 y ears and y oun g er 20 Adult Females 56 Adult Males 63

Table 4-32 Daily Fish Consumption Rates by Adults of Columbia River Tribes (Columbia River Inter-Tribal Commission, 1994) Percentile Amount (g/day) 50th 29-32 90th 97-130 95th 170 99th 389

Tribes of Puget Sound
A stud y of fish consum p tion amon g the Tulali p and S q uaxin Island Tribes of Pu g et Sound was com p leted in November 1994 (To y et al., 1995). The Tulali p and S q uaxin Island Tribes live p redominantl y on reservations near Pu g et Sound, Washin g ton. Both tribes rel y on commercial fishin g as an im p ortant p art of tribal income. Subsistence fishin g and shell-fishin g are si g nificant p arts of tribal members economies and diets.
The stud y was conducted between February and A p ril in 1994. Fish consump tion p ractices were assessed b y q uestionnaire and interview usin g dietar y recall methods, food models and a food fre q uenc y q uestionnaire. The food fre q uenc y q uestionnaire was aimed as identif y in g seasonal variabilit y . Questions in the interview included food p re p aration methods and obtained information on the p arts of the fish consumed. Fish consumed were cate g orized into anadromous fish (kin g salmon, socke y e salmon, coho salmon, chum salmon, p ink salmon, steelhead salmon, salmon unidentified and smelt); p ela g ic fish (cod, p ollock, sable fish, s p in y do g fish, rockfish, g reenlin g , herrin g and p erch); bottom fish (halibut, sole/flounder and stur g eon); and shell fish (manila clams, little clams, horse clams, butter clams, cockles, o y sters, mussels, shrimp , dun g eness crab, red rock crab, scallo p s, s q uid, sea urchin, sea cucumbers and moon snails).4-40 Amon g consumers of anadromous fish, local waters (i.e., Pu g et Sound) su pp lied a mean of 80% of the fish consumed. Res p ondents from the Tulali p Tribes p urchased a mean of app roximatel y twothirds of fish from g rocer y stores or restaurants, while amon g the S q uaxin Island Tribe, the source of fish was about 50% self-cau g ht and 50% p urchased from g rocer y stores or restaurants. For bottom fish, members of both tribes cau g ht about half of the fish the y consumed. Anadromous fish were much more likel y to be consumed with the skin attached. Most other fish were consumed minus the skin. A pp roximatel y 10% of the res p ondents consumed p arts of the fish other than muscle; i.e., head, bones, e gg s. Data on fish consump tion were obtained for 263 members from the Tulali p and S q uaxin Island tribes. The mean consump tion rate for women of both tribes was between 10-and-12-times hi g her than the default rate of 6.5 g rams/da y used b y some p arts of the U.S. g overnment to estimate fish intake. Amon g male members of both tribes, the consum p tion rate was app roximatel y 14-times hi g her than the default rate. The 50th p ercentile consum p tion rate for finfish for both tribes combined was 32 g rams/k g bod y wei g ht/da y . Male members of the Tulali p and S q uaxin Island tribes had avera g e bod y wei g hts of 189 p ounds and 204 p ounds, res p ectivel y . Female members of the Tulali p and S q uaxin Island tribes wei g hed on avera g e 166 p ounds and 150 p ounds, res p ectivel y . If an avera g e bod y wei g ht is assumed to be 70 k g , the dail y fish consump tion rate for both tribes for adults was 73 g rams p er da y with a 90th p ercentile value of 156 g rams p er da y for total fish. Fish consump tion data for selected cate g ories of fish are shown in Table 4-33.

Table 4-33 Fish Consumption (gms/kg bw/day) by the Tulalip and Squaxin Island Tribes (Toy et al., 1995)
Type of 5th 50th 90th 95th Mean SE 95th

Fish Percentile Percentile Percentile Percentile Percent CI Anadromous .0087 .2281 1.2026 1.9127 .4600 .0345 .3925, 0.5275 Pela g ic .0000 .0068 .1026 .2248 .0390 .0046 .0300, 0.0480 Bottom .0000 .0152 .1095 .2408 .0482 .0060 .0364, 0.4375 Shell .0000 .1795 1.0743 1.4475 .3701 .0343 .3027, 0.4375 Fish Other .0000 .0000 .0489 .1488 .0210 .0029 .0152, 0.0268 Fish Total .0200 .3200 .1350 2.1800 .5745 .0458 .4847, 0.6643 Finfish Total .0495 .6081 2.2267 3.2292 1.0151 .0865 .8456, 1.1846 All Fish

Durin g the surve y p eriod, 21 of the 263 tribal members surve y ed re p orted fish consump tion rates g reater than three standard deviations from the mean consum p tion rate. For examp le, six sub j ects re p orted consump tions of 5.85, 6.26, 9.85, 11.0, 22.6 and 11.2 g rams of finfish and shell fish/k g bod y wei g ht/da y . If a 70-k g bod y wei g ht is assumed these consum p tion rates corres p ond to 410, 438, 690, 770 and 1582 g rams p er da y .4-41 Mohawk Tribe
A stud y of fish consum p tion amon g 97 nursin g Mohawk women in rural New York State was conducted from 1986 to 1992 (Fitz g erald et al., 1995). Fish consump tion advisories had been issued in the area due to p ol y chlorinated bi p hen y l (PCB) contamination of the local water bod y . Usin g food fre q uenc y histor y and a lon g -term dietar y histor y , the women were asked about their consum p tion of locall y cau g ht fish durin g three s p ecific p eriods of time: durin g p re g nanc y , the y ear p rior to p re g nanc y , and more than a y ear before p re g nanc y . For com p arison, the stud y also surve y ed fish consump tion rates amon g 154 nursin g ( p rimaril y caucasian) women from nei g hborin g counties. The socioeconomic status of the women of the control g rou p were similar to that of the Mohawk women. The fish in these counties had back g round PCB concentrations. The results (Table 4-34) showed that the Mohawk women had a hi g her p revalence of consumin g locall y cau g ht fish than the comp arison g rou p in the two intervals assessed p rior to the p re g nanc y ; the p revalence of local fish consum p tion durin g p re g nanc y for the two g rou p s was comp arable. A decrease in local fish consump tion rates was also noted over time; these ma y be related to the issuance of advisories.

Table 4-34 Local Fish Meals Consumed By Time Period for the Mohawk and Comparison Nursing Mothers (Fitzgerald et al., 1995)
Fish During Pregnancy 1 Year Before Pregnancy >1 Year Before Pregnancy Meals/ Year Mohawk Control Mohawk Control Mohawk Control 0 64.9% 70.8% 43.3% 64.3% 20.6% 60.4% 1-9 24.7% 15.6% 41.2% 20.1% 43.3% 22.7% 10-19 5.2% 4.5% 4.1% 3.9% 6.2% 5.2% >19 5.1% 9.1% 11.3% 11.7% 29.9% 11.7%

Native Americans near Clear Lake, California
Harnl y et al. (1997) found that Native Americans livin g near Clear Lake, California consumed an avera g e of 84 g rams of fish/da y (60 g /da y s p ort fish p lus 24 g /da y of commercial fish). Ten p ercent of adults re p orted mercur y intakes over 30 μg /da y . The most p o p ular s p ecies of s p ortfish were: catfish, p erch, hitch, bass, and car p . Commercial s p ecies most commonl y eaten were: snapp er, tuna, salmon, crab, and shrimp . Great Lakes Tribes
Members of the O j ibwa live in the Great Lakes re g ion of the United States and Canada. Gerstenber g er et al. (1997) re p orted that app roximatel y 35% of the res p ondents (89 members of the O

j ibwa Tribes) consumed Lake Su p erior fish at least once a week with 7% of this g rou p consumin g Lake Su p erior fish at least twice a week. The most commonl y consumed Lake Su p erior-ori g in fish were lake trout, walle y e, and whitefish. In addition, fish were consumed from inland lakes with 12% of4-42 re p onsdnets eatin g inland lake fish once a week and 6% consumin g these fish twice a week. Walle y e was the most common s p ecies of fish consumed from these inland lake sources.
4.1.4 Summar y of Hawaiian Island Fish Consump tion Data The CSFII 1989-1991 did not include the Hawaiian Islands. To the knowled g e of the authors of the Mercur y Stud y Re p ort to Con g ress, data describin g fish consump tion b y the g eneral Hawaiian p o p ulation that estimate Island-wide levels of consum p tion have not been re p orted. However, re p orts on commercial utilization of seafood (Hi g uchi and Poole y , 1985; Hud g ins, 1980) and anal y sis of e p idemiolo gy data (Wilkens and Hankin, p ersonal communication, 1996) p rovide a basis to describe g eneral p atterns of consump tion. Overall, seafood consump tion in Hawaii is much hi g her than in the conti g uous United States. On a p er cap ita basis, the United States as a whole consumed 5.45 kg and 5.91 k g (12 and 13 p ounds) of seafood in 1973 and 1977, res p ectivel y (Hud g ins, 1980). B y contrast Hawaiian p er cap ita consum p tion for all fish p roducts was 11.14 k g (24.5 p ounds) in 1972 and 8.77 k g (19.3 p ounds) in 1974.
The most p o p ular s p ecies of fish and shellfish consumed were moderatel y comp arable between Hawaii and the conti g uous 48 states. The methods of food p re p aration differed, however, with raw fish bein g far more commonl y consumed in Hawaii. Samp led at the retail trade level the most commonl y p urchased fish were: tuna, mahimahi, and shellfish [see Table 4-35 which is based on data in Hi g uchi and Poole y (1985)]. A surve y of seafood consump tion b y families was identified. In 1987, the De p artment of Business and Economic Develo p ment (State of Hawaii, 1987) conducted a surve y of 400 residents selected on a random di g it dialin g basis of a p o p ulation re p resentin g 80% of total state seafood consump tion. All data were collected in Jul y and Au g ust, 1987 and would not reflect an y seasonal differences in fish/shellfish consum p tion. The res p ondents were asked to describe seafood consum p tion b y their families. Shrimp was the most p o p ular seafood with mahimahi or dol p hin fish as the second most p o p ular (Hawaii Seafood, 1988). Re p orts on fish consump tion in Hawaii se p arate various s p ecies of tuna: ahi (Hawaiian y ellowfin tuna, bi g e y e tuna & albacore tuna), aku (Hawaiian ski pj ack tuna), and tuna. In 1987, nearl y 66% of the 400 families surve y ed had seafood at least once a week and 30% twice a week. Onl y 4% did not re p ort consumin g seafood durin g the p revious week based on a tele p hone surve y . Wilkens and Hankin ( p ersonal communication, 28 February 1996) anal y zed fish intake from 1856 control sub j ects from Oahu who p artici p ated in research studies conducted b y the E p idemiolo gy Pro g ram of the Cancer Research Center of Hawaii, Universit y of Hawaii at Manoa. These sub j ects were asked about consump tion over a one- y ear p eriod p rior to the interview. Within this g rou p the most commonl y consumed fish was tuna [canned with tuna s p ecies undesi g nated (70.8 % of sub j ects re p ortin g consump tion)]; shrim p (47.7% of sub j ects); tuna ( y ellowfin fresh desi g nated aku, ahi with 42.2% of sub j ects re p ortin g consump tion); mahimahi [(or dol p hin) with 32.5% of res p ondents re p ortin g consump tion]; and canned sardines (with 29.1% of sub j ects re p ortin g consump tion).4-43 Table 4-35 Species Composition of Hawaii's Retail Seafood Trade — 1981 Purchases Higuchi and Pooley (1985)
Fish/Shellfish Pounds Purchased Percent of Total Purchases Tuna 11,600,000 20.9 Ahi (Hawaiian y ellowfin, bi g e y e & albacore) (5,400,000) Billfish (includin g swordfish) 5,900,000 11.3 and shark Mahimahi and ono (wahoo) 9,900,000 17.7 Akule (Hawaiian bi g e y e scad) 4,00,000 6.9 and o p elu Bottom fish 2,600,000 7.0 Reef fish 3,500,000 5.3 Shellfish 8,200,000 15.5 Shrim p (4,200,000) Lobster (900,000) Other s p ecies 8,300,000 15.4 Salmon/trout (1,500,000) Snapp er (1,800,000) Frozen filets (2,300,000) Frozen sticks/blocks (1,400,000) Total 54,000,000 100.0

4.1.5 Summar y of Alaskan Fish Consump tion Data
The CSFII anal y ses of food intake b y the USDA include the 48 conti g uous states but do not include Alaska or Hawaii. A number of investi g ators have p ublished data on fish consum p tion in Alaska b y members of native p o p ulations (e. g ., Inuits, Eskimos) and p ersons livin g in isolated surroundin g s. These re p orts focus on nutritional/health benefits of hi g h levels of fish consump tion, food habits of native p o p ulations, and/or effects of bioaccumulation of chemicals in the aq uatic food web.
4.1.5.1 General Po p ulation
After contactin g p rofessionals from the Alaskan health de p artments and re p resentatives of the U.S. Centers for Disease Control in Anchora g e, the authors of this re p ort have not identified g eneral p o p ulation data on fish consum p tion amon g Alaskan residents who are not p art of native p o p ulation g rou p s, subsistence fishers/hunters, or p ersons livin g in remote sites. Patterns of fish consum p tion amon g urban residents (e. g ., Juneau, Nome, Anchora g e) app ear not to be documented in the p ublished literature.4-44 4.1.5.2 Non-urban Alaskan Po p ulations
Native p eo p le livin g in the Arctic rel y on traditional or "countr y " foods for cultural and economic reasons. The p urp ose of the current discussion is not to assess the com p arative risks and benefits of these foods. The risks and benefits of these food consum p tion habits have been com p ared b y man y investi g ators and health p rofessionals (amon g others see Wormworth, 1995; Kinloch et al., 1992; Bj erre g aard, 1995).
Des p ite a de g ree of acculturation in the area of foods, native foods were still eaten fre q uentl y b y Alaskan Native p eo p les based on results of the 1987-1988 surve y . Diets that include ma j or q uantities of fish (es p eciall y salmon) and sea mammals retain a ma j or p lace in the lives of Alaskan Native p eo p les. The consump tion of traditional p re p arations of salmon and other fish continues; this includes fermented foods such as salmon heads and e gg s, other fish and their e gg s, seal, beaver, caribou and whale.
Diets of Native Alaskans differ from the g eneral p o p ulation and rel y more extensivel y on fish and marine mammals. These are p o p ulation g rou p s that are characterized b y p atterns of food consump tion that reflect availabilit y of locall y available foods and include food p re p aration techni q ues that differ from those usuall y identified in nutrient data bases. For exam p le, Nobmann et al. (1992) surve y ed a p o p ulation of Alaska Natives that included Eskimos (53%), Indians (34%), and Aleuts (13%). The distribution of stud y p artici p ants was p ro p ortional to the distribution of Alaska Natives re p orted in the 1980 Census. The 1990 Census identified an overall p o p ulation of 85,698 p ersons as Alaska Natives. Nobmann et al. (1992) indicated that Alaska Natives have traditionall y subsisted on fish; marine mammals; g ame; a few p lants such as seaweed, willow leaves, and sourdock; and berries such as blueberries and salmonberries rather than on a p lant-based diet. In p re p arin g a nutrient anal y sis of the food consumed in eleven communities that re p resented different ethnic and socioeconomic re g ions of Alaska, these investi g ators added nutrient values for 210 foods consumed b y Alaska Natives in addition to the 2400 foods p resent in the Veteran's Administration's nutrient data base. Nobmann et al. (1992) found fish were an imp ortant p art of the diet. The mean dail y intake of fish and shellfish of Alaska Natives was 109 g rams/da y . Fish consump tion was more fre q uent in the summer and fall and g ame meat was eaten more often in the winter. Quantitative information on dietary intakes of Native Alaskan p o p ulations are few. Estimates can be derived from harvest surve y data, but these have limitations because not all harvested animals are consumed nor are all edible p ortions consumed. Other edible p ortions ma y be fed to domestic animals (e. g ., sled do g s). Substantial variability in intake of foods includin g rin g ed seal, bearded seal, muktuk (belu g a skin with an underl y in g thin la y er of fat) and walrus has been re p orted (A y otte et al., 1995).
Dietar y anal y ses on seasonal food intakes of 351 Alaska Native adults from eleven communities were p erformed durin g 1987-1988 (Nobmann et al., 1992). Alaska Natives include Eskimos, Indians and Aleuts. There is no main a g ricultural cro p in Alaska which when combined with a short g rowin g season, results in limited availabilit y of edible p lants. Alaska Natives have traditionall y relied on a diet of fish, sea mammals, g ame and a few native p lants (seaweed, willow leaves, and sourdock) and berries (such as, blueberries and salmon berries). Althou g h consump tion of si g nificant amounts of commerciall y p roduced foods occurs, use of subsistence foods continues.
The surve y samp le of 351 adults, a g ed 21-60 y ears, was drawn from eleven communities. Information was obtained usin g 24-hour dietar y recalls durin g five seasons over an 18-month p eriod. Fish were consumed much more fre q uentl y b y Alaska Natives than b y the g eneral U.S. p o p ulation. Fish ranked as the fourth most fre q uentl y consumed food b y Alaska Natives comp ared with the 39th most4-45 fre q uentl y consumed food b y p artici p ants in the nationall y re p resentative Second National Health and Nutrition Assessment Surve y (NHANES II). The mean dail y intake of fish and shellfish for Alaska Natives was 109 g rams/da y contrasted with an intake of 17 g rams p er da y for the g eneral U.S. p o p ulation described in NHANES II. Amon g Alaska Natives fish was consumed more fre q uentl y in the summer and fall months.
Several extensive data sets on mercur y concentrations in marine mammals consumed b y indi g enous p o p ulations livin g in the circump olar re g ions have been p ublished (Wa g emann et al., 1996; Caurant et al., 1996; Dietz et al., 1996). Anal y ses that determined chemicall y s p eciated mercur y have shown that mercur y p resent in muscle tissue is lar g el y (>75%) or g anic mercur y (i.e., meth y lmercur y (Caurant et al., 1996)). B y contrast, mercur y p resent in or g ans such as liver and kidne y is p redominantl y in an inor g anic form (Caurant et al., 1996).
4.1.5.3 Alaskans from Subsistence Economies Wolfe and Walker (1987) described the p roductivit y and g eo g rap hic distribution of subsistence economies in Alaska durin g the 1980s. Based on a sam p le of 98 communities, the economic contributions of harvests of fish, land mammals, marine mammals and other wild resources were anal y zed. Noncommercial fishin g and huntin g p la y a ma j or role in the economic and social lives of p ersons livin g in these communities. Harvest sizes in these communities were established b y detailed retros p ective interviews with harvesters from a sam p le of households within each community . Harvests were estimated for a 12-month p eriod. Data were collected in p ounds of dressed wei g ht p er cap ita p er y ear. Althou g h it varies b y communit y and wildlife s p ecies, g enerall y "dressed wei g ht" is app roximatel y 70 to 75% of the round wei g ht for fish and 20 to 60% of round wei g ht for marine animals. Dressed wei g ht is the p ortion of the kill brou g ht into the kitchen for use, includin g bones for p articular s p ecies. The cate g or y "fish" contains s p ecies includin g salmon, whitefish, herrin g , char, halibut, and p ike. "Land mammals" included s p ecies such as moose, caribou, deer, black bear, snowshoe and tundra hare, beaver and p orcu p ines. "Marine mammals" consisted of seal, walrus and whale. "Other" contained birds, marine invertebrates, and certain p lant p roducts such as berries.
Substantial communit y -to-communit y variabilit y in the harvestin g of fish, land mammals, marine mammals and other wild resources were noted (Wolfe and Walker, 1985). Units are p ounds "dressed wei g ht" p er cap ita p er y ear. The median harvest was 252 p ounds with the hi g hest value a pp roximatel y 1500 p ounds. Wild harvests ( q uantities of fish, land mammals and marine mammals) in 46% of the sam p led Alaskan communities exceeded the western U.S. consum p tion of meat, fish, and p oultr y . These communities have been g rou p ed b y g eneral ecolo g ical zones which corres p ond to historic/cultural areas: Arctic-Subarctic Coast, Aleutian-Pacific Coast, Subarctic Interior, Northwest Coast and contem p orar y urban p o p ulation centers. The Arctic-Subarctic Coast dis p la y ed the g reatest subsistence harvests of the five ecolo g ical zones (610 p ounds p er cap ita), due p rimaril y to the relativel y g reater harvests of fish and marine animals. For all re g ions the fishin g outp ut is g reater than the huntin g ; fishin g comp rises 57 - 68% of total subsistence out p ut. Above 60  north latitude fishin g p redominates other wildlife harvests, exce p t for the extreme Arctic coastal sea mammal-caribou huntin g communities. Resource harvests of fish ("dressed wei g ht" on a p er cap ita basis) b y ecolo g ical zone (and cultural area) were these: ArcticSubarctic Coast (Inu p ia q -Yu p 'ik), 363 p ounds/y ear or 452 g rams/da y ; Aleutian-Pacific Coast (AleutSu gp ia q ), 251 p ounds/y ear or 312 g rams/da y ; Subarctic Interior (Athap askan), 256 p ounds/y ear or 318 g rams/da y ; Northwest Coast (Tin g it-Haida), 122 p ounds/y ear or 152 g rams/da y ; and Other (Anchora g e, Fairbanks, Juneau, Matanuska-Susitna Borou g h, and Southern Cook Inlet), 28 p ounds/y ear or 35 g rams/da y . Consump tion of marine mammals was re p orted amon g Yu p ik Eskimos livin g in either a coastal or river villa g e of southwest Alaska (Parkinson et al., 1994). Concentrations of p lasma ome g a-3 fatt y4-46 acids were elevated (between 6.8 and 13 times) amon g the Yu p ic-s p eakin g Eskimos livin g in two se p arate villa g es comp ared with non-Native control sub j ects (Parkinson et al., 1994). Concentrations of ome g a-3 fatt y acids in p lasma p hos p holi p id has been shown to be a valid surro g ate of fish consump tion (Silverman et al., 1990). Amon g coastal-villa g e p artici p ants the concentrations of eicosa p entaonoic and docosahexaenoic acids reflected hi g her consump tion of marine fish and marine mammals and the use of seal oil in food p re p aration. Amon g river villa g e natives, the increase reflected hi g her consump tion of salmon.
The Division of Subsistence of the Alaska De p artment of Fish and Game (Robert J. Wolfe, p ersonal communications, 1997) has p rovided estimates of the mean p er cap ita harvests of subsistence fish, shellfish, and marine mammals in rural Alaska areas (Table 4-36). Combined harvests of fish/shellfish/marine mammals avera g ed app roximatel y 350 g rams/da y for all rural areas combined. The hi g hest intakes were found in the Western, Interior and Arctic re g ions with harvests of 693, 577, and 482 g rams/da y , res p ectivel y . Marine mammal consum p tion was p articularl y hi g h in the Arctic re g ion with an avera g e of app roximatel y 270 g rams/da y consumed. Comp arable estimates of marine mammal consump tion were re p orted b y Chan (1997) for an Inuit communit y based on dietar y information g athered b y the Centre for Indi g enous Peo p les’ Nutrition and the Environment (Table 4-37). Usin g the Centre’s database for contaminants, Chan estimated that mercur y intakes were 185 μg mercur y /da y with 170 μg of mercur y comin g from marine mammal meat.
Consump tion of marine mammals results in very hi g h ex p osures to meth y lmercur y . Wolfe (1997) p rovided data on mean p er cap ita harvest of marine mammals in the Arctic re g ion of rural Alaska of about 290 g rams/da y . Greater details of t yp es of marine mammals consumed, mercur y concentrations found in these mammals, and estimates of q uantities of mammals consumed have been p ublished b y Canadian investi g ators (Jensen et al. 1997; Chan, 1997) and b y the investi g ators in Greenland and Denmark (Dietz et al., 1996).

Table 4-36 Mean Per Capita Harvest of Fish and Marine Mammals (g/day) (Wolfe, personal communication, 1997)

Alaska Rural Area Fish Shellfish Marine Fish/Shellfish Fish/Shellfish/ Mammals Marine Mammals Southcentral-Prince 114 7 4 122 126

William Sound Kodiak Island 132 17 2 149 152

Southeast 119 32 7 152 159 Southwest-Aleutian 299 7 12 307 319 Interior 577 0 0 577 577 Arctic 194 1 267 195 482 Western 605 0 88 605 693 All Rural Areas 276 11 65 267 352 Combined4-47 Table 4-37 Estimated Daily Intake of Food and Mercury for Arctic Inuit (Adapted from Chan, 1997) Food group Food (g/day) Mercury (μg/day) Marine mammal meat 199 170 Marine mammal blubber 30 2.4 Terrestrial mammal meat 147 4.0 Terrestial mammal or g ans 1 0.9 Fish 42 6.6 Birds 2 0.8 Plants 2 0.0 Total 423 185

Marine mammals are p rimaril y ex p osed to meth y lmercur y (Caurant et al., 1996). Mercur y p resent in flesh of marine mammals is lar g el y meth y lmercur y . For exam p le, Caurant et al. (1996) identified an avera g e of 78% or g anic mercur y in muscle of p ilot whales ( Globicepala melas ) and 23% or g anic mercur y in p ilot whale liver. Mercury in or g ans such as liver and kidne y app ears to be demeth y lated and stored in a form combined with selenium, which has been re g arded as a detoxification mechanism for the marine mammals (Caurant et al., 1996). Detailed date on mercur y concentration in the northern marine ecos y stem were re p orted b y Dietz et al. (1996) includin g information on mercur y concentration in molluscs, crustaceans, fish, seabirds, seals, whales, and p olar bears.
Amon g the Inuit in coastal communities of the Canadian Arctic and Greenland, marine mammals are an im p ortant source of food. Food items include the flesh and some or g ans of rin g ed seals ( Phoca hispida ) and the flesh, but p referentiall y skin meat and liver of rin g ed seals and muktuk and blubber of whales are eaten raw or cooked. Muktuk and the flesh, liver, intestines, and blubber of walrus are also eaten after fermentation (Wa g emann et al., 1996).
Throu g hout the Arctic, the mean mercury concentration in muscle of belu g a whale avera g ed between 0.7 and 1.34 μg mercur y / g ram wet wei g ht of tissue (Wa g emann et al., 1996). Muktuk (skin as a whole) of belu g a avera g ed between app roximatel y 0.6 and 0.8 μg mercur y / g wet wei g ht. The skin of cetaceans (whales, dol p hins, p orp oises) consists of four la y ers with the mercur y concentration increasin g toward the outermost la y ers of skin. In this outermost la y er of skin, mercur y concentration were about 1.5 μg / g ram. Durin g moltin g , about 20% of the total mercury in skin is lost annuall y . Muscle tissue of narwhal avera g ed between 0.8 and 1.0 μg / g , while muktuk avera g ed around 0.6 μg / g wet wei g ht (Wa g emann et al., 1996). Muscle flesh of rin g ed seals had avera g e mercur y concentrations in the ran g e of 0.4 and 0.7 μg / g with most of the mercur y p resent as meth y lmercur y . Liver mercur y concentrations avera g ed in the ran g e of 20 to 30 μg / g , but this was p rimaril y p resent as inor g anic mercur y . Kidne y contained between 1 and 3 pp m mercur y (Wa g emann et al., 1996).4-48 Overall, g rou p s consumin g muscle and muktuk from marine mammals have much hi g her ex p osures to meth y lmercur y that do g rou p s who consume p rimaril y fish and/or terrestrial mammals. Chan (in p ress) estimated ex p osures over 180 μg mercur y /da y for Arctic Inuits. To whatever extent or g ans (s p ecificall y liver and kidne y ) are consumed, these typ icall y contain hi g her concentrations of mercur y but with a lower fraction of meth y lmercur y than found in muscle tissue.
4.1.6 Summar y of Canadian Data on Mercury Intake from Fish and Marine Mammals
The Northern Contaminants Pro g ram on the De p artment of Indian Affairs and Northern Develo p ment of the Canadian Government p ublished a comp ilation of contaminant data includin g mercur y concentrations in fish and marine mammals (Jensen et al., 1997). Most of the traditionally harvested fish and land and marine animals consumed are lon g -lived and are from the hi g her tro p hic levels of the food chain which contain g reater concentrations of meth y lmercur y than are found in non p redator y fish.
Several extensive data sets on mercur y concentrations in marine mammals consumed b y indi g enous p o p ulations livin g in the circump olar re g ions have been p ublished (Wa g emann et al., 1996; Caurant et al., 1996; Dietz et al., 1996). Anal y ses that determined chemicall y s p eciated mercur y have shown that mercur y p resent in muscle tissue is lar g el y (>75%) or g anic mercur y (i.e., meth y lmercur y ) (Caurant et al., 1996). B y contrast, mercur y p resent in or g ans such as liver and kidne y is p redominantl y in an inor g anic form (Caurant et al., 1996).
Wa g emann et al. (1997) have p rovided an overview of mercur y concentrations in Arctic whales and rin g ed seals. The Inuit in coastal communities of the Canadian Arctic and Greenland hunt and consume marine mammals for food. The flesh and some or g ans of rin g ed seals ( Phoca hispida ) and flesh but p referentiall y skin (muktuk) of belu g as ( Delphinapterus leucas ) and narwal ( Monodon monoceros ) contribute si g nificantl y to the Inuit diet. Throu g hout the Arctic, the mean concentrations in Belu g a muscle avera g ed 0.70 to 1.34 μg mercur y / g ram wet wei g ht (Wa g emann et al., 1996). Mean mercury concentrations in the muktuk (skin as a whole) of belu g as samp led in the western (1993-1994) and the eastern Arctic (1993-1994) were 0.78 and 0.59 μg mercur y / g ram wet wei g ht (Wa g emann et al., 1996). Mean mercur y concentrations for narwhal samp les collected in the p eriod 1992-1994 were 0.59, 1.03, 10.8, and 1.93 μg mercur y / g ram wet wei g ht in muktuk, muscle, liver, and kidne y , res p ectivel y (Wa g emann et al., 1996). Muscle tissue of rin g ed seals contained mercury in concentrations avera g in g between 0.4 and app roximatel y 0.7 μg mercur y / g ram wet wei g ht. Liver tissue avera g ed between app roximatel y 8 and 30 μg mercur y / g ram wet wei g ht. Kidne y tissues avera g ed between 1.5 and 3.2 μg mercur y / g ram wet wei g ht.
Extensive data on mercury concentrations in multi p le tissues from a wide variety of molluscs, crustacea, fish, seabirds, and marine mammals (seals, whales, and p orp oises), and p olar bears collected in Greenland were p ublished b y Dietz et al. (1996). Chemicall y s p eciated mercur y concentrations in tissues of p ilot whales have been determined b y Caurant et al. (1996). The p ercent or g anic mercur y (i.e., meth y lmercur y ) in muscle tissue avera g ed over 75%. Liver contained a smaller fraction or g anic mercur y , avera g in g app roximatel y 23% or g anic mercur y . Marine mammals are p rinci p all y ex p osed to meth y lmercur y , which is the main p h y sico-chemical form of stora g e in fish (Caurant et al., 1996). Althou g h demeth y lation b y liver ma y serve as a means of p rotectin g the marine mammal a g ainst adverse effects of meth y lmercur y , the p resence of or g anic mercur y in the marine mammal’s muscle means that consump tion of flesh from these mammals will result in ex p osure to or g anic mercur y . Jensen et al. (1997) in the Canadian Arctic Contaminants Assessment Report identified wide variabilit y in the consump tion of fish and marine mammals b y various abori g inal g rou p s. Chan (1997) summarized results from an extensive number of dietar y surve y s of Northern p eo p les from the Dene4-49 (re g istered Indian) communities and the Inuit communities (Tables 4-38 and 4-39). The Dene were estimated to have a mean consum p tion of 80 g rams/da y of fish. The Inuit communities were estimated to have a fish consump tion of 42 g rams/da y , a marine mammal consum p tion of app roximatel y 230 g rams/da y Table 4-38 Mercury Concentrations (μg Hg/g wet weight) in Traditional Foods Consumed by Canadian Aboriginal Peoples (Modified from Chan, 1997) Food Group Number of Number of Arithmetic Standard Maximum Sites Samples Mean Deviation Marine Mammal Meat 32 764 0.85 1.05 33.4 Marine Mammal Blubber 6 71 0.08 0.05 0.13

Terrestrial Mammal Meat 6 19 0.03 0.02 0.17

Terrestrial Mammal Or g ans 14 254 0.86 0.90 3.06

Fish 799 31,441 0.46 0.52 12.3 Birds 24 216 0.38 0.59 4.4 Plants 8 14 0.02 0.02 0.05

Table 4-39 Estimated Daily Intake of Mercury Using Contaminant Data Base and Dietary Information from Dene and Inuit Communities in Canada (Adapted from Chan, 1997) Food Group Dene Community Inuit Community Food Mercury Food Mercury (g/day) (μg/day) (g/day) (μg/day) Marine Mammal Meat 0 0 199 170 Marine Mammal Blubber 0 0 30 2 Terrestrial Mammal Meat 205 6 147 4 Terrestrial Mammal 23 20 1 1 Or g ans

Fish 80 13 42 7 Birds 8 1 2 1 Plants 2 0 2 0.0 Total 318 40 423 1854-50 (199 g rams of meat and 30 g rams of blubber). These mean consump tions were associated with a mercury intake of 39 μg mercur y /da y for the Dene communit y and 185 μg mercur y /da y for an Inuit communit y . For the Inuit communit y , 170 μg mercur y /da y came from marine mammal meat. 4.2 Trends in Fish and Shellfish Consumption in the United States
Descri p tion of lon g -term trends in fish and shellfish consum p tion are based on data p rovided b y the National Marine Fisheries Service of the U.S. De p artment of Commerce. Detailed information on trends in the 1990s, and forecasts for future p roduction and consump tion of fish and shellfish, are based on p ro j ections described in the Annual Report on the United States Seafood Industry p ublished b y H.M. Johnson & Associates (1997).
4.2.1 Fish and Shellfish Consump tion: United States, 1975 to 1995
Data for the U.S. consump tion and utilization of fish and shellfish have been obtained from the World Wide Web (htt p ://remora.ss p .mnfs. g ov/commercial/landin g s/index.html). Landin g s are re p orted in p ounds of round (i.e., live) wei g ht for all s p ecies or g rou p s exce p t univalve and bivalve molluscs, such as clams, o y sters, and scallo p s. For the univalves and bivalve molluscs, landin g s are re p orted as p ounds of meat which excludes shell wei g ht. Landin g s to not include a q uaculture p roducts exce p t for clams and o y sters. A q uaculture p roducts are an increasin g source of fish and shellfish for some s p ecies of seafood (Johnson 1997).
U.S. p er cap ita consum p tion of commercial fish and shellfish has increased from the earl y p art of this centur y until p resent. The ma j or increases occurred p ost-1970. In 1910, for exam p le, U.S. citizens consumed an avera g e of 11.0 p ounds (edible meats) of commercial fish and shellfish. The consum p tion in 1970 was 11.8 p ounds p er cap ita, but b y 1990 had increased to 15.0 p ounds p er cap ita.
Two ma j or differences are associated with this trend. First, there was a ma j or increase in p o p ulation from 92.2 million p ersons in 1910, to 201.9 individuals in 1970s, and 247.8 million citizens in 1990. In 1995 (the latest y ear this source p rovide statistics), the civilian resident p o p ulation was estimated at 261.4 million p ersons. Combined with increased consum p tion on a p er cap ita basis, the seafood market has dramaticall y increased throu g hout this centur y . The second ma j or chan g e was in availabilit y of trans p ortation and in food p rocessin g . Chan g es between 1910 and 1995 are shown in Table 4-40. Consum p tion of cured fish dramaticall y decreased from about 36% of p er cap ita intake in 1910, to 2.0% in 1990. Fresh or frozen fish were about 40% of p er cap ita intake in 1910 and increased to about 67% (two-thirds) of fish and shellfish intake b y 1990 and 1995. The consump tion of canned fish and shellfish chan g ed the least re p resentin g about one-fourth of all fish/shellfish intake in 1910 and about one-third of intake in 1990 and 1995. Table 4-40 Percent of Fish/Shellfish by Processing Type between 1910 and 1995 (Source: National Marine Fisheries Service, 1997)

Year Fresh/Frozen Canned Cured 1910 39.1 24.5 36.4 1970 58.5 38.1 4.04-51 1990 64.7 33.3 2.0 1995 66.7 31.3 2.0

4.2.1.1 United States: Ma j or Im p orts and Ex p orts of Fish and Shellfish
Durin g the p eriod 1990 throu g h 1994 the United States was the second lar g est im p orter of seven fisher y commodit y g rou p s, as well as the second lar g est ex p orter of these g rou p s. The lar g est im p orter was Jap an and the third lar g est im p orter (after the United States) was France followed b y S p ain, German y , and Ital y . On a value basis, Canada in the second lar g est tradin g p artner for the United States after Jap an (Johnson, 1997).
The to p five ex p orters of seafood were Thailand, United States, Norwa y , Denmark, and China. Thailand is the leadin g su pp lier of seafood to the United States on a value basis, shi pp in g p rimaril y shrim p (Johnson, 1997). Canada was the leadin g seafood su pp lier on a volume basis (Johnson, 1997). The seven fisher y commodit y g rou p s are: 1. Fish, fresh, chilled or frozen; 2. Fish, dried, salted, or smoked; 3. Crustaceans and mollusks, fresh, dried, salted, etc.; 4. Fish p roducts and p re p arations, whether or not in airti g ht containers; 5. Crustacean and mollusk p roducts and p re p arations, whether or not in airti g ht containers; 6. Oils and fats, crude or refined, or a q uatic animal ori g in; and 7. Meals, soluble and similar animal food stuffs of a q uatic animal ori g in.
4.2.1.2 U.S. Su pp l y of Edible Commercial Fishery Products: 1990 and 1995
The su pp l y of the p roducts shown in Table 4-41 is ex p ressed as round or live wei g ht. An y com p arison of these values with food consum p tion data must consider that the edible p ortion is smaller than the live wei g ht. Factors for edible p ortion comp ared with live/round wei g ht were p ublished in the National Research Council’s re p ort on Seafood Safety (NRC/NAS, 1990). Total U.S. consum p tion of fish and shellfish must also include self-cau g ht and recreationall y cau g ht fish, as well as other sources that are not tabulated throu g h commercial channels.

Table 4-41 U.S. Supply of Edible Commercial Fishery Products: 1990 and 1995 (Round or Live Weight in Million Pounds) Source: National Marine Fisheries Service Year Domestic Commercial Imports Total Landings Million Percent Million Percent Million Pounds Pounds Pounds 1990 7,041 55.6 5,621 44.4 12,662 1995 7,783 56.8 5,917 43.2 13,7004-52 4.2.1.3 U.S. Annual Per Ca p ita Consum p tion of Canned Fisher y Products: 1990 and 1995
Canned tuna is the p redominant t yp e of canned fish consumed in the United States avera g in g 72.4% of all canned fish consumed p er cap ita. Table 4-42 shows U.S. annual p er cap ita consum p tion of canned fisher y p roducts in 1990 and 1995.

Table 4-42 U.S. Annual Per Capita Consumption of Canned Fishery Products: 1990 and 1995 (Pounds Per Capita)

Year Salmon Sardines Tuna Shellfish Other Total 1990 0.4 0.3 3.7 0.3 0.4 5.1 1995 0.5 0.2 3.4 0.3 0.3 4.7

4.2.1.4 U.S. Annual Per Ca p ita Consum p tion of Fish Items: 1990 and 1995
In fresh and frozen fish p roducts and shrimp , p er cap ita consum p tion in these cate g ories is shown in Table 4-43 based on data from the National Marine Fisheries Service.

Table 4-43 U.S. Annual Per Capita Consumption (in pounds*) of Certain Fishery Items: 1990 and 1995 Year Fillet and Steaks ** Sticks and Portions Shrimp (All Preparations) 1990 3.1 1.5 2.2 1995 2.9 1.2 2.5 * Product weight of fillets and steaks and sticks and portions, edible (meat) weight of shrimp. ** Data include ground fish and other species. Data do not include blocks, but fillets could be made into blocks from which sticks and portions could be produced.

4.2.1.5 Ma j or Im p orted Fish and Shellfish Products
The two ma j or fish/shellfish p roducts imp orted into the United States in 1994 and 1995 (ex p ressed b y wei g ht) were shrim p (621,618,000 p ounds in 1994 and 590,634,000 p ounds in 1995), and tuna (includin g albacore, canned tuna, and other tuna: 707,426,000 p ounds in 1994 and 711,241,000 p ounds in 1995). A pp roximatel y 28% of im p orted tuna was imp orted as albacore tuna and about 33% was imp orted as canned tuna. Shrimp im p orts were not differentiated b y s p ecies of shrimp or countr y of ori g in in the national Marine Fisheries Service statistics.4-53 4.2.2 Current Market Trends, 1996
The followin g data on current market trends in the seafood industr y are abstracted from the 1997 Annual Report on the United States Seafood Industrydescribin g 1996 data on seafood b y H.M. Johnson & Associates (Johnson, 1997).
The world commercial fish and shellfish su pp lies increased from 109.6 thousand metric tons in 1994 to 112.0 thousand metric tons in 1995. A q uaculture p rovided the lar g est boost to world su pp l y in 1995 increasin g 13.6% over the p revious y ear. Durin g this p eriod (1995 to 1996) cap ture fisheries declined b y 0.1 metric tons. A q uaculture re p resents 26% of all world food fish (total su pp l y less reduction fish) p roducts.
The Food and A g riculture Or g anization examined lon g -term trends in 77 ma j or fish resources (re p resentin g 77% of the world marine fish landin g s) are concluded that 35% of the resources were “overfished,” 25% were “full y fished,” and 40% had remainin g cap acit y for ex p ansion (FAO, 1996; as cited b y Johnson, 1997). Aquaculture
World a q uaculture continued to increase with 1995 p roduction increased b y 14% to 20.9 million metric tons (Johnson, 1997). Five Asian countries (China, India, Ja p an, Re p ublic of Korea, and the Phili pp ines) su pp lied 80% of a q uaculture-raised fish/shellfish. World-wise a q uaculture is p redicted b y the Food and A g riculture Or g anization to continued to increase fish and shellfish p roduction be y ond the y ears 2000. Within the United States, domestic finfish a q uaculture increased in 1996. The ma j or increases were in catfish p roduction. Catfish p roduction ver y much dominates the U.S. finfish a q uaculture p roduction y ieldin g app roximatel y 475 million p ounds round wei g ht/y ear. Tilap ia harvests were hi g her in 1996, however, trout and salmon p roduction declined. Salmon, trout, and tila p ia p roduction are substantiall y smaller than catfish p roduction. Yields from U.S. a q uaculture for salmon, trout, and tila p ia were under 50 million p ounds for each of these s p ecies.
4.2.3 Patterns in Fish and Shellfish Consum p tion: United States, 1996
4.2.3.1 Overall Patterns Between 1995 and 1996 there was a 0.2 p ound decrease in p er cap ita consum p tion of seafood in the United States. The p rinci p al decline was in canned tuna. The to p ten seafoods consumed (ex p ressed as p ounds consumed p er cap ita) were: canned tuna (3.2), shrimp (2.5), Alaska Pollock (1.6); salmon (1.4); cod ( j ust under 1 p ound); catfish (app roximatel y 0.9 p ounds); clams (app roximatel y 0.5 p ounds), flatfish (0.4 p ounds), crab (a pp roximatel y 0.3), and scallo p s (0.3). The source of these data are the National Marine Fisheries Service and the 1997 Annual Report on the United States Seafood Industry (Johnson, 1997).
4.2.3.2 Fish Intake amon g Adults
Anal y sis of the fre q uenc y of re p ortin g of fish/shellfish and menu items containin g fish and shellfish was carried out usin g data from CSFII 1994 and CSFII 1995. Seasons were g rou p ed into six two-month intervals; i.e., Jan/Feb, Mar/A p r, etc. Data for the 10 most commonl y consumed menu items are shown in Table 4-44. The most fre q uentl y re p orted menu items are “seafood salads and seafood and ve g etable dishes.” Althou g h other fisher y p roducts are p ossible, this menu cate g or y t yp icall y describes4-54 dishes made with tuna, surimi (i.e., Alaska p ollock), crab, salmon or other canned fish or shellfish. Overall, these dishes re p resent about 20% of overall seafood consump tion. This ma j or g rou p is followed b y shrim p , canned tuna, the g rou p “Seafood cakes, fritters, and casseroles without ve g etables”. Identified finfish commonl y consumed include salmon, cod, catfish, flounder, trout, seabass, ocean p erch, haddock, and p or gy . Althou g h s p ecific finfish are identified as amon g the to p ten consumed over six seasons, the y follow consump tion of p rocessed fisher y p roducts; e. g ., salads, fritters, “fast food” fillets, and shrimp .

Table 4-44 Ten Most Commonly Reported Fish/Shellfish/Mixed Dishes by Season CSFII 1994 and CSFII 1995 — Day 1 Data

Ranking Season Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec 1st Seafood Seafood Seafood Seafood Seafood Seafood salads, & salads, & salads, & salads, & salads, & salads, & seafood & seafood & seafood & seafood & seafood & seafood & ve g etable ve g etable ve g etable ve g etable ve g etable ve g etable dishes, dishes, dishes, dishes, dishes, dishes, 17.6% 16.9% 24.5% 23.2% 15.4% 20.0% 2nd Shrim p , Shrimp , Shrimp , 9.5% Seafood Tuna, canned Shrimp , 11.2% 10.5% cakes, fritters, 12.0% 11.1% & casseroles w/o ve g etables, 7.9% 3rd Seafood Tuna, canned, Tuna, canned, Tuna, canned Shrim p , Seafood cakes, fritters 10.1% 6.8% 7.5% 11.5% cakes, fritters & casseroles & casseroles w/o w/o ve g etables, ve g etables, 8.8% 10.0% 4th Catfish, 8.3% Seafood Seafood Salmon, 6.8% Seafood Fish cakes, fritters, cakes, fritters, cakes, fritters, stick/fillet, & casseroles & casseroles & casseroles 9.4% w/o w/o w/o ve g etables, ve g etables, ve g etables, 8.1% 6.4% 8.7% 5th Fish Cod, 5.6% Fish Shrimp , 6.4% Fish Fish stick/fillet stick,fillet stick/fillet, stick/fillet, 7.8% 5.5% 6.7% 9.4%Table 4-44 (continued) Ten Most Commonly Reported Fish/Shellfish/Mixed Dishes by Season CSFII 1994 and CSFII 1995 — Day 1 Data

Ranking Season Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec

4-55 6th Tuna, canned, Salmon, Salmon, 4.5% Fish Cod, Tuna, canned 6.3% 5.2%, stick/fillet, 6.3% 7.8% 5.4% 7th Salmon, 3 Fish, Seafood Catfish, Fish, Salmon, 4.4% uns p ecified, sandwiches, 4.6% uns p ecified 4.8% 4.1% 4.8% 8th Trout, 2.4% Seafood Fish, Cod, Flounder, Fish sandwiches, uns p ecified 4.6% 4.3% uns p ecified, 4.0% 3.6% 4.4% 9th Shellfish Seafood Sea bass, Ocean p erch, Salmon, Haddock, dishes in sou p s & 3.2% 3.2% 3.4% 3.9%, sauce, 2.4% casseroles Frozen with seafood ve g etables, dinners, 3.9% 3.6% 10th Frozen Por gy , 3.6% Trout, Perch, Catfish, 2.9% Flounder, seafood 2.7% 3.2% 3.3% dinners, 2.4%

Communications with ex p erts in the seafood industry as well as the im p ort/ex p ort and p roductions statistics p ublished b y the National Marine Fisheries Service and the Food and A g riculture Or g anization) indicate the p redominant s p ecies of fish and shellfish are the various s p ecies of tuna, shrim p , and the Alaskan p ollock. Su p erim p osed on these broad national trends in fish/shellfish consump tion, are re g ional trends in fish/shellfish consum p tion. Table 4-45 describes re g ional p o p ularit y of fish s p ecies within the United States.4-56 Table 4-45 Regional Popularity of Fish and Shellfish Species Region Most Popular Fish Consumed East Coast haddock, cod*, flounder, lobster, blue crab, shrim p South shrim p , catfish, g rou p er, red snapp er, blue crab West Coast salmon, dun g eness crab, shrimp , rockfish Mid-West Perch, Walle y e, Chubs, Multi p le Varities of Freshwater Fish
* In the late 1990s, cod has been replaced on menus largely by Alaskan pollock.
These im p ressions are su pp orted b y descri p tions of the best-sellin g fish/shellfish s p ecies in various t yp es of restaurants as shown in Table 4-46 (Seafood Business ma g azine cited b y Johnson, 1997, p a g e 71).

Table 4-46 Popularity of Fish/Shellfish Species in Restaurants

Rank First Second Third By Region:

North East Salmon Shrimp Swordfish South Shrim p Salmon Catfish Midwest Salmon Shrimp Cod* West/Pacific Salmon Shrimp Halibut By Restaurant Style: “Fast Food” Cod*/Shrim p Clams/Scallo p s Tuna “Dinnerhouse” Shrim p Salmon Lobster “White Tablecloth” Salmon Shrimp Swordfish By Overall Sales: 1996 Shrim p Salmon Cod* 1995 Cod* Shrim p /Salmon Swordfish 1994 Cod* Shrim p /Salmon Swordfish 1993 Cod* (& All Shrim p Hoki Whitefish)Rank First Second Third

4-57 1992 Cod* (& All Shrim p Crab Whitefish)
* In the late 1990s, cod has been replaced on menus largely by Alaskan pollock.
Althou g h the s p ecies shown in Tables 4-45 and 4-46 are p o p ular re g ionall y , for the United States as a whole, the national statistics indicate ma j or fish consumed are: tuna, shrimp , and Alaskan p ollock.
4.2.3.3 Fish and Shellfish Consump tion b y Children
The NHANES III data were anal y zed to determine the s p ecies of fish and shellfish consumed b y children in the a g e cate g ories 1-to-5 y ears, 6-to-11 y ears, and 12-to-14 y ears for male and female surve y res p ondents. S p ecific choices b y a g e g rou p s are shown in Table 4-47. The to p four fish dishes for all a g e cate g ories of children were:
• fish sticks and p atties, • tuna salad and canned tuna, • shrim p , and • catfish. Table 4-47 Frequencies of Various Fish and Shellfish Food Types for Children Ages 1 to 5 and 6 to 11 Years by Gender (Source: NHANES III) Food Type Frequency of Various Food Types Ages 1-5 Years Ages 6-11 Ages 12-14 Females Males Females Males Females Males Fish Sticks/Patties 23% 21% 23% 25% 21% 21% Tuna Salad/ Canned Tuna 33% 27% 26% 19% 25% 28% Shrimp 8% 6% 11% 10% 12% 12% Catfish 5% 5% 5% 10% 9% 4% All Other fish and Shellfish 31% 41% 35% 36% 30% 33% Total 100% 100% 100% 100% 100% 100%

4.2.4 Production Patterns and Mercur y Concentrations for S p ecific Fish and Shellfish S p ecies
Four s p ecies of fish are imp ortant p redictors of meth y lmercur y ex p osure because of the fre q uenc y with which these are consumed b y the overall p o p ulation.
4.2.4.1 Tuna4-58 Althou g h consump tion of canned tuna continues to fall (Johnson, 1997), tuna (canned and fresh or frozen) continues to be the most commonl y consumed fish based on data from contem p orar y surve y s of food intake b y individuals. The mercury concentration of tuna varies with s p ecies reflectin g variabilit y in fish size and g eo g rap hic location.
The mean mercur y concentration in tuna is 0.206 μg / g ram based on data from NMFS. This re p resents an avera g e for the mean concentrations measured in three t yp es of tuna: albacore tuna (0.264 μg / g ), ski pj ack tuna (0.136 μg / g , and y ellowfin tuna (0.218 μg / g ). Data cited b y U.S. FDA (1978) indicate the followin g mean (maximum) values in μg / g for various tuna s p ecies: tuna, li g ht ski pj ack, 0.144 (0.385); tuna li g ht y ellow, 0.271 (0.870); tuna, white 0.350 (0.904). Cramer (1994) observed that recent U.S. FDA surve y s indicated that the mean mercur y content of 1973 samp les of canned tuna was 0.21 μg / g , whereas a 1990s surve y of 245 sam p les of canned tuna was 0.17 μg / g mercur y . 4.2.4.2 Shrim p Shrim p consump tion based on contem p orar y nationall y re p resentative surve y s in the United States continues to be a to p -ten seafood choice b y both adults and children. World shrim p su pp lies are in excess of 3,000,000 metric tons (Johnson, 1997) with a pp roximatel y one-sixth of the p roduction g rown b y a q uaculture. This amounts to a pp roximatel y 500,000 metric tons g rown b y a q uaculture. The United States is a net im p orter of shrim p with ma j or su pp liers (in order of the q uantit y im p orted into the United States) Thailand, Ecuador, Mexico, and India (Johnson, 1997).
The overall avera g ed mercur y concentration in marine shrim p re p orted b y the NMFS is 0.047 μg / g . This is an avera g e of the mean concentrations measured in seven t yp es of shrim p : ro y al red shrimp (0.074 μg / g ), white shrimp (0.054 μg / g ), brown shrim p ; (0.048 μg / g ), ocean shrimp (0.053 μg / g ), p ink shrim p (0.031 μg / g ), p ink northern shrim p (0.024 μg / g ), and Alaska (sidestri p e) shrim p (0.042 μg / g ). Data cited b y U.S. FDA (1978) indicate a mean value of 0.040 with a maximum of 0.440 μg / g . Shrim p consumed in the United States are p redominantl y im p orted from Thailand, Ecuador, and India. The authors of the Re p ort to Con g ress have not identified data s p ecificall y re p ortin g mercur y concentrations in shrimp from the countries which are currentl y the ma j or su pp liers of shrim p to the United States.
4.2.4.3 Pollock
The Alaskan p ollock dominates the U.S. seafood industry . In 1996, p ollock landin g s totaled 2.6 billion p ounds (Johnson, 1997). Pollock is the fish s p ecies used in p re p aration of fish sticks, fish sandwiches served b y various “fast food” restaurant franchises in the United States, artificial “crab” or surimi.
The mercur y concentration attributed to p ollock is 0.15 μg / g based on NMFS data. Data cited b y U.S. FDA indicate a mean mercury concentration for p ollock of 0.141 (maximum value, 0.96 μg / g ). 4.2.4.4 Salmon
Salmon is a hi g hl y im p ortant fish s p ecies based on fre q uenc y of consum p tion of both the canned and fresh p roduct. S p ecies include: chinook, coho, chum, socke y e, and p ink. Production has declined in the United States between 1995 and 1996, althou g h the world su pp l y of salmon has continued to g row. Salmon is one of the ma j or fish s p ecies g rown b y a q uaculture with p roduction of app roximatel y 50 million p ounds p er y ear in the United States.4-59 The mercur y content used for salmon was the avera g e of the mean concentrations measured in five t yp es of salmon: p ink (0.019 μg / g ), chum (0.030 μg / g ), coho (0.038 μg / g ), socke y e (0.027 μg / g ), and chinook (0.063 μg / g ). Salmon that is raised b y a q uaculture based on consum p tion of corn and so y p roducts ma y have lower mercur y concentrations because of the low mercury concentration of the ve g etable p roducts fed to the a q uaculture-raised salmon. Data cited b y U.S. FDA (1978) indicated a mean value for salmon of 0.040 (maximum 0.201).
4.2.4.5 Catfish
Catfish ranks in the to p ten fish p roduced and consumed. Catfish dominates the a q uaculture p roduction in the United States with p roduction of app roximatel y 475 million p ounds round (i.e., live) wei g ht. The mercur y concentration of freshwater catfish used in the Mercury Stud y Re p ort to Con g ress was 0.088 μg / g . Data cited b y U.S. FDA (1978) indicate a mean value of 0.146 μg / g (with a maximum value of 0.38 μg / g ). As with salmon, catfish raised b y a q uaculture on ve g etable p roducts (e. g ., corn and so y ) are p redicted to have lower mercury concentrations than ca p ture catfish. 4.3 Mercury Concentrations In Fish
Mercur y concentrations in marine, estuarine, and freshwater fish were obtained from data bases maintained for marine and estuarine fish and shellfish (National Marine Fisheries Service, 1978) and freshwater fish (Lowe et al., 1985; and Bahnick et al., 1994). These data combined with estimates of fish/shellfish consump tion from various dietar y surve y s form the basis for p redicted mercur y ex p osures throu g h fish and shellfish.
4.3.1 National Marine Fisheries Service Data Base
Anal y ses of total mercur y concentrations in marine and estuarine fish and shellfish have been carried out over the p ast two to three decades. Data describin g meth y lmercur y concentrations in marine fish were p redominantl y based on the National Marine Fisheries’ Service (NMFS) data base, the lar g est p ublicl y available data base on mercur y concentrations in marine fish. In the earl y 1970s, the NMFS conducted testin g for total mercur y on over 200 seafood s p ecies of commercial and recreational interest (Hall et al., 1978). The determination of mercury in fish was based on flameless (cold va p or) atomic absorp tion s p ectro p hotometr y followin g chemical di g estion of the fish samp le. These methods were described in Hall et al. (1978).
Althou g h the NMFS data were initiall y comp iled be g innin g in the 1970s, comp arisons of the mercur y concentration identified in the NMFS’s data base with com p liance sam p les obtained b y the U.S. FDA indicate that the NMFS data are a pp ro p riate to use in estimatin g intake of mercur y from fish at the national level of data a gg re g ation. Cramer (1994) of the Office of Seafood of the Center for Food Safet y and A pp lied Nutrition of the U.S. FDA re p orted on Exposure of U.S. Consumers to Methylmercury from Fish . He noted that recent information from NMFS indicated that the fish mercur y concentrations re p orted in the 1978 re p ort do not app ear to have chan g ed si g nificantl y . The U.S. FDA continues to monitor meth y lmercur y concentration in seafood. Cramer (1994) observed that results of recent U.S. FDA surve y s indicate results p arallel to earlier findin g s b y U.S. FDA and NMFS. To illustrate, Cramer estimated the mean meth y lmercur y content of the 1973 samp les of canned tuna at 0.21 μg / g mercur y , whereas a recentl y comp leted surve y of 245 sam p les of canned tuna was 0.17 μg / g mercur y . These data are considered to be com p arable, althou g h the small decrease re p orted between these two studies may reflect increased use in canned tuna of tuna s p ecies with sli g htl y lower avera g e meth y lmercur y concentrations. The National Academy of Sciences’ National Research Council’s Subcommittee on Seafood Safet y (1991) also assessed the a pp licabilit y of the NMFS' 1970s data base to current estimates of mercur y concentrations in fish. This subcommittee also concluded that the 1978 data base differed4-60 little in mercur y concentrations from U.S. FDA comp liance sam p les estimatin g mercur y concentrations in fish.
Assessment of this data base b y p ersons with ex p ertise in anal y tical chemistr y and p atterns of mercur y contamination of the environment have indicated that tem p oral p atterns in mercur y concentrations in fish do not p reclude use of this data base in the p resent risk assessment (US EPA’s Science Advisor y Board’s ad hoc Mercur y Subcommittee; Intera g enc y Peer Review Grou p , External Peer Review Grou p ). One issue that did arise, however, concerned how zero and trace values were entered into calculation of mean mercury concentrations. This has been evaluated statisticall y throu g h com p arison of mean values when different a pp roaches were taken to mathematicall y calculated means under different assump tions of inclusion of zero and trace values.
The NMFS Re p ort p rovided data on number of samp les, number of nondetects, and mean, standard deviation, minimum and maximum mercur y levels (in p arts p er million wet wei g ht) for 1,333 combinations of fish/shellfish s p ecies, variet y , location cau g ht, and tissue (Hall et al., 1978). This data base includes 777 fish/shellfish s p ecies for which mercur y concentration data are p rovided. This re p resents 5,707 anal y ses of fish and shellfish tissues for total mercury , of which 1,467 or 26%, are re p orted as nondetectable levels. Because the mercur y concentration data are used in our anal y ses at the s p ecies level, not at the more detailed s p ecies/variet y /location/tissue level, the data have been g rou p ed to reflect 35 different fish/shellfish s p ecies.
The fre q uenc y of nondetectable or “zero” values differs with the mercur y concentration. When mean mercur y levels are relativel y “lar g e”, there are few, if an y , nondetects, so the methodolo gy em p lo y ed to handle nondetects is irrelevant. When mean mercur y levels are small, there are relativel y lar g e numbers of nondetectable values. Because the method of includin g /excludin g nondetectable values in the calculation has the g reatest im p act onl y when mercur y concentrations are very low, the overall im p act on estimated mercur y ex p osure is small.
A statistical assessment of these p otential differences was carried out b y Westat Corp oration (Memo from Robert Clickner, Se p tember 26, 1997). A descri p tion of the statistical basis for the com p arison is shown in A pp endix C. To determine the detection limit a pp licable to the data base, the lowest of all detected anal y tical values was p resumed to be the detection limit. This value is 0.010 μg / g wet wei g ht. The ma j or conclusion of this anal y sis is that different methods of handlin g nondetects have ne g li g ible im p act on the re p orted mean concentrations. Conse q uentl y the mean values as re p orted b y the NMFS will be used in p re p arin g estimates of mercury intake from marine and estuarine fish and shellfish.
Mercur y concentration in various fish s p ecies are shown in Table 4-48.

Table 4-48 Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)Table 4-48 (continued) Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)

4-61 Data Used by USEPA Data Used by US FDA Data Used by Stern et al. Mercury Study Report to Report on the Chance of U.S. 1996 Congress* Seafood Consumers Exceeding "The Current 1997 Regulatory Controls" Daily Intake for Mercury and Recommended 1978 Fish Species Average Fish Species Average Maximum Fish Average ( g Hg/g) ( g Hg/g) ( g Hg/g) Species ( g Hg/g) Abalone 0.016 Abalone 0.018 0.120 Not Re p orted (NR) Anchovies 0.047 Anchovies 0.039 0.210 NR Bass, Av g s.= 0.157 Bass, 0.752 2.000 Bass, 0.41 Freshwater (Lowe et al., Stri p ed freshwater 1985) and 0.38 (Bahnick et al., 1994) Bass, Sea Not Re p orted Bass, Sea 0.157 0.575 Bass, Sea 0.25 Bluefish Not Re p orted Bluefish 0.370 1.255 Bluefish 0.35 Blue g ills 0.033 Blue g ills 0.259 1.010 NR Bonito Not Re p orted Bonito 0.302 0.470 NR (below 3197) Bonito Not Re p orted Bonito 0.382 0.740 NR (above 3197) Butterfish Not Re p orted Butterfish 0.021 0.190 Butterfish 0.05 Carp , 0.093 Carp 0.181 0.540 Catfish, 0.15 Common freshwater Catfish 0.088 Catfish 0.146 0.380 Clams 0.05 (channel,lar g e (freshwater) mouth, rock, stri p ed, white) Catfish Not Re p orted Catfish 0.475 1.200 Cod/Scrod 0.15 (Marine) (Marine) Clams 0.023 Clams 0.049 0.260 See crab. Cod 0.121 Cod 0.125 0.590 Crab 0.15 Crab, Kin g 0.070; Crab, Kin g 0.070 0.240 NR Calculations based on 5 s p ecies of crab combined at 0.117Table 4-48 (continued) Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al. Mercury Study Report to Report on the Chance of U.S. 1996 Congress* Seafood Consumers Exceeding "The Current 1997 Regulatory Controls" Daily Intake for Mercury and Recommended 1978 Fish Species Average Fish Species Average Maximum Fish Average ( g Hg/g) ( g Hg/g) ( g Hg/g) Species ( g Hg/g)

4-62 Crab 0.117 Crab, other 0.140 0.610 NR than Kin g Crapp ie 0.114 Crapp ie 0.262 1.390 NR (black, white) Croaker 0.125 Croaker 0.124 0.810 NR Dol p hin Not Re p orted Dol p hin 0.144 0.530 Dol p hin 0.25(Mahimahi) Drums, 0.117 Drums 0.150 0.800 NR Freshwater Flounders 0.092 Flounders 0.096 0.880 Flounder 0.10 Grou p ers Grou p ers 0.595 2.450 NR Haddock 0.089 Haddock 0.109 0.368 Haddock 0.05 Hake 0.145 Hake 0.100 1.100 Hake 0.10 Halibut 0.250 Halibut 4 0.187 1.000 Halibut 0.25 Halibut 0.250 Halibut 3 0.284 1.260 Halibut 0.25 Halibut 0.250 Halibut 2H 0.440 1.460 Halibut 0.25 Halibut 0.250 Halibut 25 0.534 1.430 Halibut 0.25 Herrin g 0.013 Herrin g 0.023 0.260 Herrin g 0.05 Kin g fish 0.100 Kin g fish 0.078 0.330 Kin g fish 0.05 Lobster 0.232 Lobster, 0.339 1.603 Lobster 0.25 Northern 11 Lobster 0.232 Lobster 0.509 2.310 Lobster 0.25 Northern 10 Lobster 0.232; Lobster,S p in 0.113 0.370 Lobster 0.25 S p in y Includes s p in y y (Pacific) lobster=0.210Table 4-48 (continued) Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al. Mercury Study Report to Report on the Chance of U.S. 1996 Congress* Seafood Consumers Exceeding "The Current 1997 Regulatory Controls" Daily Intake for Mercury and Recommended 1978 Fish Species Average Fish Species Average Maximum Fish Average ( g Hg/g) ( g Hg/g) ( g Hg/g) Species ( g Hg/g)

4-63 Mackerel 0.081; Mackerel, 0.048 0.190 Mackerel 0.28 Avera g ed Atlantic Chub = 0.081; Atlantic= 0.025; Jack=0.138 Mackerel 0.081 Mackerel, 0.267 0.510 Mackerel 0.28 Jack Mackerel 0.081 Mackerel, 0.823 2.730 Mackerel 0.28 Kin g (Gulf) Mackerel 0.081 Mackerel, 1.128 2.900 Mackerel 0.28 Kin g (other) Mackerel 0.081 Mackerel, 0.542 2.470 Mackerel 0.28 S p anish 16 Mackerel 0.081 Mackerel, 0.825 1.605 Mackerel 0.28 S p anish 10 Mullet 0.009 Mullet 0.016 0.280 Mullet 0.05 O y sters 0.023 O y sters 0.027 0.460 NR Perch, 0.110 Perch, 0.290 0.880 Perch 0.18 White and Freshwater Yellow Perch, 0.116 Perch, 0.133 0.590 NR Ocean Marine Pike, 0.310 Pike 0.810 1.710 NR Northern 0.127 Pollock 0.150 Pollock 0.141 0.960 NR Pomp ano 0.104 Pom p ano 0.104 8.420 NR Rockfish Not Re p orted Rockfish 0.340 0.930 NR Sablefish Not Re p orted Sablefish 0.201 0.700 NR Salmon 0.035 Salmon 0.040 0.210 Salmon 0.05 Scallo p s 0.042 Scallo p s 0.058 0.220 NR Scu p Not Re p orted Scu p 0.106 0.520 NRTable 4-48 (continued) Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al. Mercury Study Report to Report on the Chance of U.S. 1996 Congress* Seafood Consumers Exceeding "The Current 1997 Regulatory Controls" Daily Intake for Mercury and Recommended 1978 Fish Species Average Fish Species Average Maximum Fish Average ( g Hg/g) ( g Hg/g) ( g Hg/g) Species ( g Hg/g)

4-64 Sharks 1.327 Sharks 1.244 4.528 Shark 1.11 Shrimp 0.047 Shrimp 0.040 0.440 Shrim p 0.11 Smelt 0.100 Smelt 0.016 0.058 Smelts 0.05 Snapp er 0.25 Snapp er,Red 0.454 2.170 Snapp er 0.31 Snapp er 0.25 Snapp er, 0.362 1.840 Sna pp er 0.31 Other Snook Not Re p orted Snook 0.701 1.640 NR S p ot Not Re p orted S p ot 0.041 0.180 S p otfish 0.05 S q uid 0.026 S q uid and 0.031 0.400 S q uid 0.05 Octo p i Octo p i 0.029 S q uid and 0.031 0.400 NR Octo p i Sunfish Not Re p orted Sunfish 0.312 1.200 NR Swordfish 0.95 Swordfish 1.218 2.720 Swordfish 0.93 Tillefish Not Re p orted Tillefish 1.607 3.730 NR Trout, 0.149 Trout, 0.417 1.220 Trout 0.05 Freshwater Trout 0.149 Trout, 0.212 1.190 Trout 0.05 Marine Tuna 0.206; Tuna, 0.144 0.385 Tuna, 0.17 Avera g ed: Li g ht fresh Tuna, li g ht Ski pj ack ski pj ack=0.13 6Tuna,li g ht y ellow=0.218; Albacore=0.2 64 Tuna 0.206 Tuna, 0.271 0.870 Tuna, 0.17 Li g ht fresh Yellow Tuna 0.206 Tuna, White 0.350 0.904 Tuna, 0.17 fresh Whitefish Not Re p orted Whitefish 0.054 0.230 Whitefish 0.04Table 4-48 (continued) Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al. Mercury Study Report to Report on the Chance of U.S. 1996 Congress* Seafood Consumers Exceeding "The Current 1997 Regulatory Controls" Daily Intake for Mercury and Recommended 1978 Fish Species Average Fish Species Average Maximum Fish Average ( g Hg/g) ( g Hg/g) ( g Hg/g) Species ( g Hg/g)

4-65 Other finfish Other finfish 0.287 1.020 Finfish, 0.17 other Other shellfish Not Shellfish, 0.12 Re p orted other Fish Species (Freshwater) Not Reported by FDA, 1978 Bloater 0.0.93 Smallmouth 0.096 Buffalo Northern 0.33 S q uawfish Sau g er 0.23 Sucker 0.114 (Lowe et al., 1985; 0.167 (Bahnick et al., 1994). Walle y e 0.100 (Lowe et al., 1985) and 0.52 (Bahnick et al., 1994). Trout (brown, 0.149 (Lowe lake, rainbow) et al., 1985) and 0.14 (Bahnick et al., 1994 for brown trout). Fish Species Reported by the State of New Jersey and Not Reported by EPA or FDA Blowfish 0.05 Oran g e rou g h y 0.5 Sole 0.12 Weakfish 0.15 Por gy 0.55Table 4-48 (continued) Summary of Mercury Concentrations in Fish Species (μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al. Mercury Study Report to Report on the Chance of U.S. 1996 Congress* Seafood Consumers Exceeding "The Current 1997 Regulatory Controls" Daily Intake for Mercury and Recommended 1978 Fish Species Average Fish Species Average Maximum Fish Average ( g Hg/g) ( g Hg/g) ( g Hg/g) Species ( g Hg/g)

4-66 Blackfish 0.25 Whitin g 0.05 Turbot 0.10 Sardines 0.05 Tilap ia 0.05 * See Sections 4.3.1 and 4.3.2 for data on marine species, and Section 4.3.3 for data on freshwater fish.
4.3.2 Mercur y Concentrations in Marine Fish
Data su pp lied b y NMFS g ive the mercur y concentrations in fresh wei g ht of fish muscle of numerous marine fish, shellfish, and other molluscan/crustacean s p ecies shown in Table 4-49, 4-50 and 4-51.

Table 4-49 Mercury Concentrations in Marine Finfish Fish Mercury Concentration Source of Data ( g/g, wet weight) Anchov y 0.047 NMFS 1 Barracuda, Pacific 0.177 NMFS 2 Cod 0.121 NMFS 3 Croaker, Atlantic 0.125 NMFS Eel, American 0.213 NMFS Flounder 0.092 NMFS 4 Haddock 0.089 NMFS Hake 0.145 NMFS 5 Halibut 0.25 NMFS 6 Herrin g 0.013 NMFS 7 Kin g fish 0.10 NMFS 8Table 4-49 (continued) Mercury Concentrations in Marine Finfish Fish Mercury Concentration Source of Data ( g/g, wet weight)

4-67 Mackerel 0.081 NMFS 9 Mullet 0.009 NMFS 10 Ocean Perch 0.116 NMFS 11 Pollack 0.15 NMFS Pomp ano 0.104 NMFS Por gy 0.522 NMFS Ra y 0.176 NMFS Salmon 0.035 NMFS 12 Sardines 0.1 NMFS 13 Sea Bass 0.135 NMFS Shark 1.327 NMFS 14 Skate 0.176 NMFS 15 Smelt, Rainbow 0.1 NMFS Snapp er 0.25 NMFS 16 Stur g eon 0.235 NMFS 17 Swordfish 0.95 FDA Com p liance Testin g Tuna 0.206 NMFS 18 Whitin g (silver hake) 0.041 NMFS

This is the avera g e of NMFS mean mercur y concentrations for both stri p ed anchov y (0.082 g / g ) and northern anchov y (0.010 1g / g ). USDA data base s p ecified the consump tion of the Pacific barracuda and not the Atlantic barracuda. 2 The mercur y content for cod is the avera g e of the mean concentrations in Atlantic cod (0.114 g / g and the Pacific cod (0.127 3g / g ). The mercur y content for flounder is the avera g e of the mean concentrations measured in 9 t yp es of flounder: Gulf (0.147 g / g ), 4 summer (0.127 g / g ), southern (0.078 g / g ), four-s p ot (0.090 g / g ), window p ane (0.151 g / g ), arrowtooth (0.020 g / g ), witch (0.083 g / g ), y ellowtail (0.067 g / g ), and winter (0.066 g / g ). The mercur y content for hake is the avera g e of the mean concentrations measured in 6 t yp es of hake: silver (0.041 g / g ), 5 Pacific (0.091 g / g ), s p otted (0.042 g / g ), red (0.076 g / g ), white (0.112 g / g ), and blue (0.405 g / g ). The mercur y content for halibut is the avera g e of the mean concentrations measured in 3 t yp es of halibut: Greenland, Atlantic, 6 and Pacific. The mercur y content for herrin g is the avera g e of the mean concentrations measured in 4 t yp es of herrin g : blueback (0.0 g / g ), 7 Atlantic (0.012 g / g ), Pacific (0.030 g / g ), and round (0.008 g / g ). The mercur y content for kin g fish is the avera g e of the mean concentrations measured in 3 t yp es of kin g fish: southern, Gulf, and 8 northern. The mercur y content for mackerel is the avera g e of the mean concentrations measured in 3 t yp es of mackerel: j ack (0.138 g / g ), 9 chub (0.081 g / g ), and Atlantic (0.025 g / g ).

The mercur y content for mullet is the avera g e of the mean concentrations measured in 2 t yp es of mullet: stri p ed (0.011 g / g ) 10 and silver (0.007 g / g ).

The mercur y content for ocean p erch is the avera g e of the mean concentrations measured in 2 t yp es of ocean p erch: Pacific 11 (0.083 g / g ) and redfish (0.149 g / g ).4-68 The mercur y content for salmon is the avera g e of the mean concentrations measured in 5 t yp es of salmon: p ink (0.019 g / g ), 12 chum (0.030 g / g ), coho (0.038 g / g ), socke y e (0.027 g / g ), and chinook (0.063 g / g ).

Sardines were estimated from mercury concentrations in small Atlantic herrin g . 13 The mercur y content for shark is the avera g e of the mean concentrations measured in 9 t yp es of shark: s p in y do g fish (0.607 14g / g ), (unclassified) do g fish (0.477 g / g ), smooth do g fish (0.991 g / g ), scallo p ed hammerhead (2.088 g / g ), smooth hammerhead (2.663 g / g ), shortfin mako (2.539 g / g ), blackti p shark (0.703 g / g ), sandbar shark (1.397 g / g ), and thresher shark (0.481 g / g ).

The mercur y content for skate is the avera g e of the mean concentrations measured in 3 t yp es of skate: thorn y skate (0.200 15g / g ), little skate 0.135 g / g ) and the winter skate (0.193 g / g ).

The mercur y content for snapp er is the avera g e of the mean concentrations measured in t yp es of sna pp er: 16 The mercur y content for stur g eon is the avera g e of the mean concentrations measured in 2 t yp es of stur g eon: g reen stur g eon 17 (0.218 g / g ) and white stur g eon (0.251 g / g ).

The mercur y content for tuna is the avera g e of the mean concentrations measured in 3 t yp es of tuna: albacore tuna (0.264 18g / g ), ski pj ack tuna (0.136 g / g ) and y ellowfin tuna (0.218 g / g ).

Table 4-50 Mercury Concentrations in Marine Shellfish Shellfish Mercury Concentration Source of Data ( g/g, wet weight) Abalone 0.016 NMFS 1 Clam 0.023 NMFS 2 Crab 0.117 NMFS 3 Lobster 0.232 NMFS 4 O y sters 0.023 NMFS 5 Scallo p 0.042 NMFS 6 Shrim p 0.047 NMFS 7 The mercur y content for abalone is the avera g e of the mean concentrations measured in 2 t yp es of abalone: g reen abalone 1 (0.011 g / g ) and red abalone (0.021 g / g ). The mercur y content for clam is the avera g e of the mean concentrations measured in 4 t yp es of clam: hard (or q uaho g ) clam 2 (0.034 g / g ), Pacific littleneck clam (0 g / g ), soft clam (0.027 g / g ), and g eoduck clam (0.032 g / g ). The mercur y content for crab is the avera g e of the mean concentrations measured in 5 t yp es of crab: blue crab (0.140 g / g ), 3 dun g eness crab (0.183 g / g ), kin g crab (0.070 g / g ), tanner crab (C.o p ilio ) (0.088 g / g ), and tanner crab (C.bairdi ) (0.102 g / g ). The mercur y content for lobster is the avera g e of the mean concentrations measured in 3 t yp es of lobster: s p in y (Atlantic) 4 lobster (0.108 g / g ), s p in y (Pacific) lobster (0.210 g / g ) and northern (American) lobster (0.378 g / g ). The mercur y content for o y ster is the avera g e of the mean concentrations measured in 2 t yp es of o y ster: eastern o y ster (0.022 5g / g ) and Pacific ( g iant) o y ster (0.023 g / g ). The mercur y content for scallo p is the avera g e of the mean concentrations measured in 4 t yp es of scallo p : sea (smooth) scallo p 6 (0.101 g / g ), Atlantic Ba y scallo p (0.038 g / g ), calico scallo p (0.026 g / g ), and p ink scallo p (0.004 g / g ). The mercur y content for shrimp is the avera g e of the mean concentrations measured in 7 t yp es of shrim p : ro y al red shrimp 7 (0.074 g / g ), white shrimp (0.054 g / g ), brown shrim p (0.048 g / g ), ocean shrimp (0.053 g / g ), p ink shrimp (0.031 g / g ), p ink northern shrimp (0.024 g / g ) and Alaska (sidestri p e) shrimp (0.042 g / g ).4-69 Table 4-51 Mercury Concentrations in Marine Molluscan Cephalopods Cephalopod Mercury Concentration Source of Data ( g / g wet wt.) Octo p us 0.029 NMFS S q uid 0.026 NMFS 1

The mercur y content for s q uid is the avera g e of the mean concentrations measured in 3 t yp es of s q uid: Atlantic 1 lon g finned s q uid (0.025 g / g ), short-finned s q uid (0.034 g / g ), and Pacific s q uid (0.018 g / g )

4.3.3 Freshwater Fish Mercur y Data Base Freshwater fish mercur y concentrations were re p orted b y Lowe et al. (1985) and b y Bahnick et al. (1994). Details of their anal y ses are p resented se p aratel y from those on marine fish. Lowe et al. (1985) used flameless cold va p or techni q ue absorp tion s p ectro p hotometr y in their anal y ses. Mean recover y for mercur y avera g ed 96.6±14.4 (SD) based on 72 anal y ses of s p iked samp les. Du p licate anal y ses showed a p ercent difference of 10.6±14.4 (SD) based on 51 du p licates. Values were re p orted as the g eometric means, minimum, and maximum of elemental mercur y concentrations durin g 1978 to 1979 and durin g 1980 to 1981. The limit of detection for mercury was 0.01 μg / g wet wei g ht. Standard reference materials were included and resulted of their anal y sis are shown in Table 4-52.

Table 4-52 Analyses of Mercury Standard Reference Materials Used by Lowe et al. (1985) in Support of Analyses of Freshwater Fish Mercury Reference Certified Number of Samples Measured Material Concentration Range Analyzed Concentrations (μg/g: (μg/g) mean ± 1SD) bovine liver 0.016±0.002 53 0.021±0.007

o y ster 0.057±0.015 14 0.050±0.005 tuna 0.95±0.10 32 0.86±0.07

Values of 0.01 μg mercur y / g fish tissue are routinel y re p orted in this data base. Sam p les were handled as individual fish. Mercury residues were re p orted for all s p ecies and all locations. The g eometric mean mercur y concentrations for all freshwater fish s p ecies was 0.11 μg / g in 1978 to 1979 and 0.11 μg / g in 1980-1981. The minimum value for both time p eriods was 0.01 μg / g and the maximum value was 1.10 μg / g in 1978-1979 and 0.77 μg / g in 1980-1981. The 85th p ercentile value in both time p eriods was 0.18 μg / g . Bahnick et al. (1994) used cold mercury vap or flameless atomic absor p tion and detected mercury in 92.2% of the fish samp led. Non-detects were re p orted as a zero value and avera g ed as zeros. Two se p arate detection limits were re p orted. Prior to 1990, 465 sam p les were anal y zed usin g a method havin g a detection limit of 0.05 μg / g . Modification of the method for the final 195 sam p les p roduced a detection4-70 limit of 0.0013 μg / g . The estimated standard deviation for re p licate sam p les was 0.047 μg / g in the concentration ran g e of 0.08 to 1.79 μg / g . Anal y sis of EPA reference fish havin g a re p orted ex p erimental mean value of 2.52 μg / g (s=0.64) p roduced a mean value for mercury of 2.87 (s=0.08) in this stud y . The mean value for the overall data set for 669 sam p les was 0.26 μg / g . Mercur y was detected in fish collected from the 374 sites.
Because mercur y emissions from the ambient sources considered in the current Rep ort to Con g ress have different imp acts on g lobal and local de p osition, it was considered im p ortant to se p arate fish s p ecies b y habitat. S p ecificall y , g lobal mercur y c y clin g was j ud g ed to have its g reatest im p act on marine s p ecies, whereas local de p osition was considered more likel y to affect estuarine and freshwater fish and shellfish s p ecies. The s p ecies were classified as shown in Table 4-14 on a classification s y stem described b y Jacobs et al. (in p ress). Central tendenc y estimates of seafood mercury concentrations were utilized in the re p ort. This seems a pp ro p riate since commercial seafood is widel y distributed across the United States ( Seafood Safety , 1991). The source of a p articular fish p urchase is g enerall y not noted b y the consumer (e. g ., canned tuna). As a result, a randomness and avera g in g ma y be achieved. Additionall y , onl y common names of commercial seafood were utilized; s p ecific s p ecies which could be considered to be that t yp e of fish were included in the central tendenc y estimate. A g ain, t yp ical consumers were assumed to g enerall y not be aware of the s p ecies of fish the y were consumin g , rather j ust the t yp e. As noted above, there are other estimates of mercur y concentrations in seafood. After the anal y sis of mercur y ex p osure from seafood was comp leted for this Re p ort, two other databases were obtained: U.S. FDA and Stern et al. (1996). These data are p resented in Table 4-51 for comp arison with those data used for this anal y sis.
4.3.4 Mercur y Concentrations In Freshwater Fish
Estimation of avera g e mercur y concentrations in freshwater finfish from across the United States re q uired a comp ilation of measurements of fish mercury concentrations from randoml y selected U.S. water bodies. A lar g e number of sources of mercur y concentrations in fish were not used in this p art of the assessment. Mercury concentrations in fish have been anal y zed for a number of y ears in man y local or re g ional water bodies in the United States; several of these studies are detailed in this Re p ort. Data described in this bod y of literature are a collection of individual studies which characterize mercur y concentrations in fish from s p ecific g eo g rap hic re g ions such as individual water bodies or in individual states. Man y of the studies were initiated because of a p roblem, p erceived or otherwise, with mercury concentrations in the fish or the water bod y . Thus, the samp le p resented b y a com p ilation of these data ma y be biased toward the hi g h-end of the distribution of mercur y concentrations in freshwater fish. Additionall y , the methods varied from stud y to stud y , and there is no wa y of determinin g the consistenc y of the re p orted data from stud y to stud y . Two studies, more national in sco p e, are thou g ht to p rovide a more comp lete p icture of mercur y concentrations in U.S. freshwater finfish p o p ulations: "National Contaminant Biomonitorin g Pro g ram: Concentrations of Seven Elements in Freshwater Fish, 1978-1981" b y Lowe et al. (1985) and "A National Stud y of Chemical Residues in Fish" conducted b y U.S. EPA (1992) and also re p orted in Bahnick et al. (1994). Lowe et al. (1985) re p orted mercur y concentrations in fish from the National Contaminant Biomonitorin g Pro g ram. The freshwater fish data were collected between 1978-1981 at 112 stations located across the United States. Mercur y was measured b y a flameless cold va p or techni q ue, and the detection limit was 0.01 μg / g wet wei g ht. Most of the samp led fish were taken from rivers (93 of the 1124-71 sam p le sites were rivers); the other 19 sites included lar g er lakes, canals, and streams. Fish wei g hts and len g ths were consistentl y recorded. A wide variet y of t yp es of fishes were samp led: most commonl y carp , lar g e mouth bass and white sucker. The g eometric mean mercur y concentration of all sam p led fish was 0.11 μg / g wet wei g ht; the minimum and maximum concentrations re p orted were 0.01 and 0.77 μg / g wet wei g ht, res p ectivel y . The hi g hest re p orted mercur y concentrations (0.77 μg / g wet wei g ht) occurred in the northern s q uawfish of the Columbia River. See Table 4-53 for mean mercury concentrations b y fish s p ecies.

Table 4-53 Freshwater Fish Mercury Concentrations from Lowe et al., (1985) Species Mean Mercury Concentration μg/g (fresh weight) Bass 0.157 Bloater 0.093 Blue g ill 0.033 Smallmouth Buffalo 0.096 Carp , Common 0.093 Catfish (channel, lar g emouth, rock, stri p ed, white) 0.088 Crapp ie (black, white) 0.114 Fresh-water Drum 0.117 Northern S q uawfish 0.33 Northern Pike 0.127 Perch (white and y ellow) 0.11 Sau g er 0.23 Sucker (brid g eli p , carp sucker, klamath, lar g escale, lon g nose, 0.114 rivercar p sucker, tahoe) Trout (brown, lake, rainbow) 0.149 Walle y e 0.100 Mean of all measured fish 0.11

"A National Stud y of Chemical Residues in Fish" was conducted b y U.S. EPA (1992) and also re p orted b y Bahnick et al. (1994). In this stud y mercur y concentrations in fish tissue were anal y zed. Five bottom feeders (e. g ., carp ) and five g ame fish (e. g ., bass) were samp led at each of the 314 sam p lin g sites in the United States. The sites were selected based on p roximit y to either p oint or non- p oint p ollution sources. Thirt y -five "remote" sites amon g the 314 were included to p rovide back g round p ollutant concentrations. The stud y p rimaril y tar g eted sites that were ex p ected to be im p acted b y increased dioxin levels. The p oint sources p roximate to sites of fish collection included the followin g : p ul p and p ap er mills, Su p erfund sites, p ublicl y owned treatment works and other industrial sites. Data describin g fish a g e, wei g ht, and sex were not consistentl y collected. Whole bod y mercur y concentrations were determined for bottom feeders and mercury concentrations in fillets were anal y zed for the g ame4-72 fish. Total mercur y levels were anal y zed usin g flameless atomic absor p tion; the re p orted detection limits were 0.05 μg / g earl y in the stud y and 0.0013 μg / g as anal y tical techni q ue im p roved later in the anal y sis. Mercur y was detected in fish at 92% of the sam p le sites. The maximum mercur y level detected was 1.8 μg / g , and the mean across all fish and all sites was 0.26 μg / g . The hi g hest measurements occurred in walle y e, lar g e mouth bass and car p . The mercur y concentrations in fish around p ublicl y owned treatment works were hi g hest of all p oint source data; the median value measured were 0.61 μg / g . Pa p er mills were located near man y of the sites where mercur y -laden fish was detected. Table 4-54 contains the mean mercur y concentrations of the s p ecies collected b y Bahnick et al. (1994).
Both the studies re p orted b y Lowe et al. (1985) and b y Bahnick et al. (1994) app ear to be s y stematic, national collections of fish p ollutant concentration data. Clearl y , hi g her mercur y concentrations in fish have been detected in other anal y ses, and the values obtained in these studies should be inter p reted as a rou g h app roximation of the mean concentrations in freshwater finfishes. As indicated in the ran g e of data p resented in Tables 4-53 and 4-54, as well as the aforementioned Tables in Cha p ter 2, wide variations are ex p ected in data on mercur y concentrations in freshwater fish.
The mean mercur y concentrations in all fish sam p led var y b y a factor of two between the studies. The mean mercur y concentration re p orted b y Lowe et al.(1985) was 0.11 μg / g , whereas the mean mercur y concentration re p orted b y Bahnick et al. (1994) was 0.26 μg / g . This difference can be extended to the hi g hest re p orted mean concentrations in fish s p ecies. Note that the avera g e mercur y concentrations in bass and walle y e re p orted b y Bahnick's data are hi g her than the northern s q uawfish, which is the s p ecies with the hi g hest mean concentration of mercury identified b y Lowe et al. (1985).
The bases for these differences in meth y lmercur y concentrations are not immediatel y obvious. The tro p hic p ositions of the s p ecies sam p led, the sizes of the fish, or a g es of fish samp led could si g nificantl y increase or decrease the re p orted mean mercur y concentration. Older and lar g er fish, which occu py hi g her tro p hic p ositions in the a q uatic food chain, would, all other factors bein g e q ual, be ex p ected to have hi g her mercur y concentrations. The sources of the fish also influence fish mercur y concentrations. Most of the fish obtained b y Lowe et al. (1985) were from rivers. The fate and trans p ort of mercur y in river s y stems is less well characterized than in small lakes. Most of the data collected b y Bahnick et al. (1994) were collected with a bias toward more contaminated/industrialized sites, althou g h not sites s p ecificall y contaminated with mercury . It could be that there is more mercury available to the a q uatic food chains at the sites re p orted b y Bahnick et al. (1994). Finall y , the increase in the more recent data as re p orted in Bahnick et al. (1994) could be the result of tem p oral increases in mercury concentrations.
There is a de g ree of uncertaint y in the mercur y concentrations selected for this assessment. This uncertaint y reflects both the ade q uac y of the samp lin g p rotocol for this app lication and the known variabilit y in fish bod y burden. The variabilit y in these data is as broad as the ran g e of re p orted concentrations, which extends from non-detect (below 0.01 μg / g wet wei g ht) u p to 9 μg / g wet wei g ht. Where p ossible, when s p ecific freshwater fish s p ecies are described in the USDA 3-da y consump tion studies, the mean meth y lmercur y concentration for that p articular s p ecies was derived in two se p arate calculations based on the data on meth y lmercur y concentration in the fish re p orted b y Lowe et al. (1985) and b y Bahnick et al. (1994).
Data for mean mercur y concentration in freshwater fish from Bahnick et al. (1994) were combined with the U.S. consump tion rates for freshwater fish from the CSFII 89-91, CSFII 1994, CSFII 1995, and NHANES III to estimate meth y lmercur y intakes for the p o p ulation. The concentrations in the fish utilized are shown in Table 4-54. The ex p osure estimates for freshwater fin fish consump tion are found in Table 4-55. Bahnick et al. (1994) freshwater fish concentration data were utilized, alon g with data on mercur y concentrations in marine fish and shellfish (Tables 4-48, 4-49, 4-50) to calculate total4-73 ex p osure, for g eneral U.S. p o p ulation, to mercur y throu g h consump tion of fish and shellfish (shown in Table 4-55).
Some s p ecies of freshwater fishes were not samp led b y Bahnick et al. (1994), and some res p ondents in the USDA CSFII 89-91 surve y did not identif y the t yp e of freshwater fish consumed. In these situations, it was assumed that the fish consumed contained 0.26 μg meth y lmercur y / g , which is the avera g e of all sam p led fish Bahnick et al. (1994). It is im p ortant to note that the freshwater fish data are for wild p o p ulations not farm-raised fish.

Table 4-54 Mercury Concentrations in Freshwater Fish

U.S. EPA (1992) and Bahnick et al. (1994)

Freshwater Fish Average Mercury Concentration ( g/g, wet weight) Carp 0.11 Sucker 0.167 1 Catfish, Channel and Flathead 0.16 Bass 0.38 2 Walle y e 0.52 Northern Pike 0.31 Cra pp ie 0.22 Brown Trout 0.14 Mean All Fish Samp led 0.26

The value p resented is the mean of the avera g e concentrations found in three typ es of sucker fish (white, redhorse and s p otter). 1 The value p resented is the mean of the avera g e concentrations found in three typ es of bass (white, lar g emouth and smallmouth). 2

4.3.5 Calculation of Mercury Concentrations in Fish Dishes
To estimate the mercur y intake from fish and fish dishes re p orted as consumed b y res p ondents in the CSFII surve y s and NHANES III surve y , several ste p s were taken. Usin g the Reci p e File available from USDA, the fish s p ecies for a p articular re p orted food was identified. The avera g e mercur y concentration in fish tissue on a fresh (or wet) wei g ht basis was identified usin g the NMFS data or the data re p orted b y Bahnick et al. (1994). The food intake of the U.S. p o p ulation includes a lar g e number of com p onents of a q uatic ori g in. A few of these app ear not to have been anal y zed for mercur y concentrations. Meth y lmercur y concentration data were not available for some infre q uentl y consumed food items; e. g ., turtle, roe or j ell y fish. Data on the q uantit y of fish p resent in commerciall y p re p ared sou p s were also not available and were excluded from the anal y sis.
Ph y sical chan g es occur to a food when it is p rocessed and/or cooked. The NMFS and Bahnick et al. (1994) data bases were used to estimate mercur y intake re p ort mercur y concentrations on a g mercur y p er g ram of fresh tissue basis. Earlier research (Bloom, 1992) indicated that over 90% of mercur y p resent in fish and shellfish is chemicall y s p eciated as meth y lmercur y which is bound to p rotein4-74 in fish tissue. Mor g an et al. (1994) indicated that over 90% of mercury p resent in fish and shellfish is chemicall y s p eciated as meth y lmercur y . Conse q uentl y the q uantit y of meth y lmercur y p resent in the fish tissue in the raw state will remain in the cooked or p rocessed fish. In cookin g or p rocessin g raw fish, there is t yp icall y a reduction in the p ercent moisture in the food. Thus, mercury concentration data were recalculated to reflect the loss of moisture durin g food p rocessin g , as well as retention of meth y lmercur y in the remainin g lowered-moisture content fish tissues. Standard estimates of cookin g / p rocessin g -related wei g ht reductions were p rovided b y Dr. Bett y Perloff and Dr. Jacob Exler, ex p erts in the USDA reci p e file and in USDA's food comp osition. Percent moisture lost for baked or broiled fish was 25%. Fried fish p roducts lose wei g ht throu g h loss of moisture but add wei g ht from fat added durin g fr y in g for a total wei g ht loss of minus 12%. The p ercent moisture in fish that were dried, p ickled or smoked was identified for individual fish s p ecies (e. g ., herrin g , cod, trout, etc.) from USDA handbooks of food com p osition. Information on the p ercent moisture in the raw, and in the dried, smoked or p ickled fish was obtained. The meth y lmercur y concentration in the fish was recalculated to reflect the increased meth y lmercur y concentration of the fish as the p ercent moisture decreased in the dry in g , p icklin g or smokin g p rocess. The mean mercur y concentrations for all fish from Lowe et al. (1985) and Bahnick et al. (1994) were combined with the freshwater fish consum p tion data to estimate a ran g e of ex p osure from total fish consump tion. Given the human fish consum p tion rates and the differences between the mercur y concentrations in the two data sets, it is im p ortant to use data from both studies of mercury ex p osures to assess mean concentrations in fish. For p urp oses of comp arison both sets of data were utilized to illustrate the p redicted meth y lmercur y ex p osure. For this comp arison, the avera g e mercur y concentrations for fish in the Lowe and the Bahnick data were anal y zed se p aratel y b y combinin g the freshwater fish data with the data in Tables 4-48 throu g h 4-50. The bod y wei g ht data and the freshwater fish consump tion rates were obtained from Table 4-12. Ex p osure to meth y lmercur y based on an assump tion of 0.11 μg meth y lmercur y / g fish tissue (wet wei g ht) (Lowe et al., 1985). These values are estimated on a bod y wei g ht basis. Tables 4-53 and 4-54 were develo p ed usin g the mean data on mercur y concentrations for all fishes sam p led for these two studies.
Human mercur y intake from fish was estimated b y combinin g data on mercur y concentration in fish s p ecies with the re p orted q uantities and t yp es of fish s p ecies re p orted as consumed b y "users" in the national food consump tion surve y s. The mercur y concentrations in the consumed fish re p orted b y the national surve y s were estimated usin g data on mercur y concentration in fish ex p ressed as micro g rams of mercur y p er g ram fresh-wei g ht of fish tissue. The CSFII 89-91, CSFII 1994, and CSFII 1995 are three of the USDA's food consum p tion surve y s. An additional nationall y -based food consump tion surve y is the third National Health and Nutrition Examination Surve y . The food items re p orted b y individuals interviewed in these surve y s are identified b y 7-di g it food codes. The USDA has develo p ed a reci p e file identif y in g the p rimar y com p onents that make u p the food or dish re p orted "as Eaten" b y a surve y res p ondent. The total wei g ht of a fish-containin g food is t yp icall y not 100% fish. The food code s p ecifies a p re p aration method and g ives additional in g redients used in p re p aration of the dish. For examp le, in the Reci p e File "Fish, floured or breaded, fried" contains 84% fish, b y wei g ht. Fish dishes contained a wide ran g e of fish; from app roximatel y 5% for a frozen "shrim p chow mein dinner with e gg roll and p e pp ers" to 100% for fish consumed raw, such as raw shark.4-75 4.4 Intake of Methylmercury from Fish/fish Dishes
Estimates of meth y lmercur y intake from fish and shellfish have been made based on dietar y surve y data from the nationall y re p resentative surve y s (CSFII 89-91, CSFII 94, CSFII 95, and NHANES III). Pro j ected month-lon g estimates of fish/shellfish intake and mercur y ex p osure have been develo p ed from the NHANES III fre q uenc y of fish consum p tion data usin g data from the adult p artici p ants in NHANES III and the 24-hour recall data from children and adults in NHANES III. These month-lon g p ro j ections are considered to be the descri p tions of mercur y ex p osure from fish and shellfish that are most relevant to the health end p oint used as the basis for the RfD; i.e. develo p mental deficits in children followin g maternal ex p osure to meth y lmercur y . Based on in p ut from the intera g enc y review a determination has been made that com p arison of 24-hour “ p er user” data is g enerall y inapp ro p riate and will not be done exce p t when describin g sub p o p ulations who eat fish/shellfish almost ever y da y . 4.4.1 Intakes " p er User" and " p er Cap ita"
The data from ma j or nationall y based surve y s of the g eneral p o p ulation are from CSFII 89-91, CSFII 1994, CSFII 1995, and NHANES III conducted between 1988 and 1994. CSFII 89-91 obtained 3- da y s of dietar y histor y based on 24-hour recall interviews. CSFII 1994 and CSFII 1995 obtained two da y s of dietar y histor y also obtained b y 24-hour recall interview techni q ues. These two da y s of dietar y recalls were not necessaril y se q uential da y s. Interviewers in NHANES III obtained the res p ondents’ estimate of the number of times p er da y , p er week, and p er months the res p ondent consumed fish/shellfish over the p ast 12-month p eriod. These data were obtained onl y for p ersons 12 y ears of a g e and older. In addition, recall data on fish/shellfish consum p tion were obtained on the same res p ondents as were q uestionnaire res p onses of the fre q uenc y of food consum p tion. These recall data cover the 24- hour p eriod p rior to the interview.
The number and p ercent of res p ondents re p ortin g consump tion of fish and/or shellfish in these surve y s in shown in Tables 4-55 to 4-57. Intake data can be ex p ressed on a “ p er cap ita” basis which re p orts the statistics calculated for all surve y p artici p ants whether or not the y re p orted consumin g fish and/or shellfish durin g the recall p eriod. B y contrast, “ p er user” statistics are calculated for onl y those individuals who re p orted consumin g fish and/or shellfish durin g the recall p eriods. The p ercent of surve y res p ondents who re p orted consumin g fish and/or shellfish on one 24-hour recall ran g ed from 11.3 to 12.9% in the nationall y -based contemp orar y food consum p tion surve y s (Table 4-54).

Table 4-55 CSFII 89-91 Number of Respondents - All Age Groups

Ages 14 and Ages 15 through Ages 46 and Total

Younger 45 Older Total 2893 4968 3545 11,706 Fish Consumers 720 1510 1384 36144-76 Table 4-56 CSFII 89-91 Adult Respondents Gender Ages 15 to 45 Years Ages 46 Years and Older Total for All Age Groups Males 2131 1537 3668 Females 2837 2308 5145 Respondents Reporting Fish Consumption Gender Ages 15 to 45 Years Ages 46 Years and Older Total for All Age Groups Males 646 556 1202 Females 864 828 1692 Total 1510 1384 2894

Table 4-57 Contemporary Dietary Surveys — 1990s General U.S. Population Survey Total Number of Number Reporting Percent Consuming Subjects Fish/shellfish Fish/shellfish Consumption NHANES III 29,989 3864 12.9 CSFII 94 - Day 1 5,296 598 11.3 CSFII 94 - Day 2 5,293 596 11.3 CSFII 95 - Day 1 5063 601 11.9 CSFII 95 - Day 2 5062 620 12.2

4.4.1.1 “Per Cap ita” Consum p tion
“Per cap ita” data for CSFII 89-91 are shown in Table 4-58. Data in CSFII 89-91 reflect avera g es calculated from three da y s of 24-hour recall data. Data for the more-recentl y conducted national surve y s are shown in Table 4-59. These data were obtained b y interview and describe fish/shellfish consum p tion in the p revious 24-hour p eriod. Interviewers describe two 24-hour recalls p er res p ondent.4-77 Table 4-58 Per Capita Fish/Shellfish Consumption (gms/day) and Mercury Exposure (μg/kg body weight/day) From CSFII 89-91 Based on Average of Three 24-Hour Recalls

25th 50th 75th 95th Maximum Fish/shellfish Consumption Zero Zero 16 73 461
Mercury Exposure Zero Zero 0.04 0.24 2.76

Table 4-59 Per Capita Fish/Shellfish Consumption Based on Individual Days of 24-Hour Recall Data General U.S. Population Surveys — 1990s Survey 10th 50th 90th 95th Maximum CSFII 94 - Da y 1 Zero Zero 32 85 457 0.03 0.13 3.76 CSFII 94 - Da y 2 Zero Zero 37 85 606 0.03 0.14 4.03 CSFII 95 - Da y 1 Zero Zero 43 105 960 0.04 0.13 5.93 CSFII 95 - Da y 2 Zero Zero 43 98 1084 0.05 0.17 2.63 NHANES III Zero Zero 56 114 1260 0.08 0.19 6.96

4.4.1.2 “Per User” Consump tion
If statistics are calculated onl y on those individuals who re p orted consumin g fish and/or shellfish durin g the recall p eriod “ p er user” values are calculated. Data from the avera g e (i.e., mean) of three da y s of 24-hour recalls re p orted in the CSFII 1989-1991 surve y are shown in Table 4-60. Data for the individual two da y s recorded in CSFII 1994 and in CSFII 1995, and for the sin g le da y ’s 24-hour recall in NHANES III are shown in Table 4-61.4-78 Table 4-60 Per User Fish/Shellfish Consumption (grams per day) and Mercury Exposure (μg/kg bw/day) Based on Average of Three 24-Hour Recalls — CSFII 89-91

25th 50th 75th 95th Maximum Fish/shellfish Consumption 19 32 57 117 461
Mercury Exposure 0.04 0.09 0.18 0.45 2.76

Table 4-61 “Per User” Intake of Fish and Shellfish (gms/day) and Exposure to Mercury (μg Hg/kg bw/day) Among Individuals Reporting Consumption, Based on Individual Day Recall Data Study 10th 50th 90th 95th Maximum CSFII 94 - Da y 1 28 76 186 252 458 n=598 0.02 0.11 0.43 0.65 3.76 CSFII 94 - Da y 2 26 74 200 282 606 n=596 0.03 0.11 0.40 0.65 1.03 CSFII 95 - Da y 1 28 84 197 261 960 n=601 0.03 0.10 0.42 0.61 5.93 CSFII 95 - Da y 2 24 79 216 285 1084 n = 620 0.03 0.12 0.47 0.64 2.63 NHANES III 22 73 242 336 1260 n=3,864 0.01 0.11 0.44 0.63 6.95

4.4.2 Meth y lmercur y Intake from Fish and Shellfish amon g Women of Child-bearin g A g e and Children
Sub g rou p s at increased risk of ex p osure and/or toxic effects of mercur y amon g the g eneral p o p ulation include women of childbearin g a g e and children. Ex p osures to women of childbearin g a g e are of p articular interest because meth y lmercur y is a develo p mental toxin (Tables 4-62 and 4-63).4-79 Table 4-62 “Per Capita” Fish/Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg bw/day) Based on Average of Three 24-Hour Dietary Recalls — CSFII 89-91 Females Aged 15-45 25th 50th 75th 95th Maximum Value Fish/shellfish Consumption Zero Zero 15 72 461 Mercury Exposure Zero Zero 0.03 0.20 2.76
Table 4-63 “Per User” Fish/Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg bw/day) Based on Average of Three 24-Hour Dietary Recalls — CSFII 89-91 Females Aged 15-45 25th 50th 75th 95th Maximum Value Fish/shellfish Consumption 19 31 56 113 461 Mercury Exposure 0.04 0.08 0.16 0.33 2.76
Children consume more food on a bod y wei g ht basis than do adults. Conse q uentl y children have hi g her ex p osures to a variet y of food contaminants (National Academ y of Sciences, 1993 ) includin g mercur y . Overall, app roximatel y 11 to 13 % of adults re p ort fish/shellfish consump tion in short-term consump tion estimates based on sin g le 24-hour recall data. For children, the p ercent who re p ort fish consump tion in similar surve y s is about 8 to 9%.
Lookin g at the q uantit y of fish consumed and the intake of mercury on a bod y wei g ht basis (i.e., μg H g /k g bod y wei g ht/da y ), the hi g hest environmental dose of mercury from consum p tion of fish and shellfish is found amon g children (Tables 4-64 and 4-65) based on fish intake and mercury ex p osures estimated from sin g le-da y estimates. Ex p osure (on a p er k g /bw basis) amon g children a g es 10 and y oun g er are elevated com p ared with adult values. Children in the a g e ran g e 11 throu g h 14 y ears have mercur y doses ( μg H g /k g bod y wei g ht/da y ) more com p arable to adult values than to those of y oun g er children. When the NHANES III data are g rou p ed b y a g e cate g or y , ex p osure p atterns shown in Table 4- 64 are identified. Hi g her doses of mercur y relative to bod y wei g ht ( μg /k g bod y wei g ht/da y ) were also observed in data from CSFII 94 and CSFII 95 (Table 4-66).4-80 Table 4-64 Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) among Different Age Categories of Children, Based on Individual Day Data (Data from the NHANES III, 1988-1994) Age Group, Years Fish Consumption Mercury Exposure grams/day μg/kg body weight/day Less than 2 years 50th Percentile 29 0.33 90th Percentile 95 0.98 95th Percentile 115 1.33 3 through 6 years 50th Percentile 43 0.28 90th Percentile 113 0.77 95th Percentile 151 1.08 7 through 10 years 50th Percentile 77 0.31 90th Percentile 178 0.86 95th Percentile 270 1.08 11 through 14 years

50th Percentile 63 0.15 90th Percentile 158 0.42 95th Percentile 215 0.68

Table 4-65 Fish and Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg body weight/day) for Children Aged 14 years and Younger — CSFII 89-91 Based on Average of Three 24-Hour Recalls
Gender 25th 50th 75th 95th Maximum Value “Per User” Females 13 24 38 75 154 0.08 0.17 0.34 0.85 1.69 Males 14 23 43 87 139 0.09 0.17 0.29 0.63 1.51 “Per Capita” Females Zero Zero 7 43 155 Zero Zero Zero 0.39 1.69 Males Zero Zero 5 52 139 Zero Zero 0.01 0.33 1.514-81 Table 4-66 “Per User” Fish and/or Shellfish Consumption (grams/day) and Mercury Exposure (μg Hg/kg bw/day) by Children ages 14 and Younger Based on Individual Day Data. Survey 10th 50th 90th 95th Maximum CSFII 94 - Da y 1 15 53 127 176 293 n=148 0.04 0.13 0.77 1.06 1.56 CSFII 94 - Da y 2 16 53 156 171 384 n=162 0.07 0.20 0.67 0.91 2.70 CSFII 95 - Da y 1 16 57 185 204 305 n=126 0.04 0.23 0.69 0.81 5.93 CSFII 95 - Da y 2 13 53 170 243 305 n=148 0.03 0.23 1.00 1.98 2.63 NHANES III 14 51 155 185 915 1988-1994 0.04 0.25 0.83 1.08 6.95 n=1,062

Com p arison of the “ p er cap ita” and “ p er user” values indicate that Asian Americans and Pacific Islanders consume fish and shellfish more fre q uentl y than other sub p o p ulations. However, the q uantit y of fish and shellfish consumed p er p erson is actuall y smaller than for the other sub p o p ulations Table 4- 67). If mercur y ex p osure is ex p ressed on a bod y wei g ht basis ( μg H g /k g bod y wei g ht), the ex p osures are more comp arable althou g h Asian Americans/Pacific Islanders have lower ex p osure to mercur y (on a bod y wei g ht basis) than do other ethnicall y diverse sub p o p ulations (Table 4-67).

Table 4-67 Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) Among Ethnically Diverse Groups, Based on Individual Day Recalls (Source: CSFII 94 and CSFII 95) Ethnic Group Per Capita Per User 1 2 Fish Mercury Fish Mercury Consumption Exposure Consumption Exposure grams/day μg/kg bw/day grams/day μg/kg bw/day White

50th Percentile 90th Percentile 95th Percentile 80 0.14 243 0.67 Zero Zero 72 0.12 24 0.03 192 0.46

Black

50th Percentile 90th Percentile 95th Percentile 104 0.19 302 0.96 Zero Zero 82 0.14 48 0.05 228 0.54Table 4-67 (continued) Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) Among Ethnically Diverse Groups, Based on Individual Day Recalls

Ethnic Group Per Capita Per User 1 2 Fish Mercury Fish Mercury Consumption Exposure Consumption Exposure grams/day μg/kg bw/day grams/day μg/kg bw/day

4-82 Asian and Pacific Islander

50th Percentile 90th Percentile 95th Percentile 127 0.30 292 0.56 Zero Zero 62 0.10 80 0.15 189 0.39

Native American and Alaska Native 50th Percentile 90th Percentile Zero Zero of small of small 95th Percentile Zero Zero made because made because
56 0.03 numbers of numbers of Estimate not Exposures not

respondents

. respondents. Other

50th Percentile 90th Percentile 95th Percentile Zero Zero 83 0.18 Zero Zero 294 0.64 62 0.13 327 0.81 Total number of 24-hour food consumption recall reports: White (16,241); Black (2,580); Asian and 1 Pacific Islander (532); Native American and Alaska Native (166): and Other (1,195).

Number of 24-hour food consumption recall reports: White (1,821); Black (329); Asian and Pacific 2 Islander (155); Native American and Alaska Native (12); and Other (98).

4.4.3 Month-Lon g Estimates for Consumers
The third NHANES included surve y q uestions on the fre q uenc y of consum p tion of fish and shellfish that p ermitted nationall y based estimates on how fre q uentl y p eo p le in the g eneral United States p o p ulation consume fish and shellfish over a month-lon g p eriod. The t yp ical fre q uenc y of consum p tion combined with a “sna p shot” of t yp ical consum p tion on any single day as shown in the “per user” 24- hour recall data p ermit p ro j ection of moderate-term p atterns of fish/shellfish consum p tion. It is these moderate-term p atterns that are the most relevant ex p osure p eriod for the health-based end p oint that formed the basis of the RfD - i.e., develo p mental deficits in children followin g maternal ex p osure to meth y lmercur y . Additional descri p tion of the p articular im p ortance of moderate-term p atterns of mercur y ex p osure from fish/shellfish intakes is found in Section 4.1.1 ( p a g e 4-1 throu g h 4-3 of this Volume).
The fre q uenc y of fish and shellfish consump tion can be determined from the food fre q uenc y data obtained in NHANES III. B y combinin g the number of times p er month a p erson eats fish and shellfish with the 24-hour recall data that p rovide an estimate of p ortion size and s p ecies of fish/shellfish selected,4-83 a p ro j ection can be made of the consum p tion p attern over a month. The statistical methods describin g how these two fre q uenc y distributions were combined is p resented in A pp endix D. The month-lon g p ro j ection of fish/shellfish consum p tion for the g eneral p o p ulation is shown in Table 4-68a and 4-68b; the estimate for women of childbearin g a g e (assumed to be 15 throu g h 44 y ears) is shown in Tables 4-69 and 4-70, and the estimates for children are shown in Tables 4-71 and 4-72. Table 4-68a Month-Long Estimates of Fish and Shellfish Consumption (gms/day) General Population by Ethnic/Racial Group National Estimates Based on NHANES III Data

White/NonHis p anic Black/NonHis p anic Other Percentile Fish/Shellfish Percentile Fish/Shellfish Percentile Fish/Shellfish g ms/da y g ms/da y g ms/da y 50th 8 50th 10 50th 9 75th 19 75th 25 75th 27 90th 44 90th 58 90th 70 95th 73 95th 97 95th 123 Percentile at Percentile at Percentile at which 97.3th which 95.1th which 94.6th consum p tion Percentile consum p tion Percentile consum p tion p ercentile e q uals e q uals e q uals a pp roximatel y app roximatel y app roximatel y 100 g rams/da y . 100 g rams/da y . 100 g rams/da y .

Table 4-68b Month-Long Estimates of Mercury Exposure (μg/kgbw/day) Population by Ethnic/Racial Group National Estimates Based on NHANES III Data White/NonHispanic Black/NonHispanic Other Percentile Mercury Percentile Mercury Percentile Mercury Exposure Exposure Exposure μg/kgbw/day μg/kgbw/day μg/kgbw/day 50th 0.02 50th 0.02 50th 0.02 75th 0.04 75th 0.05 75th 0.06 90th 0.09 90th 0.13 90th 0.17 95th 0.15 95th 0.21 95th 0.314-84 Table 4-69 Month-Long Estimates of Exposure to Fish and Shellfish (gms/day) for Women Ages 15 through 44 Years Combined Distributions Based on NHANES III Data
Percentile Fish/Shellfish (gms/day) 50th 9 75th 21 90th 46 95th 77 Percentile at which consumption exceeds approximately 100 grams/day 97th percentile based on month-long projections

Table 4-70 Month-Long Estimates of Mercury Exposure (μg/kgbw /day) for Women Ages 15 through 44 All Subpopulations Combined National Estimates Based on NHANES III Data Percentiles Mercury Exposure μg/kgbw /day Month-Long Estimates 50th 0.01 75th 0.03 90th 0.08 95th 0.13 99th 0.374-85 Table 4-71 Month-Long Estimates of Fish/Shellfish Consumption (gms/day) among Children Ages 3 through 6 Years. National Estimates Based on NHANES III Data

Percentile Per User Month-Long Estimate Fish/Shellfish Consumption Mercury Exposure (grams/day) (μg/kg bw /day) 50th 5 0.03 75th 12 0.08 90th 25 0.18 95th 39 0.29

Table 4-72 Month-Long Estimates of Exposure to Fish and Shellfish (gms/day) and Mercury (μg/kgbw/day ) among Children Ages 3 through 6 Years National Estimates for Individual Ethnic/Racial Groups Percentile All Groups White/ Black/ Other NonHispanic NonHispanic 50th Fish (grams/day) 5567 Mercury (μg/kgbw/day ) 0.03 0.03 0.03 0.04

75th Fish (grams/day) 12 11 13 17 Mercury (μg/kgbw/day ) 0.08 0.08 0.08 0.11

90th Fish (grams/day) 25 24 28 36 Mercury (μg/kgbw/day ) 0.18 0.17 0.19 0.25

95th Fish (grams/day) 39 37 44 57 Mercury (μg/kgbw/day ) 0.29 0.28 0.33 0.424-86 4.4.4 Habitat of Fish Consumed and Mercury Ex p osure from Fish of Marine, Estuarine and Freshwater Ori g in
Fish and shellfish s p ecies have been g rou p ed into those inhabitin g marine, estuarine, and freshwater environments. This classification was develo p ed b y US EPA’s Office of Water based on advise from fisheries biolo g ists. Cate g ories of fish and shellfish into those p rimaril y inhabitin g marine, estuarine, and freshwater environments was shown in Table 4-17.
State and local authorities fre q uentl y have obtained data on mercury concentrations in fish in waterwa y s within their boundaries. Thirt y -ei g ht states in the United States have issued advisories re g ardin g mercur y ex p osures that will occur throu g h consump tion of these fish. Nine states have statewide advisories that either are based p rimaril y u p on or include concern for mercur y ex p osures from these fish. At a local level, the mercury concentrations in fish vary widel y . Ex p osures to meth y lmercur y will var y with the p ro p ortion of fish obtained from local sources and from interstate commerce.
Estimates have been made of a national p attern indicatin g the mixture of marine, estuarine, and freshwater source of fish and shellfish. Tables 4-73 and 4-74 are based on the fish/shellfish consum p tion data from NHANES III combined with the mercur y concentration data of the NMFS and data re p orted b y Bahnick et al. (1988) on mercury concentrations in freshwater fish comin g from a nationall y based sam p le of fish and shellfish. Consump tion of fish and shellfish from a p articular g eo g rap hic site ma y result in hi g her or lower ex p osures to meth y lmercur y . Amon g the three habitat t yp es, overall consump tion of freshwater fish and shellfish resulted in the hi g hest mercur y ex p osure p er kilo g ram bod y wei g ht, followed b y marine and estuarine fish and shellfish. Men re p orted hi g her mercur y ex p osures from freshwater fish than did women. The hi g her external doses from freshwater fish are, in p art, a reflection of lar g er servin g sizes re p orted when freshwater s p ecies are consumed. Table 4-73 Exposure of Men Ages 15 to 44 Years to Mercury (μg Hg/kg bw/day) from Fish and Shellfish of Marine, Estuarine, and Freshwater Origin Based on Individual Day Recalls (Food Consumption Data from NHANES III and Mercury Concentration Data from NMFS and Bahnick et al. (1988) )

Statistic Marine Estuarine Freshwater Combined Origin Origin Origin Origin, i.e., Total n=386 n = 198 n=60 Exposure n = 644 Percentiles 10th 0 0 0.01 0.01 50th 0.10 0.03 0.33 0.11 90th 0.35 0.30 1.26 0.44 95th 0.60 0.44 1.37 0.60 Maximum Values Reported 4.43 0.71 1.91 4.434-87 Table 4-74 Exposure of Women Aged 15-44 Years to Mercury (μg Hg/kg bw/day) from Fish and Shellfish of Marine, Estuarine, and Freshwater Origin Based on Individual Day Recalls (Food Consumption Data from NHANES III and Mercury Concentration Data from NMFS and Bahnick et al. (1988) )

Statistic Marine Estuarine Freshwater Combined Origin Origin Origin Origin, i.e., Total n = 581 n = 221 n = 82 Exposure n = 882 Percentiles 10th 0.01 0.01 0.04 0.01 50th 0.10 0.03 0.18 0.10 90th 0.41 0.14 0.50 0.39 95th 0.56 0.23 0.77 0.53 Maximum Reported Value 3.59 0.39 0.91 3.59

4.4.5 Meth y lmercur y Consump tion Quantities of meth y lmercur y consumed in fish de p end u p on both the q uantit y of fish consumed and the meth y lmercur y concentration of the fish. Althou g h the y are infre q uentl y consumed, swordfish, barracuda and shark have a much hi g her meth y lmercur y concentration than other marine finfish, freshwater finfish or shellfish. B y contrast most shellfish contain low concentrations of meth y lmercur y resultin g in far lower meth y lmercur y ex p osures than would occur if finfish s p ecies were chosen. 4.5 Conclusions on Methylmercury Intake from Fish
Meth y lmercur y intakes calculated in this cha p ter have been develo p ed for a nationall y based p o p ulation rather than site-s p ecific estimates. Food consum p tion data was p rovided from the CSFII 89/91, CSFII 94, CSFII 95, and NHANES III surve y s. Meth y lmercur y intakes calculated in this cha p ter have been develo p ed for a nationall y based rather than site-s p ecific estimates. The CSFII 89-91 from USDA was desi g ned to re p resent the U.S. p o p ulation. The concentrations of meth y lmercur y in marine fish and shellfish were from a data base that is national in sco p e. Data on freshwater finfish were taken from two lar g e studies that sam p led fish at a number of sites throu g hout the United States. The extent of app licabilit y of these data to site-s p ecific assessments must rest with the p rofessional j ud g ments of the assessor. Because of the ma g nitude of anthro p o g enic, ambient mercur y contamination, the estimates of meth y lmercur y from fish do not p rovide a value that reflects meth y lmercur y from nonindustrial sources. "Back g round" values imp l y an ex p osure a g ainst which the increments of anthro p o g enic activit y could be added. This is not the situation due to release of substantial q uantities into the environment.
Issues dealin g with confidence in data on the meth y lmercur y concentration of fish consumed include the followin g :4-88  In a number of situations individuals cannot identif y with accurac y the s p ecies of fish consumed. The USDA Reci p e File Data Base has "default" fish s p ecies s p ecified if the res p ondent does not identif y the fish s p ecies consumed. There is no way , however to estimate the ma g nitude of uncertaint y encountered b y misidentification of fish s p ecies b y the surve y res p ondents.

 The data base used to estimate meth y lmercur y concentrations in marine fish and shellfish was p rovided b y the NMFS. This data base has been g athered over app roximatel y the p ast 20 y ears and covers a wide number of s p ecies of marine fish and shellfish. The number of fish samp les for each s p ecies varies but typ icall y exceeds 20 fish p er s p ecies.

 The anal y tical q ualit y of the data base has been evaluated b y comp arison of these data with comp liance sam p les run for the U.S. FDA. The National Academ y of Sciences' Re p ort on Seafood Safety and the U.S. FDA have found this data base from NMFS to be consistent with 1990s data on meth y lmercur y concentrations in fish.

 The meth y lmercur y concentrations in freshwater fish come from two p ublications, each g ivin g data that re p resent freshwater fish from a number of locations. These data were g athered between the earl y 1980s and earl y 1990s. These surve y s b y U.S. EPA (1992), Bahnick et al. (1994), and Lowe et al. (1985) re p ort different mean mercur y concentrations; 0.260 μg / g mercur y (wet wei g ht) and 0.114 μg / g mercur y (wet wei g ht), res p ectivel y . The extent to which either of these data sets re p resents nationall y based data on freshwater fish meth y lmercur y concentrations remains uncertain.

 Month-lon g estimates of mercury ex p osure from fish and shellfish consump tion are considered the ex p osure p ro j ection most relevant to the health end p oint of concern; i.e., develo p mental deficits amon g children followin g maternal fish consump tion.

 Because meth y lmercur y is a develo p mental toxin, a sub p o p ulation of interest is women of child-bearin g a g e. In this anal y sis of meth y lmercur y intake, dietar y intakes of women a g ed 15 throu g h 44 y ears were used to app roximate the diet of the p re g nant woman. From data on Vital and Health Statistics, it has been determined that 9.5% of women of re p roductive a g e can be antici p ated to be p re g nant within a g iven y ear. Generall y food intake increases durin g p re g nanc y (Naismith, 1980). Information on dietar y p atterns of p re g nant women has been assessed (amon g other see Bowen, 1992; Greele y et al., 1992). Most of these anal y ses have focussed on intake of nutrients rather than contaminants. It is uncertain whether or not p re g nanc y would modif y q uantities and fre q uenc y of fish consumed be y ond an y increase that ma y result from increased ener gy (i.e., caloric) intake that typ icall y accom p anies p re g nanc y .

 Based on available data on fish consum p tion in the 3 throu g h 6 y ear a g e g rou p , it is estimated that 19 to 26% of these children consume relativel y more fish on a p er kilo g ram p er bod y -wei g ht basis than do adults, which may result in hi g her mercur y ex p osure these children. The ran g e reflects differences in mercury ex p osures between sub p o p ulations cate g orized on the basis of race and ethnicit y . Persons of Asian/Pacific Islander, non-Mexican His p anics (lar g el y p ersons of Caribbean ethnicity ), Native Americans, and Alaskan Natives have the hi g hest ex p osures.

 Because mercur y concentrations in fish/shellfish are hi g hl y variable, information on fish/shellfish consump tion ( g rams/da y ) are also of interest. It is estimated that 3% of4-89 women have month-lon g fish/shellfish intakes of 100 g rams p er da y and hi g her based on the NHANES III data.5-1 

### 5. POPULATION EXPOSURES - NON-DIETARY SOURCES 5.1 Dental Amalgams Dental amalgams have been the most commonly used restorative material in dentistry. A typical amalgam consists of approximately 50% mercury by weight. The mercury in the amalgam is continuously released over time as elemental mercury vapor (Begerow et al., 1994). Research indicates that this pathway contributes to the total mercury body burden, with mercury levels in some body fluids correlating with the amount and surface area of fillings for non-occupationally exposed individuals (Langworth et al., 1991; Olstad et al., 1987; Snapp et al., 1989). For the average individual an intake of 2-20 μg/day of elemental mercury vapor is estimated from this pathway (Begerow et al., 1994). Additionally, during and immediately following removal or installation of dental amalgams supplementary exposures of 1-5 μg/day for several days can be expected (Geurtsen 1990). Approximately 80% of the elemental mercury vapor released by dental amalgams is expected to be re-absorbed by the lungs (Begerow et al., 1994). In contrast, dietary inorganic mercury absorption via the gastrointestinal tract is known the be about 7%. The contribution to the body burden of inorganic mercury is thus, greater from dental amalgams than from the diet or any other source. The inorganic mercury is excreted in urine, and methylmercury is mainly excreted in feces. Since urinary mercury levels will only result from inorganic mercury intake, which occurs almost exclusively from dietary and dental pathways for members of the general public, it is a reasonable biomonitor of inorganic mercury exposure. Urinary mercury concentrations from individuals with dental amalgams generally range from 1-5 μg/day, while for persons without these fillings it is generally less than 1 μg/day (Zander et al., 1990). It can be inferred that the difference represents mercury that originated in dental amalgams. Begerow et al., (1994) studied the effects of dental amalgams on inhalation intake of elemental mercury and the resulting body burden of mercury from this pathway. The mercury levels in urine of 17 people aged 28-55 years were monitored before and at varying times after removal of all dental amalgam fillings (number of fillings was between 4-24 per person). Before amalgam removal, urinary mercury concentrations averaged 1.44 μg/g creatinine. In the immediate post-removal phase (up to 6 days), concentrations increased by an average of 30%, peaking at 3 days post-removal. After this phase mercury concentrations in urine decreased continuously and by twelve months had dropped to an average of 0.36 μg/g creatinine. This represents a four-fold decrease from pre-removal steady-state urinary mercury levels. 5.2 Occupational Exposures to Mercury Industries in which occupational exposure to mercury may occur include chemical and drug synthesis, hospitals, laboratories, dental practices, instrument manufacture, and battery manufacture (National Institute for Occupational Safety and Health, (NIOSH) 1977). Jobs and processes involving mercury exposure include manufacture of measuring instruments (barometers, thermometers, etc.), mercury arc lamps, mercury switches, fluorescent lamps, mercury broilers, mirrors, electric rectifiers, electrolysis cathodes, pulp and paper, zinc carbon and mercury cell batteries, dental amalgams, antifouling paints, explosives, photographs, disinfectants, and fur processing. Occupational mercury exposure can also result from the synthesis and use of metallic mercury, mercury salts, mercury catalysts (in making urethane and epoxy resins), mercury fulminate, Millon's reagent, chlorine and caustic soda, pharmaceuticals, and antimicrobial agents (Occupational Safety and Health Administration (OSHA) 1989).5-2 OSHA (1975) estimated that approximately 150,000 US workers are exposed to mercury in at least 56 occupations (OSHA 1975). More recently, Campbell et al., (1992) reported that about 70,000 workers are annually exposed to mercury. Inorganic mercury accounts for nearly all occupational exposures, with airborne elemental mercury vapor the main pathway of concern in most industries, in particular those with the greatest number of mercury exposures. Occupational exposure to methylmercury appears to be insignificant. Table 3-10 summarizes workplace standards for airborne mercury (vapor + particulate).A number of studies have been reported that monitored workers' exposure to mercury (GonzalezFernandez et al., 1984; Ehrenberg et al., 1991; Cardenas et al., 1993; Kishi et al., 1993, 1994; Yang et al., 1994). Some studies have reported employees working in areas which contain extremely high air mercury concentrations: 0.2 to over 1.0 mg/m of mercury. Such workplaces include lamp sock 3 manufacturers in Taiwan (Yang et al., 1994), mercury mines in Japan (Kishi et al., 1993,1994), a small thermometer and scientific glass manufacturer in the US (Ehrenberg et al., 1991), and a factory producing mercury glass bubble relays (Gonzalez-Fernandez et al., 1984). High mercury levels have been reported in blood and urine samples collected from these employees (reportedly over 100 μg/L in blood and over 200 - 300 μg/L or 100 - 150 μg/g creatinine for urine). At exposures near or over 1.0 mg/m3, workers show clear signs of toxic mercury exposure (fatigue, memory impairment, irritability, tremors, and mental deterioration). The chronic problems include neurobehavioral deficits that persist long after blood and urine mercury levels have returned to normal; many workers required hospitalization and/or drug treatments. With the exception of mercury mines, workplaces producing these mercury levels are typically small and specialized, often employing only a few workers who were exposed to high mercury concentrations. Many other studies have monitored employees’ work areas and reported measured mercury air concentrations of 0.02 - 0.2 mg/m ; these levels are generally in excess of present occupational standards 3 (see Table 5-1). These mercury levels were most often reported at chlor-alkali plants (Ellingsen et al., 1993; Dangwal 1993; Barregard et al., 1992; Barregard et al., 1991; Cardenas et al., 1993). Employees at these facilities had elevated bodily mercury levels of approximately 10-100 μg/L for urine and about 30 μg/L in blood. At these lower levels, chronic problems persisting after retirement included visual response and peripheral sensory nerve effects. Exposures to mercury levels under 0.02 mg/m typically result in blood and urine levels 3 statistically higher than the general population, but health effects are usually not observed.

Table 5-1 Occupational Standards for Airborne Mercury Exposure
Concentration Standard Type Mercury Species Reference Standard (mg/m ) 3 0.10 STEL inorganic CFR (1989) 0.01 TWA organic CFR (1989) 0.03 STEL alkyl CFR (1989) 0.05 TWA all besides alkyl ACGIH (1986)Table 5-1 (continued) Occupational Standards for Airborne Mercury Exposure Concentration Standard Type Mercury Species Reference Standard (mg/m ) 3

5-3 0.01 TWA alkyl ACGIH (1986) 0.03 STEL alkyl ACGIH (1986) 0.10 TWA aryl and inorganic ACGIH (1986) 0.05 TWA all besides alkyl NIOSH (1977)

Abbreviations: ACGIH - American Conference of Governmental Industrial Hygienists CFR - Code of Federal Regulations STEL - Short term exposure limit (15 minutes) TWA - Time weighted average (8 hour workday)

5.3 Miscellaneous Sources of Mercur y Ex p osure Inorganic mercury is used in some ritualistic practices (Wendroff, 1995). The extent of this use in the United States is undocumented, although it is considered to be more commonly encountered in Hispanic and Latino communities. Inorganic mercury is distributed around the household in a variety of ways and may result in dermal contact or it potentially be inhaled.
5.4 Cases of Mercur y Poisonin g Numerous examples may be found in the literature of unintentional mercury poisoning. The following examples were taken from Morbidity and Mortality Weekly Report , a publication of the U.S. Public Health Service, Centers for Disease Control. These cases studies indicate that mercury has diverse — although, in many cases, illegal — applications. The studies illustrate the wide range of potential health effects from mercury exposure including death. Unsafe Levels of Mercury Found in Beauty Cream Between September 1995 and May 1996, the Texas Department of Health, the New Mexico Department of Health, and the San Diego County Health Department investigated three cases of mercury poisoning associated with the use of a mercury-containing beauty cream produced in Mexico. The cream, marketed as “Crema de Belleza-Manning” for skin cleansing and prevention of acne, has been produced since 1971. The product listed “calomel” (mercurous chloride) as an ingredient and contained 6% to 10% mercury by weight. Because mercury compounds are readily absorbed through the skin, FDA regulations restrict the use of these compounds as cosmetic ingredients. Specifically, mercury compounds can be used only as preservatives in eye-area cosmetics at concentrations not exceeding 65 ppm of mercury; no effective and safe nonmercurial substitute preservative is available for use in such cosmetics.5-4 An ongoing investigation of the cream located it in shops and flea markets in the United States near the U.S.-Mexico border, and identified a U.S. organization in Los Angeles as the distributor. Media announcements, warning of the mercury containing cream, were then made in Arizona, California, New Mexico, and Texas. In response to these announcements, 238 people contacted their local health departments to report using the cream. Urinalysis was conducted for 119 people, and of these, 104 had elevated mercury levels. Elevated urine mercury levels were also detected in people who did not use the cream but who were close household contacts of cream users. Indoor Latex Paint Found to Contain Unsafe Mercury Levels In August 1989, a previously healthy 4-year-old boy in Michigan was diagnosed with acrodynia, a rare manifestation of childhood mercury poisoning. A urine mercury level of 65 μg/L was measured in a urine sample collected over 24 hours. Examinations of his parents and two siblings also revealed elevated urine mercury levels. The Michigan Department of Public Health (MDPH) determined that inhalation of mercury-containing vapors from phenylmercuric acetate contained in latex paint was the probable route of mercury exposure for the family; 17 gallons of the paint had been applied to the inside of the family’s home during the first week of July. During that month, the air conditioning was turned on and the windows were closed, so that mercury vapors from the paint were not properly vented. In addition, samples of the paint contained 930-955 mg/L mercury, while the EPA limit for mercury as a preservative in interior paint is 300 mg/L. In October, the Michigan Department of Agriculture prohibited further sales of the inappropriately formulated paint, and the MDPH advised people not to use the paint, to thoroughly ventilate freshly painted areas, and to consult a physician if unexplained health problems occurred. In November, the MDPH and Centers for Disease Control began an ongoing investigation in selected communities in southeastern Michigan to assess mercury levels in the air of homes in which this paint had been applied and in urine samples from the occupants. Jar of Mercury Spilled in Ohio Apartment In November 1989, a 15-year-old male from Columbus, Ohio was diagnosed with acrodynia, a form of mercury poisoning. A 24-hour urine collection detected a mercury level of 840 μg/L in the patient’s urine. The patient’s sister and both his parents were also found to have elevated mercury urine levels. Therefore, on November 29, the Columbus Health Department investigated the apartment where the family had lived since August 26, 1989. Neighbors reported that the previous tenant had spilled a large jar of elemental mercury within the apartment. Mercury vapor concentrations in seven rooms ranged from 50-400 μg/m . The Agency for Toxic Substances and Disease Registry’s acceptable 3 residential indoor air mercury concentration is less than or equal to 0.5 μg/m . 3 Mercury Vapors Released in House During Smelting Operation On August 7, 1989, four adults from Michigan, ranging from age 40 to 88, were hospitalized for acute mercury poisoning. All four patients lived in the same house, where one of the patients had been smelting dental amalgam in a casting furnace in the basement of the house in an attempt to recover silver. Mercury fumes were released during the smelting operation, entered air ducts in the basement, and were circulated throughout the house. All four patients died of mercury poisoning within 11-24 days after exposure.5-5 Mercury Spilled in Michigan House During the summer of 1989, a boy in Michigan spilled about 20 cm of liquid mercury in his 3 bedroom. In September of that year, both of his sisters were diagnosed with mercury poisoning, after exhibiting clinical symptoms associated with such poisoning. The boy, although asymptomatic, was also tested and was found to have elevated mercury levels. Florida School Children Find Elemental Mercury in Abandoned Van During August 1994, five children residing in a neighborhood in Palm Beach County, Florida found 5 pints of elemental mercury in an abandoned van. During the ensuing 25 days, the children shared and played with the mercury outdoors, inside homes, and at local schools. On August 25, 1994, a parent notified local police and fire authorities that her children had brought mercury into the home. That same day, 50 homes were immediately vacated and an assessment of environmental and health impacts was initiated by the State of Florida Department of Environmental Protection, the Health and Rehabilitation Services of the Palm Beach County Public Health Unit, and the U.S. Environmental Protection Agency. A total of 58 residential structures were monitored for indoor mercury vapor concentrations; unsafe indoor air levels of mercury (>15 μg/m ) were detected in 17. Several classrooms at the local 3 high schools were determined to be contaminated. In addition, 477 people were identified by the survey as possibly exposed to mercury vapors and were evaluated at the emergency department of the local hospital or the health department clinic for mercury poisoning. Of these people, 54 were found to have elevated mercury levels. Unsafe Mercury Levels Found in North Carolina Home In July 1988, the Environmental Epidemiology Section of the North Carolina Department of Environment, Health, and Natural Resources (DEHNR), investigated chronic mercury poisoning diagnosed in a 3-year-old boy from North Carolina. Results of 24-hour urine specimens for mercury collected from both the patient and his parents revealed elevated mercury levels. Although the family reported no known mercury exposures, in April 1988, they had moved into a house whose previous owner had collected elemental mercury. Several containers of mercury had reportedly been spilled in the house during the previous owner’s occupancy. As a result of the determination that the house was the probable source of exposure, the family temporarily relocated. The DEHNR conducted an extensive investigation of the house. Elevated mercury levels were detected in five rooms and two bathrooms. The vacuum cleaner filter bag was tested for mercury as well, and found to have extremely high mercury levels. The carpets were also heavily contaminated with mercury. When the contaminated carpets were vacuumed, mercury particles and vapor were probably dispersed throughout the house. Vaporization probably increased with the spread of the mercury and the onset of warmer weather.6-1 

### 6. COMPARISON OF ESTIMATED EXPOSURE WITH BIOMONITORING 6.1 Biomarkers of Exposure Biologic markers, as described by the U.S. National Research Council (NRC, 1989) are indicators signaling events in biological systems or samples. These are classified as biologic markers of exposure, effect and susceptibility. A biological marker of exposure is defined by the National Research Council (1989) an “exogenous substance or its metabolite(s) or the product of an interaction between a zenobiotic agent and some target molecule or cell that is measured in a compartment within an organism” (NRC, 1989, pg. 2). Concentrations of mercury and of methylmercury in biological materials are used as biomarkers of exposure to mercury in the environment. Mercury accumulates in body organs. Although concentrations of mercury in organs adversely affected by mercury (e.g., neural tissue, the kidney) may be more predictive of levels of exposure at the site of organ system damage, for purposes of monitoring exposures mercury concentrations in tissues less proximal are relied upon. Typically mercury concentrations in blood, hair, and urine are used to assess exposure to organic and inorganic mercury. 6.2 Biomarkers of Exposure Predictive of Intake of Methylmercury Humans are exposed to both organic (e.g., methylmercury) and inorganic mercury. The proportion of organic to inorganic mercury exposure depends on exposure conditions. Organic methylmercury almost exclusively occurs through consumption of fish and shellfish. Occupational exposure to organic mercury compounds is far less common than are occupational exposures to inorganic mercury compounds. Within occupations where exposures to organic mercury compounds occur, great caution must be taken to assure that people handling such compounds do not come into contact with organic mercury because of its extreme toxicity. Inorganic mercury exposures reflect sources including dental amalgams and occupational sources with minor contributions from certain hobbies and ritualistic uses of mercury. Contribution from “minor” sources refers to their overall use in the general population. Such “minor” sources can produce highly elevated exposures and poisoning of individuals who use these products. Blood and hair concentrations of mercury can be used to back calculate estimates of methylmercury ingested. Because methylmercury in the diet comes almost exclusively from consumption of fish and shellfish, methylmercury concentrations in blood and hair are very strong predictors of methylmercury ingestion from fish and shellfish. The fraction of methylmercury absorbed via the gastrointestinal tract from fish and shellfish is extremely high; typically more than 95% (REFS). After absorption methylmercury is transported in the blood. There is a strong affinity for the erythrocyte (Aberg et al., 1969; Miettinen, 1971). Standard reference values for blood mercury concentrations indicate packed cells are 10-times more concentrated in mercury than is whole blood (Cornelis et al., 1996). Methylmercury is distributed throughout the body including distribution into the central nervous system. Postabsorption and distribution to tissues, methylmercury is slowly demethylated and converted to inorganic mercury (Burbacker and Mottet, 1996). A portion of the inorganic mercury arising from demethylation of methylmercury is present in blood (Smith and Farris, 1996). Additional sources of inorganic mercury include dental amalgams in persons with silver-mercury dental restorations, small amounts of inorganic mercury absorbed from diet, and for some individuals occupational and/or miscellaneous sources. Although inorganic mercury is6-2 present in blood, under most conditions the predominant chemical species of mercury in blood is methylmercury arising from consumption of fish and shellfish. 6.3 Sample Handling and Analysis of Blood Samples for Mercury The predominant method of chemical analysis of total mercury in blood is based on cold vapor absorbance techniques (IUPAC, 1996; Nixon et al., 1996). Atomic fluorescence is also a very sensitive and reliable technique for mercury measurement in blood, serum and urine (IUPAC, 1996). The various mercury-species are converted by reducing agents to elemental mercury and released as a vapor which is either directly pumped through the cell of the atomic absorption spectrophotometer or analyzed after amalgamation and enrichment on gold (IUPAC, 1996). Sample pretreatment to destroy the organic matter in samples and avoid losses of mercury through volatilization are key considerations in the analytic procedure for determination of inorganic and total mercury. Digestion procedures have been developed that permit conversion of organic mercury compounds and arylmercury to inorganic mercury, but do not convert significant quantities of alkylmercury (i.e., methylmercury) to inorganic mercury (Nixon et al., 1996). The expected concentration cited by IUPAC (1996) for mercury in serum of healthy individuals is 0.5 μg/L. In packed cells the level is about 5 μg/kg. Standard reference materials for mercury in whole blood are available in the range of 4 to 14 μg/L. Using the IUPAC (1996) expected concentration, whole blood mercury would be less than 2.5 μg/L. Sample handling prior to analysis is always critical in obtaining optimal analytical results. The Commission of Toxicology of the IUPAC has described an organized system for collection and handling of human blood and urine for the analysis of trace elements including mercury (1996). 6.4 Association of Blood Mercury with Fish Consumption 6.4.1 Half-Lives of Methylmercury in Blood The half-life of mercury in blood varies with prior intake of methylmercury and individual characteristics. Previous investigations with methylmercury ingestion under controlled conditions provide estimates of half-lives among adults. Data on half-lives among children do not appear to exist. Two studies among adults are particularly informative. Sherlock et al. (1984) evaluated half-lives for methylmercury ingested via halibut by 14 adult male and 7 adult female volunteers over a period of 96 days. Overall, the half-life for mercury in blood was calculated by Sherlock et al. as 50±1 days (mean±standard error; range 42 to 70 days) for adult subjects. Another approach is that used by Birke et al. (1972) based on repeated blood sampling of subjects after termination of chronic ingestion at higher levels of methylmercury consumption. Data from the study of Birke et al. (1972) showed two subjectswith half-lives of 99 and 120 days in blood cells and 47 and 130 days in plasma. Additional data on halflives of methylmercury ingested via fish were reported by Miettinen et al. (1971) following single ingestion of radiolabelled fish. Miettinen et al. (1971) using Hg-labelled methylmercury incorporated 203 into burbot ( Lota vulgaris ) fed as a single dose to 15 adult volunteers determined a mean biological halftime of 50±7 days (mean±standard deviation of the mean) in red blood cells for five male subjects and one female subject. Overall the metabolic data support the use of blood mercury as an indicator of recent methylmercury intake. The range surround mean half-lives reflect the combined influence of individual person-to-person characteristics, previous intake of methylmercury, and level of methylmercury6-3 ingestion. During the 1990s, a number of additional reports on total blood mercury and on organic methylmercury in blood have confirmed that higher intakes of fish/shellfish are associated with increasing concentrations of total mercury, and in particular a higher fraction of methylmercury (Mahaffey and Mergler, in press). 6.4.2 Fraction of Total Blood Mercury that Is Organic or Methylmercury Among subjects with blood total mercury levels less than 5 μg/L, Oskarsson et al. (1996) reporting on 30 women living in northern Sweden found that 26% of blood mercury was organic mercury. By contrast women who consumed large amounts of seafood had 80% organic mercury at delivery in maternal blood from Inuit women in Greenland (Hansen et al., 1990), and approximately 83% organic mercury in Faroese women (Grandjean et al., 1992). High blood levels of total mercury were reported by Akagi et al. (1995) among residents of the Amazon. In fishing villages where blood total mercury levels were approximately 100 μg/L, 98% of total mercury was organic (methyl) mercury. Aks et al. (1995) in another study of adult Amazon villagers, found approximately 90% of total mercury to be organic mercury when blood levels were approximately 25 to 30 μg/L. Mahaffey and Mergler (in press) found that there was a linear increase (when the data were log transformed) in the fraction of total blood mercury that was present as organic mercury over a blood total mercury up to 70 μg/L. 6.4.3 Methylmercury Consumption from Fish and Blood Mercury Values Increasing frequency of fish consumption is predictive of higher total blood mercury concentrations; particularly increased concentrations of organic mercury (i.e., methylmercury) in blood(Brune et al., 1991; Hansen et al., 1990; Svensson et al., 1992; Weihe et al., 1996). Within the nonoccupationally mercury exposed population, frequency, quantity and species of fish consumed produce differences in methylmercury ingestion and in blood mercury concentrations. Brune et al. (1991) reviewed the literature on total mercury concentrations in whole blood and associated these with the number of fish meals/week (Table 6-1). Although there is a clear increase in mean values with increasing frequency of fish consumption, the ranges of values (e.g., 10th and 90th percentiles) overlap with the next highest category of consumption. These ranges illustrate some of the difficulty of characterizing methylmercury intake simply by the reports describing number of fish meals consumed per week.
Table 6-1 Literature Derived Values for Total Mercury Concentrations in Whole Blood (from Brune et al., 1990)

Level of Fish Mean Value 10th and 90th 25th and 75th Number of Consumption Percentiles Percentiles Observations Category I, No Fish Consumption 20 0, 4.3 0.8, 3.2 223 Category II, < 2 Fish Meals/Week 4.8 2.4, 7.2 3.5, 6.1 339 Category III,  2-4 Fish Meals/ Week 8.4 2.6, 14.2 5.4, 11.4 658Table 6-1 (continued) Literature Derived Values for Total Mercury Concentrations in Whole Blood (from Brune et al., 1990)

Level of Fish Mean Value 10th and 90th 25th and 75th Number of Consumption Percentiles Percentiles Observations

6-4 Category IV, > 4 Fish Meals/Week 44.4 6.1, 82.7 24.4, 64.4 613 Category V, Unknown Fish Consumption 5.8 1.2, 10.4 3.4, 8.2 3182

The analysis by Brune et al. (1990) demonstrated the limitations of determining a methylmercury intake based on the number of fish meals/week. Nonetheless there is an association between frequency of fish meals and blood mercury levels. If the exposure analysis is further refined to include a description of the size of the serving of fish consumed, and information on the mercury content of the fish, the association with blood mercury concentration is strengthened. 6.4.4 North American Reports on Blood Mercury Concentrations
6.4.2.1 United States Normative data to predict blood mercury concentrations for the United States population are not available. With a very few exceptions all of the data that have been identified are for adult subjects. The largest single study appears to be that of former United States Air Force pilots. Kingman et al. (Kingman et al., in press; Nixon et al., 1996) analyzed urine and blood levels among 1127 Vietnam-era United States Air Force pilots (all men, average age 53 years at the time of blood collection ) for whom extensive dental records were available. Blood values were determined for total mercury, inorganic mercury and organic/methylmercury. Mean total blood mercury concentration was 3.1 μg/L with a range of “zero” (i.e., detection limit of 0.2) to 44 μg/L. Overall, 75% of total blood mercury was present as organic/methylmercury. Less than 1% of the variability in total blood mercury was attributable to variation in the number and size of silver-mercury amalgam dental restorations. Dietary data on the former pilots were very limited, so typical patterns of fish consumption are not reported. Additional North American studies have been reported by various individual states in the United States. These are described below and summarized in Table 6-2.
Arkansas The Arkansas Department of Health reported on total blood mercury for 236 individuals with a mean of 10.5 μg/L (range “zero” to 75 μg/L) (Burge and Evans, 1996). Of these, 139 participants had total blood mercury above 5 μg/L and 36 participants had blood mercury concentrations more than 20 μg/L. To have been included in the survey, subjects had to confirm that their fish consumption rate was a minimum of two meals per month with eight ounces of fish per meal.6-5 Table 6-2 Blood Mercury Concentrations Values Reported for the United States Study Community Measure of Maximum Additional Central Information on Tendency Study Bur g e and Evans 236 participants Mean: 10.5 μg /L; All sub j ects: 75 139 participants (1996) from Arkansas amon g men: 12.8 μg /L exceeded 5 μg /L. μg /L; amon g women, 6.9 μg /L. Males: 75 μg /L 30 participants in
Median: All Females: 27 μg /L. 75 μg /L or 15% sub j ects 7.1 μg /L >20 μg /L. Men: 9.0 μg /L Women: 4.8 μg /L 5% of men had >30 the ran g e of 20 to

μg /L. No women had values > 30 μg /L. Centers for Disease Micousukee Indian Mean: 2.5 μg /L 13.8 μg /L Control (1993) Tribe of South Median: 1.6 μg /L Florida. 50 blood samples from sub j ects with mean a g e=34 y ears (Ran g e 8 to 86 y ears). Gerstenber g er et al. 68 O j ibwa Tribal 57 participants < 16 53 μg /L 11 individuals had (1997) members from the μg /L. Remainin g blood mercur y in Great Lakes Re g ion 11 sub j ects the ran g e 20 to 53 avera g ed 37 μg /L. μg /L. Harnl y et al. (1997) Native Americans Mean for 44 Tribal Amon g Tribal 20% of all livin g near Clear members: 18.5 μg /L members: Total H g participants (9 Lake, California. (2.9 μg /L inor g anic was 43.5 μg /L (4.7 persons includin g Group studied H g + 15.6 μg /L for μg /L inor g anic + four women of include 44 Tribal or g anic H g ). 38.8 μg /L or g anic). childbearin g a g e) members, and 4 had blood mercury nontribal members. Mean for 4 For nontribal concentrations  20 nontribal members: members: Total H g μg /L. 11.5 μg /L (2.7 μg /L 15.6 μg /L (3.4 μg /L inor g anic + 8.8 inor g anic + 12.2 μg /L or g anic H g ). μg /L or g anic).Table 6-2 (continued) Blood Mercury Concentrations Values Reported for the United States Study Community Measure of Maximum Additional Central Information on Tendency Study

6-6 Humphre y and Lake Michi g an Al g onac, Lake St. Al g onac, Lake St. Mercur y Budd (1996) residents studied in Clair: Clair contamination less 1971. Fisheaters (n=42) intense in South mean 36.4 Fisheaters: 3.0-95.6 Haven compared compared with 65 μg /L with Al g onac. low fish consumers havin g mean of 5.7 Comparison: μg /L. 1.1 - 20.6 μg /L South Haven, Lake Michi g an with lower H g South Haven, Lake contamination. Michi g an Fisheaters (n=54) had mean 11.8 μg /L Fisheaters: 3.7-44.6 and the comparison μg /L g roup of low fish consumers mean Comparison: (n=42) of 5.2 μg /L 1.6-11.5 μg /L Knobeloch et al. Famil y consumin g Initial blood values Six months after (1995) commerciall y for wife (37 μg /L) famil y stopped obtained seafood. and husband (58 consumin g seabass, μg /L) followin g blood mercur y re g ular concentrations for consumption of the wife (3 μg /L) imported seabass and husband (5 havin g mercur y μg /L) had returned concentrations to “back g round” estimated at 0.5 to concentrations. 0.7 ppm H g . Schantz et al. Adult men and 104 fisheaters: Maximum for Questionnaire on (1996) women a g ed 50 to mean=2.3 μg H g /L fisheaters: 20.5 μg fish-eatin g patterns 90 y ears. Michi g an H g /L included sportresidents. 84 nonfisheaters: cau g ht Great Lakes mean=1.1 μg H g /L. Maximum for fish and purchased nonfisheaters: 5.0 fish, as well as μg H g /L. questions on patterns of wild g ame consumption.6-7 Great Lakes Region Schantz et al. (1996) reported on blood mercury levels in an older-adult population (ages 50 to 90 years). Blood mercury levels for non-fisheaters averaged 1.1 μg/L and for fish-eaters the average was 2.3 μg/L. Gerstenberger et al. (1997) determined blood mercury levels for 57 Ojibwas Tribal Members from the Great Lakes Region. Among the 68 participants 57 had blood mercury concentrations < 16 μg/L. The remaining 11 subjects had average blood mercury concentrations of 37 μg/L with a maximum value of 53 μg/L.
Wisconsin Blood mercury levels among 175 Wisconsin Chippewas Indians who consumed fish from northern Wisconsin lakes that have fish with high mercury concentrations (>1 ppm) were determined (Peterson et al., 1994). Values ranged from nondetectable (i.e., < 1 μg/L) to a high of 33 μg/L. Twenty percent (64 individuals) had blood mercury levels > 5 μg/L. Recent consumption of the fish, walleye, was associated with elevated blood mercury concentrations.Knobeloch et al. (1995) investigated mercury exposure in a husband and wife and their two-yearold son living in Wisconsin. The individuals had total blood mercury ranging from 37 to 58 μg/L. The family’s diet included three to four fish meals per week. The fish was purchased commercially from a local market. Seabass were found to contain mercury at 0.5 to 0.7 ppm. Six months after the family stopped consuming the seabass, blood mercury levels in this man and women declined dramatically to 5 and 3 μg/L, respectively. California Harnly et al. (1997) determined blood mercury concentrations for 44 members of Native American tribes and 4 nontribal members living near Clear Lake, California. The mean for the 44 tribal members was 18.5 μg/L total mercury (15.6 μg/L organic and 2.9 μg/L inorganic). The maximum value was 43.5 μg/L (38.8 μg/L organic and 4.7 μg/L inorganic). Twenty percent of all participants (including four women of childbearing age) had blood mercury concentrations  20 μg/L. Among nontribal members total mercury concentrations were lower with a total mercury value of 11.5 (8.8 organic + 2.7 inorganic) μg/L. The highest value for nontribal members was 15.6 (12.2 organic and 3.4 inorganic) μg/L. Florida The U.S. Centers for Disease Control (CDC, 1993) conducted a community survey of the tribal representatives of the Miccousukee Indian Tribe living in South Florida. Blood mercury levels were determined for 100 participants who were adult tribal members. Fish consumption among this group was low with a maximum of approximately 170 grams/day and 3.5 grams calculated as a daily average. Total blood mercury ranged from 0.2 to 13.8 μg/L with median and mean values of 1.6 and 2.5 μg/L, respectively. There was a correlation between blood mercury levels and consumption of locally caught fish.
Maine6-8 An additional source of data on blood mercury levels is the heavy metal profiles (for lead, arsenic, cadmium, and mercury) conducted as part of occupational surveillance. Typically the persons who receive this type of screening are expected to have exposures to at least one of these metals. Occupational surveillance may be based on state requirements or Federal statutes. For example, the State of Maine has an occupational disease reporting requirement on individuals whose blood mercury concentrations for total mercury are 5 μg/L and higher and whose urinary total mercury is greater than or equal to 20 μg/L. The State of Maine evaluated data on occupational screening for heavy metal exposure and identified a group of adults having total blood mercury concentrations more than 5 ppb. Several cases of elevated blood mercury concentrations were identified. One case has been reported by Dr. Allison Hawkes (personal communication, 1997). The individual was identified with a blood mercury of 21.4 μg/L. The subject had no known occupational exposure to mercury, but self-reported eating 3 or 4 fish meals per week. The individual was asked to abstain from consuming fish for 4 or 5 weeks and then return for follow-up blood testing. On retesting blood total mercury was only 5 μg/L.
6.4.2.1 Canadian As in the United States, normative data for the general population of Canada have not been identified in compiling information for this Report to Congress. By contrast to the United States, information on mercury exposures in the northern regions of the country has been obtained. The Department of Indian and Northern Affairs of the Government of Canada reported on Arctic contaminants in the Canadian Arctic Contaminants Assessment Report in 1997. Methylmercury levels in blood since 1970. For all Aboriginal Peoples the mean blood mercury concentration was 14.13 (standard deviation 22.63) and a range of 1 to 660 μg/L (Wheatley and Paradis, 1995) based on 38,571 data points from 514 native communities across Canada. Overall, blood mercury concentrations are considered closely tied to consumption of fish and marine mammals. The highest levels are found among Aboriginal residents with particular high levels found in northern Quebec and among the northern and eastern Inuit communities. No downward trend was evident in Inuit blood mercury concentrations between 1975 and 1987, but more recent data (1992 to 1995) indicated lower levels of mercury in some groups (Jensen et al., 1997, page 336). Quebec Within the values reported in the Canadian Arctic Contaminants Assessment Report (Jensen et al., 1997) particularly high mean concentrations were observed among the Inuit (Nunavik) of Quebec. Mean total mercury concentration of 47 μg/L (SD 33, range 3 to 267 μg/L) was identified among 1114 Inuit of Quebec. The Northern (Cree) had mean values of 34 (SD 41, range 2 to 649 μg/L) among 4,670 blood values and 42.9 (SD 52, range 2 to 649) based on 1,129 blood values. North West Territory The Nunavut (Inuit) of the North West Territory also have elevated blood mercury levels with mean values during the 1970s through late 1980s averaging between 17 and 40 μg/L (upper extent of this range going to 226 μg/L). The Western (Dene) population had lower blood mercury levels with means between 11 and 17 μg/L (upper extent of the Dene range to 138 μg/L). 6.5 Hair Mercury as a Biomarker of Methylmercury Exposure 6.5.1 Hair Composition6-9 Hair is approximately 95% proteinaceous and 5% a mixture of lipids, glycoproteins, remnants of nucleic acids, and in the case of pigmented hairs, of melanin and phaeomelanin. Hair contains a central core of closely packed spindle-shaped cortical cells, each filled with macrofibrils which in turn consist of a microfibril/matrix composite. The long axes of the cells and their fibrous constituents are oriented along the long axis of the hair. The amino acid composition of hair is high in those amino acids with side-chains (particularly, those containing “reactive” groups such as cystine, cysteine, tyrosine, tryptophan, acidic and basic amino acids, as well as terminal carboxyl or amino groups). The cortical core is covered by sheet-like cells of the cuticle. The surfaces of all the cells of the hair shaft have a thin layer of lipid which is covalently attached to the underlying proteins. Hair has been assumed to grow at the rate of one centimeter a month (Kjellstrom et al., 1989; Marsh et al., 1980). However, there is variability in the rate of hair growth. Growth determined experimentally is between 0.9 and 1.3 cm per month (Barman et al., 1963; Munro, 1966; and Saitoh, 1967). Mercury is incorporated into hair during the growth of hair. Hair mercury concentrations are presumed to reflect blood mercury concentrations at the moment of hair growth. Whether the predominant chemical species is inorganic mercury or methylmercury depends on exposure patterns and on the extent of demethylation of methylmercury. Hair mercury (ug/g) and blood mercury (ng/L) ratios range from 190:1 up to 370:1 (Skerfving, 1974; Phelps et al., 1980; Turner et al., 1980; Sherlock et al., 1984). Higher ratios have recently been reported. Additional discussion of the hair to blood mercury ratio is found in the volume on human health. This is one source of person-to-person variability considered in selection of uncertainty factors in determining U.S. EPA’s Reference Dose for methylmercury. Chemical analyses to determine mercury concentrations in hair determine total mercury rather than chemical species of mercury. In order to dissolve hair samples, they must be put through an acid digestion. The process of acid digestion will convert virtually all of the mercury in the biological sample to inorganic mercury (Nixon et al., 1996). Consequently the fraction of hair mercury that is methylmercury is only an estimate based on what is known of environmental/occupational exposure patterns. The frequency of fish consumption has been used as a guide to differences in hair mercury concentrations (Airey, 1983). Within a general population as fish consumption increases, hair mercury concentration will also increase. However, the amount of mercury in hair depends on the concentration of mercury present in fish consumed. Comparison of recent studies from Bangladesh (Holsbeek et al., 1996) and Papua New Guinea (Abe et al., 1995) illustrates these differences. Holsbeek et al. (1996) found a highly significant positive correlation (r=0.88, P<0.001) between fish consumption and hair mercury concentrations. Total hair mercury concentrations had a mean value of 0.44±0.19 μg/g (range 0.02 to 0.95) and a fish consumption of 2.1 kg/month (range 1.4 to 2.6). The low concentrations in hair reflect the low concentrations of methylmercury in Bangladesh fish. Abe et al. (1995) evaluated 134 fish-consuming subjects and 13 nonfish-eating subjects in Papua, New Guinea. Among the fish consumers hair mercury levels had a mean mercury concentrations of 21.9 μg/g (range 3.7 to 71.9). Average fish consumption was 280 grams/day (range=52 to 425) or about 8.4 kg/month producing an average methylmercury intake of 84 μg/day. Among the nonfish consumers the mean hair mercury was 0.75±0.4 μg/g. The difference in hair mercury concentration in Bangladesh and New Guinea were considerably greater than the differences in fish mercury.6-10 6.5.2 Hair Mercury Concentrations in North America
6.5.2.1 United States Data do not exist describing hair mercury concentrations that are representative of the United States population as a whole. This is similar to the situation for blood mercury concentrations. Limited data from smaller studies are described below and summarized in Table 6-3.
U.S. Communities Crispin-Smith et al. (1997) analyzed hair mercury concentrations in 1431 individuals living in the United States. The communities in which these individuals resided were not identified. Mean values in these studies were < 1 μg/g. Fish consumers had slightly higher blood mercury concentrations than did nonfish consumers (0.52 vs. 0.48). The maximal value reported in this survey was 6.3 μg/g. Statistical information on these data is not available currently. New York Metropolitan Area, New Jersey, Alabama (Birmingham), and North Carolina (Charlotte) Creason et al. (1978a, 1978b, and 1978c) evaluated children and adults living in these cities in the early 1970s. Mean values for all groups of children and adults were less than 1 μg/g. Maximum values were in the range of 5 to 11.3 μg/g of hair. Adult values were slightly higher than those of children. California Airey (1983) determined hair mercury concentrations among about 100 subjects living in Southern California (LaJolla and San Diego). Mean values were in the range of 2 to 3 μg Hg/gram, with maximum values in the range of 4.5 to 6.6 μg/g.. Harnly et al. (1997) determined hair mercury among Tribal and nontribal group members living near Clear Lake, California. Mean values were typically less than 1 μg/g., with maximum values of 1.8 μg/g. among Tribal members and 2.3 μg/g among non-Tribal members. Maryland Airey (1983) found mean concentrations of about 1.5 to 2.3 μg/g in adults living in Maryland (communities were not identified). Maximum concentrations were 4.5 μg/g.. State of Washington Lazaret et al. (1991) identified hair mercury concentrations < 1 μg/g. and a maximum value of 1.5 μg/g. Earlier Airey (1983) reported mean values of 1.5 to 3.8 μg/g among small numbers of subjects. The maximum value reported was 7. 9 μg/g.
Florida CDC (1993) surveyed 330 subjects living in the Florida Everglades and determined that average hair mercury concentrations were 1.3 μg/g.. The maximum value was 15.6 μg/g.6-11 Wisconsin Knobeloch et al. (1995) reporting on two individuals with blood mercury concentrations of 38 and >50 μg/g. found the individuals hair mercury concentrations were 11 and 12 μg/g. Great Lakes Region Gerstenberger et al. (1997) determined mean mercury concentrations were less than one μg/g. among 78 Ojibwa Tribal members. The maximum hair mercury concentration was 2.6 μg/g.
Alaska Lazaret et al., (1991) reported hair mercury concentrations averaging 1.4 μg/g among 80 women of childbearing age. The maximum hair mercury concentrations were 15.2 μg/g.
Table 6-3 Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents of Various Communities in the United States Study Community Mean Maximum Additional Concentration Concentration Information on Study Creason et al., New York Children (n=280); Children, 11.3 ppm Surve y conducted 1978a Metropolitan Area 0.67 ppm in 1971 and 1972 Adults (n=203); Adults, 14.0 ppm 0.77 Creason et al., Four communities in Children (n=204), Children, 4.4 ppm Surve y conducted 1978b New Jerse y : 0.77 ppm in 1972 and 1973 Rid g ewood, Adults (n=117), Adults, 5.6 ppm Fairlawn, Matawan 0.78 ppm and Elizabeth Creason et al., Birmin g ham, Children (n=322), Children, 5.4 ppm; Surve y conducted 1978c Alabama, and 0.46 ppm in 1972 and 1973 Charlotte, North Adults (n-117) 0.78 Adults, 7.5 ppm Carolina ppmTable 6-3 (continued) Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents of Various Communities in the United States Study Community Mean Maximum Additional Concentration Concentration Information on Study

6-12 Aire y , 1983 U.S. data cited b y 1) Males (n=22), 1) 6.2 ppm Aire y , 1983. 2.7 ppm;
Communit y not (n=16), 2.6 ppm; identified. 3) Males and 3) 5.6 ppm 2). Females 2) 5.5 ppm

Females (24 sub j ects), 2.1 ppm; 4) Males and 4) 6.6 ppm Females (31 sub j ects), 2.2 ppm; 5) Males and 5) 7.9 ppm Females 924 sub j ects) 2.9 ppm; 6) Males and 6) 7.9 ppm Females (79 sub j ects), 2.4 ppm. Aire y , 1983 U.S. data cited b y 1) 2.4 ppm (13 1) 6.2 ppm Aire y , 1983 men);
Communit y women); identified: LaJolla- 3) 2.3 ppm (8 3) 4.5 ppm San Die g o sub j ects includin g 2) 2.7 ppm (13 2) 5.5 ppm

men and women); 4) 2.9 ppm (17 sub j ects includin g men and women); 5) 2.6 ppm (5 5) 6.2 ppm sub j ects includin g men and women); 6) 2.8 ppm (30 6) 6.6 ppm sub j ects includin g men and women). Aire y , 1983 U.S. data cited b y 1) 1.8 ppm (11 1) 3.8 ppm Aire y , 1983. Area sub j ects, men and identified: Mar y land women); 2) 1.5 ppm (11 2) 3.9 ppm sub j ects, men and women); 3) 2.3 ppm (11 3) 4.5 ppm sub j ects, men and women); 4) 1.9 ppm (33 4) 4.4 ppm sub j ects, men and women).Table 6-3 (continued) Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents of Various Communities in the United States Study Community Mean Maximum Additional Concentration Concentration Information on Study

6-13 Aire y , 1983 U.S. data cited b y 1) 3.3 ppm (9 men); 1) 5.6 ppm Aire y , 1983 2. 2.2 ppm (3 2) 4.1 ppm Communit y women); identified: Seattle. 3) 2.6 ppm (5 3) 5.6 ppm sub j ects men and women); 4) 1.5 ppm (3 4) 2.1 ppm sub j ects, men and women); 5) 3.8 ppm (8 5) 7.9 ppm sub j ects, men and women); 6) 3.0 ppm (16 6) 7.9 ppm sub j ects, men and women). Crispin-Smith et U.S., Communities 0.48 ppm (1,431 6.3 ppm The 1009 al., 1997 and distribution not individuals); individuals are a identified 0.52 ppm (1009 subset of the 1431 individuals sub j ects. reportin g some seafood consumption) Lasora et al., 1991 Nome, Alaska 1.36 ppm 15.2 ppm (80 women of childbearin g a g e) Lasora et al., 1991 Sequim, Washin g ton 0.70 ppm (7 women 1.5 ppm of childbearin g a g e) Flemin g et al., Florida Ever g lades 1.3 ppm (330 15.6 ppm To be included in 1995 sub j ects, men and the surve y the women) sub j ects had to have consumed fish or wildlife from the Ever g lades. Knobeloch et al., Wisconsin, urban 2 adults (1 man, 1 1995 woman); values 11 and 12 ppmTable 6-3 (continued) Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents of Various Communities in the United States Study Community Mean Maximum Additional Concentration Concentration Information on Study

6-14 Gerstenber g er et O j ibwa Tribal 47% > 0.28 ppm. 2.6 ppm al., 1997 members from the Amon g individuals Great Lakes Re g ion with values above the level of detection, the mean was 0.83 ppm based on 78 sub j ects Harnl y et al., 1997 Native Americans 68 Tribal members. Maximum value for livin g near Clear Mean value: 0.64 Tribal members: Lake, California. ppm. 1.8 ppm 4 non-Tribal Maximum value for members. Mean non-Tribal value: 1.6 ppm members: 2.3 ppm 6.5.2.2 Summary of Data on Hair Mercury Concentrations Available data indicate that mean mercury concentrations in the U.S. population are typically less than 3 μg/g and often less than 1 μg/g, although, maximum concentrations of more than 15 μg/g are reported. Hair mercury concentrations of greater than 10 μg/g have been associated with mercury exposure from fish. The shape of the distribution of hair mercury concentrations in the United States is not well documented. Comparison of data summarized by Airey (1983) on the association between frequency of fish meals, mean and range of hair mercury concentrations reveals (see Table 6-4): • The arithmetic mean of hair mercury from the U.S. surveys is consistent with the lower bound of the range associated with fish ingestion rates of less than once a month to as frequent as once a week. • The maximum values identified in the surveys are consistent with fish consumption of every week to every day.
Table 6-4 Association of Hair Mercury Concentrations (ug Hg/gram hair) with Frequency of Fish Ingestion by Adult Men and Women Living in 32 Locations within 13 Countries (Airey, 1983) Frequency of Fish Meals Arithmetic Mean Range Once a Month or Less 1.4 0.1-6.2 Twice a Month 1.9 0.2-9.2 Every Week 2.5 0.2-16.2 Every Day 11.6 3.6-24.06-15 6.6 Conclusions 6.6.1 Blood Mercury Levels Mercury in blood is a reflection of exposures in recent days and weeks to environmental mercury. Typically blood mercury values are reported as total mercury, although chemically speciated mercury analyses often are included in reports published in the 1990s. Organic mercury in blood generally reflects methylmercury intake from fish and shellfish. At progressively higher dietary intakes of fish and shellfish, the fraction of total blood mercury that is organic mercury increases becoming more than 95% at high levels of fish consumption. Blood mercury concentrations (μg Hg/L) in healthy populations are less than 3 μg/L (5 μg/kg packed cells and 0.5 μg/L serum) based on values published by the International Union for Pure and Applied Chemistry (1996). The U.S. EPA RfD is associated with a whole blood mercury concentration of 4 to 5 μg/L. The “benchmark dose” for methylmercury used in setting the RfD is 44 μg/L based on neurotoxic effects observed in Iraqi children exposed in utero. There are no representative data on blood mercury for the U.S. population as a whole. In the United States (in the peer-reviewed literature published in the 1990s), blood mercury concentrations in the range of 50 to 95 μg/L have been reported and attributed to the consumption of fish and shellfish. Among groups of anglers and Native American Tribal groups, mean blood mercury levels in the range of 10 to 20 μg/L have been reported. Blood mercury concentrations greater than 20 μg/L and attributable to consumption of fish and shellfish have been identified among women of childbearing age in the United States. 6.6.2 Hair Mercury Levels Mercury is incorporated in hair as it grows. Typically the centimeter of hair nearest the scalp reflects mercury exposure during the past month. The extent to which the predominant chemical species in hair is a function of methylmercury exposure depends on environmental exposure patterns. Methylmercury in the diet results in elevated hair mercury concentrations. Dietary sources documented to produce elevated hair mercury concentrations include fish, shellfish, and flesh from marine mammals.
There are no representative data on hair mercury concentrations for the U.S. population as a whole. Typical values in the United States are less than 1 μg/g. Maximum hair mercury concentrations of 15 μg/gram and higher have been reported in the United States. Hair mercury concentrations greater than 10 μg/gram have been reported for women of childbearing age living in the United States. U.S. EPA’s RfD is associated with a hair mercury concentration of approximately 1 μg/g. The “benchmark” dose is associated with a hair mercury concentration of 11.1 μg/g and is based on neurotoxic effects observed in Iraqi children exposed in utero to methylmercury.7-1 

### 7. CONCLUSIONS

 The results of the current exposure of the U.S. population from fish consumption indicate that most of the population consumes fish and is exposed to methylmercury as a result. Approximately 85% of adults in the United States consumer fish and shellfish at least once a month with about half of adults selecting fish and shellfish as part of their diets at least once a week (based on food frequency data collected among more than 19,000 adult respondents in the NHANES III conducted between 1988 and 1994). This same survey identified 1-2% of adults who indicated they consume fish and shellfish almost daily.

 For the modeled fish ingestion scenarios, the local emission sources are predicted to account for the majority of the total mercury exposure for water bodies close to the sources. This is particularly true for the hypothetical western site, where background and regional atmospheric contributions to the total mercury concentration in the water column are predicted to be lower.

 Consumption of fish is the dominant pathway of exposure to methylmercury for fish-consuming humans. There is a great deal of variability among individuals in these populations with respect to food sources and fish consumption rates. As a result, there is a great deal of variability in exposure to methylmercury in these populations. The anthropogenic contribution to the total amount of methylmercury in fish is, in part, the result of anthropogenic mercury releases from industrial and combustion sources which increases mercury body burdens in fish. As a consequence of human consumption of the affected fish, there is an incremental increase in exposure to methylmercury. Terrestrial exposures were evaluated in the modeling analysis; inorganic mercury species were predicted to be the dominant chemical species to which humans are exposed.

 In the nationally-based dietary surveys, the types of fish most frequently reported to be eaten by consumers are tuna, shrimp, and Alaskan pollock. The importance of these species is corroborated by U.S. National Marine Fisheries Service data on per capita consumption rates of commercial fish species.

 National surveys indicate that Asian/Pacific Islander-American and Black-American subpopulations report more frequent consumption of fish and shellfish than other survey participants.

 Superimposed on this general pattern of fish and shellfish consumption is freshwater fish consumption, which may pose a significant source of methylmercury exposure to consumers of such fish. The magnitude of methylmercury exposure from freshwater fish varies with local consumption rates and methylmercury concentrations in the fish. The modeling exercise indicated that some of these methylmercury concentrations in freshwater fish may be elevated as a result of mercury emissions from anthropogenic sources. Exposures may be elevated among some members of this subpopulation; these may be evidenced by analyses of blood mercury showing concentrations in excess of 10 micrograms per liter (μg/L) that have been reported among multiple freshwater fish-consumer subpopulations. The mean value of blood mercury in an Arkansas study was 10μg/L. Because general populations data on the distribution of blood mercury concentrations have not been gathered, it is not known how common blood mercury concentration above 10μg/L are.7-2  An assessment of consumption of fish and shellfish was based on data obtained from contemporary nationally based dietary surveys conducted by the United States government: the third National Health and Nutrition Examination Survey conducted between 1988 and 1994 (National Center for Health Statistics of the Centers for Disease Control) and the 1994 and 1995 Continuing Surveys of Food Intakes by Individuals (United States Department of Agriculture). Data on mercury concentrations in fish and shellfish were obtained from national database compiled by the National Marine Fisheries Service and the U.S. Environment Protection Agency. The results of the assessment show that the predicted average exposure among make and female fish consumers of reproductive age is 0.1 micrograms of methylmercury per kilogram of body weight per day based on a single day’s estimate. The comparable 90th percentile estimate is approximately four times this level. Median “per user” fish/shellfish consumption values across these nationally representative surveys were between 73 and 79 grams/day based on single-day estimates. The comparable 90th percentile values ranged between 186 and 242 grams/day based on single-day estimates. The single-day estimates are used to project month-long fish/shellfish consumption when combined with frequency of fish/shellfish consumption estimates obtained from adult participants in NHANES III. The single-day estimates of fish/shellfish consumption provide portion sizes to estimated the impact of intermittent consumption of fish containing mercury at concentrations considerably above that commonly encountered in the commercial market, e.g., approximately 0.5 ppm and higher. Fish with mercury concentrations averaging over 0.5 ppm include swordfish and shark among marine fish and smallmouth bass, largemouth bass, channel catfish, walleye, and northern pike among freshwater fish.

 Exposure rates to methylmercury among fish-consuming children are predicted to be higher thanfor fish-consuming adults on a body weight basis. The 50th percentile exposure rate among fishconsuming children ages 3 through 6 years is approximately 0.3 micrograms per kilogram of body weight per day based on single day estimates. Predicted exposures at the 90th percentile are approximately three-times greater or 0.8 to one microgram of mercury per kilogram of body weight on a single day. Estimated month long mercury exposures among 3 through 6 year-old children are 0.03 at the 50th percentile and 0.17 at the 90th percentile using adult data to predict how often children consume fish and shellfish. It is uncertain how well the adult data are predictive for children because data for children are not available.

 Exposures among specific subpopulations including anglers, Asian-Americans, and members of some Native American Tribes indicate that their average exposures to methylmercury may be more than two-times greater than those experience by the average population.

 Predicted high-end exposures to methylmercury are caused by one or two factors or their combination: 1) high consumption rates of methylmercury contaminated fish, water and/or 2) consumption of types of fish which exhibit elevated methylmercury concentrations in their tissues. Of these two factors the former appears to be more significant for overall population exposures.

 Blood mercury concentrations and hair mercury levels are biomarkers used to indicate exposure to mercury. Inorganic mercury exposures occur occupationally and for some individuals through folk/hobby exposures to inorganic mercury. Dental restorations with silver-mercury amalgams can also contribute to inorganic mercury exposures. Methylmercury exposure is almost7-3 exclusively through consumption of fish, shellfish, and marine mammals. Occupational exposures to methylmercury are rare. Data describing blood and/or hair mercury for a population representative of the United States do not exist, however, some data are available. Blood mercury concentrations, attributable to consumption of fish and shellfish, in excess of 30 μg/L have been reported in the United States. Hair mercury concentrations in the United States are typically less than 1μg/g, however, hair mercury concentration greater than 10μ/g have been reported for women of childbearing age living in the United States. U.S. EPA’s RfD is associated with a blood mercury concentration of 4-5 μg/L and a hair mercury concentration of approximately 1μg/g. The “benchmark” dose is associated with mercury concentrations of 44μg/L in blood and 11.1 μg/g in hair. The “benchmark” dose for methylmercury is based on neurotoxic effects observed in Iraqi children exposed in utero to methylmercury.

 To improve the quantitative exposure assessment modeling component of the risk assessment for mercury and mercury compounds, U.S. EPA would need more and better mercury emissions data and measured data near sources of concern, as well as a better quantitative understanding of mercury chemistry in the emission plume, the atmosphere, soils, water bodies, and biota.

 To improve the exposure estimated based on surveys of fish consumption, more study is needed among potentially high-end fish consumers, which examines specific biomarkers indicating mercury exposure (e.g., blood mercury concentrations and hair mercury concentrations).

 A pharmacokinetic-based understanding of mercury partitioning in children is needed. Additional studies of fish intake and methylmercury exposure among children are needed.8-1 

### 8. RESEARCH NEEDS

 To im p rove the q uantitative ex p osure assessment modelin g comp onent of the risk assessment for mercur y and mercur y comp ounds, U.S. EPA would need more and better mercur y emissions data and measured data near sources of concern, as well as a better q uantitative understandin g mor mercur y chemistr y I the emission p lume, the atmos p here, soils, water bodies, and biota.

 To im p rove the ex p osure estimated based on surve y s of fish consump tion, more stud y in needed amon g p otentiall y hi g h-end fish consumers, which examines s p ecific biomarkers indicatin g mercur y ex p osure (e. g ., blood mercur y concentrations and hair mercury concentrations).

 A p harmacokinetic-based understandin g of mercur y p artitionin g in children is needed. Additional studies of fish intake and meth y lmercur y ex p osure amon g children are needed.9-1 

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### APPENDIX A EXPOSURE PARAMETER JUSTIFICATIONSA-i 

### TABLE OF CONTENTS

DISTRIBUTION NOTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
A. SCENARIO INDEPENDENT PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 A.1 Chemical Inde p endent Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 A.1.1 Basic Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 A.1.2 Rece p tor Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 A.1.2.1 Bod y Wei g ht . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2 A.1.2.2 Ex p osure Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2 A.1.3 A g ricultural Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3 A.1.3.1 Interce p tion Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3 A.1.3.2 Len g th of Plant Ex p osure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-4 A.1.3.3 Plant Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-5 A.1.3.4 Plant In g estion b y Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-6 A.1.3.5 Soil In g estion b y Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-7 A.1.4 Ex p osure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-7 A.1.4.1 Inhalation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-8 A.1.4.2 Consump tion Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-9 A.1.4.3 Soil In g estion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-10 A.1.4.4 Groundwater In g estion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-11 A.1.4.5 Fish In g estion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-12 A.1.4.6 Contact Fractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-14 A.2 Chemical De p endent Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15 A.2.1 Basic Chemical Pro p erties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15 A.2.1.1 Molecular Wei g ht . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15 A.2.1.2 Henr y 's Law Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15 A.2.1.3 Soil-Water Partition Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . A-16 A.2.1.4 Sediment-to-Water Partition Coefficient . . . . . . . . . . . . . . . . . . . . . A-16 A.2.1.5 Sus p ended Sediment-Water Partition Coefficient . . . . . . . . . . . . . . A-17 A.2.1.6 Soil and Water Loss De g radation Constants . . . . . . . . . . . . . . . . . . A-18 A.2.1.7 E q uilibrium Fraction for Chemical in Soil . . . . . . . . . . . . . . . . . . . A-18 A.2.1.8 E q uilibrium Fraction for Chemical in Water . . . . . . . . . . . . . . . . . . A-19 A.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-35A-ii 

### LIST OF TABLES

A-1 Chemical Inde p endent Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1A-iii DISTRIBUTION NOTATION

A com p rehensive uncertainty anal y sis was not conducted as p art of this stud y . Initiall y , p reliminar y p arameter p robabilit y distributions were develo p ed. These are listed in A pp endicies A and B. These were not utilized in the g eneration of q uantative ex p osure estimates. The y are p rovided as a matter of interest for the reader. Unless noted otherwise in the text, distribution notations are p resented as follows.

Distribution Description

Lo g (A,B) Lo g normal distribution with mean A and standard deviation B Lo g *(A,B) Lo g normal distribution, but A and B are mean and standard deviation of underl y in g normal distribution. Norm (A,B) Normal distribution with mean A and standard deviation B

U (A,B) Uniform distribution over the ran g e (A,B) T (A,B,C) Trian g ular distribution over the ran g e (A,C) with mode of BA-1 A. EXPOSURE MODEL PARAMETERS
This app endix describes the p arameters used in the ex p osure modelin g for the Mercur y Stud y Re p ort to Con g ress. For other environmental fate model p arameters the reader is referred to A pp endicies A-C of Volume 3. A.1 Chemical Independent Parameters Chemical inde p endent p arameters are variables that remain constant des p ite the s p ecific contaminant bein g evaluated. The chemical inde p endent variables used in this stud y are described in the followin g sections.
A.1.1 Basic Constants
Table A-1 lists the chemical inde p endent constants used in the stud y , their definitions, and values.

Table A-1 Chemical Independent Constants
Parameter Description Value R ideal g as constant 8.21E-5 m -atm/mole-K 3 p a air densit y 1.19E-3 g /cm 3 ua viscosit y of air 1.84E-4 g /cm-second Psed solids densit y 2.7 k g /L Cdra g dra g coefficient 1.1E-3

 Von Karman's coefficient 7.40E-1boundar y thickness 4.0

2

A.1.2 Rece p tor Parameters
Rece p tor p arameters are variables that reflect information about p otential rece p tors modeled in the stud y . These p arameters include bod y wei g ht, ex p osure duration, and other characteristics of p otential rece p tors. A.1.2.1 Bod y Wei g ht
Parameter: BWa, BWc Definition: Bod y wei g hts (or masses) of individual human rece p tors
Units: k gA-2 Receptor Default Value (kg) Child 17 Adult 70
Technical Basis: The default values for children and adults are those assumed in U.S. EPA, 1990.

A.1.2.2 Ex p osure Duration
Parameter: ED Definition: Len g th of time that ex p osure occurs. Units: y ears Receptor Default Value Distribution Range (years) (years) Child 18 U(1,18) 1-18 Adult 30 U(7,70) 7-70
Technical Basis:
The 18- y ear ex p osure duration for the child is based on U.S. EPA g uidance for this stud y . For adults, the 30- y ear duration is the assumed lifetime of the facilit y (U.S. EPA, 1990). It should be noted for noncarcino g enic chemicals the ex p osure duration is not used in the calculations. The ran g e and distribution are arbitrar y to determine the relative sensitivit y of this variable, when app ro p riate.

A.1.4 Ex p osure Parameters
Ex p osure p arameters are variables that directl y affect an individual's dose or intake of a contaminant. Such p arameters include inhalation and in g estion rates of air, water and cro p s and the surface area of skin for the p urp oses of dermal contact scenarios.A-3 A.1.4.1 Inhalation Rate
Parameter: INH Definition: Rate of inhalation of air containin g contaminants.
Units: m /da y 3
Receptor Default Value Distribution (m /day) 3 Infant 5.14 T(1.7,5.14,15.4) Child 16 T(2.9,16,53.9) Adult 20 T(6,20,60)

Technical Basis:
The default value for infants is the central value of the distribution used for 1 y ear olds in Hanford Environmental Dose Reconstruction Pro j ect (HEDR) (1992) and is from Ro y and Courta y (1991). The default value for children is based on U.S. EPA (1990). The default value for adults is that recommended in U.S. EPA (1991), which states that this value re p resents a reasonable u pp er bound for individuals that s p end a ma j orit y of time at home.
The ran g e for infants is that used for 1 y ear olds in HEDR (1992) and was determined b y scalin g the value 5.14 b y 0.3 and 3.0, res p ectivel y . The ran g e for children is the smallest ran g e containin g the values used for 5-, 10-, and 15- y ear-old children in HEDR (1992). The ran g e for the adult was obtained b y scalin g the default value b y the same numbers used for infants of 0.3 and 3.0 (we note that HEDR, 1992 used a sli g htl y hi g her central value of 22 m /da y ). 3
To p revent a bias towards u pp er-end inhalation rates, trian g ular distributions were considered more app ro p riate than more arbitrar y uniform distributions, with a most likel y value e q ual to the default value.A-4 A.1.4.2 Consump tion Rates
Parameter: CPi, CA j Definition: Consump tion rate of food p roduct p er k g of bod y wei g ht p er da y . Units: g dr y wei g ht/k g BW/da y Food Type Child (gDW/kgBW/day) Adult (g DW/kg BW/day) Leaf y Ve g etables 0.008 0.0281 Grains and cereals 3.77 1.87 Le g umes 0.666 0.381 Potatoes 0.274 0.170 Fruits 0.223 0.570 Fruitin g ve g etables 0.120 0.064 Rootin g Ve g etables 0.036 0.024 Beef, excludin g liver 0.553 0.341 Beef liver 0.025 0.066 a Dair y (milk) 2.04 0.599 Pork 0.236 0.169 Poultr y 0.214 0.111 E gg s 0.093 0.073 Lamb 0.061 0.057 a
Onl y the 95-100 p ercentile of the data from TAS (1991) was nonzero. a

Technical Basis:
All of the values re p orted above are g iven on a g ram dr y wei g ht p er k g of bod y wei g ht p er da y basis. With the exce p tion of the in g estion rates for adults for leaf y ve g etables and fruits, the values are either the 50-55 p ercentile (or the 95-100 p ercentile if the median was zero) of the data from Technical Assessment S y stems, Inc. (TAS). The values for the p ercentiles were re p orted in g DW/k g of bod y wei g ht p er da y . TAS conducted this anal y sis of food consump tion habits of the total p o p ulation and five p o p ulation sub g rou p s in the United States. The data used were the results of the Nationwide Food Consump tion Surve y (NFCS) of 1987-88 conducted b y the United States De p artment of A g riculture. The information in the NFCS was collected durin g home visits b y trained interviewers usin g one-da y interviewer-recorded recall and a two-da y self-administered record. A stratified area- p robabilit y samp le of households was drawn in the 48 conti g uous states from A p ril 1987 to 1988. More than 10,000 individuals p rovided information for the basic surve y .A-5 Each individual's intake of food was avera g ed across the 3 da y s of the ori g inal NFCS surve y , and food consum p tion for each food g rou p was determined for each individual. Percentiles were then com p uted for six p o p ulation sub g rou p s: • U.S. p o p ulation • males  13 y ears • females  13 y ears • children 1-6 y ears • children 7-12 y ears • infants < 1 y ear. The values for children in the p revious table are based on the data for children between 7 and 12 y ear of a g e, while the adult values are for males older than 12 y ears of a g e. The males older than 12 y ears of a g e were chosen to re p resent the adult since rates for females are lower; this is reco g anized to be somewhat conservative. The United States p o p ulation rates include the rates of children which were considered inapp ro p riate for the h yp othetical adult rece p tors modeled in this anal y sis.
The values for leaf y ve g etables and fruits for adults are from (USU.S. EPA 1989).
A.1.4.3 Soil In g estion Rate
Parameter: Cs Definition: Amount of soil in g ested dail y . Units: g /da y Receptor Default Value (g/day) Distribution Range (g/day) Pica Child 7.5 U(5,10) 5-10 Child 0.2 U(0.016,0.2) 0.016-0.2 Adult 0.1 U(0.016,0.1) 0.016-0.1

Technical Basis:
Soil in g estion ma y occur inadvertentl y throu g h hand-to-mouth contact or intentionall y in the case of a child who en g a g es in p ica. The default values for adults and non- p ica children are those su gg ested for use in U.S. EPA (1989). More recent studies have found that these values are rather conservative. For exam p le, Calabrese and Stanek (1991) found that avera g e soil intake b y children was found to ran g e from 0.016 to 0.055 g /da y . This ran g e, in con j unction with the su gg ested U.S. EPA values, was used to obtain the ran g es shown.
Several studies su gg est that a p ica child ma y in g est u p to 5 to 10 g /da y (LaGo y , 1987, U.S. EPA, 1989). This ran g e was selected, and the mid p oint was chosen as the default value.A-6 A.1.4.4 Groundwater In g estion Rate
Parameter: Cw Definition: The amount of water consumed each day . Units: L/da y Receptor Default Values Distribution (L/day) Child 1.0 Lo g *(0.378; 0.079) Adult 2.0 Lo g *(0.1; 0.007)

Technical Basis:
The default values for children and adult are those also su gg ested in U.S. EPA (1989) and were first p ublished b y the Safe Drinkin g Water Committee of the National Academ y of Sciences (NAS, 1977).
The distributions are those com p uted in Roseberr y and Burmaster (1992). In that p ap er, lo g normal distributions were fit to data collected in a national surve y for both total water intake and ta p water intake b y children and adults. These data were ori g inall y g athered in the 1977-1978 Nationwide Food Consump tion Surve y of the United States De p artment of A g riculture and were anal y zed b y Ershow and Cantor (1989).
In Roseberr y and Burmaster (1992), distributions were fit to the intake rates for humans a g es 0-1 y ear, 1-11 y ears, 11-20 y ears, 20-65 y ears and older than 65 y ears. The distribution for children a g es 1- 11 was chosen for the child's distribution g iven in the p revious table and the distribution for adults a g es 20-65 was used for the adult. For the p urp ose of the p resent anal y sis, the ta p water intake was deemed more app ro p riate than total water intake. The total water intake included water intrinsic in foods that are accounted for in the a g ricultural p athwa y s, while the ta p water intake was the sum of water consumed directl y as a bevera g e and water added to foods and bevera g es durin g p re p aration. The minima and maxima were selected as the 2.5 and 97.5 p ercentiles, res p ectivel y .A-7 A.1.4.5 Fish In g estion Rate
Parameter: Cf Definition: Quantit y of locall y - cau g ht fish in g ested p er da y . Units: g /da y Receptor Default Value (g/day) Hi g h End Fisher 60 Child of hi g h end fisher 20 Recreational An g ler 30

Technical Basis:
Because of the bioaccumulation of meth y lmercur y in fish, the fish in g estion rate is an im p ortant p arameter for modelin g mercur y ex p osure. Fish consump tion rates are difficult to determine for a g eneral p o p ulation stud y because individual fish in g estion rates var y widel y across the United States. This animal p rotein source ma y be readil y consumed or avoided on a seasonal, social, economic or demo g rap hic basis. Ideall y , for an actual site, s p ecific surve y s identif y in g the t yp e, source, and q uantit y of fish consumed b y area residents would be used. Within the context of this study , it is not p ossible to characterize this variabilit y comp letel y . For this p art of the assessment, individuals in three broad g rou p s of ex p osed p o p ulations will be considered: hi g h end fishers, recreational an g lers and the g eneral p o p ulation. For the g eneral p o p ulation, no commercial distribution of locall y cau g ht fish was assumed. All consumers of locall y cau g ht fish were assumed to be recreational an g lers or subsistence fishers.
In U.S. EPA's 1989 Ex p osure Factors Handbook, fish consump tion data from Puffer (1981) and Pierce et al. (1981) are su gg ested as most a pp ro p riate for fish consump tion of recreational an g lers from lar g e water bodies. The median of this sub p o p ulation is 30 g /da y with a 90th p ercentile of 140 g /da y (340 meals/y ear). The median was used as the surro g ate value for recreational an g lers. For subsistence fishers, human fish consum p tion data were obtained from the re p ort of the Columbia River Inter-Tribal Fish Commission (1994), which estimated fish consum p tion rates for members of four tribes inhabitin g the Columbia River Basin. The estimated fish consum p tion rates were based on interviews with 513 adult tribe members who lived on or near the reservation. The p artici p ants had been selected from p atient re g istration lists p rovided b y the Indian Health Service. Adults interviewed p rovided information on fish consump tion for themselves and for 204 children under 5 y ears of a g e. Durin g the stud y fish were consumed b y over 90% of the p o p ulation with onl y 9% of the res p ondents re p ortin g no fish consump tion. Monthl y variations in consump tion rates were re p orted. The avera g e dail y consump tion rate durin g the two hi g hest intake months was 107.8 g rams/da y , and the dail y consump tion rate durin g the two lowest consump tion months was 30.7 g rams/da y . Members who were a g ed 60 y ears and older had an avera g e dail y consump tion rate of 74.4 g rams/da y . Durin g the p ast two decades, a decrease in fish consum p tion was g enerall y noted amon g res p ondents in this surve y . The maximum dail y consump tion rate for fish re p orted for this g rou p was 972 g rams/da y .A-8 The mean dail y fish consump tion rate for the total adult p o p ulation (a g ed 18 y ears and older) was re p orted to be 59 g rams/da y . The mean dail y fish consump tion rate for the adult females surve y ed was 56 g /da y and the mean dail y fish consump tion rate for the adult males surve y ed was 63 g rams. A value of 60 g rams of fish p er da y was selected for the subsistence an g ler modeled in this re p ort. Other fish consump tion rate studies for s p ecific sub p o p ulations (i.e., an g lers and subsistence consumers) have been conducted. These studies are briefl y described in Volume IV. These studies demonstrate the wide ran g e of fish consump tion rates exhibited across the U.S. p o p ulation. The y also tend to corroborate the estimates to be used in this anal y sis. These anal y ses also illustrate the difficult y in determinin g avera g e and hi g h-end consump tion rates for sub p o p ulations considered to be more likel y to consume more fish. In the lacustrine scenarios of this assessment, all fish were assumed to ori g inate from the lakes, which are considered to re p resent several small lakes that ma y be p resent in a h yp othetical location.
The effects of fish p re p aration for food on extant mercur y levels in fish have also been evaluated (Mor g an et al., 1994). Total mercury levels in walle y e were found to be constant before and after p re p aration; however, mercur y concentrations in the cooked fish were increased 1.3 to 2.0 times when com p ared to mercur y levels in the raw fish. It was su gg ested that this increase was p robabl y due to water and fat loss durin g cookin g and fish skin removal. A p re p aration factor ad j ustment was noted but not im p lemented in this anal y sis because human consum p tion levels were measured on uncooked fish. For more information see Volume IV.A-9 A.1.4.6 Contact Fractions
Parameter: FPi, Fa j Definition: that fraction of the food t yp e g rown or raised on contaminated land
Units: Unitless

Food Subsistence Rural Home Urban Gardener Comment Farmer Gardener/ Subsistence Fisher Grains 1 0.667 0.195 Values are for corn from Table 2-7 in U.S. EPA (1989) Le g umes 1 0.8 0.5 Values are for p eas from Table 2-7 in U.S. EPA (1989). Potatoes 1 0.225 0.031 Values are for total fresh p otatoes from Table 2-7 in U.S. EPA (1989). Root Ve g etables 1 0.268 0.073 Values are for carrots from Table 2-7 in U.S. EPA (1989).

Fruits 1 0.233 0.076 citrus fruit from Table 2-7Values are for Total nonin U.S. EPA (1989). Fruitin g 1 0.623 0.317 Values are for tomatoes Ve g etables from Table 2-7 in U.S. EPA (1989). Leaf y Ve g etables 1 0.058 0.026 Values are for lettuce from U.S. EPA (1989). Beef 1 0 0 Beef liver 1 0 0 Dair y 100 Pork 1 0 0 Poultr y 100 E gg s 100 Lamb 1 0 0

Technical Basis:
The values for the subsistence farmer are consistent with the assum p tions re g ardin g this scenario. The values for the g ardeners are from U.S. EPA (1989), p er U.S. EPA g uidance. Because it is assumed that onl y the subsistence farmers will consume contaminated animal p roducts, the contact fractions for g ardeners is 0 for consump tion of local animal p roducts.A-10 A.2 Chemical Dependent Parameters Chemical de p endent p arameters are variables that chan g e de p endin g on the s p ecific contaminant bein g evaluated. The chemical de p endent variables used in this stud y are described in the followin g sections.
A.2.1 Basic Chemical Pro p erties
The followin g sections list the chemical p ro p erties used in the stud y , their definitions, and values.
A.2.1.1 Molecular Wei g ht
Parameter: Mw Definition: The mass in g rams of one mole of molecules of a com p ound.
Units: g /mole
Chemical Default Value (g/mole) H g , H g 201 0 2+ Meth y lmercur y 216 Meth y l mercuric chloride 251 Mercuric chloride 272

A.2.1.2 Henr y 's Law Constant
Parameter: H Definition: Provides a measure of the extent of chemical p artitionin g between air and water at e q uilibrium.
Units: atm-m /mole 3
Chemical Default Value (atm-m /mole) 3 H g 7.1x10 0 -3

H g (H g Cl ) 7.1x10 2+ 2 -10

Meth y lmercur y 4.7x10 -7
Technical Basis:
The hi g her the Henr y 's Law Constant, the more likel y a chemical is to volatilize than to remain in the water. The value for H g is from Iverfeldt and Persson (1985), while the other values are from 0 Lind q uist and Rodhe (1985).A-11 A.3 References
Aka g i H., D.C. Mortimer, and D.R. Miller (1979). Mercur y Meth y lation and Partition in A q uatic S y stems. Bull. Environ. Contam. Toxicol. 23: 372-376. Alberts, J.J., J.E. Schindler, and R.W. Miller. (1974). Elemental mercur y evolution mediated b y humic acid. Albeuts, J.J., J.E. Schindler, and R.W. Miller (1974). Elemental Mercur y Evolution Mediated b y Humic Acid. Science 184: 895-897. Anderson, A., N. Browne, S. Duletsk y , J. Rami g , and T. Warn (1985). Development of Statistical Distributions or Ranges of Standard Factors Used in Exposure Assessments. EPA 600/8-85-101, U.S. Environmental Protection A g enc y , Office of Health and Environmental Assessment, Washin g ton, D.C. Baes, C.F., R.D. Shar p , A.L. S j oreen, and R.W. Shor (1984). A Review and Analysis of Parameters for Assessing Transport of Environmentally Released Radionuclides Through Agriculture. Pre p ared under contract No. DE-AC05-84OR21400. U.S. De p artment of Ener gy , Washin g ton, D.C. Belcher, G.D. and C.C. Travis (1989). Modeling Support for the Rura and Municipal Waste Combustion Projects: Final Report on Sensitivity and Uncertainty Analysis for the Terrestrial Food Chain Model. Pre p ared for the U.S. EPA. Bloom, N.S. and C.J. Watras (1989). Observations of Meth y lmercur y in Preci p itation. The Sci. Tot. Environ. 87/88: 199-207. Bloom, N. and S.W. Effler. (1990). Water, Air, and Soil Poll. 53:251-265. Bloom, N.S., C.J. Watras, and J.P. Hurle y (1991). Im p act of Acidification on the Meth y lmercur y C y cle of Remote See p a g e Lakes. Water, Air, and Soil Poll. 56: 477-491. Bousset, C. (1981). The Mercur y C y cle. Water, Air and Soil Pollution 16: 253-255. Butcher, B., B. Davidoff, M.C. Amacher, C. Hinz, I.K. Iskandar, and H.M. Selim (1989). Correlation of Freundlich K and retention p arameters with soils and elements. Soil Science 148: 370-379. d Calabrese, E.J. and E.J. Stanek, III (1991). A Guide to Inter p retin g Soil In g estion Studies. II. Qualitative and Quantitative Evidence of Soil In g estion. Regul. Toxicol. Pharmacol. 13:278-292.
Capp on, C.J. (1981). Mercur y and Selenium Content and Chemical Form in Ve g etable Cro p s Grown on Slud g e-Amended Soil. Arch. Environm. Contam. Toxicol. 10: 673-689.
Capp on, C. (1984). Content and Chemical Form of mercury and selenium in soil, slud g e and fertilizer materials. Water, Air, Soil Pollut 22:95-104.
Capp on, C.J. (1987). U p take and S p eciation of Mercur y and Selenium in Ve g etable Cro p sGrown on Com p ost-Treated Soil. Water, Air, Soil Poll. 34: 353-361. Carsel, R.F., C.N. Smith, L.A. Mulke y , J.D. Dean, and P. Jowise (1984). User's Manual for the Pesticide Root Zone Model (PRZM) Release 1. U.S. EPA, Athens, GA. EPA-600/3-84-109.A-12 Ershow, A.G. and K.P. Cantor (1989). Total Water and Tapwater Intake in the United States: Population-based Estimates of Quantities and Sources . Life Sciences Research Office, Federation of American Societies for Ex p erimental Biolo gy . Bethesda, Mary land.
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### APPENDIX B ESTIMATED NATIONAL AND REGIONAL POPULATIONS OF WOMEN OF CHILD-BEARING AGE: UNITED STATES, 1990B-1 Estimated National and Regional Populations of Women of Child-Bearing Age: United States, 1990

Because methylmercury is a developmental toxin, the subpopulation judged of particular concern in this Mercury Study: Report to Congress was women of child-bearing age. Estimates of the size of the population of women of reproductive age, number of live births, number of fetal deaths, and number of legal abortions can be used to predict the percent of the population and number of women of reproductive age who are pregnant in a given year. This methodology has been previously used in the Agency for Toxic Substances and Disease Registry's (ATSDR's) Report to Congress on The Nature and Extent of Lead Poisoning in Children in the United States (Mushak and Crocetti, 1990). The estimates of number of women of child-bearing age calculated for this Mercury Study: Report to Congress were prepared by Dr. A.M. Crocetti under purchase order from the EPA Office of Air Quality, Planning and Standards (OAQPS). The techniques used by Dr. Crocetti parallel those used to prepared the 1984 estimates for ATSDR. To estimate the size of this population on a national basis Vital and Health Statistics data for number of live births (National Center for Health Statistics of the United States, 1990; Volume I, Natality, Table 1-60, pages 134-140), and fetal deaths (National Center for Health Statistics of the United States, 1990; Volume II, Mortality; Table 3-10, pages 16, 18, and 20). Fetal wastage, that is, spontaneous abortions prior to 20 weeks of gestation were not considered since no systematically collected, nationally based data exist. The estimate of number of women of child-bearing age includes some proportion of women who will never experience pregnancy. However, substitution of the number of pregnancies in a given year provides some measure of assessing the size of the surrogate population at risk. Estimates of the size of the population were based on "Estimates of Resident Population of the United States Regions and Divisions by Age and Sex" (Byerly, 1993). The Census data for 1990 were grouped by age and gender. The sizes of these populations are shown in Table B-1. Women ages 15 through 44 are the age group of greatest interest in identifying a subpopulation of concern for the effects of a developmental toxin such as methylmercury. This population consisted of 58,222,000 women living within the contiguous United States. This population was chosen rather than for the total United States (population 58,620,000 women ages 15 through 44 years) because the dietary survey information from CSFII 89-91 did not include Hawaii and Alaska. Based on estimates of fish consumption data for Alaska by Nobmann et al. (1992) the quantities of fish eaten by Alaskans exceeds those of the contiguous U.S. population. It is also estimated that residents of the Hawaiian Islands also have fish consumption patterns that differ from those of the contiguous United States. The number of pregnancies per year was estimated by combining the number of live births, number of fetal deaths (past 20 weeks of gestation) and the number of legal abortions. The legal abortion data were based on information published by Koonin et al. (1993) in Morbidity and Mortality Weekly Report . These totals are presented in Table B-2. As noted in this table, the total of legal abortions includes those with unknown age which were not included in the body of each table entry. There were 2,929 such cases for the United States in 1990 or 0.2% of all legal abortions. Another complication in the legal abortion data was for the age group 45 and older. The available data provide abortion data for 40 years and older only. To estimate the size of the population older than 45 years, the number of legal abortions for women age 40 years and older were allocated by using the proportions of Live Births and Fetal Deaths for the two age groups 40-44 and 45 and older.B-2 It was estimated that within the contiguous United States 9.5% of women ages 15 to 44 years were pregnant in a given year. The total number of live births reported in 1990 for this age group was 4,112,579 with 30,974 reported fetal deaths and 1,407,830 reported legal abortions. The estimated number of total pregnancies for women ages 15 to 44 years was 5,551,383 in a population of 58,222,000 women.

REFERENCES Byerly, E.R. (1993) State Population Estimates by Age and Sex: 1980-1992, U.S. Bureau of the Census, Current Population Reports P25-1106, U.S. Government Printing Office, Washington, DC. Koonin, L.M., Smith, J.C., and Ramick, M. (1993) Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion: Abortion Surveillance - United States, 1990: Morbidity Mortality Weekly Report, Vol 42/No. SS-6, pps. 29-57, December 17. Mushak, P., and Crocetti, A.M. (1988). The Nature and Extent of Lead Poisoning in Children in the United States: A Report to Congress. Agency for Toxic Substances and Disease Registry, United States Public Health Service, United States Department of Health and Human Services. National Center for Health Statistics of the United States (1990) Volume I. Natality: Table 1-60; pages 134-140. National Center for Health Statistics of the United States (1990) Volume II. Mortality; Table 3-10, pages 16, 18, and 20. Nobmann, E.D., Byers, T., Lanier, A.P., Hankin, J.H., and Jackson, M.Y. (1992) The diet of Alaska Native adults: 1987-1988. Amer. J. Clin. Nutr. 55: 1024-1032.B-3 Table B-1 Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age United States 248,710 53,853 117,610 77,248 Male 121,239 27,570 58,989 34,680 Female 127,471 26,284 58,620 42,567 % Female 51.3 48.8 49.8 55.1
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age Contiguous United States 247,052 53,462 116,772 76,817

Male 120,385 27,369 58,548 34,467 Female 126,667 26,094 58,222 42,348 % Female 51.3 48.8 49.9 55.1
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age New England 13,207 2,590 6,379 4,239 Male 6,380 1,327 3,174 1,878 Female 6,827 1,264 3,202 2,361 % Female 51.7 48.8 50.2 55.7B-4 Table B-1 (continued)
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 45 Years Gender of Age of Age of Age Middle Atlantic States 37,602 7,471 17,495 12,638

Male 18,056 3,824 8,676 5,554 Female 19,547 3,645 8,818 7,083 % Female 52 49 50 56
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age E North Central 42,009 9,233 19,596 13,180 Male 20,373 4,728 9,744 5,899 Female 21,636 4,505 9,851 7,279 % Female 51.5 48.8 50.3 55.2
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age West North Central 17,660 3,967 8,017 5,676

Male 8,599 2,032 4,020 2,546 Female 9,061 1,935 3,997 3,129 % Female 51.3 48.8 49.9 55.1B-5 Table B-1 (continued)
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age South Atlantic 43,567 8,864 20,579 14,122

Male 21,129 4,531 10,279 6,321 Female 22,438 4,333 10,301 7,804 % Female 51.5 48.9 50.1 55.3
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age East South Central 15,176 3,316 7,037 4,823

Male 7,301 1,698 3,472 2,132 Female 7,875 1,618 3,565 2,692 % Female 51.9 48.8 50.7 55.8
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age West South Central 26,703 6,366 12,687 7,651

Male 13,061 3,256 6,359 3,445 Female 13,641 3,110 6,328 4,204 % Female 51.1 48.9 49.9 54.9B-6 Table B-1 (continued)
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age Mountain States 13,659 3,313 6,435 3,910

Male 6,779 1,696 3,259 1,825 Female 6,880 1,616 3,176 2,087 % Female 50.4 48.8 49.4 53.4
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age West North Central 17,660 3,967 8,017 5,676

Male 8,599 2,032 4,020 2,546 Female 9,061 1,935 3,997 3,129 % Female 51.3 48.8 49.9 55.1
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age Pacific (5 States including Alaska and Hawaii) 39,127 8,734 19,394 11,011

Male 19,562 4,476 10,004 5,083 Female 19,565 4,258 9,379 5,929 % Female 50.0 48.8 48.4 53.8B-7 Table B-1 (continued)
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and Age; in Thousands, including Armed Forces Residing in Region. Division/ Total < 15 Years 15-44 Years > 44 Years Gender of Age of Age of Age Pacific (Washington, Oregon and California only) 37,469 8,343 18,546 10,580

Male 18,708 4,275 9,563 4,870 Female 18,761 4,068 8,981 5,710 % Female 50.1 48.8 48.4 54.0B-8 Table B-2 Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* United Total** < 15 Years 15-44 Years > 44 States Years*** Females 127,471,000 26,284,000 58,620,000 42,567,000 Live births 4,158,212 11,657 4,144,917 1,638 Fetal Deaths 31,386 174 31,176 36 Legal Abortions 1,429,577 11,819 1,413,992 837 Total Pregnancies 5,619,175 23,650 5,590,085 2,511
% Pregnant - 9.5 -
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* Contiguous Total** < 15 Years 15-44 Years > 44 Years United States Females 126,667,000 26,094,000 58,222,000 42,348,000 Live births 4,125,821 11,615 4,112,579 1,627 Fetal Deaths 31,183 173 30,974 36 Legal Abortions 1,423,340 11,765 1,407,830 833 Total Pregnancies 5,580,344 23,553 5,551,383 2,496
% Pregnant - - 9.5 -
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* New Total** < 15 Years 15-44 Years >44 Years England Females 6,827,000 1,264,000 3,202,000 2,361,000 Live births 201,173 270 200,827 76 Fetal Deaths 1,226 4 1,220 2 Legal Abortions 78,347 487 77,358 37 Total Pregnancies 280,746 761 279,405 115
% Pregnant - - 8.7 -B-9 Table B-2 (continued)
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* Middle Total** < 15 Years 15-44 Years > 44 Years Atlantic Females 19,547,000 3,645,000 8,818,000 7,083,000 Live births 591,826 1,305 590,238 283 Fetal Deaths 5,653 25 5,622 6 Legal Abortions 252,599 1,912 250,484 157 Total Pregnancies 850,078 3,242 846,344 446
% Pregnant 9.6
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* East Total** < 15 Years 15-44 Years > 44 Years North Central Females 21,636,000 4,505,000 9,851,000 7,279,000

Live births 675,512 1,838 673,449 225 Fetal Deaths 4,555 14 4,537 4 Legal Abortions 166,897 1,056 165,434 109 Total Pregnancies 846,964 2,908 843,420 338
% Pregnant 8.6
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* West Total** < 15 Years 15-44 Years > 44 Years North Central Females 9,061,000 1,935,000 3,997,000 3,129,000 Live births 270,331 457 269,792 82 Fetal Deaths 1,741 6 1,733 2 Legal Abortions 57,219 398 56,562 30 Total Pregnancies 329,291 861 328,087 114
% Pregnant - 8.2 -B-10 Table B-2 (continued)
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* South Total** < 15 Years 15-44 Years > 44 Years Atlantic Females 22,438,000 4,333,000 10,301,000 7,804,000 Live births 700,285 2,644 697,424 217 Fetal Deaths 6,453 57 6,389 7 Legal Abortions 238,538 2,242 235,536 123 Total Pregnancies 945,276 4,943 939,349 347
% Pregnant - 9.1 -
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* East Total** < 15 Years 15-44 Years > 44 Years South Central Females 7,875,000 1,618,000 3,565,000 2,692,000 Live births 236,374 1,143 235,195 36 Fetal Deaths 2,954 25 2,027 2 Legal Abortions 53,919 662 53,030 19 Total Pregnancies 292,347 1,830 290,252 57
% Pregnant - 8.1 -
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* West Total** < 15 Years 15-44 Years > 44 Years South Central Females 13,641,000 3,110,000 6,328,000 4,204,000 Live births 472,721 1,852 470,715 154 Fetal Deaths 3,258 21 3,234 3 Legal Abortions 122,261 781 121,100 90 Total Pregnancies 598,240 2,654 595,049 247
% Pregnant - 9.4 -B-11 Table B-2 (continued)
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* Mountain Total** < 15 Years 15-44 Years > 44 Years Females 6,880,000 1,616,000 3,176,000 2,087,000 Live births 242,829 500 242,235 94 Fetal Deaths 1,492 6 1,483 3 Legal Abortions 50,880 288 50,330 31 Total Pregnancies 295,201 794 294,048 128
% Pregnant - 9.3 -
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* Pacific Total** < 15 Years 15-44 Years > 44 Years (5 states including Alaska and Hawaii) Females 19,565,000 4,258,000 9,379,000 5,929,000 Live births 767.161 1,648 765,042 471 Fetal Deaths 4,954 16 4,931 7 Legal Abortions 408,917 3,993 404,158 241 Total Pregnancies 1,181,032 5,657 1,174,131 719
% Pregnant - 12.5 -
Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age* Pacific Total** < 15 Years 15-44 Years > 44 Years (Washington, Oregon, and California) Females 18,761,000 4,068,000 8,981,000 5,710,000 Live births 734,770 1,606 732,704 460 Fetal Deaths 4,751 15 4,729 7 Legal Abortions 402,680 3,939 397,996 237

Total Pregnancies 1,142,201 5,560 1,135,429 704
% Pregnant - - 12.6 -C-1 APPENDIX C ANALYSIS OF MERCURY LEVELS IN FISH AND SHELLFISH REPORTED IN NATIONAL MARINE FISHERIES SERVICE SURVEY OF TRACE ELEMENTS IN THE FISHERY RESERVEC-1 C.1 Introduction Some reviewers of data on the levels of mercury in fish and shellfish have expressed concern about the methods used to handle “nondetects” by the investigators who originally reported the data on the concentrations of mercury in fish and shellfish tissues. Specifically, these reviewers have expressed concern about the potential impact that different methods of handling nondetects may have on the reported mean concentrations of mercury. The purpose of this memo is to report the results of a data analysis performed on the nondetects in the mercury data reported in the report National Marine Fisheries Service Survey of Trace Elements in the Fishery Reserve , hereinafter referenced as the NMFS Report. The major conclusion of this analysis is that different methods of handling nondetects have negligible impact on the reported mean concentrations. This conclusion follows from two findings from the data analysis, set forth below. First, when mean mercury levels are relatively “large”, there are few, if any, nondetects, so the methodology employed to handle nondetects is irrelevant. Second, when mean mercury levels are small, there are relatively large numbers of nondetects. However, the differences between different methods of handling nondetects result in small differences in the resultant mean values. The NMFS Report reports number of samples, number of nondetects, and mean, standard deviation, minimum and maximum mercury level in ppm for 1,333 combinations of fish/shellfish species, variety, location caught, and tissue. Of these, 777 correspond to fish/shellfish species for which we have mercury concentration data. These 777 combinations form the basis for the analyses reported in this memorandum. They represent 5,707 analyses of fish and shellfish tissues for mercury, of which 1,467, or 26 percent, are reported as nondetects. Because the mercury concentration data is used in our analyses at the species level, not at the more detailed species/variety/location/tissue level, we have aggregated, or pooled, the 777 combinations to 35 different species for the purposes of this analysis. In the following sections, we first discuss various methods of handling nondetects in calculating mean mercury concentrations, then the analysis method adopted, and finally the results of that analysis.

C.2 Methods for Handling the Detection Limits There are five methods commonly used to handle values below the detection limits in calculating the mean mercury levels.
1. All nondetects are treated as being equal to 0. The total number of samples for which mercury was measured is used in the mean calculation and it is assumed that the concentration of mercury is 0.000 whenever the chemical analysis was reported as “not detected”. This approach may lead to an underestimation of the true mean. 2. All nondetects are excluded from the calculation of the mean. The mean is calculated as if these samples were not selected. The number of nondetects is subtracted from the total number of samples for which mercury was measured, and the resulting number is used to calculate the mean. This method may overestimate the true mean and always yields a mean estimate greater than that obtained by method 1 (see formulae in Addendum A).C-2 3. All nondetects are replaced with a fixed value, usually one-half of the detection limit. This method is the most widely used and accepted of the five methods. It is difficult to know whether this method will lead to an underestimation or to an overestimation of the true mean. But it will always lead to an estimate that falls between the estimates obtained from method 1 and method 2. 4. All nondetects are replaced with simulated mercury levels randomly selected in the interval (0, detection limit) according to an appropriate statistical distribution. This method is close in spirit to method 3 and, like method 3, will lead to an estimate falling between estimates obtained from method 1 and method 2. 5. All nondetects are replaced with the detection limit. This method may overestimate the mean as all nondetects are smaller or equal to the detection limit. The mean calculated by method 5 will also be between the means obtained from method 1 and method 2. The NMFS Report says that method 2 -- nondetects dropped from the calculation -- was used to calculate their reported mean mercury levels. However, an examination of their data indicates that the investigators did not always use method 2. It appears that other methods, including method 1 -- nondetects set equal to zero -- may have sometimes been used.

C.3 Method of Analysis The approach adopted amounts to comparing means obtained by two different methods. Since we do not have access to the raw data, it was necessary to first assume that the reported mean mercury levels were calculated by one of the five methods mentioned above. Then we calculated the mean that would have been obtained if another method had been used. Although it is possible to consider all ten possible combinations of two methods that can be obtained from the five under analysis, we have confined ourselves to the case where the other methods are compared with method 3, the latter being the most commonly used in such situations. The following three scenarios are studied:

 The reported means are assumed to have been calculated by method 1. The corresponding mean mercury levels that would have been obtained by method 3 were then calculated. The two sets of corresponding means are then compared. The calculation method is reported in Addendum A.

 The above analysis was repeated for method 2 and method 3.

 The above analysis was repeated for method 5 and method 3. It should be noted that if the reported mean is 0 and is assumed to be obtained by method 5 then method 3 might yield a negative value. In that case the mean was set to 0.000.C-3 It is unlikely that method 4 was used to calculate the reported means since this would likely have appeared in the NMFS report. Therefore method 4 is ruled out of this analysis. To be able to calculate the mean mercury level by method 3, a value for the limit of detection is needed. We have been told that the limit of detection was 0.100 ppm. However, the data reported in the NMFS Report have numerous reported positive values less than 0.100 ppm. We therefore used the lowest of all detected analytical values as the presumed limit of detection. This value is 0.010 ppm. Addendum B lists and graphs the mean mercury levels in ppm by fish and shellfish species, as reported by NMFS, then as calculated according to the methodology described above. That is, the mean mercury level that would be obtained by method 3, assuming NMFS used method 1 is presented, followed by the other two comparisons listed above. Then the mean differences between pairs of methods are presented. C.4 Data Analysis Results The calculations comparing method 1 -- nondetects dropped -- and method 3 -- nondetects set to one-half the detection limit, viz., 0.005 -- are reported in Figure C-1a and C-1b. The straight line in Figure C-1a is the line y = x; points on the line correspond to mean values that are the same for both methods. All points are on the line y = x, or nearly on it; the two methods yield identical results for most species. This result follows from the fact that when mean mercury levels are relatively large, very few nondetects were reported (see Figure C-4a). In order to have a better assessment of the magnitude of the differences between method 1 and method 3, we plotted the differences between the two methods versus method 1 in Figure C-1b. The differences between methods 1 and 3 are never as high as 0.004 ppm. Further, they never exceed 0.001 ppm when the mean is above 0.200 ppm.C-5 The results comparing methods 2 and 3 are in Figures C-2a and C-2b. They lead to the same conclusions as the comparison of methods 1 and 3. The differences between methods 2 and 3 never exceed 0.030 ppm in magnitude. Because the differences between methods 2 and 3 are an order of magnitude greater than the other two comparisons, it was decided to investigate the larger differences between these methods to see if there were any significant patterns. The results comparing methods 5 and 3 are in Figures C-3a and C-3b. They lead to the same conclusions as the two previous comparisons. The differences between methods 5 and 3 never exceed 0.003 ppm in magnitude. They never exceed 0.001 ppm when the mean mercury level is above 0.200 ppm. These results follow from the fact that the number of nondetects is especially high when the reported mean is very small. When that mean is larger, there are very few nondetects, so that all methods yield virtually the same results. This phenomenon is well illustrated in Figures C-4a and C-4b, which present the number and percentage of nondetects against the mean mercury levels, respectively.C-9 ADDENDUM A

This addendum provides the formulae used to calculate the mean Mercury levels according to the four methods used in the analysis.

Let N 0 be the total number of samples for which the fish was measured, N d the total number of samples in which no Mercury was detected and d 0 the limit of detection. Suppose that

x i stands for the Mercury level (ppm) detected in the i th sample and that XX X 123

, , and X 5 are the mean Mercury levels calculated by methods 1,2,3 and 5 respectively. Then we have that,

X N x X N N x

X N x Nd X N x Nd i d i i N N

i N N

i d i N N i d i N N d d

d d 1 0 2 0 1 1

3 0 0 1 5 0 0 1 1 1

1 2 1 0 0

0 0 = =

−

= +         = +         = −

= −

= −

= − 

# ∑ ∑ ∑ ∑ ,
/ ,

Let X 3/1 , X 3 2 / and X 3 5 / be the means calculated by method 3 under the assumption that the reported data are calculated by method 1, 2 and 5 respectively. These conditional means are obtained as follows:

X NX N d N d d 3 1 0 0 2 /1 = + ×÷

,

X N NX N d N d d 3 2 0 2 0 0 2 / ( )
= − + ×÷

and

X NX N d N d 3 5 0 5 0 0 2 / = − ×÷

.C-10 ADDENDUM B


### Mercury Levels by Species

NMFS Data: Table and Graphs

Comparisons of Different Methods of Handling Nondetects: Table and GraphsC-11 Table C-1 Records in NMFS Report for which the difference between Method 3 and Method 2 is greater than 0.010 (sorted according to the magnitude of the difference, DIFF)
SPECIES VARIETY LOCATION TISSUE NO. N. DET MEAN DIFF Herring Pacific Pacific NWest whole 20 19 .260 -0.242 Sole Petrale Pacific NWest muscle 11 6 .347 -0.187 Tuna Bigeye Hawaii liver 2 1 .250 -0.123 Squid Atl. Longfinned N. Atlantic mantle, skinless 6 5 .130 -0.104 Cod Atlantic N. Atlantic liver 2 1 .210 -0.103 Crab Tanner (Bairdi) Alaska meat 10 5 .208 -0.102 Squid Atl. Longfinned N. Atlantic mantle, skinless 7 5 .140 -0.096 Shrimp Alaska (Sidestriped) Alaska tail, peeled 7 4 .168 -0.093 Cod Atlantic N. Atlantic liver 6 5 .110 -0.088 Shrimp Ocean Pacific NWest tail, peeled 10 6 .136 -0.079 Cod Atlantic N. Atlantic liver 4 2 .158 -0.077 Clam Butter Pacific NWest shucked, large 10 8 .100 -0.076 Mullet Striped Hawaii muscle 18 16 .090 -0.076 Salmon Coho (Silver) Alaska muscle 10 7 .110 -0.074 Crab Tanner (Bairdi) Alaska meat 10 5 .152 -0.074 Mullet Striped South Atlantic muscle 19 15 .098 -0.073 Oyster Pacific (Giant) California shucked 10 8 .090 -0.068 Scallop Calico S. Atlantic abductor muscle 10 8 .090 -0.068 Clam Hard N. Atlantic shucked, cherrysto 10 5 .141 -0.068 Squid Shortfinned N. Atlantic mantle, skinless 4 2 .135 -0.065 Shrimp Brown Gulf tail, peeled 10 8 .085 -0.064 Oyster Pacific (Giant) California shucked 20 12 .111 -0.064 Squid Atl. Longfinned N. Atlantic mantle, skinless 20 13 .100 -0.062 Squid Shortfinned N. Atlantic mantle, skinless 2 1 .120 -0.058 Tuna Yellowfin Hawaii liver 2 1 .120 -0.058 Clam Razor Alaska shucked 11 8 .083 -0.057 Croaker Atlantic Gulf muscle 9 6 .090 -0.057 Pollock Walleye (Alaska) Alaska muscle 28 12 .135 -0.056 Squid Shortfinned N. Atlantic mantle, skinless 11 6 .105 -0.055 Shrimp Pink Gulf tail, peeled 20 10 .114 -0.055 Salmon Coho (Silver) Pacific NWest liver 2 1 .110 -0.053 Mackerel Jack California headed 4 3 .070 -0.049 Trout (Sea) Silver (White) Gulf muscle 10 5 .100 -0.048 Clam Soft N. Atlantic shucked 19 11 .086 -0.047 Flounder Fourspot N. Atlantic muscle 3 1 .145 -0.047 Mullet Striped Gulf muscle 12 10 .060 -0.046 Shrimp White Gulf tail, peeled 10 8 .060 -0.044 Squid Shortfinned N. Atlantic mantle, skinless 5 4 .060 -0.044 Cod Atlantic N. Atlantic muscle 16 6 .121 -0.044 Pollock N. Atlantic liver 3 2 .070 -0.043 Flounder Winter North Atlantic muscle 10 4 .113 -0.043C-12 Mackerel King Gulf ROE 9 2 .199 -0.043 Flounder Witch N. Atlantic muscle 2 1 .090 -0.043 Tuna Skipjack Pacific liver 2 1 .090 -0.043 Herring Atlantic North Atlantic whole 12 11 .050 -0.041 Scallop Calico S. Atlantic shucked 10 6 .073 -0.041 Squid Shortfinned N. Atlantic mantle, skinless 10 6 .073 -0.041 Mullet Striped South Atlantic muscle 10 9 .050 -0.041 Shrimp Pink (Northern) Alaska tail, peeled 10 9 .050 -0.041 Flounder Winter North Atlantic muscle 2 1 .085 -0.040 Squid Pacific California whole 29 19 .064 -0.039 Oyster Eastern S. Atlantic shucked 10 3 .133 -0.038 Flounder Winter North Atlantic muscle 5 2 .100 -0.038 Salmon Sockeye (Red) Pacific NWest muscle 12 7 .068 -0.037 Abalone Red California shucked 10 5 .078 -0.037 Oyster Eastern N. Atlantic shucked, std. 10 7 .057 -0.036 Crab Tanner (Bairdi) Alaska meat 10 3 .126 -0.036 Herring Round North Atlantic H & G tailless 10 6 .065 -0.036 Flounder Southern S. Atlantic muscle 10 4 .095 -0.036 Pollock N. Atlantic liver 7 5 .055 -0.036 Squid Atl. Longfinned N. Atlantic mantle, skinless 7 3 .088 -0.036 Scallop Calico S. Atlantic abductor muscle 10 5 .076 -0.036 Flounder Summer (Fluke) S. Atlantic muscle 20 6 .119 -0.034 Trout (Sea) Sand Gulf muscle 5 3 .060 -0.033 Crab Rock N. Atlantic meat 5 1 .169 -0.033 Mullet Striped Gulf muscle 15 14 .040 -0.033 Flounder Winter North Atlantic muscle 4 2 .070 -0.033 Scup North Atlantic muscle 2 1 .070 -0.033 Salmon Chum (Keta) Alaska muscle 10 4 .086 -0.032 Shrimp Pink (Northern) Alaska tail, peeled 9 5 .063 -0.032 Mullet Striped South Atlantic muscle 4 1 .133 -0.032 Squid Shortfinned N. Atlantic mantle, skinless 14 8 .060 -0.031 Clam Surf N. Atlantic shucked, whole 19 9 .070 -0.031 Pollock Walleye (Alaska) Alaska liver 3 2 .050 -0.030 Anchovy Northern California whole 10 4 .080 -0.030 Scallop Sea (smooth) N. Atlantic abductor muscle 10 7 .047 -0.029 Squid Atl. Longfinned N. Atlantic mantle, skinless 10 4 .078 -0.029 Herring Atlantic North Atlantic headed 6 5 .040 -0.029 Herring Atlantic North Atlantic whole 29 14 .065 -0.029 Shrimp Brown Gulf tail, peeled 10 4 .077 -0.029 Salmon Chinock (King) Pacific NWest liver 5 1 .149 -0.029 Snapper Red (EMU) Hawaii muscle 18 1 .522 -0.029 Flounder Witch N. Atlantic muscle 16 3 .156 -0.028 Flounder Yellowtail North Atlantic muscle 10 3 .099 -0.028 Mackerel Atlantic North Atlantic muscle 8 5 .050 -0.028 Oyster Eastern N. Atlantic shucked, select 10 8 .040 -0.028 Shrimp White Gulf tail, peeled 10 8 .040 -0.028 Mullet Striped Hawaii muscle 9 6 .047 -0.028C-13 Shrimp Pink Gulf tail, peeled 9 2 .130 -0.028 Pollock N. Atlantic liver 14 8 .053 -0.027 Shrimp Pink (Northern) N. Atlantic tail, peeled 11 7 .048 -0.027 Shark Blue North Atlantic liver 9 2 .127 -0.027 Scup North Atlantic muscle 6 2 .086 -0.027 Flounder Winter North Atlantic muscle 10 4 .072 -0.027 Flounder Yellowtail North Atlantic muscle 3 2 .045 -0.027 Salmon Chinock (King) Alaska muscle 10 8 .038 -0.026 Mackerel Spanish South Atlantic muscle 20 3 .181 -0.026 Squid Atl. Longfinned N. Atlantic mantle, skinless 4 3 .040 -0.026 Squid Shortfinned N. Atlantic mantle, skinless 4 3 .040 -0.026 Flounder Gulf Gulf muscle 19 5 .101 -0.025 Trout (Sea) Gray (Weakfish) N. Atlantic whole 10 4 .068 -0.025 Octopus Marmuratus Hawaii mantle, skinless 36 17 .058 -0.025 Flounder Fourspot N. Atlantic muscle 4 2 .055 -0.025 Herring Atlantic North Atlantic whole 3 1 .080 -0.025 Croaker Atlantic N. Atlantic muscle 5 1 .130 -0.025 Perch Ocean (Pacific) Pacific NWest muscle 10 7 .040 -0.025 Shrimp Alaska (Sidestriped) Alaska tail, peeled 10 7 .040 -0.025 Oyster Pacific (Giant) Pacific NWest shucked, medium 9 4 .060 -0.024 Flounder Winter North Atlantic muscle 7 4 .047 -0.024 Flounder Witch N. Atlantic muscle 5 2 .065 -0.024 Flounder Winter North Atlantic muscle 15 9 .045 -0.024 Salmon Chum (Keta) Alaska muscle 9 4 .059 -0.024 Sole Dover Pacific NWest muscle 10 3 .085 -0.024 Flounder Winter North Atlantic muscle 6 1 .147 -0.024 Bass striped N. Atlantic muscle 16 8 .052 -0.024 Cod Atlantic N. Atlantic liver 3 2 .040 -0.023 Halibut Pacific Pacific NWest liver 3 2 .040 -0.023 Mackerel Atlantic North Atlantic muscle 11 4 .069 -0.023 Squid Atl. Longfinned N. Atlantic mantle, skinless 13 7 .048 -0.023 Mullet Striped South Atlantic muscle 2 1 .050 -0.023 Herring Atlantic North Atlantic muscle 10 9 .030 -0.023 Mullet Silver (white) South Atlantic muscle 24 18 .035 -0.023 Oyster Pacific (Giant) Pacific NWest shucked, small 10 5 .050 -0.023 Shrimp Pink Gulf tail, peeled 9 8 .030 -0.022 Herring Round North Atlantic H & G tailless 27 21 .033 -0.022 Bass striped Pacific NWest muscle 40 1 .858 -0.021 Flounder Witch N. Atlantic muscle 15 3 .111 -0.021 Mullet Striped Gulf muscle 20 11 .043 -0.021 Cod Atlantic N. Atlantic liver 5 2 .057 -0.021 Flounder Witch N. Atlantic muscle 4 1 .088 -0.021 Clam Razor Pacific NWest shucked 10 5 .046 -0.021 Flounder Winter North Atlantic muscle 21 6 .076 -0.020 Herring Atlantic North Atlantic muscle 12 8 .035 -0.020 Scup North Atlantic muscle 5 1 .105 -0.020 Sole Petrale Pacific NWest muscle 2 1 .045 -0.020C-14 Shark Blacktip South Atlantic liver 3 1 .065 -0.020 Squid Pacific California whole 10 6 .038 -0.020 Squid Atl. Longfinned N. Atlantic mantle, skinless 17 7 .053 -0.020 Mackerel Atlantic North Atlantic muscle 36 17 .046 -0.019 Oyster Eastern N. Atlantic shucked 20 9 .048 -0.019 Trout Rainbow/Steelhead Pacific NWest muscle 6 2 .063 -0.019 Trout (Sea) Silver (White) Gulf muscle 10 3 .069 -0.019 Shrimp Brown Gulf tail, peeled 17 3 .113 -0.019 Scup North Atlantic muscle 6 3 .043 -0.019 Croaker Atlantic S. Atlantic muscle 12 4 .061 -0.019 Clam Razor Pacific NWest shucked 10 5 .042 -0.019 Shrimp White S. Atlantic tail, peeled 10 2 .096 -0.018 Shrimp Pink (Northern) N. Atlantic tail, peeled 10 4 .050 -0.018 Salmon Chum (Keta) Pacific NWest muscle 7 5 .030 -0.018 Squid Atl. Longfinned N. Atlantic whole 23 9 .050 -0.018 Flounder Witch N. Atlantic muscle 10 2 .093 -0.018 Scallop Atlantic Bay S. Atlantic abductor muscle 10 6 .034 -0.017 Flounder Fourspot N. Atlantic muscle 6 1 .109 -0.017 Anchovy Northern California whole 10 4 .048 -0.017 Scallop Atlantic Bay S. Atlantic abductor muscle 10 2 .091 -0.017 Halibut Pacific Pacific NWest muscle 10 3 .062 -0.017 Salmon Sockeye (Red) Alaska muscle 19 9 .041 -0.017 Croaker Atlantic N. Atlantic muscle 10 6 .033 -0.017 Cod Pacific (Gray) Alaska liver 5 2 .047 -0.017 Trout (Sea) Silver (White) Gulf muscle 13 2 .114 -0.017 Shrimp Pink (Northern) N. Atlantic tail, peeled 3 1 .055 -0.017 Anchovy Northern California whole 10 8 .025 -0.016 Crab Blue N. Atlantic claw & body meat 10 5 .037 -0.016 Mackerel Atlantic North Atlantic muscle 7 4 .033 -0.016 Squid Shortfinned N. Atlantic mantle, skinless 20 5 .069 -0.016 Flounder Southern Gulf muscle 4 1 .067 -0.016 Flounder Fourspot N. Atlantic muscle 19 3 .103 -0.015 Flounder Winter North Atlantic muscle 12 4 .051 -0.015 Bass striped California muscle 28 1 .432 -0.015 Salmon Pink Alaska muscle 9 4 .039 -0.015 Clam Razor Alaska shucked 8 4 .035 -0.015 Croaker Atlantic Gulf muscle 2 1 .035 -0.015 Halibut Pacific Pacific NWest liver 8 6 .025 -0.015 Clam Hard N. Atlantic shucked, mixed 20 5 .065 -0.015 Oyster Eastern Gulf shucked 11 5 .038 -0.015 Squid Shortfinned N. Atlantic mantle, skinless 5 3 .030 -0.015 Tuna Yellowfin Hawaii muscle 10 3 .054 -0.015 Flounder Fourspot N. Atlantic muscle 6 1 .093 -0.015 Shrimp Pink Gulf tail, peeled 10 5 .034 -0.015 Clam Hard N. Atlantic shucked, littleneck 16 7 .038 -0.014 Abalone Green California shucked 10 6 .029 -0.014 Herring Round North Atlantic H & G tailless 7 5 .025 -0.014C-15 Shrimp Pink (Northern) N. Atlantic tail, peeled 9 4 .037 -0.014 Herring Atlantic North Atlantic whole 17 16 .020 -0.014 Shrimp White Gulf tail, peeled 17 3 .085 -0.014 Shrimp Brown Gulf tail, peeled 13 7 .031 -0.014 Clam Hard N. Atlantic shucked, cherrysto 30 13 .037 -0.014 Mullet Striped Hawaii muscle 13 12 .020 -0.014 Oyster Eastern Gulf shucked 20 12 .028 -0.014 Oyster Pacific (Giant) Pacific NWest shucked, medium 10 6 .028 -0.014 Scup North Atlantic muscle 11 10 .020 -0.014 Oyster Eastern N. Atlantic shucked, select 16 9 .029 -0.014 Anchovy Northern California whole 10 9 .020 -0.014 Croaker Atlantic Gulf muscle 10 9 .020 -0.014 Salmon Sockeye (Red) Alaska muscle 10 9 .020 -0.014 Oyster Pacific (Giant) Pacific NWest shucked 10 4 .038 -0.013 Clam Hard N. Atlantic shucked, chowder 49 14 .050 -0.013 Croaker Atlantic S. Atlantic muscle 2 1 .030 -0.013 Haddock N. Atlantic liver 2 1 .030 -0.013 Oyster Eastern S.Atlantic shucked 10 5 .030 -0.013 Perch Ocean (Pacific) Pacific NWest liver 8 4 .030 -0.013 Snapper Vermilion South Atlantic muscle 2 1 .030 -0.013 Lobster Atlantic Spiny Gulf tail meat 12 3 .055 -0.013 Tuna Skipjack Pacific muscle 20 3 .088 -0.012 Clam Butter Pacific NWest shucked, ex. large 9 4 .033 -0.012 Salmon Chinock (King) Alaska muscle 9 3 .042 -0.012 Flounder Windowpane N. Atlantic muscle 7 1 .090 -0.012 Salmon Chinock (King) Alaska muscle 10 8 .020 -0.012 Scallop Pink Alaska abductor muscle 5 4 .020 -0.012 Scup North Atlantic muscle 5 3 .025 -0.012 Squid Shortfinned N. Atlantic mantle, skinless 5 4 .020 -0.012 Haddock N. Atlantic muscle 5 1 .065 -0.012 Squid Shortfinned N. Atlantic mantle, skinless 9 1 .112 -0.012 Shrimp Brown Gulf tail, peeled 3 1 .040 -0.012 Flounder Witch N. Atlantic muscle 15 2 .092 -0.012 Salmon Coho (Silver) Pacific NWest muscle 10 5 .028 -0.012 Flounder Fourspot N. Atlantic muscle 18 2 .108 -0.011 Clam Butter Pacific NWest shucked 4 3 .020 -0.011 Shrimp Pink (Northern) N. Atlantic tail, peeled 4 1 .050 -0.011 Salmon Sockeye (Red) Alaska muscle 10 4 .033 -0.011 Perch Ocean (Redfish) North Atlantic muscle 14 1 .161 -0.011 Haddock N. Atlantic muscle 9 1 .105 -0.011 Crab King Alaska meat 9 3 .038 -0.011 Salmon Pink Alaska muscle 10 6 .023 -0.011

### APPENDIX D HUMAN FISH CONSUMPTION AND MERCURY INGESTION DISTRIBUTIONSD-i TABLE OF CONTENTS Page

D.1 Introduction .................................................................. 1 D.2 Methods and Assumptions ...................................................... 1 D.3 Population Exposure Equations .................................................. 1 D.4 Input Distributions ............................................................ 2 D.4.1 Mercury Ingestion per Fish Meal (Hg ) ................................... 2 MEAL D.4.2 Fish Consumption per Fish Meal (Fish ) .................................. 3 MEAL D.4.3 Number of Fish Meals per Month (Nmeals) ................................. 4 D.5 Simulation Output ............................................................ 6 D.6 Sensitivity Analysis ........................................................... 8 D.6.1 Adequacy of Input Distribution Fit ......................................... 8 D.6.2 Impact of Assumptions on Simulation Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 D.6.2 Other Sources of Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 D.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
D.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16D-ii LIST OF TABLES Page D-1 Hg Distributions for Selected Populations ....................................... 3 MEAL D-2 Fish Distributions for Selected Populations ...................................... 4 MEAL D-3 Nmeals Distributions for Selected Populations ...................................... 5 D-4 Hg Distributions for Selected Populations: Adults ............................... 6 DAILY D-5 Hg Distributions for Selected Populations: Children .............................. 7 DAILY D-6 FC Distributions for Selected Populations: Adults ............................... 7 DAILY D-7 FC Distributions for Selected Populations: Children .............................. 8 DAILY D-8 Comparison of Hg Output for Alternate Fits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 DAILYD-iii LISTOF FIGURES Page D-1 Quantile-Quantile Plots for Hg Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 MEAL D-2 Quantile-Quantile Plots for Fish Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 MEAL D-3 Quantile-Quantile Plots for Nmeals Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 D-4 Quantile-Quantile Plots for Mixtures-Distribution Fits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Hg DAILY  Hg MEAL x Nmeals 30

D-1 D.1 Introduction This Appendix presents an analysis of the third National Health and Nutrition Examination Survey (NHANES III) data on frequency of fish and shellfish consumption over an one-month interval, 24-hour recall data for consumption of fish and shellfish, body weight (in kilograms) and mean mercury concentrations in fish and shellfish. These data were utilized to estimate national exposure distributions for ingestion of mercury from fish and shellfish for a time period defined as one month or 30 days. Mathematical distributions were fit to data addressing the number and size of fish meals and associated mercury ingestion for several ethnic and racial groups within the general U.S. population. Analyses for higher-frequency fish consumers, women of child-bearing age and children were also performed. D.2 Methods and Assumptions All variables in this analysis were assumed to be lognormally distributed and independent. Parameters of the lognormal distributions are expressed as the geometric mean (GM) and the geometric standard deviation (GSD). The geometric mean (and median) is defined as e , where μ is the mean of the μ logarithms of the observations. The geometric standard deviation is defined as e , where  is the  standard deviation of the logarithms of the observations. The data available for estimation of distribution parameters were in the form of cumulative distribution percentiles and moments (arithmetic mean and standard deviation). The primary approach to fitting lognormal distributions to the data was by the method of moments, in which the sample mean and sample standard deviation, themselves, are used as estimates of the parameters. For the lognormal, the parameters are determined in log space (mean and standard deviation of the logs of the observations). In this analysis, the GM and GSD were estimated from the arithmetic mean and standard deviation using analytic formulas relating the arithmetic and geometric moments (Evans et al., 1993). In some cases the arithmetic moments did not provide reasonable estimates of the geometric moments. In these cases parameter estimation focused on the range between the 50th (median) and 95th percentiles. μ was assumed to be the log of the median.  was estimated as the average of the difference of the logs of the 75th, 90th and 95th percentiles and μ, divided by the corresponding z-score from the standard unit normal distribution. Distributions derived by the percentile method should be considered to be less reliable than by the method of moments. The fit of the distributions to the data in this range was assessed by graphical analysis and percentile matching. D.3 Population Exposure Equations Daily mercury ingestion from fish consumption is given as Equation 1.

(1)
where Hg is daily ingestion of total mercury (μg/kg bw -day), DAILY Hg is the ingestion of total mercury per fish meal (μg/kg bw -meal), MEAL Nmeals is the number of fish meals per month (month ) and -1 30 is the number of days per month (days/month).FC DAILY  Fish MEAL x Nmeals 30

D-2 Daily fish consumption is given as Equation 2.

(2)
where FC is daily per capita fish consumption (g/day), DAILY Fish is fish consumption per fish meal (g/meal), MEAL Nmeals is the number of fish meals per month (month ) and -1 30 is the number of days per month (days/month). Equations 1 and 2 are solved using analytic methods for multiplying lognormal distributions (Aitchison and Brown, 1966; see also Appendix D to Volume 3 of this Report). D.4 Input Distributions This section presents the development of each of the input distributions for Equations 1 and 2. The basis for each distribution is given. Moments and percentiles for all empirical distributions were based on population weighted frequencies. That is, the sample observation frequencies were projected to the national population weighted by sex and age frequencies in the national population (NHANES III). D.4.1 Mercury Ingestion per Fish Meal (Hg ) MEAL Hg distributions were based on 24-hour fish (and shellfish) consumption recall data for MEAL consumers, only (per user), reported in NHANES III and average mercury concentrations reported for each fish species consumed. Consumption-mass-weighted mercury concentrations for individual species were summed across all species consumed by each survey respondent (consumers only) and divided by the respondent's body weight. Simplifying assumption were made that all the mercury was methylmercury (MeHg) and was ingested in a single meal. Empirical Hg distributions were MEAL constructed for six subpopulations: the Caucasian (nonHispanic) general population ("White"), the African-American (nonHispanic) general population ("Black"), the Mexican-American general population ("Hispanic"), a more frequent fish-consuming population that included Asians, Pacific Islanders, Native Americans and Caribbean Islanders ("Other"), 15 to 44 year-old females across all groups ("Women") and 3 to 6 year-old children across all groups ("Children"). Women of this age group were selected as the MeHg Reference Dose (RfD) based primarily on effects in offspring of women exposed to MeHg during pregnancy. This particular age group of children was selected because of its much higher mercury exposure rate than other child age groups. The Hg empirical distributions and MEAL lognormal approximations for each of these subpopulations are given in Table D-1.D-3 Table D-1 Hg Distributions for Selected Populations MEAL (μg/kgbw -meal)
Population Distribution: White Black Hispanic Other Women Children Empirical n 1392 1278 914 265 882 415 mean 0.19 0.23 0.22 0.23 0.17 0.40 std. dev. 43.05 19.69 11.42 50.00 0.28 0.56 50th percentile 0.12* 0.15 0.15 0.12 0.10 0.28 75th percentile 0.26 0.32 0.31 0.32 0.22 0.49 90th percentile 0.50 0.57 0.58 0.61 0.39 0.77 95th percentile 0.73 0.77 0.77 0.97 0.53 1.08 Lo g normal method percentiles percentiles percentiles percentiles moments moments GM 0.12 0.15 0.15 0.12 0.09 0.23 a GSD 3.01 2.82 2.91 3.77 3.14 2.83 b 75th percentile 0.25 0.31 0.30 0.30 0.19 0.47 90th percentile 0.50 0.57 0.58 0.66 0.38 0.88 95th percentile 0.74 0.83 0.85 1.07 0.58 1.29 mean 0.22 0.26 0.26 0.29 - - std. dev. 0.34 0.36 0.38 0.64 - - Geometric Mean (and 50th percentile) a Geometric Standard Deviation b *Rounded to 2 significant figures.
D.4.2 Fish Consumption per Fish Meal (Fish ) MEAL Fish distributions were based on 24-hour fish (and shellfish) consumption recall data for MEAL consumers, only (per user), reported in NHANES III. A simplifying assumption was made that all the fish was consumed in a single meal. Fish distributions were constructed for the same five MEAL subpopulations as for Hg . The Fish empirical distributions and lognormal approximations for MEAL MEAL each of these subpopulations are given in Table D-2.D-4 Table D-2 Fish Distributions for Selected Populations MEAL (g/meal)
Population Distribution: White Black Hispanic Other Women Children Empirical n 1394 1282 920 266 883 415 mean 109 128 108 106 103 57 std. dev. 16752 8004 4856 15277 116 55 50th percentile 65.5* 77.5 64.7 67.5 66.0 43.3 75th percentile 126 151 129 122 131 66.2 90th percentile 222 263 222 234 228 113 95th percentile 291 356 318 297 288 151 Lo g normal method percentiles percentiles percentiles percentiles moments moments Gm 65.5 77.5 64.7 67.5 68.6 40.7 a GSD 2.57 2.60 2.67 2.50 2.47 2.26 b 75th percentile 124 148 125 125 126 70.6 90th percentile 220 264 228 219 219 116 95th percentile 310 373 326 305 304 156 mean 102 122 105 103 - - std. dev. 123 150 134 119 - - Geometric Mean (and 50th percentile) a Geometric Standard Deviation b * Rounded to 3 si g nificant fi g ures.
D.4.3 Number of Fish Meals per Month (Nmeals) Nmeals distributions were based on monthly fish (and shellfish) consumption frequency data for all respondents ( per capita ) reported in NHANES III. The frequency of fish meals consumed per month was treated as a continuous variable for estimation of long-term fish consumption rates. Values at the reference percentiles (50th, 75th, 90th and 95th) were estimated by linear interpolation from cumulative discrete frequency distributions. As these data are from the general population (not just fish consumers), a significant fraction of respondents reported eating no fish in the last month (11-14%). NmealsD-5 distributions were constructed for the same subpopulations as for Hg and Fish except for MEAL MEAL "Women" and "Children," for which data were not available. An Nmeals distribution for the general population across all other groups ("All") was used as a surrogate for "Women" and "Children." Nmeals empirical distributions and lognormal approximations for each of these subpopulations are given in Table D-3. Table D-3 Nmeals Distributions for Selected Populations (month ) -1
Population Distribution White Black Hispanic Other All Empirical n 7410 5594 5394 785 19,200 mean 5.6 6.5 4.7 8.3 5.8 std. dev. 6.2 8.2 5.8 2.6 6.9 50th percentile 3.4* 3.8 2.9 4.1 3.5 75th percentile 7.2 8.0 5.8 9.9 7.4 90th percentile 12 13 11 22 12 95th percentile 16 18 14 31 17 99th percentile 30 31 28 43 30 maximum 150 220 150 61 220 Lo g normal method moments moments moments moments moments GM 3.7 4.0 3.0 5.3 3.8 a GSD 2.5 2.7 2.6 2.6 2.5 b 75th percentile 6.8 7.8 5.7 10 7.1 90th percentile 12 14 10 18 12 95th percentile 16 20 14 25 18 99th percentile 30 39 28 19 33 Geometric Mean (and 50th percentile) a Geometric Standard Deviation b * Rounded to 2 si g nificant fi g ures.D-6 D.5 Simulation Output The results of the solution of Equation 1 (Hg ) are given for adults and children in Tables DAILY D-4 and D-5, respectively. The percentile at which the MeHg RfD falls in the Hg output is given DAILY for adults (Table D-4). Direct comparison to the RfD is most appropriate for women of child-bearing age, as the MeHg RfD is based, primarily, on effects in the offspring of exposures to their mothers during pregnancy (see Volume V of this report; also U. S. EPA, 1997). That is, although the effects were observed in children, the exposure (and it's associated metric) was to the mother. The RfD is designed to be protective of all sensitive subpopulations. In this case (MeHg), the developing fetus was judged to be the most sensitive population. An uncertainty factor was included in the RfD to account for the lack of data on post-natal development, among other factors. The results of the solution of Equation 2 (FC ) are given for adults and children in Tables DAILY D-6 and D-7, respectively. The percentile at which fish ingestion exceeds 100 g/day in the Fish DAILY output is also shown.
Table D-4 Hg Distributions for Selected Populations: Adults DAILY (μg/kgbw -day)
Population Percentile White Black Hispanic Other Women a b cd e 50th 0.015 0.020 0.015 0.021 0.011 75th 0.039 0.053 0.047 0.064 0.030 90th 0.092 0.13 0.11 0.17 0.074 95th 0.15 0.21 0.18 0.31 0.13 RfD Percentile 91.0 86.8 91.0 82.7 93.2

GM = 0.0149, GSD = 4.145 a GM = 0.0204, GSD = 4.153 b GM = 0.0145, GSD = 4.216 c GM = 0.0214, GSD = 5.123 d GM = 0.0111, GSD = 4.382 dD-7 Table D-5 Hg Distributions for Selected Populations: Children DAILY (μg/kgbw -day)
Ethnicity Percentile All White Black Hispanic Other Groups a b c de

50th 0.029 0.029 0.031 0.023 0.041 75th 0.075 0.072 0.082 0.060 0.11 90th 0.18 0.17 0.19 0.14 0.25 95th 0.29 0.28 0.33 0.24 0.42 GM = 0.0292, GSD = 4.050 a GM = 0.0286, GSD = 3.961 b GM = 0.0311, GSD = 4.173 c GM = 0.0230, GSD = 4.130 d GM = 0.0411, GSD = 4.102 e Nmeals distributions from g eneral population for each g roup (not child-specific) H g distribution from 3-6 y ear-old children across ethnicities (not g roup-specific) MEAL

Table D-6 FC Distributions for Selected Populations: Adults DAILY (g/day)
Population Percentile White Black Hispanic Other Women a b cc d 50th 8.1 10 6.4 12 8.6 75th 19 26 16 29 21 90th 43 60 37 65 46 95th 69 99 62 105 73 100 g percentile 97.3 95.1 97.7 94.6 97.0 GM = 8.08, GSD = 3.685 a GM = 10.4, GSD = 3.925 b GM = 6.43, GSD = 3.957 c GM = 11.9, GSD = 3.751 c GM = 8.63, GSD = 3.668 dD-8 Table D-7 FC Distributions for Selected Populations: Children DAILY (g/day)
Ethnicity Percentile All White Black Hispanic Other Groups a b c de

50th 5.1 5.0 5.5 4.0 7.2 75th 12 11 13 9.5 17 90th 25 24 28 20 36 95th 39 37 44 32 57 100 g percentile >99 >99 99 >99 98 GM = 5.12, GSD = 3.456 a GM = 5.01, GSD = 3.370 b GM = 5.46, GSD = 3.573 c GM = 4.04, GSD = 3.532 d GM = 7.18, GSD = 3.506 e Nmeals distributions from g eneral population for each g roup (not child-specific) Fish distribution from 3-6 y ear-old children across ethnicities (not g roup-specific) MEAL
D.6 Sensitivity Analysis D.6.1 Adequacy of Input Distribution Fit A general trend for fitting input distributions by the percentile method was for higher estimates of  at lower percentiles but with fairly good agreement in the targeted range (75th to 95th percentiles); coefficients of variation for  estimates for a given data set were in the range of 0.03 to 0.1. Distributions fit by this method were not particularly good approximations of the data outside these percentile ranges. The impact of overestimating the lower end of the input distributions on the output of Equations 1 and 2 is discussed in the next section. Quantile-quantile plots (QQ plots) are shown for each of the distributions in Figures D-1, D-2 and D-3, which show the Hg , Fish , and Nmeals distributions, respectively. These figures plot MEAL MEAL the z-scores of the logs of the observations against the z-scores for the corresponding fitted lognormal distribution (normal in log space). The z-scores are the number of standard deviations above or below the median. A z-score of 2 corresponds to about the 95 percentile (z= -2  5 percentile). The 99 and th th th 99.9 percentiles correspond to z-scores of 2.33 and 3.1, respectively. As these plots compare the logs th of the distributions, zeroes in the raw data are not included. Zeroes were included, however, in the fitting process for those variables fit by the method of moments. For those distributions fit by the percentile method, the data points (50 , 75 , 90 and 95 percentiles) used in the fitting process are indicated by th th th th filled symbols on the Figures.D-9 The solid straight lines on the QQ plots represent perfect fits. That is, a perfect fit would result in all the points lining up along the line. The direction of deviations from the line can be used to assess the direction of the prediction error. If the points curve below the line at either end, the fitted distribution will under predict actual values at that end. Conversely, if the points curve above the line, the fitted distribution will over predict. The tendency to over predict the lower tail can be seen for all of the variables. This tendency is quite marked for a number of variables, particularly for the ones fitted by the percentile method. The upper tails of the empirical distributions are all fairly well represented by the fitted distributions, even for extreme values. Nmeals/Other is an exception, but the poor fit is well beyond the 99th percentile; the data points above the 99th percentile are single observations. The effect of over prediction in the lower tail on the analytic solutions of Equations 1 and 2 will be to greatly exaggerate the lower percentiles. There will also be a tendency to over predict the upper percentiles, but probably not by a large amount. Deviations from the fit line at z-scores of less than -3 should have no effect on the output. In general, the magnitude of the over prediction is difficult to assess from the QQ plots, but will be considerably less than that resulting from over prediction in the upper tails of the input distributions. The best predictions should be for both outputs for "Women" and "Children," given the better combined fit for Hg , Fish , and Nmeals for these two groups. MEAL MEALempirical z-scorenormal z-score

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 WHITE

empirical z-scorenormal z-score

-7.0 -5.5 -4.0 -2.5 -1.0 0.0 1.0 2.0 3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 BLACK

empirical z-scorenormal z-score

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 HISPANIC

empirical z-scorenormal z-score

-3.0 -2.0 -1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 OTHER

empirical z-scorenormal z-score

-2.0 -1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 WOMEN

empirical z-scorenormal z-score

-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 CHILDREN

D-10 Figure D-1 Quantile-Quantile Plots for Hg Distributions MEALempirical z-scorenormal z-score

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 WHITE

empirical z-scorenormal z-score

-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 BLACK

empirical z-scorenormal z-score

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 HISPANIC

empirical z-scorenormal z-score

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 OTHER

empirical z-scorenormal z-score

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 WOMEN

empirical z-scorenormal z-score

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 CHILDREN

D-11 Figure D-2 Quantile-Quantile Plots for Fish Distributions MEALempirical z-scorenormal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 WHITE

empirical z-scorenormal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 BLACK

empirical z-scorenormal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 HISPANIC

empirical z-scorenormal z-score

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 OTHER

empirical z-scorenormal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 ALL

D-12 Figure D-3 Quantile-Quantile Plots for Nmeals DistributionsD-13 D.6.2 Impact of Assumptions on Simulation Output The assumption that the 24-hour recall data represent one fish meal is obviously false for all respondents who reported more than 30 fish meals per month. The assumption will result in overestimation of both Hg and FC at higher percentiles. The 30 fish meal per month mark falls DAILY DAILY at the 99th percentile or higher for all groups except “Other,” for which the 95th percentile is 31.4 fish meals per month. The bias in Hg and FC for groups other than “Other” should not be DAILY DAILY significant at the 95th percentile and lower, but this assumption was not tested. The results for “Other” above the 90th percentile should be considered to be conservative. Correlation of input variables was not considered in this analysis. Data for “Women” suggest that there is a slight positive correlation between Nmeals and the other two variables, with a more noticeable difference in Fish for those respondents reporting zero or one fish meal in the last month. MEAL That is, those individuals who had a low frequency of fish consumption also tended to eat less fish per meal (70 g/meal vs 108 g/meal for respondents reporting two or more fish meals per month). The result of this correlation would be an over prediction of FC . The magnitude of the over prediction could DAILY not be estimated without the specific body weight of the individuals, but was judged to be small. The correlation of Nmeals and Hg was very weak and was not expected to have any impact on the output. MEAL The effect of correlations on simulation output is generally smaller than that arising from the form of the assigned distribution (Bukowski et al., 1995). The impact of the simplifying lognormal assumptions on the output of Equations 1 and 2 was investigated by defining the input distributions as mixtures (mixtures approach) and then solving the equations by Monte Carlo analysis. That is, separate distributions were fit to discrete segments of the empirical data rather than assuming a single mathematical form for the entire distribution. For several data sets where the number of zeroes was high, the proportion of zeroes was modeled as a delta function (spike), with a lognormal distribution fit to the nonzero data (delta method). For one data set with no zeroes, a log-triangular distribution was fit to the proportion of the data set that did not appear to be lognormal (the lower 25%) and a lognormal was fit to the remainder (two-distribution method). In each case, a composite mixtures distribution was constructed by Monte Carlo simulation. Figure D-4 shows the QQ-plots for the mixtures distribution fits to selected variables. Two of the worst-fitting Hg data sets (Hispanic and Other) were selected for this part of the analysis. The MEAL corresponding Nmeals data sets were also analyzed so that output distributions (Equation 1) could be generated. Hg /Hispanic, was fit by the two-distribution method and the rest by the delta method. MEAL Distribution quantiles, in natural log units, are shown in these plots instead of z-scores, as the fitted distributions are not entirely lognormal. Otherwise, the visual fit of the distributions can be compared directly with the corresponding QQ-plots in Figures D-1 and D-3. The mixtures approach provided a better overall fit for Hg , particularly at the lower end, the lower three points for Hg /Hispanic MEAL MEAL being an exception. These data points, however, represent less than 1% of the distribution and would have no effect on the output. Upper percentile estimates for the mixtures approach are similar to those estimated by the simple lognormal assumptions. The Nmeals distributions estimated by the mixtures approach showed only slightly better fit (or none at all) in the lower percentiles at the expense of a slightly poorer fit at the upper extreme. Fits to Nmeals/White and Nmeals/All were similar to Nmeals/Hispanic. Overall, the mixtures approach did not improve the fit to Nmeals.empirical quantiles (natural log)mixture distribution quantiles (natural log)

-6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 HgMeal (Hispanic)

empirical quantiles (natural log)mixture distribution quantiles (natural log)

-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 HgMeal (Other)

empirical quantiles (natural log)mixture distribution quantiles (natural log)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Nmeals (Hispanic)

empirical quantiles (natural log)mixture distribution quantiles (natural log)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Nmeals (Other)

D-14 Figure D-4 Quantile-Quantile Plots for Mixtures-Distribution Fits

Results of the Monte Carlo simulations of Equation 1 using the mixtures distributions are given in Table D-8. The output was simulated with mixtures distributions for both inputs (Hg and Nmeals) MEAL and for Hg , only, as the mixtures approach did not provide a better fit for Nmeals. The results in MEAL Table D-8 show little effect from the simple lognormal assumption for the inputs in this limited comparison. Further analysis using the full data sets and other parametric fitting or nonparametric methods would be useful for resolving the remaining distribution fit issues.D-15 Table D-8 Comparison of Hg Output for Alternate Fits DAILY (μg/kgbw -day)
Group Hispanic Other method of simple Hg both simple Hg both distribution lognormal mixture mixtures lognormal mixture mixtures fit a MEAL bc a MEAL b c

Percentiles 50th 0.015* 0.014 0.019 0.021 0.021 0.020 75th 0.038 0.038 0.047 0.064 0.066 0.071 90th 0.092 0.086 0.11 0.17 0.18 0.20 95th 0.15 0.14 0.18 0.31 0.33 0.36 99th 0.41 0.40 0.45 0.96 0.98 1.1 from Table D-4 a mixture for H g , onl y ; lo g normal Nmeals from Table D-3 b MEAL

mixtures for both inputs c * Rounded to 2 si g nificant fi g ures.
D.6.2 Other Sources of Uncertainty Sources of uncertainty or bias that have not been considered in this analysis include fish mercury concentrations, mercury speciation in fish and shellfish, and population weights. The mercury concentrations in the fish and shellfish were average concentrations for the identified fish species. Data were available on the distribution of mercury in each species but were not considered for this analysis. These data would provide bounds on the percentile values estimated in this analysis but would not change the median estimates for each percentile. The mercury in all “fish” species was assumed to be methylmercury, which is a fairly sound assumption for finfish (Bloom, 1992), but somewhat less so for shellfish and other species. The impact of this assumption on the simulation output was not investigated but was assumed to be small. The uncertainty in the population weighting protocol in NHANES III was not investigated either. D.7 Conclusions The derived distributions are thought to be more characteristic of month-long patterns of fish and shellfish consumption than are either of the two individual distributions that formed the input variables. The resulting derived distribution was done to maximize fit between the 75th and 95th percentiles.D-16 D.8 References Aitchison, J. and J. A. C. Brown (1966). The Lognormal Distribution . University Press, Cambridge. Barnes, D. G. and M. L. Dourson (1988). “Reference dose (RfD): Description and use in health risk assessment,” Regul. Toxicol. Pharmacol . 8:471-486. Bloom, N.S. (1992). On the chemical form of mercury in edible fish and marine invertebrate tissue. Can. J. Fish. Aquat. Sci . 49:1010-1017. Bukowski, J., L. Korn and D. Wartenberg (1995).“Correlated Inputs in Quantitative Risk Assessment: The Effects of Distributional Shape,” Risk Analysis 15:215-219. Evans, M., N. Hastings and B. Peacock (1993). Statistical Distributions. 2nd Edition. John Wiley and Sons, Inc., New York, NY, pp. 42-44. U. S. EPA (1988). Integrated Risk Information System (IRIS), Background Document 1, National Center for Environmental Assessment, Cincinnati Office, Cincinnati, OH. U. S. EPA (1997). Integrated Risk Information System (IRIS), On-Line Assessments, National Center for Environmental Assessment, Cincinnati Office, Cincinnati, OH.