# Economic Valuation of Human Health Benefits of Controlling Mercury Emissions from U.S. Coal-Fired Power Plants

February 2005ii 

### Members of Northeast States for Coordinated Air Use Management

Anne Gobin, Bureau Chief Connecticut Department of Environmenta l Protection, Bureau of Air Management James P. Brooks, Bureau Director Maine Department of Environmental Protection, Bureau of Air Quality Barbara Kwetz, Director Massachusetts Department of Environmenta l Protection, Bureau of Waste Prevention Robert Scott, Director New Hampshire Department of Environm ental Services, Air Resources Division William O’Sullivan, Director New Jersey Department of Environmental Pr otection, Office of Air Quality Management David Shaw, Director New York Depart m ent of Environmental Conservation, Division of Air Re s our c es Stephen Majkut, Chief Rhode Island Department of Environmen tal Management, Office of Air Resources Richard A. Valentinetti, Director Vermont Department of Environmental Cons ervation, Air Pollution Control Division

Arthur Marin, Executive Director Northeast States for Coordinated Air Use Managementiii 

# Economic Valuation of Human

Health Benefits of Controlling Mercury Emissions from U.S. Coal-Fired Power Plants

February 2005

Prepared by: Glenn Rice (ScD Candidate) and James K. Hammitt (Director) Harvard Center for Risk Analysis 718 Huntington Ave.

Boston, MA 02115

Project Director Praveen K. Amar, Ph.D., P.E. Director, Science and Policy, NESCAUM

NESCAUM Northeast States for Coordinated Air Use Management www.nescaum.orgivv TABLE OF CONTENTS Page

LIST OF T ABLES.......................................................................................................... viii LIST OF FI GURES.........................................................................................................xv EXECUTIVE SU MMARY...............................................................................................xvi ACKNOWLEDGME NTS.................................................................................................xx

1. INTRODUCTION AND BACKG ROUND............................................................... 1 1.1. ATMOSPHER IC MER CURY ..................................................................... 4 1.2. BIOACCUMULATION OF METHYLME RCURY ...................................... 10 1.3. HUMAN EXPOSURE TO METHYLME RCURY ....................................... 12 1.4. TOXICO KINETICS .................................................................................. 22 1.5. HUMAN HEAL TH EFFECT S ................................................................... 24 1.5.1. Neurological Decrem ents Associated with Intraurine Methylmercur y Exposur es............................................................. 26 1.5.2. Myocardial Effects As sociated with Adult Methylmercury Expos ures ..................................................................................... 37 1.5.3. Elevated Childhood Blood Pressure and Cardiac Rhythm Effects Associated with In Utero Methylmercury Exposures ......... 48 1.6. REGULATION AND CONTROL OF MERCURY EMISSIONS FROM POWE R PLANTS ......................................................................... 54 1.7. PURPOSE OF REPO RT ......................................................................... 55 1.8. OVERVIEW OF ANALYSI S..................................................................... 55

2. METHOD S.......................................................................................................... 57 2.1. CHANGES IN ME RCURY DEPOSI TION ................................................ 62 2.2. METHYLMERCURY EXPO SURES THROUGH FISH CONSUM PTION...................................................................................... 64 2.2.1. Methylmercury Expos ures Through Commercial Seafood Consum ption................................................................................. 65 2.2.2. Methylmercury Exposures Through Consumption of Non-Commercia l Seafood ............................................................. 73 2.2.3. Estimates of Population Size ........................................................ 83 2.3. NEUROT OXICITY ................................................................................... 85 2.3.1. Population Toxicity Threshold: Background .................................. 87 2.3.2. Population Toxicity Thres hold for Methylmercur y Exposures........ 89 2.3.3. Analysis of Ri sk Assuming Thre shold ........................................... 91 2.4. DOSE-RESPONS E FOR ACUTE MYOCARDIAL INFARCTION AND ALL CAUSE MORTALITY ............................................................... 91 2.4.1. Estimating the Dose-Response F unctions .................................... 93 2.4.2. Applying the Do se-Response Fu nctions ....................................... 94vi TABLE OF CONTENTS cont. Page

2.5. VALUING CHANGES IN CHILDREN’S INTE LLIGENCE......................... 99 2.5.1. Proportional Impact of a One-Point Change in IQ on Lifetime Earn ings...................................................................................... 100 2.5.2. Baseline Lifetime Earnings Va lue (E) .......................................... 103 2.5.3. The Absolute Impact of IQ Changes on Lifetime Earnings.......... 104 2.5.4. Remedial Education Cost s and Shifts in the Population IQ Distri bution .................................................................................. 105 2.6. HEALTH-RELATED QUALITY OF LIFE MEASURES FOR NEUROLOGICAL DECREMENT S ........................................................ 107 2.6.1. Utility Weight for Health -Related Quality of Life Associated with Neurodevelop mental Toxi city............................................... 108 2.6.2. Exposure-Response Function for Health-Related Quality of Life Weights ............................................................................ 110 2.7. VALUING CHANG ES ASSOCIATED WITH ADVERSE MYOCARDIAL EVENTS AND ALL CA USE MORTAL ITY.............................................. 110 2.7.1. Cost-of-Illness Estimates for Myocardial Infarctions and All Cause Mo rtality ........................................................................... 111 2.7.2. Value of a Statistical Life............................................................. 112 2.7.3. Health-Related Quality of Life Utility Weights for Myocardial Infarc tions ................................................................................... 112 2.8. SENSITIVITY ANALY SES..................................................................... 113

3. RESULT S......................................................................................................... 116 3.1. CHANGES IN ANNUAL MERCURY DEPOSITION RATES IN THE 8 REGION S ........................................................................................... 116 3.2. CHANGES IN FISH METHYLMERCURY CONCENTRATIONS IN THE 8 RE GIONS ................................................................................... 118 3.3. CHANGES IN METHYLME RCURY EXPOSURES AMONG FISH CONSUMERS IN THE 8 REGION S ...................................................... 132 3.3.1. Popu lation Da ta........................................................................... 132 3.3.2. Predicted Exposures in General U.S. Populatio n........................ 132 3.3.3. Predicted Exposures in Consumers of Non-commercial Marine Fish ................................................................................. 135 3.3.4. Predicted Exposures in Consumers of Non-commercial Freshwat er Fish .......................................................................... 138 3.4. ESTIMATES OF IN UTERO NEUROTOXICITY RISKS ASSOCIATED WITH MATERNAL CONSUMPTION OF METHYLMERCURY IN FISH 144 3.4.1. IQ Points Lost and Associated Costs due to Current Methylmercur y Exposur es........................................................... 144 3.4.2. Averted Costs Associated with IQ Point Gains per Birth Cohort Under Alternative Emissi ons Scenarios 1 and 2 ......................... 160 3.4.3. IQ Results C onsidering QA LYs................................................... 162vii TABLE OF CONTENTS cont. Page

3.5. COSTS ASSOCIATED WITH ACUTE MYOCARDIAL INFARCTIONS AND ALL CAUSE MORTALIT Y ............................................................. 162 3.5.1. Pike Consumer s.......................................................................... 162 3.5.2. Entire U. S. Popula tion................................................................. 168 3.6. RESULT S SUMMARY ........................................................................... 189 3.7. RESULTS OF LIMITED SENSITIVITY AN ALYSES .............................. 194

4. DISCU SSION ................................................................................................... 198

5. REFERE NCES................................................................................................. 212viii LIST OF TABLES

Table Page 1 Mercury Emissi ons Sour ces ................................................................................. 2 2 The Pounds Per Capita, Methylmercur y Concentration, Market Share, and Fractional Contribution of Fish in Co mmerce from Each U.S. Fishery or Import for Top 24 Types of Fish Consumed in U.S. ............................................ 17

3 Estimates of the Size of the Saltwater Angler Population Annually in the U.S. and the Number of Consumers of Recr eationally-Caught Sa ltwater Fish........... 18 4 Median, Mean, and Maximum Methylmerc ury Concentrations (μg/g) Reported Fish Species Harvested via Recreationa l Angling in the At lantic Ocean ............ 20 5 Median, Mean, and Maximum Methylmerc ury Concentrations (μg/g) Reported Fish Species Harvested via Recreational Angling in the Gulf of Mexico............. 21 6 Tests Employed by Kjellstrom et al. (1986, 1989) in the New Zealand St udies.................................................................................................. 29 7 Regression Coefficients and 95% Confid ence Intervals for Hair Mercury Concentrations (ppm) Calculated by Crump et al . (1998)................................... 30 8 Tests Employed in the Seychelles Is lands Child Develo pment Study ................ 32 9 Tests Employed in Studies of Faroese Ch ildren................................................. 36 10 Coefficients for Logarithmic Trans formation of Cord Blood Mercury Concentrations on Selected Neur opsychological Tests...................................... 37 11 Measures of Cohort Methylmercury Intake Rates Reported in Salonen et al. (1995) ........................................................................................................ 40 12 Results of Cox Proportional Haza rds Models Reported by Salonen et al. (1995) ........................................................................................................ 41 13 Relative Risk of Acute Coronary Events in a Middle-Aged Finish Male Cohort Based on Serum Fatty Acid Composition, Stratified by Hair Mercury Levels ..... 44 14 Co-Occurrence Of Minamata Disease Diagnoses and Diagnosis of Hypertension and the Occurrence of Hy pertension in the Control Group from the City of Ine ............................................................................................. 52ix LIST OF TABLES cont.

Table Page 15 Comparison of Hypertensive Dia gnoses Between Those with and Without Minamata Disease in Two Diffe rent Age Cat egories .......................................... 52 16 Blood Methylmercury Concentrations (μg/ L) in U.S. Women Aged 16 to 49 ...... 65 17 Comparison of Body Weight, Blo od Volume and Fish Intake Between U.S. Males and Females .................................................................................... 73

18 Fish Size Restrictions Imposed on Model Data .................................................. 77 19 Percentage of Fishing Days Ta rgeting Selected Species................................... 78 20 Commercial Fish Intake Rates Among Consumers of Recreationally Caught Fi sh ........................................................................................................ 83 21 Fishing Days by U.S. R egion and Estimated Number of Consumers of Fish Caught in Each U.S. Fr eshwater R egion............................................................ 85 22 Parameter Values Develope d by Salkever (1995) ............................................ 102 23 Description of Cognitive Decrement and Associated Utility Weight Based on Torrance et al. (19 96) .................................................................................. 109 24 Description of Cognitive Decrement and Associated Utility Weight Based on Feeny et al. (2002) ...................................................................................... 110 25 Predicted Percent Decreases in Mercury Deposition to the Coastal Atlantic Ocean Region, the Gulf of Mexico Region, and the All Other Waters Region Under CSI ............................................................................................ 117

26 Predicted Percent Decreases in Mercur y Deposition in the Five Freshwater Regions Relative to Current Emis sions ............................................................ 118 27 Weighted Mean Methylmercury Concent rations in Comme rcial Fish ............... 121 28 Predicted Weighted Mean Non-comme rcial Fish Methylmercury Concentrations (μg/g) ....................................................................................... 125

29 Northeastern Fish C onsumption Data .............................................................. 126

30 Mid Atlantic Fish Cons umption Summary Data ................................................ 127x LIST OF TABLES cont.

Table Page

31 Southeastern Fish C onsumption Data.............................................................. 128 32 Midwest Fish Consumpt ion Summary Data...................................................... 129 33 Western Fish Consumpt ion Summary Data...................................................... 130 34 Estimated Popula tion Size s.............................................................................. 131 35 Predicted Tissue Methylmercury Concentrations in Commercial Fish Consumer s ....................................................................................................... 134 36 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial Atla ntic Ocean Fish............................................................... 136 37 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial Gulf Fi sh ............................................................................... 136 38 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial No rtheast Fi sh ...................................................................... 139 39 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial Mid- Atlantic Fish ................................................................... 139 40 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial S outheast Fi sh...................................................................... 140 41 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial Mi dwest Fi sh......................................................................... 140 42 Predicted Methylmercury Intake Rate s (μg/kg-day) in Consumers of Non-Commercial West Fi sh.............................................................................. 141 43 Predicted Methylmercury Intakes Among High-End Freshwater Fish Consumers in the U.S. ..................................................................................... 143 44 Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of N on-Commercial Atlantic Ocean Fish .......... 145xi LIST OF TABLES cont.

Table Page 45 Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of Non-Commercial Gulf Fi sh........................... 146 46 Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of Non-Commercial No rtheast Fish.................. 147 47 Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of N on-Commercial Mid-At lantic Fish............... 148 48 Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of Non-Commercial So utheast Fi sh ................. 149 49 Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of Non-Commercial Mi dwest Fi sh .................... 150 50 Predicted Mean Hair Methylmercury Concentrations, Mean IQ point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers of Non-Commercial West Fish ......................... 151 51 Predicted Mean Hair Methylmercur y Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Illness) (2000$) in Cons umers of Commercial Fish and Non-Fish C onsumers ........................................................................................ 151 52 Summary of IQ Point Losses and A ssociated Costs per Annual Birth Cohort for the Entire U.S. Populat ion ............................................................... 153

53 Predicted Incremental IQ Gains per Annual U.S. Birth Cohort and Incremental Estimated Monetary Value of the IQ Gains ................................... 154 54 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Commerc ial Atlantic Oc ean Fish ............................ 156 55 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Co mmercial Gulf Fish ............................................. 156xii LIST OF TABLES cont.

Table Page 56 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Comme rcial Northeas t Fish.................................... 157 57 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Commerc ial Mid-Atlant ic Fish ................................ 157 58 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Co mmercial Southeas t Fish ................................... 158 59 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Co mmercial Midwes t Fish ...................................... 158 60 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-I llness) (2000$) in Consumers of Commercial Fish and Non-Co mmercial West Fish ........................................... 159 61 Predicted Mean IQ Point Loss, IQ Lo sses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Ill ness) (2000$) in Commercial Fish Consumer s ....................................................................................................... 159

62 Distribution of Predicted Annual Case s of Acute Myocardial Infarction (AMI) and Premature Deaths (ACM) in Male Northern Pike Consumers.......... 165 63 Using a Cost-of-Illness Approach and VSL, Annual Costs Associated with Cases of Non-Fatal AMI and Premat ure Death in Male Northern Pike Consumer s ....................................................................................................... 167

64 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish and N on-Commercial Atlantic Ocean Fish..... 169

65 Predicted Annual AMI and ACM Inci dence and Valuation (2000$) in Male Consumers of Commercial Fish and Non-Commercial Gulf Fish............. 170

66 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish a nd Non-Commercial No rtheast Fish ............ 171

67 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish and N on-Commercial Mid-Atlantic Fish ......... 172xiii LIST OF TABLES cont.

Table Page

68 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish and Non-Commercial Southeastern Fish....... 173

69 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish and Non-Commercial Midwestern Fish.......... 174

70 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish a nd Non-Commercial We stern Fish............... 175

71 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Co mmercial Fi sh ....................................................................... 176

72 Predicted Annual Decreased AMI and AC M Incidence and Annual Benefit (2000$) in Males............................................................................................... 177

73 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of Commercial Fish and N on-Commercial Atlantic Ocean Fish..... 179

74 Predicted Annual AMI and ACM Incidenc e and Valuation (2000$) in Female Consumers of Commercial Fish and Non-Commercial Gulf Fish ........ 180

75 Predicted Annual AMI and ACM Inci dence and Valuation (2000$) in Female Consumers of Commercial Fish and Non-Commercial Northeast Fish 181

76 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of Commercial Fish and N on-Commercial Mid-Atlantic Fish ......... 182

77 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of Commercial Fish a nd Non-Commercial S outheast Fish............ 183

78 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of Commercial Fish a nd Non-Commercial Mi dwest Fish............... 184

79 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of Commercial Fish a nd Non-Commercial West Fish.................... 185

80 Predicted Annual AMI and ACM Inci dence and Valuation (2000$) in Female Consumers of Commercial Fish .......................................................... 186

81 Predicted Annual Decreased AMI and AC M Incidence and Annual Benefit (2000$) in Fe males ........................................................................................... 187xiv LIST OF TABLES cont.

Table Page

82 Predicted Annual Decreased AMI and AC M Incidence and Annual Benefit (2000$) in Males and Female s ......................................................................... 187

83 Predicted Annual QALY Increase Result ing from Decreased AMI and ACM Incidence in Male s and Female s ...................................................................... 188 84 Summary of Cost-of-Illness and Value-of -Statistical Life Approaches for Neurotoxicity and Cardio vascular Toxi city........................................................ 190 85 Summary of Estimates of QALY Gains due to Reductions in Neurotoxicity and Cardio vascular Toxi city........................................................ 191 86 Predicted IQ Point Loss per Annual Bi rth Cohort in the Northeast Region and the Associated Cost-of-Illness Esti mate (2000$), if Deposition Rates are Doubled or Halved ...................................................................................... 195 87 Comparison of Predicted Incrementa l IQ Gains per Annual U.S. Birth Cohort and Incremental Estimated M onetary Value of the IQ Gains (Cost-of-Illness) (2000$) for 3 Neurotoxicit y Models ......................................... 196xv LIST OF FIGURES Page

1 Global Mercur y Cycle ........................................................................................... 4 2 Average Methylmercury Concentrations for "Top 24" Types of Fish Consumed in the U.S. Co mmercial Seafood Market .......................................... 16 3 Conceptual Model of Hum an Mercury Ex posures .............................................. 59 4 Regions Consider ed in M odel............................................................................. 60 5 DHA and Methylmercury Levels in U.S. Freshwat er Fish................................... 96 6 EPA and Methylmercury Levels in U.S. Freshwat er Fish ................................... 97 7 Total Polyunsaturated Fatty Acid and Methylmercury Levels in U.S. Freshwater Fish.................................................................................................. 98 8 Model for Relationship Betw een IQ and Wages and Labor Force Participat ion...................................................................................................... 102 9 For "Top 24" Types of Fish in U.S. Commercial Seafood Market, the Percentage of Methymercury C ontributed by Fi sh Type ................................... 120

10 Percent Contribution of the Atlantic Ocean and Gulf of Mexico Harvests to U.S. Commercial Mark et by Fish Type ......................................................... 122

11 Fractional Contribution of Consumers of Non-Commercial Fish in Each Region and Commercial Fish to To tal IQ Point Loss, Assuming No Neurotoxicity Threshol d.................................................................................... 152 12 Spectrum of Certainty of Causal Asso ciation of Health Effect with Mercury Exposure with Estimated Benefit Over lay in Millions ($M) and Billions ($B) of Dolla rs (2000$) ..................................................................................... 192xvi EXECUTIVE SUMMARY

This report describes the results of a co mprehensive study to estimate the health benefits of reducing mercury em issions from coal-fired power plants in the United States. Reductions in mercury emissions from coal-fired power plants are anticipated to decrease methylmercury concentrations in fi sh. Fish consumption is the primary pathway of human exposure to methylmercury, which is a human neurotoxicant and possibly a cardiovascular to xicant. Some babies born to U.S. residents, a population sensitive to the neurotoxic effects of me thylmercury, are currently exposed to intrauterine methylmercury concentrations above the EPA's Reference Dose (RfD). (The RfD is considered by some to be an acceptable level of exposure.) The modeling analysis is based on the assumption that equilibria currently exist between deposited mercury and fish methylme rcury concentrations and between fish methylmercury concentrations and methylme rcury exposures to individuals who consume these fish. Changes in the quantity of mercury depos ited are assumed to lead to linear and proportional changes in fish me thylmercury concentrations, assuming no other factors change. In the model, the waters from whic h fish are caught for human consumption are divided into eight regi ons. Based on broad regional patterns of mercury deposition rates, the cont inental U.S. is divided into five freshwater regions, the Northeast, Mid-Atlantic, Southeast, Midwest, and West. There are three saltwater regions, the Atlantic Coastal, Gulf of Mexi co, and All Other Waters . Each region is modeled as an isolated compartment, connected only with the atmospheric compartment above. Thus, in each region, the predicted change in mercury deposition rate results in a proportional change in fish methylmercury levels. Changes in mercury deposition rates asso ciated with reductions in power plant mercury emissions are based on regional d eposition modeling results from the EPA's analysis of the Clear Skies Init iative. In its analysis, t he EPA simulated current mercury deposition rates and the changes in these rate s that would result if power plants reduced their mercury emissions fr om the current rate of 49 tons per year to either 26 or 15 tons per year. We used thes e predictions to estimate c hanges in deposition rates for the freshwater regions, the Atlantic Coastal Region, and the Gulf of Mexico. Estimated decreases range from approximat ely 1% to 10%. The change in deposition rates to the All Other Waters region is assumed to be proportional to the change in total global emissions that would result from U.S. power plant emissi ons reductions, which is less than 1%.The model accounts for human exposure through commercially and noncommercially harvested fish. Commercial and non-commercial fish consumption rates are based on data reported by the U.S. F ood and Drug Administration (FDA) and the EPA. Methylmercury concentrations in commercially harvested fish are based on published reports of scientists from the FDA. Using per capita consumption rate data, we develop a weighted mean methylmercury concentration for commercial fish. This concentration changes in response to predict ed deposition rate changes in the Atlanticxvii Coastal Region, the Gulf of Mexico, and the All Other Waters Region. Noncommercially harvested fish are caught in each of the five freshwater regions plus the Atlantic Coastal and the Gulf of Mexico Regions. Current methylmercury concentrations are estimated from EPA databases. In each fr eshwater region, the mix of species that is consumed is based on regional data on th e frequencies at which individual species are targeted. For the Atlant ic Coastal and the Gulf of Mexico Regions, the mix is assumed to be consistent with annual harvest weights for non-commercial marine fish published by the National Marine Fisheries Serv ice (NMFS). The regional populations of non-commercial fish consuming populati ons are estimated using data from NMFS and the U.S. Fish and Wildlife Service. Changes in non-commercial fish concentrations under different mercury emissions control scenarios are proportional to changes in deposition in each region. The model accounts for potential changes in two health effects: cognitive abilities and cardiovascular events. We assume that increases in a child's intelligence quotient (IQ) that result from decr eases in intrauterine methylmercury exposures capture some of the neurodevelopmental delays reported in positive epidemio logic studies. We use a recent estimate of the dose-response function that integrates the results of the three primary methylmercury epidemiologic studi es (from the Faroe Islands, Seychelle Islands, and New Zealand). Using a cost-of-illness approach, we estimate the value of a lost IQ point to be approximately $16,500 (y ear 2000 dollars). We note that the body of scientific evidence that has evaluat ed the relationship between intrauterine methylmercury exposures and childhood neurodevelopmental delays has been thoroughly reviewed by the National Academy of Sciences. Credible epidemiological studies ha ve reported an association between methylmercury exposures in males and in creased risks of myocardial infarction and premature mortality. T he exposed population in a group of these epidemiological studies consumed non-fatty freshwater fish. We use regression coefficients reported in these studies to estimate dose-response re lationships for premature mortality and nonfatal myocardial infarction. We apply thes e dose-response estimates alternatively to males who eat non-fatty freshwater fish and to all fish consumers. Using a cost-ofillness approach, we estimate the value of a myocardial infarction to be approximately $50,000 (2000$). Using a willingness-to-pay approach, we estimate the value of a premature fatality to be approximately $6,000,000 (2000$). While the individual epidemiological studies have been published in the peer-reviewed literature, the body of scientific evidence concerning the relations hip between adult methylmercury exposures and increases in cases of premature morta lity and non-fatal myocardial infarction has not been thoroughly reviewed. Fu rthermore, this relationsh ip is potentially confounded with the cardio-protective effect of fish consumption. Based on the available data, independent scientific bodies eval uating the weight of evidence for fish cardio-protective effects have recommended that adults increase t heir fish consumption. Whether there is an increased cardiovascular risk associated wit h methylmercury exposures is not clear at this time. Thus, we recommend that the predicted benefit s associated with premature mortality and non- fatal myocardial infarction be viewed with caution.xviii Figure ES-1 describes a range of possible benefits of U.S. power plant mercury emissions controls. The benefits associated with Scenarios 1 and 2 are predicted to result when power plants fa ce annual mercury emissions caps of 26 tons and 15 tons, respectively. While the benefits estimates incr ease from left to right in this figure, our confidence that the U.S. popul ation experiences these effe cts decreases. For Scenario 1, the predicted annual benefit a ssociated with IQ increases in the annual birth cohort ranges from $75 million (assuming a neurotoxic ity threshold equal to RfD) to $194 million (assuming no threshold). The corresponding annual benefit predictions for Scenario 2 are $119 million to $288 million. The monetized benefits associated with avoided cardiovascular events and premature mortality are predicted to be much larger than the neurotoxicity benefits; however, there are additional uncertainties in the external generalization of the results of the epidemiologic studies upon which these estimates are based to the U.S. population. If these cardiovascular effects are only experienced by male populations that c onsume non-fatty freshwater fish, then the monetized annual benefits are $48 million an d $86 million in Scenarios 1 and 2, respectively. If these cardiovascular effects are experienced by the whole U.S. population, then the monetized a nnual benefits are predicted to be $3.3 billion and $4.9 billion in Scenario 1 and Scenario 2, respectively. This report highlights numerous mercury research needs. The following four are likely among the most import ant. Long-term monitoring studi es that measure mercury concentrations in environmental media and biot a over time are needed to determine the extent to which reductions in mercury em issions decrease mercury concentrations in the environment and reduce human exposures. Studies that evaluate the atmospheric fate of mercury emitted from coal-fired power plants need to be implemented on local, regional, and global scales. Additional studies need to be undertaken to evaluate shortterm and long-term fish consumpt ion rates in the U.S. populat ion; these studies need to identify the types of fish c onsumed and their sources. Additional studies are needed to evaluate the relationship between methylme rcury exposures and the development of cardiovascular effects; these should incl ude studies of human physiological responses to fish fatty acids (the com ponent of fish thought to be cardio-protective) and to methylmercury.xix Figure ES-1 Spectrum of Certainty of Causal Associat ion of Health Effect with Mercury Exposure with Estimated Benefit Overlay in Millions ($M) and Billions ($B) of Dollars (2000$) 

# Spectrum of Health Effect Certainty

Persistent IQ deficits from fetal exposures above MeHg RfD Persistent IQ deficits in all children from fetal MeHg exposures Cardiovascular effects and premature mortality in male consumers of non - fatty freshwater fish with high MeHg levels Cardiovascular effects and premature mortality in male fish consumers Cardiovascular effects and premature mortality in all fish consumers



### Decreasing Certainty Increasing Benefit Scenario 1 $75M $194M $48M $1.5B $ 3.3B Scenario 2 $119M $288M $86M $2.3B $ 4.9 Bxx ACKNOWLEDGMENTS

We thank Steve Koplin, National Oceanic an d Atmospheric Administration, for the information he provided regarding the sources of commercial fish. We thank Thomas Braverman, U.S. Enviro nmental Protection Agency, Michael Troyer, U.S. Environmental Protec tion Agency, and Tara Maddock, U.S. Environmental Protection Agency, for their help wit h the REMSAD modeling results. We gratefully acknowledge the following i ndividuals for their helpful discussions regarding cognitive and cardiovascular health effects: Steve Daniels, Cincinnati Childr en's Hospital Medical Center Mark H. Deis, Pediatric Associates Kim N. Dietrich, University of Cincinnati College of Medicine. We are grateful to the fo llowing individuals for their discussions regarding the environmental fate of mercury an d U.S. methylmercury exposures: Robert Ambrose, U.S. Envir onmental Protection Agency Rona Birnbaum, U.S. Envir onmental Protection Agency Ellen Brown, U.S. Envir onmental Protection Agency O. Russell Bullock, National Oceanic and Atmospheric Association Paul Cocca, U.S. Environmental Protection Agency John S. Evans, Harvard University George M. Gray, Harvard University. Matt Heberling, U.S. Environmental Protection Agency Matt Irvine, NESCAUM Amy Kinner, U.S. Envir onmental Protection Agency Carl Lamborg, University of Connecticut Debora Martin, U.S. Environment al Protection Agency (Retired) Mary Rothermich, U.S. Envi ronmental Protection Agency Tamara Saltman, U.S. Envi ronmental Protection Agency Jeff Swartout, U.S. Envir onmental Protection Agency Finally, we thank members of our exter nal peer-reviewer panel. Their thoughtful and extensive comments and suggestions were extremely valuable and greatly improved this report. Michael Aucott, New Jersey Depart ment of Environmental Protection Myrick Freeman III, Bowdoin College David P. Krabbenhoft, U.S. Geological Survey Jacqueline Moya, U.S. Envir onmental Protection Agency Rita Schoeny, U.S. Environm ental Protection Agency Joel Schwartz, Harvard University C. Mark Smith, Massachusetts Depar tment of Environmental Protection Alan H. Stern, New Jersey Departm ent of Environmental Protection1 1. INTRODUCTION AND BACKGROUND

In recent years, the U.S. Environment al Protection Agency (EPA) issued final regulations mandating the reduction of me rcury emissions from municipal waste combustors (1995), medical waste in cinerators (1997), and hazardous waste incinerators (1999). The impetus for thes e federal regulations was the plausible connection between anthropogenic mercury emissions and potentially harmful methylmercury levels in consumable fish. Despite these recent regulatory actions intended to reduce methylmercury levels in fish, concerns about the safety of fish consumers persist among U.S. environmental and public health officials. This concern

continues because the fetuses of some pregnant women in the U.S., a population sensitive to the effects of methylmercury, are exposed to this toxicant above the U.S. EPA’s reference dose (RfD) 1 (Schober et al., 2003; Mahaffe y et al., 2004; U.S. NCHS, 2003; U.S. EPA, 2001a; NRC, 2000; CDC, 2004). New federal and state legislation, regulation and policies desi gned to reduce mercury emi ssions further are being pursued. Some of these policies target coal -fired power plants because they emit over 35% of total U.S. anthropogenic mercury emissions (Table 1).

1 The EPA defines the RfD to be "an estimate (with unc ertainty perhaps spanning an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effect s during a lifetime" (U.S. EPA, 1999).2 Table 1. Mercury Emissions Sources Sources to Atmosphere Annual Emission Rate (tons yr -1 ) Reference

Natural Emissions Land 1000 1100 Mason et al., 2002; Lamborg et al., 2002 Oceanic Evasion 2850 a 900 Mason et al., 2002; Lamborg et al., 2002 Anthropogenic Northern Hemisphere a 2450 Lamborg et al., 2002 Anthropogenic Southern Hemisphere 450 Lamborg et al., 2002 Total Global Anthropogenic 2650 2850 Mason et al., 2002; Lamborg et al., 2002 Total Global Emissions 4850 U.S. EPA, 2003a U.S. Utility Boilers Coal Oil Natural gas 48.9 (36%) b 48.0 0.5 0.4 U.S. EPA, 2003a

U.S. Ore Gold Ore Iron Ore Silver Ore Ferroalloy Ores, Except Vanadium 11.7 (9%) 11.5 0.2 4.0E-3 5.5E-4 U.S. EPA, 2003a

U.S. Chlorine Production 6.5 (5%) U.S. EPA, 2003a U.S. Municipal Waste Combustors 5.1 (4%) U.S. EPA, 2003a U.S. Hazardous Waste Combustion Commercial Hazardous Waste Incinerators On-Site Hazardous Waste Incinerators Hazardous Waste Incineration 5.0 (4%)

2.48 2.38 0.98 U.S. EPA, 2003a

U.S. Industrial Boilers Industrial/Commercial/Institutional Boilers & Process Heaters Stationary Combustion Turbines 3.8 (3%)

3.28 0.51 U.S. EPA, 2003a

U.S. Medical Waste Incinerators 2.8 (2%) U.S. EPA, 2003a Subtotal (U.S. Sources) 83.8 (61%) U.S. EPA, 2003a Total Point and Non-point U.S. Emissions 136.3 Natural Emissions from U.S. c 64

a In the Mason and Scheu (2002) model much of the mercury rel eased to the atmosphere from the ocean re deposits into ocean. b The percentage of total U.S. anthropogenic emissions as simula ted in U.S. EPA (2003a) is based on 1999 emission estimates. U.S. anthropogenic emission estimates have been updated ( www.epa.gov/ttn/chief ).

c We developed this estimate based on natural global mercury emissions estimates of Lamborg et al. (2002). Using Lamborg’s approach, the U.S. estimate is based on the ratio of U. S. landmass to total landmass of northern hemisphere.3 Figure 1 summarizes the results of a global dynamic simulation model for mercury (Lamborg et al., 2002). In this model, anthropogenic mercury emissions (2900 tons annually) comprise roughly 60% of the to tal quantity emitted global ly (4900 tons). Annually, 900 tons of merc ury are emitted from aquat ic systems and 1100 tons are emitted from terrestrial systems; thes e emissions consist of both re-emitted anthropogenic mercury and mercury naturally pr esent in these environmental media. Deposition of atmospheric mercury to land a ccounts for slightly more than 50% of the total deposition; the remainder of the atmospheric mercury deposits to water bodies. In this model, concentrations in compartment s of the biosphere (e.g., atmosphere and upper levels of the oceans) are predicted to be increasing approxim ately 1% per year. 2 Estimating the human-health ben efits of reductions in mercury emissions from U.S. power plants requires accounting for the atmospheric transport and deposition of mercury, methylation, uptake and bioconcent ration in fish, human consumption, the relationships between human exposure to me thylmercury and health effects, and the

valuation of those effects. The remai nder of this section provides background information on these topics.

2 Seigneur et al. (2004) compare four global mercury si mulation models, including Lamborg et al. (2002). The comparison (see Table 2 in Seigneur et al.) shows Lamborg’s estimate of anthropogenic mercury emissions to be 15% higher than those asso ciated with other models. See Table 1.4 Figure 1 Global Mercury Cycle

1.1. ATMOSPHERIC MERCURY Understanding the transport and deposition of inorganic me rcury emitted to the atmosphere is fundamental to analyzing the changes in methylmercury exposures that may be associated with future mandated me rcury emissions reductions. In the environment, mercury exists in two gener al forms: inorganic forms, which include elemental mercury, mercury oxides and merc ury salts, and organic forms, which include alkylated species such as methylmercury. Inorganic forms of mercury are released to the atmosphere by natural (e.g., off-ga ssing from surface waters and volcanic emissions) and anthropogenic sources (Table 1). The distribution of mercury in the 

# Global Mercury Cycle

MeHg Aquatic Cycle Aquatic Ecosystem Aquatic Deposition 22 Emissions Anthropogenic: 29 Natural: 11 Atmosphere

Food Web Oceanic Evasion 9 Terrestrial Deposition 24



### Crude Global Exposure Efficiency 2E-3

Adapted from Lamborg et al., 2002 

### Units: 100 tons/yr 

# Global Mercury Cycle

MeHg Aquatic Cycle Aquatic Ecosystem Aquatic Deposition 22 Emissions Anthropogenic: 29 Natural: 11 Atmosphere

Food Web Oceanic Evasion 9 Terrestrial Deposition 24



### Crude Global Exposure Efficiency 2E-3

Adapted from Lamborg et al., 2002 

### Units: 100 tons/yr5 environment is dominated by tr ansport during its atmospheric phase. Although the atmospheric chemistry of mercury is extr emely complex and incompletely understood, most experts agree that mercury emissions can deposit potentially causing problems both locally (i.e., close to emissions s ources), regionally (i .e., within a few hundred kilometres of a source) and globally. T hus, policy makers are concerned about the impacts of local and long-range trans port of atmospheric mercury. Three different forms of inorganic mercur y emissions are typically modeled in atmospheric transport models. T hese are elemental mercury (Hg 0 ), gas phase divalent mercury (Hg II ) (also referred to as reactive gaseous mercury), and particulate-bound divalent mercury (Hg p ). Based on the curr ent understanding of the fate of atmospheric mercury (e.g., Petersen et al., 1995; C ohen et al., 2004; Seigneur et al., 2004), Hg II and Hg p are deposited to the surface of the earth relatively rapidly through wet and dry deposition processes. 3 Thus, when a mix of Hg 0 , Hg II and Hg p is released from an anthropogenic source, Hg II and Hg p are thought to deposit loca lly and regionally around

the source. Due to the difficulties in measuring the dry deposition of Hg II , estimates of its deposition are uncertain and a source of disagreement among modelers and policy analysts. For example, U.S. EPA (1997c, 1998a) predicted that over 80% of the mercury that deposited within 50 Km of hypothetical coal-fir ed power plant was emitted as Hg II and Hg p , 4 although the two forms comprised onl y 50% of the total emitted.

3 Hg II and Hg p are readily scavenged by atmospheric precipit ation and deposited to the earth through wet deposition. Hg p is dry deposited due to gravitational settling. Gaseous Hg II also can dry deposit. Hg II dry deposition velocities appear to depend on both ground cover and weather conditions; a typical deposition velocity for Hg II is estimated to be 2.9 cm/s (U.S. EPA, 1997a, 1998a). While the wet deposition of mercury and dry deposition of Hg p can be measured in a relatively direct manner, measuring the quantities of gaseous Hg II that dry deposit is complex and estimate s of these deposition rates are less certain (Lindberg and Stratton, 1998).6 Others contend that the fraction depositing locally from such sources is much less than

estimated in EPA’s simulation. Because th ese two forms of mercury deposit near anthropogenic sources, policy makers need to take into account the local and regional nature of impacts of mercury emissions. On the other hand, Hg 0 is volatile and much less soluble than Hg II . Its estimated atmospheric half-life is approxim ately 1.5 years. Given this long half-life, emitted Hg 0 is unlikely to deposit locally; rather, it ent ers regional and global atmospheric mercury pools. In these regional and global pools, Hg 0 slowly converts to Hg II and deposits (Petersen et al., 1995). For example, the results of the same U.S. EPA simulation described previously (U.S. EPA, 1997c, 1998a) predicted that only 30% of the total mercury emitted from all U.S. power plant s deposits in the conti guous U.S. If these predictions are accurate, then policy makers also need to consider regional and global impacts of this form of emitted mercury. To generate as accurate an understanding as possible of the deposition of atmospheric mercury, both t he U.S. EPA and the Electric Power Research Institute (EPRI) have developed models to simulate the long-range transport and deposition of atmospheric mercury in the contiguous U.S. 5 U.S. EPA used a Lagr angian or trajectory

model to estimate the fate of emitted me rcury in two separate Reports to the U.S. Congress (Bullock, 1997; U.S. EPA, 1997c, d, 1998a, b). Limitations in these earlier

4 Very little Hg p is emitted by power plants; however, roughl y 20% of the measured mercury in power plant emissions plumes is Hg p (U.S. EPA, 1997b). When the U.S. EPA si mulated the atmospheric fate of mercury, they assumed that 20% was bound to par ticulate matter based on measurements in the emission plume.

5 Other groups have also developed regional atmospheri c fate models for mercury; for example, see Cohen et al. (2004) and Petersen et al. (1995).7 simulation models (e.g., limited treatment of atmospheric mercury chemistry) have led both EPA and EPRI to develop regional scale Eulerian or grid-based air quality models that simulate long-term deposit ion fluxes of atmospheric mercury over large geographic

domains In 2002, the U.S. EPA proposed the Clear Sk ies Initiative (CSI) to reduce power plant emissions of mercury through the application of a mark et-based “cap and trade” approach over the next two decades. 6 In the analysis of the CSI, the U.S. EPA used the Regulatory Modeling System for Aero sols and Deposition (REMSAD) model to predict potential changes in mercury deposit ion associated with changes in power plant mercury emissions. The model predicted annual mercury deposition rates for each 36 x 36 Km grid cell in the contiguous U.S., t he western Atlantic Ocean and the Gulf of

Mexico. The EPA’s CSI simulation included predi ctions of current mercury deposition rates using the 1999 U.S. mercury emissions inventory (U.S. EPA, 2002); in this analysis 36% (48.9 tons) of the 136.3 tons of mercur y emitted annually by U.S. anthropogenic sources was a ttributed to power plants. 7 Separate simulations were developed to compare predict ed changes in mercury deposit ion under the CSI at two different points in time. The mercury emissi ons cap in 2010 for utilities subject to CSI is 26 tons. Based on these simulations, in the year 2010, the EPA (U.S. EPA, 2003a, b)

6 In 2005, CSI was reintroduced in the U.S. Congress wi th a higher first phase cap of 34 TPY of mercury emissions in 2010 instead of the 26 TPY cap in the 2002 proposal. However, all analyses in this report are based on 26 TPY cap in 2010. 7 Recent updates to the inventory estimate that power plants account for approximately 40% of mercury emitted from U.S. anthr opogenic sources annually. ( www.epa.gov/ttn/chief/net (See 1999 NEI data).8 predicts U.S. anthropogenic mercury emissi ons to be 104.7 tons; if the CSI is implemented, total U.S. anthr opogenic emissions are predicted to be 85.6 tons per year because power plant mercury emissions will be 29. 8 tons per year (26 tons per year for power plants that are subject to CSI). By 2020, U.S. EPA ( 2003a,b) predicts annual anthropogenic mercury emissions to be 105.7 tons and if the CSI is implemented, total annual anthropogenic emissions are predicted to be 79 tons because power plant

emissions will be at 23.2 tons (power plants that are subjec t to CSI would emit 18 tons in that year assuming no “safety valve.”) 8 The mercury emissions cap in 2018 for utilities subject to CSI is 15 tons. In thei r simulations, the EPA included differences in the quantities and species of mercury emitte d from power plants using a model that predicts the U.S. power sect or’s response (including foss il fuel consumption) to regulatory and economic changes. If the CSI is fully implemented, EPA predicts slightly more than a 10% decrease in mercury deposit ion across the U.S., when compared to current deposition rates. EPRI (2003) applied the Trace Element Analysis Model (TEAM) to predict the changes in mercury deposition in the U.S. under the CSI and predicted notably different impacts than were predicted by EPA (2003a). The TEAM is a regional-scale Eulerian model that simulates the atmospheric chem istry of mercury over the North American continent using 100 x 100 Km grids. C hanges in coal consumption patterns and mercury emissions by the electric power industry under the CSI were modeled. EPRI (2003) predicted that current me rcury emissions from power plants account for less than

8 The “safety valve” is a provision of the cap and tr ade program that allows power plants to meet their obligations by purchasing emission allowances at a predetermined cost per ounce of mercury.9 8% of the mercury deposited in the U.S.; the model predicts that most deposition is the

result of emissions from sources outside t he U.S. The impact of mercury emissions changes was analyzed using “transfer coefficient s” estimated from the model. Transfer coefficients are model parameter s that relate changes in mercury emissions to changes in mercury deposition at specific locations . For example, deposition at a New York receptor is most influenced by changes in emissions in New York and New Jersey; changes in emissions from the U.S. Midwes t influence mercury deposition at the New York receptor more than changes in emission s from the South (Figure 2-2 in the EPRI report). Many of the transfe r coefficients were estimated for sites monitored by the National Atmospheric Deposit ion Program Mercury Deposit ion Network (NADP-MDN), although this network was designed to m easure background mercury concentrations

and not contributions from local anthropogenic sources. Based on their analysis of transfer coefficients at 19 receptor site s, EPRI (2003) estimates that, with CSI regulations in place, ther e will be a 1.5% (range of 0.1- 5.3%) decrease in mercury deposition rates across the U.S. by 2020. While the predicted mercury deposition ra te decreases vary between the two models, the TEAM and REMSAD simulation s predict similar geographic patterns of mercury deposition. For exam ple, more mercury deposition is predicted in the Eastern U.S. than in the Western U.S., with high deposition rates in the Mid-Atlantic and Northeast Regions of the U.S. The EPRI m odel results suggest significantly smaller

contributions to overall me rcury deposition from power pl ants than do the EPA models. The EPRI report does not detail changes in deposition within t he contiguous U.S.;10 rather, it averages deposition changes over the entire U.S. and does not explore local and regional variability in mercury deposition.

1.2. BIOACCUMULATION OF METHYLMERCURY Following deposition, inor ganic mercury enters bodies of water where sulfatereducing micro organisms in the water colu mn and in the sediments convert small fractions (typically about 1 or 2%) to the form of methylmercury (Hamdy and Noyes, 1975; Gilmour and Henry, 1991). 9 Invertebrates such as zooplankton and insects that feed on organisms at the base of the aquatic food web accumulate methylmercury. Methylmercury binds to amino acids through sulfhydral bonds and is incorporated into muscle protein. Small fish feed on t he contaminated aquatic invertebrates, preferentially retain methylmercury, and eventually are eaten by predatory (i.e., piscivorous) fish. Because methylmercury is highly retained in organisms, it rapidly bioaccumulates up the food web and as a resul t, in a body of water, the large predatory fish have the highest methylmercury concentrations in their muscle tissues. 10

9 Soil microorganisms can also methylate inorganic mercury (St. Louis et al., 1996) . This likely occurs primarily in wetlands during anoxic periods. The mercury methylated by soil microorganisms can be transported to a water body. 10 Inorganic forms of mercury are less efficiently absorbed through the fish gut and more efficiently eliminated from fish than methylmercury; consequently they do not bioaccumulate in aquatic organisms to the extent that methylmercury does.11 There is a great deal of variability in the a ccumulation of methylmercury in fish across

different bodies of water. 11 Thus, the methylmercury level in a fish depends on geography, geochemistry, the age and size of the fi sh and its position in the food web. Understanding the fate of mercury ent ering a water body is fundamental to predicting the changes in methylmercury concent rations in fish that may be associated with reductions in mercury deposition. In an analysis of the applicat ions of the primary models used to estimate methyl mercury bioaccumulation in studies of specific bodies of water (Harris et al., 1996; U.S. EPA, 1997a), the Mercury Maps project (U.S. EPA, 2001d) showed that predicted changes in meth ylmercury concentrations in fish were proportional to changes in mercury enter ing the water body under steady-state assumptions. If the atmospheric mercur y deposition was the dominant source of mercury to a water body, then the changes in deposition that might follow mercury emission controls would result in proportional changes over time in fish concentrations (U.S. EPA, 2001d). If non-atmos pheric sources, such as mines and relic accumulations in soil and sediments, are significant source s of inorganic mercury to a body of water, then reducing atmospheric deposition of mercur y is predicted to result in less than proportional (i.e., nonlinear) reductions of fish methylmercury concentrations.

11 The following factors reportedly impact the accumulation of methylmercury in fish:

• mercury transport into the aquatic system includi ng the fraction of a watershed that consists of wetlands (St. Louis et al., 1996),

• food chain length (i.e., short food chains result in less methylmercury bioaccumulation among apex predators) (Cabana and Rasmussen, 1994)

• breadth of lower trophic levels (i.e., dilution of methylmercury across biomass at low end of food chain leads to less accumulation among pisc ivorous species) (Pickhardt et al., 2002)

• demethylation rates (DisPasquale et al., 2000)

• nutrient loading (Hammerschmidt and Fitzgerald, 2004)

• water pH (Brumbaugh et al., 2000)

• dissolved organic carbon (Brumbaugh et al., 2000)

• sulfate concentration (Brumbaugh et al., 2000).12 The model developed in the Mercury M aps project (U.S. EPA, 2001d) was used to conduct a national analysis at a watershed level to estimate the percent change in mercury deposition needed to remove fish consumption advisories. U.S. EPA (2001d) suggested that by using fish methylmercur y concentrations reported in the National Listing of Fish and Wildlife Advisories (NLFWA) database (U.S. EPA, 2003d) and simulation results from atmospheric mercur y models, the Mercury Maps model could be used to estimate the change in the number of fresh water bodies with fish consumption advisories for freshwater fish, if anthr opogenic mercury emissions were reduced. The EPRI (2003) analysis also evaluated the impact of reducing power plant mercury emissions on fish methylmercury c oncentrations. The analysis assumed that a linear relationship existed between reduction s in mercury deposition and methylmercury

levels in fish. All U.S. freshwater fish were assigned to a single model compartment and all commercial fish were assigned to a separate model compartment. Methylmercury concentrations in freshwater fi sh were assumed to be in equilibrium with mercury deposition across the contiguous U. S. and methylmercury concentrations in

commercial fish were assumed to be in equilibrium with global mercury deposition. Decreases in mercury deposition that resu lted from decreased power plant emissions were assumed to result in linear and propor tional reductions in fish methylmercury concentrations. In the EPRI analysis of the CS I, U.S. exposures to methylmercury from fish consumption were predicted to c hange very little from current exposures.

1.3. HUMAN EXPOSURE TO METHYLMERCURY Humans are exposed to methylmercury pr imarily through fish consumption (i.e., eating the contaminated fish muscle). Schober et al. (2003) and Mahaffey et al. (2004)13 showed that individuals reporti ng some fish consumption hav e higher levels of blood mercury and blood methylmercury, respec tively, than those who report no fish consumption. Self-reported num ber of fish meals is a str ong predictor of blood mercury (Schober et al., 2003) and methylmercury conc entrations (Mahaffey et al., 2004). The magnitude of human methylmercury exposur es depends on the fish methylmercury levels and the quantity of fish consumed.

Fish consumed in the U.S. are obtained from two general sources: the U.S.

commercial seafood market and non-commercial capture by individuals for personal consumption or consumption by others known to them (e.g., fa mily members or friends).

Between 70% and 90% of the U. S. population (roughly, 200 to 250 million individuals)

consumes seafood from the commercial mark et (Carrington and Bolger , 2002). In the U.S., this market is comprised of farmed fi sh and wild fish that have been caught off the U.S. coasts or imported from countries around the world. Three types of seafood, tuna, shrimp and pollock account for over 45% of the mass of fish purchased by U.S. consumers and Table 2 lists the 24 types of seafood,

which combined account for 92% of the ma rketed fish (Carrington and Bolger, 2002). Based on Carrington and Bolger’s input data, 22 of t he top 24 types of fish in commerce are wild and captured in marine waters. Commercial catfish and crawfish, the two freshwater species listed among the top 24 species, are typically raised in aquaculture. In general, commercial fish consumers are u naware of the origins of the fish they consume. For example, consumers likely do not know the country from which their canned tuna was imported. They also may be unaware of the types of seafood they consume; for example, they may not know the types of fish comprising “fish sticks.”14 Most marine fish inhabit upwelling regions of the oceans, nutri ent rich estuarine waters, or continental shelves (Ryther , 1969). Policy makers concerned about the safety of commercial seafood obtained from marine environments must consider the influence of U.S. mercury emissions on meth ylmercury concentrations in the food webs of the commercial seafood stocks living in t hese areas of the ocean. The continental shelf adjacent to the North American cont inent under the Atlantic Ocean is one such area of concern; the Gulf of Mexico is another. In the U.S., the Food and Drug Administra tion (FDA) monito rs methylmercury levels in commercial seafood. Methylmercury concentrations are typically highest in the wild finfish such as shark and swordfish (F igure 2). Carrington and Bolger (2002) developed an exposure model, based on the frequency, quantity and types of seafood consumed. The model predicts changes in methylmercury intake in the general population associated with chang es in fish consumption patterns. Table 2 lists per capita consumption rates and mean methylmercur y concentrations fo r the commercial fish typically consumed in the U.S. ( adapted from Carrington and Bolger, 2002). Concentrations in fish raised in aquaculture are typically low; these animals are not part of the wild food web where methylmercury can accumulate.15 Fish meal is a dietary component of some fi sh raised in aquaculture (fish-eating species raised in aquaculture include salmon and c od). Lower trophic level marine species comprise these fish meals. 12 Shellfish are low trophic level organisms and, in general, have low levels of methylmercury. While m any marine species only inhabit and feed in one location, three important commercial fish, tuna, shark and swordfis h, are migratory. Thus, the methylmercury concentrations of t hese predatory fish may reflect participation

in marine food webs at a number of different locations over time.

12 Pauly et al. (1998) reported that, based on global fi sheries statistics, the mean trophic level of fish caught has declined from 1950. The decline is a cons equence of limited availability of higher trophic level species and an increase in the quantity of lower trophic level fish being targeted. Many of these species are used in fish meal and fish oils fed to some aquaculture species.1600.20.40.60.811.2 Sw o r d f is h S h a r k Lob s ter - Americ a n H a libu t Sable fish Rockfish Tu n a-canned

Crab s - D u n g e ne s s Po l lo c k C r a bs-Snow C r a b s-B l ue Lobst e r-S p iney C o d Flatfi s h Crabs-King Pe r c h -O c ean Shrimp S a lmo n Oysters C r awfi s h C a t fis h S callo p s Sa r din e s C l amsFish Type

Methylmercury Concentration (ppm)Figure 2Average Methylmercury Concentrations for "Top 24" Types of Fish Consumed in the U.S. Commercial SeafoodMarket17 Table 2. The Pounds Per Capita, Methylmercury Concentration, Market Share, and Fractional Contribution of Fish in Commerce From Each U.S. Fishery or Import for Top 24 Types of Fish Consumed in U.S. (Sources: Carrington and Bolger, 2003; NMFS, 2002)

Type Annual Consumption Rate (Pounds

per capita) Arithmetic Mean MeHg Concentration (ppm) Atlantic (%) Gulf (%) Pacific (%) Import (%)

Tuna-canned* 3.1 0.17 migratory species Shrimp 2.7 0.05 1.1 10.3 2.5 86.2 Pollock 1.64 0.15 0.2 0.0 84.8 14.9 Salmon 1.299 0.05 0.0 0.0 41.7 58.2 Cod 1.057 0.12 2.2 0.0 30.5 67.4 Catfish 1.02 0.05 aquaculture Clams 0.46 0.02 84.0 0.4 1.8 13.9 Flatfish 0.33 0.09 9.4 0.1 41.8 48.8 Halibut 0.29 0.31 0.0 0.0 62.0 38.0 Scallops 0.25 0.04 49.5 0.0 0.3 50.3 Crabs-Blue 0.24 0.15 12.8 6.7 0.0 80.5 Oysters 0.22 0.05 4.8 34.9 15.0 45.3 Sardines 0.18 0.03 32.0 0.8 39.6 27.7 Rockfish 0.127 0.20 0.0 0.0 63.6 36.4 Crabs-Snow 0.092 0.15 0.0 0.0 0.0 100.0LobsterAmerican 0.09 0.46 16.0 0.0 0.0 84.0

LobsterSpiney 0.09 0.12 1.5 9.8 2.4 86.3 Swordfish 0.08 1.07 migratory species Crawfish 0.065 0.05 aquaculture Perch-Ocean 0.056 0.06 4.0 0.0 55.4 40.6CrabsDungeness 0.054 0.17 0.0 0.0 99.8 0.2 Crabs-King 0.037 0.09 0.0 0.0 81.0 19.0 Sable fish 0.024 0.27 0.0 0.0 100.0 0.0 Shark 0.02 0.96 migratory species * Recent analyses have shown that canned albacore/white tuna have higher mean methylmercury concentrations (0.29 ppm) than light tuna (0.12 ppm) (FDA, 2004; www.cfsan.fda.gov ).18 Non-commercial consumers eat fish caught by individual anglers in saltwaters

and freshwaters. Unlike the fish consumed from the comme rcial market, which may be caught in different locations, the fish eaten by non-commercial fish consumers are typically obtained from a small geographic area. Recent surveys by the National Marine Fisheries Service (NMFS, 2003) and the U.S. Fish and Wildlife Service (U.S. FWS, 2003) indicate that over 9 million people fish U.S. saltwaters (Table 3). Based on their National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, U.S. FWS (2003) estimated that there were 28.4 million freshwater anglers. If the fish in a geographic area have high levels of methyl mercury (for example, due to elevated mercury deposition rates associated with emi ssions from local or regional sources), then exposures in these fish consumers may be elevated. U.S. EPA policy makers are concerned about elevated methylmercury exposures from consumption of fish caught in these potential mercury “hot spots” in marine and freshwater environments.

Table 3. Estimates of the Size of the Saltwater Angler Population Annually in the U.S. and the Number of Consumers of Recreationally-Caught Saltwater Fish

Waters Fished Population Size Source Estimated Number of Consumers Recreationally-caught Marine Fish U.S. Saltwaters 9,051,000 10,577,000 U.S. FWS (for year 2001) NMFS (for year 2002) -- Gulf of Mexico 3,138,000 2,655,000 U.S. FWS (for year 2001) NMFS (for year 2002) 5,793,000 Atlantic Ocean 4,766,000 5,258,000 U.S. FWS (for year 2001) NMFS (for year 2002) 10,024,00019 Non-commercial fish caught in marine and estuarine waters along the U.S.

coasts are monitored thr ough the annual National Marine Recreational Fisheries Statistics Survey (NMFS, 2003). Their su rveys ascertain the numbers, weights and types of fish caught recreationally. Tables 4 and 5 list the 10 most frequently captured fish, based on total mass of fish caught for c onsumption by recreati onal anglers in the

Atlantic Ocean and in the Gulf of Mexico. T he estimated total catch weights of these 10 types of fish comprise roughly 70% of the estimated total catc h weight of all fish caught recreationally in the Atlantic and in the Gulf. The NMFS data are limited to finfish. U.S. EPA (2003a) has compiled methylmercury c oncentration data for marine species targeted for recreational capture. Methylmercury concentrations in U.S. freshwater fish are compiled in the National Listing of Fish and Wildlife Adviso ries (NLFWA) Mercury Fish Tissue Database (U.S. EPA, 2003d). States voluntarily r eport information to this database, which contains approximately 58,000 samples repor ting methylmercury c oncentrations. In addition to the methylmercury concentration, many of the ent ries include the type of fish, its length and weight. The U.S. FWS does not directly monitor the types of freshwater fish individuals consume, but conducts surv eys to identify the types of fish anglers target and the amount of time they spend targeting specific fish.20 Table 4. Median, Mean, and Maximum Methylmercury Concentrations (μg/g) Reported Fish Species Harvested via Recreat ional Angling in the Atlantic Ocean Type Median Mean Maximum Number of Samples Harvest (lbs) a Striped Bass 0.1 0. 15 0.8 215 12,919,000 Summer Flounder 0.03 0.04 0.1 34 12,523,000 Bluefish 0.35 0.4 1.6 174 12,334,000 Other Tunas/Mackerels b 8,135,000 Blackfin Tuna 1.16 1.16 1.2 1 Cero Mackerel 0.15 0.19 0.3 3 Dolphins 0.06 0.07 0.2 14 7,676,000 Atlantic Croaker 0.06 0.09 0.6 58 7,913,000 King Mackerel 0.67 0.98 3.5 118 4,789,000 Weakfish 0.2 0.27 0.8 61 4,045,000 Black Sea Bass 0.15 0.15 0.2 2 1,514,000 Scup 0.03 0.03 0.1 10 875,000 Subtotal (for 10 species listed) 72,721,000 Total Recreational Catch 105,215,000

a NMFS (1998) Data b Note that we divided the estimated harvest weight for the cat egory of other tunas and cero mackerels evenly betw een the two types of fish.21 Table 5. Median, Mean, and Maximum Methylmercury Concentrations (μg/g) Reported Fish Species Harvested via Recreat ional Angling in the Gulf of Mexico Type Median Mean Max # Samples Harvest (lbs)* Red Drum 0.19 0.5 4.62 590 8,522,000 Spotted Seatrout 0.28 0.32 1.5 546 8,256,000 Red Snapper 0.11 0.09 0.16 13 4,259,000 Dolphins 0.06 0.13 0.49 29 4,246,000 Groupers (myctera) 0.29 0.37 1.4 94 4,146,000 King Mackerel 0.86 1.09 4.47 385 3,933,000 Sheepshead 0.12 0.18 1.73 224 3,471,000 Black Drum 0.15 0.44 6.62 233 2,146,000 Spanish Mackerel 0.47 0.53 2.9 204 1,910,000 Sand Seatrout 0.45 0.48 1.2 99 1,815,000 Subtotal (for 10 species listed) 42,705,000 Total Recreational Catch 62,548,000

* Source of marine recreational catches: NMFS, 1998.

Fish consumption rate data have been collected by several U.S. federal agencies depending on the source of the fish. T he FDA has evaluated commercial fish consumption rates (Carrington and Bolger , 2002). Table 2 reports per capita consumption rates for selected types of co mmercial fish based on FDA dietary surveys of the U.S. population. Ba sed on an extensive analysis of NMFS survey data, U.S. EPA (1997a) estimated the distribut ion of finfish consumption rates for Atlantic and Gulf recreational anglers. U.S. EPA (1997a) also r eports the distribution of freshwater fish consumption rates for U.S. re creational anglers. These val ues were based on studies of freshwater anglers fishing in the states of Maine, New York and Michigan (U.S. EPA, 1997a citing Ebert et al., 1993; Connely et al., 1996; West et al., 1989). Some individuals and groups catch and consume lar ge quantities of non-commercial fish. For22 example, the Columbia River Inter-Tribal Fish Commission Report (1994) describes consumption rates among a group of Native Americans who frequently consume large quantities of fish. 13 Policy makers are concerned about methylmercury exposures in

individuals who either cons ume high quantities of commerci al and non-commercial fish

or consume contaminated fish.

1.4. TOXICOKINETICS Once ingested, roughly 95% of the methyl mercury entering the gastrointestinal tract is absorbed. This compound passes thr ough the lining of the gut to the liver and

enters the blood stream wher e it primarily binds red blood cell proteins, quickly distributing from blood to t he liver, as well as the kidney and skin. Methylmercury appears to pass the adult blood brain barrier by binding to a thiol group on cystiene, which is then recognized by a neutral amino ac id carrier protein (Aschner, 1989); entry

into the brain tissues can lead to adul t neurotoxicity. In pregnant females, methylmercury can also bind and pass through the placenta tissues; it may also enter placental tissues through an active transport pr ocess via an amino acid carrier protein. Stern and Smith (2003) r eport that methylmercury levels in cord blood are higher than levels in maternal blood; the reason(s) for this observation have not been fully explained. The blood brain barrier does not effectively occlude methylmercury from the brain tissue of the fetus. Entry into these brain tissues likely leads to the neurocognitive deficits observed in some children expos ed to methylmercury. The possible

13 While described as a study of “subsistence” angler c onsumption (U.S. EPA, 1997b), there is not a specific intake quantity or percent of total daily ca loric intake or protein intake that results in a categorization of “subsistence.”23 cardiovascular effects associated with in creased methylmercury exposures may result from the oxidative pr operties of mercury in the bloodstream (Salonen et al., 2000). The mean half-life of methylmercury in humans is estimated to be between 47 and 72 days. Methylmercury is demethyl ated and converted back to inorganic forms primarily by gut microflora; the liver and brain also demethylate methylmercury. Demethylation in the brain may, in effect, trap inorganic mercury in the brain, because inorganic mercury passes out of the adult brain more slowly than it enters (if it passes out at all). It is unlikely that the fetus can demethylate methylmercury (Dock et al., 1994). Nearly all of the ingested methylme rcury (90%) is eventually eliminated as inorganic mercury through the feces via the b ile and exfoliated gastr ointestinal epithelial cells. Methylmercury in the bile can be reabsorbed by the gall bladder and the small intestine; this apparent recycling is a factor in the long half-life of methylmercury in the body. Methylmercury also appears to bind sulfhydral groups in amino acids that comprise hair (Farris et al., 1993; U.S. EPA, 1997e). The binding of methylmercury to

hair is an additional eliminat ion pathway from the body. While some researchers have used blood sera and toenails to analyze mercury exposure, measures of mercury concentrati ons in whole blood and hair are the most common biomarkers used in analyses of human exposures (Grandjean et al., 1999). Blood and hair mercury concentrations can be used to estimate the quantity of methylmercury ingested through the “methyl mercury 1-compartment model.” This model provides reasonable ap proximations of intake, blood, and hair mercury levels under steady-state conditions.24 d c* b* v / (a* f * Bw) = (Eq. 1) where: d = oral dose (μg MeHg/kg-day) c = blood concentration (μg/L)

b = elimination c onstant (0.014 day -1 ) v = blood volume (5 L) a = gastrointestinal absorption factor (0.95) f = fraction of absorbed dose found in blood (0.059) Bw = body weight (kg) The parameter values above were obtained from U.S. EPA (1997e, 2001a, 2001c). The ratio of hair mercury concentration to blood mercury concentration is typically considered to be approximately 250:1 (e.g ., 8 μg/L blood = 2 ppm hair) (U.S. EPA, 2001c). The mercury hair-to-blood ratio appears to be highly variable across individuals

and studies.

1.5. HUMAN HEALTH EFFECTS This discussion of mercury health effects focuses on neurological effects 14

observed in children that result from intr auterine methylmercury exposures and adult myocardial effects associated with methylmerc ury exposures through fish consumption. The risks of incurring these two effects are quant ified in Section 3 of this report and they likely account for a large frac tion of the total damage to hum ans that is associated with

14 In laboratory animal studies, ingested methylmerc ury binds placental tissue and diffuses across these membranes, entering the fetal blood (U.S. EPA, 1997d). Methylmercury may also be actively transported across placental membranes by binding to cysti ene. Methylmercury causes diffuse damage throughout the entire developing brain. When compared to norm al brains, affected brains weigh less, have fewer cells, and exhibit a less organized structure (Burbacher et al., 1990). This systematic damage may result from brain cell death.25 methylmercury exposures. We also discu ss two additional effects that have been

observed in children and associated with intrauterine methylmercury exposures, increased blood pressure and decreased heart rate variability. We do not quantify these risks, because the increased blood pre ssure does not appear to persist and the clinical significance of changes in heart rate variability of otherwis e healthy children is

not known. The following reported effects associ ated with intrauterine methylmercury exposures and several reported effects asso ciated with adult methylmercury exposures are also not addressed in this report.

• Delays in auditory brainstem evoked potentials that were observed in Faroese children at ages 7 and 14 y ears and associated with intrauterine methylmercury exposures are not evaluated because the clinical significance of such small delays in these evoked potentials is unknown (Murata et al., 1999, 2004).

• Intrauterine methylmercury expos ures have also been associated with decreases in muscle tone and reflexes in 2-week-old children (McKeownEyssen et al., 1983), and with decreased neurological optimality scores in male offspring at ages 12 and 30 months (Steurwald et al., 2000). These possible effects of intrauterine methylmercury exposures are not addressed due to unknown significance of the endpoints in young children (e.g., does the effect persist? does it cause a significant decrease in the quality of life?).

• Cordier et al. (2002) reported that decreased leg coordination in males between the ages of 5 and 12 years and decreases on a Copying Test, which evaluates reasoning and visuos patial organization, in children ages 5 to 7 years was associated with intr auterine methylmercury exposures. We did not model the leg coordination because the significance of such decreases is unknown. We did not evaluate the Copying Test results because they would be a component of IQ decreases which are evaluated (Section 1.5.1) and because they did not appear to persist.

• Adult neurological effects possibl y associated with methylmercury exposures through fish consumpti on have been reported. These include reduced motor control (Mergler and Do lbec, 1998) and reduced function on tests of fine motor speed and dexte rity (Yokoo et al., 2003) and on tests of verbal memory (Yokoo et al., 2003), difficulty with accuracy and26 sharpness of visual fixation and pursuit in dynamic eye movements (Beuter and Edwards, 2003) and restricted visual fields (Mergler and Dolbec, 1998). These effects were not modeled because of their unknown economic significance and impacts on individuals’ quality of life. 1.5.1. Neurological Decrements Associat ed with Intrauterine Methylmercury Exposures.

The need for increased scientific underst anding of the relationship between exposures to organic forms of mercury and hu man neurological decr ements was initially identified through epidemiologic investigations of poisoning episodes in Japan (Harada, 1995) and Iraq (Bakir et al., 1973; Amin-Zaki et al., 1974). The victims were poisoned by methylmercury through consumption of contaminated fish in Japan and by an organic mercury compound through consumpt ion of treated seed grain in Iraq (summarized in Clarkson, 2002). Exposures to these high levels of mercury led to severe neurological effects in adults, child ren and fetuses; however, the neurotoxicity that was associated with intrauterine exposu res (i.e., severe cerebral palsy, delayed walking and talking) occurred at lower dos es than the doses that resulted in adult effects (summary discussions in NRC, 2000; U.S. EPA, 2001b,c) . For example, asymptomatic mothers (i.e., mothers in whom no neurological effects were observed) bore affected children indicating that the developing fetal nervous system was more vulnerable to methylmercury than the mature maternal nervous system. While the effects associated with expo sures to environmental levels of methylmercury in fish continue to be the subj ect of intensive scientific investigation and there have been additional epidem iologic studies of such exposures (U.S. EPA, 2001b), epidemiologic studies conducted in New Zeal and, the Seychelle Islands and the Faroe Islands have been the subject of most recent scientific interest. All three studies27 evaluated maternal methylmercury exposures during pregnancy and have examined the relationship between these exposure measures and pediatric neurological test results in the mothers’ children during multiple devel opmental stages. The National Research Council (NRC, 2000), which evaluated all thr ee studies, reported that none of the studies “appear to have serious flaws.” Two of the studies, the Faroe Islands and New Zealand studies, showed an association between elevated in utero methylmercury exposures and adverse neurological outcomes in the offspring (e.g., IQ decreases), while the Seychelle Islands study has not r eported such associations. At present, no adequate explanation has been offer ed for the differences in the results between the

Faroe Islands and New Zealand studies and those found in the Seychelle (NRC, 2000). Kjellstrom et al. (1986, 1989) conducted a prospective case-control study in New Zealand to examine the neurological devel opment of children who had elevated intrauterine methylmercury exposures. From the 10,930 mother-child pairs enrolled in the cohort, 935 mothers who repor ted eating more than three fish meals per week while pregnant were identified. The fetal expos ure measure was the mercury level in maternal hair over the entire pregnancy. T he high-exposure group (n=31 children) was defined to be women with hair mercury levels greater than 6 mg/kg (or ppm) hair. Each child in this high exposure group was matched with a reference child based on maternal ethnicity, the child's age and birth hospital. The average maternal hair mercury concentrations in the high-exposure and re ference groups were 8.8 ppm and 1.9 ppm, respectively. At 4 years of age, the childre n were administered a group of tests (Table 6). Based on the Denver Deve lopmental Screening Te sts (DDST) results, a statistically significant difference existed between the neur ological functions of the high-exposure28 and reference group. Developmental dela ys (primarily in the motor and language domains) were observed in 52% (16/31) of the children in the high-exposure group and in 17% (5/30) of the reference group. The second phase of the study, undertaken when the children in the cohort were 6 years of age, employed additi onal neuropsychological and scholastic tests (Table 6) (Kjelstrom et al., 1989). Based on the child 's gender, and the mother's age, ethnicity, residence, smoking status, and residence time in New Zealand, each child (n=61) in the high-exposure group was matched with three children whose mothers had lower hair mercury concentrations during their pregnanci es. The average maternal hair mercury concentrations during pregnancy in members of the two control gr oups that reported frequent fish consumption were less than 3 ppm and between 3 and 6 ppm. A statistically significant association wa s reported between high maternal mercury exposure and decreased test scores on the McCa rthy Scale of Children's Abilities in the perceptual and motor domains. Results of the linear multiple regression analyses showed significant associations between high maternal mercury exposure and decreased performance on three neurological tests. Kjellstrom and collaborators reported no differences in the two groups based on “observed” behavior (i.e., in the absence of sophisticated neurological testing).29 Table 6. Tests Employed by Kjellstrom et al. (1986, 1989) in the New Zealand Studies Developmental Domain 4 Years of Age 6 Years of Age

Vision vision test Sensory sensory test Clay Diagnostic Survey Concepts Letter Test Word Test Reading Accuracy Burt Word Recognition Test Age Equivalent Score Academic attainment

Key Mathematical test Grade Score Denver Developmental Screening Tests (DDST) Test of Language Development (TOLD) Grammar completion Grammar understanding Oral Vocabulary Picture Vocabulary Sentence Imitation Spoken Language Quotient (TOLD-SL) Language Development

Peabody Percentile Rank Standard Score Stanine Motor Coordination DDST-gro ss and fine McCarthy Scales Motoric (MCC-MOT) McCarthy Scales Verbal Quantitative Memory General Cognitive Intelligence

Wechsler Intelligence Scale for Children-Revised Verbal IQ Performance IQ Full scale IQ Visuospatial/ Visuomotor McCarthy Scales Perceptual (MCC-PP) Personal-social DDST Crump et al. (1998) reanalyzed the Kj ellstrom et al. (1989) study conducting30 Multiple regression analyses on the r eported average maternal hair mercury concentrations during pregnancy as a continuous variable (i.e., unlike Kjellstrom et al. they used the reported values of hair merc ury concentrations in their analyses rather than as a categorical variable as Kjells trom had done). They did not identify a statistically significant relationship bet ween maternal hair mercury levels and the neuropsychological test scores unless they omitted the single subject whose mother had the highest average maternal hair mercur y concentration in the cohort, which was over four times higher than the second highest measurem ent. The maternal hair mercury variable was a statisti cally significant predictive variable in six tests. The regression coefficients for five of the tests are shown in Table 7.

Table 7. Regression Coefficients and 95% Confidence Intervals for Hair Mercury Concentrations (ppm) Calculated by Crump et al. (1998) Test of Language Development - Spoken Language Quotient Wechsler Intelligence

Scale forChildrenRevised Performance Wechsler Intelligence

Scale forChildrenRevised Full scale IQ McCarthy Scales Perceptual McCarthy Scales Motoric

1 st Regression Analysis a,b -0.60 (-1.2,-0.03) -0.54 (-0.45,0.21) -0.53 (-1.1,0.069) -0.53 (-0.95,-0.11) -0.01 (-0.02,0.003)

2 nd Regression Analysis a,b,c -0.42 (-0.98,0.13) -0.47 (-1.1,0.16) -0.42 (-1.1,0.18) -0.50 (-0.92,-0.08) -0.01 (-0.02,0.002)

a Omitted maternal-infant pair with highest maternal hair mercury level b Statistically controlled for smoking, alc ohol intake, social class, birth weight, maternal age, breastfeeding, gender, ethnicit y, residence, residence time in Ne w Zealand, and other siblings.

c Statistically controlled for age of child at testing and parental education levels31 The Seychelles Child Development Study (SCDS) examined the effects of lowdose fetal exposure to methylmercury from maternal fish consumption. The main prospective study consisted of a cohort of 740 mother-infant pairs that were selected between 1989 and 1990. Average maternal hair mercury levels over the entire pregnancy (range 0.5 to 26.7 ppm; median = 5.9 ppm) were used as the marker of fetal mercury exposure. The cohort has been evaluat ed at 6.5, 19, 29, and 66 months of age (Marsh et al., 1995; Myers et al., 1995) and 9 years of age (Myers et al., 2003). The main prospective study reports no statis tically significant associations between environmental prenatal methylmercury exposure s (primarily through maternal saltwater fish consumption) and adverse neurological outcomes in Seychellois children over a 9-year period (Myers et al., 2000, 2003; Axtell et al., 2000). 15 Myers et al. (2003) reported an association with decreased performance in the peg board test with males, but noted that, given the total number of test s, this association could have been by

chance alone. Table 8 indicates the devel opmental domain tested and age and type of

tests conducted in the SCDS. Based on the resu lts of these studies, it is plausible that

in utero exposures to environmental concentrat ions of methylmercury through fish consumption are not associat ed with neurological decrements. Given the large sample size in the main Seychelles cohort, the SCDS appears to have sufficient statistical power to detect neurological effects and reject the null hypothesis. However, the NAS (2000) report cautions that a large epidemiologic study, such as the SCDS, may lack adequate power to detect adverse associations if a relatively small number of subjects

15 Myers et al. (1995) noted a statistica lly significant association between in utero mercury exposure and the DDST-R (when the abnormal and questionable scores were combined); however, this association was observed in the pilot study only.32 are exposed at levels where effects are likely to be found (i.e., the upper end of the exposure distribution). 16

Table 8. Tests Employed in the Se ychelles Islands Child Development Study Age of Child (months) Developmental Domain 6.5 19 29 66 168 Marsh et al. (1995)Globalcognitive DDST-R BSID, MDI BSID, MDI MSCA, GCI

Visualperceptive Kohen-Raz Kohen-Raz Bender-Gestalt, MSCA Perceptual Speech language DDST-R MSCA Verbal PLS Total Language Aud. Comprehension Verbal Ability Memory Fagen Infantest MSCA Memory

Visual Attention Fagen Infantest Neuromotor exam Neurological DDST-R BSID PDI BSID PDI Bender-Gestalt MSCA Motor Behavioral DDST-R BSID IBR CBCLLearningachievement WoodcockJohnson

16 The NAS report states “power analyses that are based on total sample size can be misleading if adverse effects occur primarily amon g the most heavily exposed children, who typically comprise a very small proportion of the sample. Although the sample size of 700 children in the SCDS would seem to be more than adequate, only about 35 children were exposed at 15 μg/g or higher. Because multiple regression analysis examines associations that are averaged across the entire distribution of exposure, associations that hold only for the most highly exposed children can be difficult to detect. Thus, if adverse effects of prenatal MeHg exposure occur primarily in the upper range, the power to detect them will be limited...”33 Age of Child (months) Developmental Domain 6.5 19 29 66 168 Auditory response Audiometry Tympanometry Davidson et al. (1998)Globalcognitive MSCA, GCI

Visualperceptive Bender-Gestalt

Speechlanguage PLS Total Score

Behavioral CBCLLearningachievement WoodcockJohnson Letter and Word Recognition, Applied Problems Myers et al. (2003)Globalcognitive WISC-FSIQ

Speechlanguage BNT

Memory visual memory subtest ofthe widerange assessment of memory and learning Sustained Attention Connors Continuous Performance Test Behavioral Connors Teacher34 Age of Child (months) Developmental Domain 6.5 19 29 66 168 rating scale, parent-child behavior checklistLearningachievement WoodcockJohnson Letter and Word Recognition, Applied Problems CVLT Motor functions fingertapping, trail making, grooved peg board,BruininksOseretsky, Visual motor integration BeeryBuktenica, test of haptic matching Adapted from: U.S. EPA, 2000 Symbols and Abbreviations: BSID = Bailey Scales of Infant Development; IBR = Infant Behavior Record; MDI = Mental Developmental Index; PDI = Psychomotor Developmental I ndex; CBCL = Child Behavior Checklist; DDST-R = Denver Developmental Screening Test - Revised; GCI = General Cognitive Index; MSCA = McCarthy Scales of Children's Abilities; PLS = Preschool Language Scale. WISC-FSIQ= Wechsler intelligence scale for children- full scale IQ, BNT= Boston naming test, CVLT= California Verbal Learning Test Sources: Marsh et al., 1995; Davidson et al., 1998; Myers et al., 2003

A longitudinal prospective study was conducted in the Faroe Islands to evaluate the relationship between maternal me thylmercury exposure through seafood consumption during pregnancy (including ep isodic pilot whale consumption) and neurodevelopment in the offspring (Grandj ean et al., 1995, 1997, 1998, 1999, 2003; Dahl et al., 1996; Murata et al., 1999, 2004). Following a pilot study (Grandjean et al., 1992), a cohort of 1022 singleton births was assembled during a 21-month period35 between 1986 and 1987. The ex posure measures relevant to intrauterine exposures were maternal hair mercury concentrations and cord blood mercury concentrations at

childbirth. In the main study (Grandjean et al., 1997), t he geometric mean cord blood concentration was 22.8 μg/L (interquartile range 13.4-41.3 μg/L) and the geometric mean maternal hair mercury concentration wa s 4.27 μg/g (interquart ile range 2.6-7.7 μg/g). No mercury-related abnormalities were identified during clinic al examination of the cohort. Intrauterine methylmercury expo sures were statistically significantly

associated with decreased scores on a number of neurological tests in the children. Table 9 indicates the developmental domain tested, subjects’ ages, and type of tests conducted in the Faroe Islands study. Gr andjean et al. (1997) conducted multiple regression analyses on the cord blood mercury concentrations. The cord blood mercury concentration was a statistically significant predictive variable in six tests. The regression coefficients for five neuropsyc hological tests appear in Table 10; the

coefficients were calculated for those ch ildren born to mothers whose hair mercury concentrations were less than 10 ppm. In summary, NRC (2000) and, more recently, a subset of the committee (Stern et al., 2004) have concluded that, based on evidenc e from two of the three studies, low

dose in utero exposures to methylmercury likel y lead to subtle but measurable neurological effects in children. Because the three studies used different test batteries, it is difficult to directly co mpare their results. In the fu ture, there may be opportunities to

evaluate confidence intervals associated with significant and non-significant test results at the level of developmental domains acro ss the three studies (see first column in Tables 6, 8, and 9 for listing of the domains ev aluated in the three studies). This type of36 evaluation may provide alternative measur es of consistencies between the three

studies. Additional evaluatio ns of potential confounding effe cts such as the intake of

selenium are needed. Table 9. Tests Employed in Studies of Faroese Children Age of Child Developmental Domain 12 months Grandjean et al. (1992) 7 years Grandjean et al. (1997) - Main Prospective Study; Grandjean et al. (1998) - Nested Case Control Study; Dahl et al. (1996); Murata et al. (1999) 14 yearsMurata et al. (2004)

Developmental milestones sitting creeping standing Motor coordination Hand-Eye Coordination

General cognitive WISC-R Similarities Visuospatial WISC-R Block Designs Bender Motor Visual Gestalt Test Attention NES2 Continuous Performance WISC-R Digit Spans Forward Speech-language Boston Naming Test Memory California Verbal Learning Test Motor speed NES2 Finger Tapping NES2 Hand-Eye Coordination NES2 Tactual Performance Personal-social Nonverbal Analogue Profile of Mood States Neuropathological Abnormalities Brain-stem auditory evoked potentials, visual-evoked potentials Brain-stem auditory evoked potentials37 Table 10. Coefficients for Logarithmic Transformation of Cord Blood Mercury Concentrations on Selected Neuropsychological Tests (onl y for mothers with hair mercury concentrations less than 10 ppm) (Grandjean et al., 1997) Test Regression Coefficient p-value Wechsler intelligence scale for children-Revised -0.31 0.05 Bender Visual Motor Gestalt Test Reproduction -0.43 0.02 Boston naming test, No cues -1.42 0.01 Boston naming test with cues -1.57 <0.01 California Verbal Learning Test-Children short term reproduction -0.74 <0.01

Statistically controlled for age of child at testing, gender, maternal cognitive function as measure by scores on Raven's Progressive Matrices, major m edical risk factors, smoking, alcohol intake, parental educati on levels, father's employment status, current residence, child's computer acquai ntance, day care, and other siblings. NRC (2000) presents a summary t able (Table 7-1) that prov ides estimates of the regression coefficients fo r all of the subjects.

1.5.2. Myocardial Effects Associated wi th Adult Methylmercury Exposures. In this section, we discuss adult myocardial effects associated with methylmercury exposures (also, see recent review by Stern, 2004). While scientific panels have evaluated the methylmercury neurol ogic studies as a group, similar panels have not evaluated the group of studies that have exami ned the relationship between adult methylmercury intakes and myocardial effe cts. Studies conduc ted by Salonen et al. (1995, 2000), Rissanen et al. (2000), and Guallar et al. (2002) suggest that methylmercury exposures may be a risk fact or for myocardial events and premature deaths. Studies by Tamashiro et al. (1986), Ah lqwist et al. (1999) and Yoshizawa et al. (2002) did not observe such a relationship, indica ting that it may not exist. We note that cardiovascular diseases have been associated with many other behavioral, genetic, and38 dietary risk factors. Typically, in adult populations, the primary source of methylmercury exposure is consumption of contaminated fi sh and, in general, the consumption of fish and fish oils has been consistently associ ated with reduced risk of myocardial events (e.g., Kromhaut et al., 1985; Da viglus et al., 1997; Hu et al ., 2002, see also summary by Bouzan et al., in preparation). For example, the study of Daviglus et al. (1997), which examined 1,822 U.S. males ov er 30 years, showed tha t, when compared to those reporting no fish consumption, individuals w ho reported consuming 35 or more grams of fish per day had roughly a 40% reduced risk of incurring a fatal myocardial event. The strength of this substantia l body of evidence on the card iovascular benefits of fish consumption has led to U.S. nutritional recommendations that individuals consume multiple fish meals per week (Kris-Etherton et al., 2002). However, this benefit is not observed in every study of fish intake (C urb and Reed, 1985; Vollset et al., 1985, Morris et al., 1982; Folsom and Demissie, 2004). It is plausible that fish contaminants such as methylmercury could attenuate the cardiovascula r benefits of fish intake. That is, one explanation for the fish consumption studies that show no fish consumption-associated

cardiovascular benefits or even adverse ca rdiovascular effects could be that the cardiotoxic effect of methylmercury offsets or exceeds the cardioprot ective effect of the polyunsaturated fatty acids in fish. Of c ourse, there may be ot her contaminants and

other factors in these studies that lead to these observations. We note that these positive epidemiologic studies appear to be well conduc ted controlling for many known cardiovascular risk factors and the study results appear to be credible. Next, we describe the four studies that have shown an association between adult methylmercury intakes and myocardial events . Based on the study by Salonen et al.39 (1995), we develop dose-response rela tionships between adult methylmercury exposures, as measured by hair mercur y concentrations and acute myocardial infarction (AMI) and all cause mortality (ACM) 17 in Section 2. We also describe the studies that do not report a statistically si gnificant association between methylmercury and myocardial events. A study of 1,833 males in Eastern Finland, aged 42- 60 years, evaluated the relationship between methylmercury intake vi a fish consumption and the incidence of AMI and all cause mortality (Salonen et al., 1995). Upon study entry, the subjects were free of cancer, coronary heart disease, st roke and claudication. Mercury exposures were measured using hair and urine merc ury concentrations. Hair mercury concentrations reflect long-term average mercury exposures (months) and urinary mercury concentrations are influenced strongl y by concentrations of inorganic mercury in blood, which reflect short durations of exposure (e.g., the past 45 days). Among the study participants, mean fish intake was 46.5 g/day and the mean daily dietary intake of methylmercury was estimated to be 7.6 μg/ day. The mean mercury concentration in hair was 1.92 ppm and the mean daily urinary ex cretion rate was 1.18 μg/day. Table 11 provides additional details of these meas ures. In this study, the mean and maximum follow-up periods were 5 years and 7.75 y ears for cardiac events and 6 years and 8.75 years for deaths, respectively. Salonen et al. used Cox propor tional hazards models to evaluate the relationship between hair mercury levels and AMI and premature death.

17 All cause mortality (ACM) is an epidemiologic term th at refers to all deaths occurring in a study cohort regardless of the cause of death.40 They modeled hair mercury concentrations as a continuous variable and statistically

controlled for a number of risk fa ctors of myocardial events (Table 12 lists these for both Cox models used). We use the values estimated in the second model, (which controls for additional risk factors associated with my ocardial events) and are more likely to estimate the isolated effects of methylme rcury exposure than those estimated by the

first model. The second model estimates that for each 1 ppm increase in hair mercury, the 5-year risk of non-fatal and fatal AMI incr eased by 6.8% and the 5-year risk of all cause mortality increased by 9.0% (Table 12, see results of the second set of Cox models). We note that the obs erved increase in AMI risks is not statistically significant (p=0.17).

Table 11. Measures of Cohort Methylmercur y Intake Rates Reported in Salonen et al. (1995)

Mean Standard Deviation Minimum Maximum Self-reported fish intake g/day 46.5 55.5 0 619

daily dietary intake of mercury μg/day 7.6 7.7 1.1 95

Hair mercury concentration ppm 1.92 1.98 0 16

Urinary excretion rate μg/day 1.18 1.1 0 541 Table 12. Results of Cox Proportional Hazards Models Reported by Salonen et al. (1995)

Fatal and Nonfatal AMI All Cause Mortality Model 1 RR p value 95% CI RR p value 95% CI Hair Mercury (ppm) 1.094 0.037 1.01, 1.19 1.132 0.001 1.05, 1.22

Hair Mercury (>2 ppm) 1.96 0.005 1.23, 3.13 2.26 0.001 1.43, 3.56

Statistically Controlled for age, exam year , ischemic exercise ECG, maximal oxygen uptake Model 2 Hair Mercury (ppm) 1.068 0.175 0.97, 1.18 1. 09 0.043 1.003, 1.186

Hair Mercury (>2 ppm) 1.69 0.038 1.03, 2.76 1.93 0.007 1.2, 3.10

Statistically Controlled for Same variables as Model 1 + family CH D history, smoking, systolic blood pressure, diabetes, socioecon omic status, residence, dietary iron intake, serum apolipoprotein B, HDL2 ch olesterol, and ferritin concentrations The Cox proportional hazards model is described in Equat ion 6 in Section 2.5.1.

Salonen et al. also reported an analysis t hat compared risks to subjects with hair mercury concentrations of 2.0 ppm or more with t hose that had less than 2.0 ppm. In the group with hair mercury concentrations of 2.0 ppm or more, t he authors reported a 69% (p=0.038) greater risk of fatal or nonfat al AMI and a 93% greater (p=0.007) risk of premature death fr om all causes.

Based on an additional multivariate anal ysis of a small subset (n=187), the authors reported that merc ury levels in the hair and urine we re statistically significantly associated with immune complexes containing oxidized low density lipoprotein (LDL). These complexes are believed to be related to adverse myocardial events. The authors42 posited that the increased incidence of myoc ardial events may be caused by oxidation of serum lipids. Of the va riables analyzed, hair mercury levels were the strongest predictor of serum immune complexes containing oxidized LDL. 18 Two follow-up studies (Salonen et al., 2000; Rissanen et al., 2000) were implemented to further analyze these results. The Salonen et al. (2000) study (n=1014) examined the association between the progre ssion of arteriosclerosis, as measured by changes in carotid artery thickness ov er a 4-year period, and hair mercury

concentration. Based on the results of a mu ltivariate model, hair mercury concentration was a statistically significant predictor of the progression of common carotid artery thickening; based on this finding, the follow- up study further supported the possibility that methylmercury exposures may oxidize serum lipids. The second follow-up study (n=1871) ( Rissanen et al., 2000) evaluated whether hair mercury concentrations modified the re lationship between acute myocardial events and two serum fatty acids derived from α -linoleic and linoleic fatty acids in fish, docosahexaenoic acid (DHA, also identified in the biochemical literature as 22:6 n-3) and docosapentaenoic acid (DPA, also identified in the biochemical literature as 22:5 n-6). Although evaluated, no significant asso ciations were observed between a third fish-derived fatty acid, eicosapentaenoic ac id, and the risk of ac ute coronary events. The mean hair mercury concentration increas ed with the proportion of the serum fatty

18 Salonen et al. suggested three possible mechanisms that might explain the association between tissue methylmercury levels and increased incidence of myocardial events: 1. Mercury could catalyze the formation of free radicals that would lead to oxidized lipids. 2. Due to the affinity of mercury for sulhydrol grou ps, it could reduce the antioxidative capacity of the plasma by binding to proteins such as glutathiols. 3. When bound to mercury, selenium does not func tion as a cofactor in peroxide-scavenging enzymes.43 acids that was comprised of DHA and DPA; this was expected because fish intake is

the source of a ll three compounds. To analyze the relationship between t he two fatty acids and acute coronary events, the cohort was divided into quintil es based on the proportion of serum fatty acids comprised of DHA and DPA. Rissanen et al. used Cox proportional hazards models and statistically controlled for a number of factors related to cardiac events (Table 13). Men in the highest quintile of DHA and DPA had a 44% lower risk of acute coronary events (95% confidence interv al 11%, 65%; p=0.014) when compared to subjects in the lowest quintile. To evaluat e whether mercury exposu re might modify the relationship between fish-derived serum acid s and myocardial events, the data were

stratified based on hair merc ury content and a dichotomous analysis was developed that compared risks to subjects that had hai r mercury concentrations of 2.0 ppm or more with those that had less than 2.0 ppm. Men in the highest quintile for serum DPA

and DHA and the lower level of hair mercur y content had a 67% (p= 0.016) reduced risk of acute coronary events, when compared to subjects in the lowest DHA and DPA quintiles having hair mercury levels above 2.0 ppm. Within each quintile, subjects with higher hair mercury concentrations had hi gher risks of acut e coronary events, suggesting that mercury exposure may attenuate the cardioprotective effects of fishderived fatty acids.44 Table 13. Relative Risk of Acute Coronary Events in a Middle-Aged Finish Male Cohort Based on Serum Fatty Acid Compos ition, Stratified by Hair Mercury Levels (Rissanen, 2000)

Quintiles, by Proportion of Serum Fatty Acids comprised of DHA and DPA Hair mercury concentration <2.38% 2.38%-2.73% 2.74%-3 .07% 3.08%-3.58% >3.58%

< 2 ppm 0.85 0.50 0.48 0.41 0.33 > 2 ppm 1.00 0.83 0.63 0.76 0.76 Statistically Controlled for age, examination year, ischem ic exercise ECG, maximal oxygen uptake, family CHD hist ory, smoking, systolic blood pressure, diabetes, body mass index, socioeconomic status, serum in sulin, ADP-induced plat elet aggregation, residence, dietary iron intake, dietary ener gy intake, serum apolipoprotein B, HDL2 cholesterol, and ferritin concentrations.

In a case-control study, Guallar et al . (2002) evaluated the association between toenail mercury concentrations, adipose tiss ue DHA concentrations, and AMI risk in men of no more than 70 years of age from nine different countries. 19 The relationship between toenail mercury levels, the biomarker of mercury exposure in this study, and blood mercury levels, although assumed to be related in the same manner that hair mercury concentrations are related to blood concentrations, is not known. The cases consisted of 684 men having first-time diagnosis of myocardial infarction confirmed by electrocardiology and blood enzyme analysis; the controls consisted of 724 agematched males, with no history of myocardial infarction and from the same study center

catchment areas as the cases. DHA and mercury concentrations were correlated (correlation coefficient = 0.34). Toenail mercury levels among the cases were 15%

19 Cases and controls were obtained from each of 9 countries. The total number of cases and controls (i.e., the sum) from any one site numbered no mo re than 200. The countries from which cases and controls were obtained included Spain, Norway, Russ ia, Germany, Israel, the Netherlands, Finland, Switzerland and the United Kingdom.45 higher than those of the c ontrols after adjustment for DHA levels and coronary risk factors (95% confidence interval 5%, 25% ); the case-control ratio was 1.10 (95% confidence interval 1.03, 1.18). After adju sting for mercury concentrations, DHA levels were inversely associated with risk of AMI; when highest and lowest DHA quintiles were compared, the odds ratio (OR) was 0.59 (95% confidence inte rval 0.3, 1.19; the trend test was statistically significant). In a follow-up study of the Japanese popul ation poisoned by methylmercury, Tamashiro et al. (1986) studied mortality patterns based on death certificates between

1970 and 1981 for individuals who resided in the section of Minamata that had the highest reported number of cases of Minamata disease. 20 The frequencies of heart disease in the study population were lo wer than those of the general Japanese population. In a prospective cohort study, Ahlqwist et al. (1999) examined the association between mercury concentrations in blood sera and myocardial infarction, among 1,462 Swedish women 38 to 60 years of age at re cruitment who were followed for up to 25 years. Serum mercury concentrations, wh ich disproportionately reflect inorganic mercury exposures, were measured at cohort in itiation. No association between serum mercury concentrations and myocardial infarctions was found. In a prospective nested case-control st udy, Hallgren et al. (2001) examined the relationship between first ti me myocardial infarction and erythrocyte mercury concentrations. They reported an inverse association between the risk of a first

20 Adequate death records were not maintained prior to 1970.46 myocardial infarction and erythrocyt e mercury concentrations and plasma polyunsaturated fatty acid concentrations. Using a nested case-control design, Yo shizawa et al. (2002) studied the association between toenail mercury concent rations and risks of coronary heart disease in male health professionals aged 40 to 75 years. They excl uded from the study subjects reporting myocardial infarction, angina, coronary artery by-pass surgery, stroke, diagnosis of cancer or having a very hi gh or very low daily caloric intake. Based on several different analyses (e.g., odds ratios , multivariate regres sion), there was no statistically significant difference between t he mercury levels of cases and controls. A separate multivariate analysis that excluded dentists (due to occupational exposure to inorganic mercury) compared the highest and lowe st mercury quintiles. A relative risk (RR) of 1.27 (95% confidence interval 0.62, 2.59) was observed, suggesting an association of toenail mercury with risk of coronary heart disease, but the association was not statistically significant. The number of subjects included in this separate analysis was roughly half of the total in the main analysis. In summary, this group of studies does not definitively show whether methylmercury exposures increase the risk of incurring adverse myocardial events. The evidence that supports the cardioprotective e ffects of fish consumption, in general, is the strongest. The questions are whether co-e xposure to methylmercury attenuates the cardiovascular benefit of fish consumption and, if so, under what conditions and by how

much. We have noted that so me epidemiologic studies that evaluated the relationship between fish consumption and cardiovascular e ffects did not report a positive effect. When compared to the body of epidemiologic dat a indicating that fish consumption may47 reduce the risk of myocardial events (e.g., Daviglus et al., 1997) , the epidemiologic studies showing an association between meth ylmercury exposures and cardiovascular effects are comprised of a relatively small number of subjects and only three independent cohorts. Only one study evaluated cardiovascular disease and mercury exposures in women (Ahlqwist et al., 1999) and this study did not report a statistically significant association. T he exposure measure used in th is study may be inappropriate for inferring whether there is an association between meth ylmercury intake and fish consumption. Serum levels of mercury, wh ich were used as a measure of exposure in this study, may be a biomarker for inorganic mercury exposures rather than exposures to methylmercury, which binds to red blood cells (Stern, 2004). The Tamashiro et al. study, which also did not report a statisti cally significant association, evaluated methylmercury poisoning, and the effects of the large methylmercury doses in these individuals may differ substantially from t hose exposed to typical environmental levels. Finally, the Yoshizawa et al. data showed a po sitive but not statistically significant association between toenail mercury concent rations and cardiovascular effects, if

dentists were not included. In Section 2, we use the regression coe fficients of Salonen et al. to estimatemethylmercury-related risks of AMI and ACM, because the studies appear wellconducted and the results appear to be credible. The studies that have evaluated the relationship between methylmercury exposur es and adult cardiovascular effects have, to date, not been subjected as a group to su ch an independent evaluation. We are not as confident in our external generalization of the AMI and ACM data as we are in our external generalization of the neurotoxicity studies. T he neurotoxicity studies have48 been independently evaluated as a group. T he studies reporting the cardiovascular benefits of fish consumption have also been subjected as a group to thorough reevaluation.

1.5.3. Elevated Chil dhood Blood Pressure and Cardiac Rhythm Effects

Associated with In Utero Methylmercury Exposures. In this section, we review the results of the Sorensen et al . (1999) and Grandjean et al. (2004) studies. We also discu ss human blood pressure reports from the methylmercury poisoning in Minamata, Japa n and several relevant rodent bioassays. Although blood pressure increases in childre n can be detrimental to health, typically these are not as significant as effects in adults. We note that because the increased childhood blood pressure among children aged 7 years that was reported by Sorensen et al. (1999) did not persist (Grandjean et al., 2004), it is unlikely to be of clinical significance. Finally, we discuss the association of in utero methylmercury exposures with decreased heart rate variability. This a ssociation was first observed in a cohort of children aged 7 years (Sorensen et al., 1999) and was shown to persist in this cohort Grandjean et al. (2004) until 14 y ears of age. The clinical significance of this

observation in children is not known.

1.5.3.1. Childhood Blood Pressure

― In the Faroe Islands birth cohort (Grandj ean et al., 1995), Sorensen et al. (1999) reported statistically significant associ ations between elevated cord blood mercury levels or maternal hair mercury levels and increased diastolic and systolic blood pressures for 7-year-old children (n=917). Obstetrical charts and interviews were used49 to obtain pregnancy and pertinent maternal information. A single measure of blood pressures was made for each 7-year-old ch ild in the cohort standardized conditions. A series of multiple regression analyse s were conducted for maternal and child risk factors and elevated blood pressure. As anticipated, both incr easing body weight (regression coefficient = 0.54 and 95% CL=0.4, 0.68) and increasing height (regression coefficient = 0.25 and 95% CL=0.15, 0.36) were statistically significantly associated with increases in diastolic blood pressure (DBP) and systolic blood pressure (SBP). Adjusting for body weight and maternal hy pertension, the author s report that DBP increased on average by 13.9 mm Hg (95% CL=7.4, 20.4) as mercury levels increased from 1 to 10 μg/L cord blood. SBP increased on average by 14.6 mmHg (95% CL=8.3, 20.8) over the same range of mercury levels in cord blood. These increases were observed only in the range of 1 to 10 μg/ L; above 10 μg mercury/L cord blood, no additional increase in blood pressures wa s observed and the effect appeared to plateau. The mean cord blood mercury concentration among all subjects was 32 μg/L and the standard deviation was 29 μg/ L; the cord blood concentration was below 10 μg/L in 145 children. Birth weight was noted to be a possible effect modifier for the association between mercury levels in cord blood and bl ood pressure. When the association was

evaluated in children below the median birth weight (3.7 kg), a cord blood mercury increase from 1 to 10 μg/L was associat ed with an average increa se of 24.4 mmHg and 20.9 mmHg in DBP and SBP, respectively. Smaller increases in diastolic (6.7 mmHg) and systolic (9.6 mmHg) blood pressures we re observed in the cohort whose birth weights were greater than the median.50 Blood pressure increases were also associated with increasing maternal hair mercury concentrations. Increases from 0. 2 to 2 μg/g in hair mercury concentrations resulted in a 3.6 mmHg (95% CL=4.3, 11. 5) increase in DBP and SBP increased 8.6 mmHg (95% CL=0.9, 16.2). Potential maternal risk factors such as smoking, alcohol intake, age, weight and height and other pot ential child risk factors such as breastfeeding duration and parity did not appear to be significant predictor s of the subjects’ blood pressures. The authors also evaluat ed birth weight, placenta weight, and gestational age simultaneously an d found that these coefficient s were not statistically significant predictors for DBP or SBP. T he authors then divided current body weight, birth weight and placenta weight into quar tiles and found no statistically significant effects. Assessment of mercury exposure using a biomarker such as cord blood mercury levels is indicative of fetal expos ures over a relatively short time period (between 1 and 2 months). Ha ir mercury concentrations in tegrate a broader period of exposure than cord blood meas ures (Grandjean et al., 1999). Blood pressure data were collected again in this cohort at 14 years of age (Grandjean et al., 2004). At t hat age, no effect of intrau terine mercury exposure was observed on systolic or diastolic blood pres sure. Shifts in childhood blood pressure, while potentially important, do not appear to be as consequentia l as those that persist

into adulthood. Other epidemiologic data describe a ssociations between methylmercury exposures and hypertension. Tsubaki and Ir ukayama (1977) describe a retrospective epidemiological study designed to estimate t he prevalence of Minamata disease, which was caused by exposures to high levels of methylmercury, among residents of three51 Japanese cities around Minamata Bay. Fr om 1932 to 1968 a factory polluted the bay with mercury leading to elevated concentrations of methylmercury in the fish consumed by the residents (e.g., marine products from Minamata Bay had 5.61 to 35.7 ppm mercury (Harada, 1995)). Residents in cities of Minamata (n=965), Goshonoura (n=1723) and Ine (n=608) were evaluated. Fi sh in the Minamata area were the most heavily contaminated by methylmercury; those in Goshonoura were considered moderately contaminated, and th ose in Ine were unlikely to be contaminated by the factory. This is supported by the reported frequencies of Minamata disease diagnosis in the three cities; in Minamata 28.5% of the individuals ex amined had Minamata disease, compared to 1.8% in Goshonoura, and 0.2% in Ine. Tables 14 and 15 identify the frequencies with which individuals with Minamat a disease and those in the control group were diagnosed as hypertensive. In this study, the criteria for a diagnosis of hypertension were a SBP great er than 160 mm Hg or a DBP greater than 95 mmHg; these criteria are slightly higher than the current criteria for hypertension of 140/90 mmHg. The incidence of hypertension in residents of Minamata and Goshonoura who were not diagnosed with Minamata disease is not given. Discussion of the six individuals with congenital Minamata di sease (born in Minamata between 1955 and 1962) did not identify hypertension as a “main symptom.” While the co-occurrence of hypertension and Minamata disease is inte resting, these individuals experienced methylmercury exposures that were much greater than those occurring through fish consumption currently in the U.S. and in the Faroe Islands.52 Table 14. Co-Occurrence of Minama ta Disease Diagnoses and Diagnosis of Hypertension and the Occurrence of Hypert ension in the Control Group from the City of Ine Disease Status Hypertensive/ Total Disease* Minamata Goshonoura Minamata Disease 83/269 (31%) 19/34 (56%) Suspected Minamata Diseas e 7/19 (37%) 12/31 (39%) Deferred Diagnosis 5/15 (33%) 10/29 (34%) Hypertensive/ Total Evaluated No Disease (Residents of Ine) 109/608 (18%) *Data exclude congenit al Minamata Cases

Table 15. Comparison of Hypertensi ve Diagnoses Between Those with and Without Minamata Disease in Tw o Different Age Categories Minamata Disease Status/City/Age ca tegory Hypertensive/Total Examined Positive Diagnosis/Minamata/> 39 Years old 79/214 (36.9%) Positive Diagnosis/Minamata/< 39 Years old 4/55 (7.3%) Disease-Free/Ine/>39 Year s old 107/378 (28.3%) Disease-Free/Ine/<39 Y ears old 2/230 (0.9%)

In the section of Minamata that had t he highest reported number of cases of Minamata disease, Tamashiro et al. (1986) studied mortality patterns based on death

certificates between 1970 and 1981. The frequency at whic h the cause of death was listed as hypertension or hyper tensive heart disease did not di ffer significantly from their comparison population that wa s not exposed to elevated mercury levels. As noted previously, the frequencies of heart disease and cerebral infarction in the study53 population were lower than t hose of the general Japanese population but the frequency of cerebral hemorrhage, a s equela of elevated blood pressure, was significantly higher. Adequate death records were not maintained prior to 1970. Further analyses of the

Minamata disease cases do not appear to address the incid ence of hypertension (e.g., Fukuda et al., 1999). 21

1.5.3.2. Childhood Cardiac Rhythms

― Sorensen et al. (1999) also reported t hat heart rate variability decreased with

increased in utero methylmercury exposures. Specifically, in the children aged 7 years,

the variation for the R-R interval was decre ased (i.e., variability of cardiac electrical impulses was decreased). 22 At age 14 years, the heart ra te variability was evaluated again in the same cohort (Grandjean et al ., 2004). The autonomic nervous system controls heart rate function through both low frequency and high frequency activities.

21 Two relevant studies that evaluated the effect of methylmercury on blood pressure in rodents were identified. Tamashiro et al. (1986) administer ed methylmercury (2 mg/kg-day) to a 7-week-old spontaneously hypertensive strain of rats for 26 days (n=10 males and 10 females). Relative to untreated controls, the SBP of the treated fe males was significantly higher du ring weeks 3 and 5 of the 5 week experiment. The blood pressures in the treated male s were lower than controls during weeks 3 and 4, but only 2 males survived through week 4 and no males survived week 5. Wakita (1987) administered 0.5 mg/kg methylmercuric chloride via oral gavage to four groups of male Wistar rats (age and exact number of animals (likely n < 8) in each group is unknown) for 23 to 28 days (total dose = 11.5-14 mg/kg), 120 days (total dose = 60 mg/kg), 180 to 210 days (total dose = 90-105 mg/kg), or 220 to 240 days (total dose = 110 to 120 mg/kg). SBP of the treated animals did not differ from controls (n=9) until afte r day 60. After this point the SBP of all treated rats were static ally significantly elevated above those in the control group. The last blood pressure measurement of the low dose group occurred at 120 days. Blood pressures were measured to approximately 450 days in the other dose groups and the controls; SBP in these groups were also statistically significantly hi gher than the controls. Small numbers of experimental animals, short study durations, high doses, and inade quate data reporting render further interpretation of the relationship of these studies to the association reported by Sorensen difficult. A series of chronic methylmercuric chloride feeding studies did not repo rt blood pressure measures in the treated animals (Mitsumori et al., 1983, 1984, 1990). In conclusion, the bioassay data, like the data from the Minamata disease cases, suggest that high levels of me thylmercury may be associated with blood pressure increases, but neither body of data directly addre ss the issue of fetal methylmercury exposures. 22 Each heartbeat is a discrete event affected by both sympathet ic and parasympathetic influences. When the heart beats, electrical impulses are dischar ged. Electrocardiography can be used to record these electrical discharges as a series of defined waves. One heart beat consists of a P wave, a QRS wave, a T wave, and finally a U wave. The R-R wave intervals are used to define the period of time between sequential heart beats.54 Both low frequency and high frequency activiti es decreased with increasing intrauterine exposures in a statistically significant manner . Heart rate variability also declined with increasing exposures in a statistically sign ificant manner. While there is clinical significance to these declines in heart rate variability among the elderly and those who have suffered a heart attack, the clinical signi ficance of such decreases in apparently healthy children remains an active research ar ea in pediatric cardiology but is unknown

at this time. Given that the decreased heart rate variability persisted until age 14 years, and that this effect is prognostic of sudden deat h from arrhythmia in adults, additional research into this possible effect of intrauterine methylmercury is warranted.

1.6. REGULATION AND CONTROL OF MERCURY EMISSIONS FROM POWER PLANTS The proposed CSI, a bill introduced in February 2003 in the U.S. House of Representatives (HR 999) and the U.S. S enate (S. 485), would create a mandatory program that would reduce power plant em issions of mercury, sulfur dioxide and nitrogen oxides. Under Section 473 of this proposal, mercury emissions from the U.S. coal-fired power plants would be reduced in two steps. In the years 2010 and 2018, annual mercury emissions from power plant s would be capped under a national marketbased “cap and trade” approach. The U.S. power plants subjec ted to CSI are estimated to currently emit 49 tons of mercury per year; under CSI they would face a cap of 26 and 15 tons per year in 2010 and 2018, respectively (U.S. EPA, 2003a). Other bills that would reduce mercury emissions have been in troduced to the US Congress and EPA is considering regulations under the Clean Air Act as well. 23

23 As we note in the Introduction, CSI has been reintr oduced in the U.S. Congress in 2005 with a major change in mercury control requirements: the first-phas e cap in 2010 now is 34 TPY instead of 26 TPY,55 1.7. PURPOSE OF REPORT Previous studies that evaluated the impac t of reducing mercury emissions from power plants either did not evaluate the pot entially resultant changes in methylmercury exposure in the U.S. populat ion (U.S. EPA, 2003b) or, if ex posure was evaluated, were unable to adequately evaluate differences in source s of fish in U.S. diets (EPRI, 2003). Our goals were to characterize the impact t hat reductions in mercury emissions from power plants would have on methylmercur y exposures in the U.S. population and to estimate quantitatively the economic benefit of the plausible improvements in the health of the U.S. population.

1.8. OVERVIEW OF ANALYSIS We investigate five U.S. mercury emi ssions scenarios. The first scenario is based on current mercury emi ssions, two scenarios are based on U.S. EPA projections of changes in mercury emissions that are projected to occur in 2010 and 2020 under current U.S. regulations (taking into account factors such as economic growth), and the final two are based on likely emissions if the CSI passes into law. 24 To estimate the change in exposure, we identify eight geographic regions that

are sources of consumable fish and estimate t he quantity of fish from each region that is consumed, noting the current population expo sures and the size of the consuming

the second-phase cap remains at 15 TPY in 2018. The analysis in this report, however, is based on the 2003 version of the CSI. 24 Specifically, the geographic distribution and total me rcury emissions, as predicted by U.S. EPA’s application of the Integrated Planning Model, were used as inputs to the REMSAD model. U.S. EPA simulated mercury deposition using the REMSAD model. The Agency used this model to predict current atmospheric deposition levels and to project mercury deposition levels corresponding to the two future “base case” (without CSI controls) and “control case” (with CSI controls) scenarios for the years 2010 and56 population. The impact of decreased mercury emissions from power plants on mercury deposition in these regions is estimated and related to decreases in methylmercury

concentrations in these fish. Based on data that describe the types of fish consumed and current fish consumption rates, we esti mate changes in methylmercury exposure in the U.S. population. Next, we assign monetary estimates to reductions in human health effects plausibly associated with exposures to me thylmercury through consumption of U.S.

commercial and non-commercial fish. The monet ary estimates are primarily based on human capital approaches. We also estima te these values based on health-related quality of life measures. To estimate the economic impact, we estimate the number of children that may incur neurodevelopmental e ffects of the type that have been observed in epidemiologic studies of pregnant women and t heir children. We note that this is the primary health concern associated with methyl mercury intake from fish (Grandjean et al., 1997; Kjellstrom et al., 1986, 1989). We also estimate cases of adult myocardial infarction and premature death. These effect s are valued using a cost-of-illness and a willingness-to-pay approach, respectively. Our model for estimating current exposures and the changes in U.S. methylmercury exposures and the economic im pacts that might result from decreased mercury emissions from power plants is pres ented in Section 2. In Section 3, the results of the modeling effort is presented and in Section 4 these results are discussed.

2020. The model simulated deposition of elemental, di valent particulate mercury, and gaseous divalent mercury at a 36 Km x 36 Km grid resolution fo r the continental USA and the surrounding waters.57 2. METHODS Figure 3 depicts our conceptual model for estimating the impact of decreases in U.S. power plant mercury emissions on methyl mercury intake in the U.S. population. Mercury emissions sources are divided into two categories: coal-fired power plants and all other sources (depicted as ovals). Merc ury emissions from a ll non-U.S. sources are

assumed to remain constant. 25 The arrows represent the movement of mercury from

environmental media to fish and fish to hum ans (depicted as boxes). Because changes in power plant mercury emissions result in spatially varying decreases in the predicted quantities of mercury deposited, the sources of wild fish for U.S. consumers are divided into eight geographic regions (Figure 4). In the model, each region is an isolated compartment, connected only with the atmos pheric compartment. There are five freshwater regions in the c ontiguous U.S.; these are depict ed as a single box in Figure 3 and geographically in Figure 4. The boundaries of the five freshwater regions follow. 1. Northeast Region includes New England and New York State, 2. Mid-Atlantic Region includes the St ates of Virginia, West Virginia, Pennsylvania, New Jersey, Maryland and Delaware, 3. Southeast Region includes the States of Arkansas, Tennessee, and North Carolina and the States to the south of these States, 4. Midwest Region includes the Stat es of Kentucky and Missouri and the States to their North incl uding Ohio and Minnesota, and 5. West Region includes the States of Texas, Ok lahoma, Kansas, Nebraska, Iowa and the Dakotas and States to t he west in the conterminous U.S.

25 While there is evidence that global mercury emis sion and deposition rates are decreasing (Schuster et al., 2002; Slemr et al., 2003), the model results of La mborg et al. (2002) indicate that atmospheric concentrations are increasing, suggesting that anthropogenic emissions may be increasing globally.58 The three saltwater regions are depicted as separate boxes in Figure 3 and also geographically in Figure 4. 1. Atlantic Coastal Region includes the continental shelf adjacent to the eastern seaboard of the U.S. This region is bounded on the west by the U.S. coastline from Maine to the easte rn coast of Florida and extends east 200 miles in width from the U.S. coast. 2. Gulf of Mexico Region includes the continental shelf under the Gulf. This region is bounded by a point 50 miles s outh of Florida ex tending west to Mexico and from that line north to the continental U.S., and, 3. All Other Waters Region includes all other oceans and seas and the parts of the Atlantic Ocean not included in the Atlantic Coastal Region.59Figure 3Conceptual Model of Human Mercury ExposuresFreshwaterFishGulf ofMexico FishRegional Modelsof Atmospheric HgDepositionGlobal Model forDeposition ofAtmospheric HgOtherMarine FishUS Power PlantHg EmissionsOther Hg Naturaland AnthropogenicEmissions SourcesConsumers ofNon-CommercialFreshwater FishConsumers ofCommercial FishConsumers ofNon-CommercialAtlantic FishAtlanticCoastalFishConsumers ofNon-CommercialGulf Fish60Figure 4Regions Considered in Model61 The model is based on two primary assumptions: 1. Equilibria exist between total deposited mercury and fish methylmercury concentrations and between the fish methylmercury concentrations and methylmercury exposures in the i ndividuals who consume these fish. 2. Changes in the total quant ity of mercury deposited lead to proportional changes in fish methylmercury concentrations 26 , assuming no other factors change (i.e., ceteris paribus ). As a consequence of these two assump tions, changes in mercury deposition rates result in proportional changes in human methylmercury intakes, because there are no changes in other factors in cluding the regions where fish are captured and types or quantities of fish that humans consume. The first assumption is implemented, in part, through Equation 2, which is a general equation applicable to the commercia l and non-commercial fish included in the

model.

Cf = Cc x Df Dc ij ij j j (Eq. 2)

where:

Cf ij = mean methylmercury concentration in the i th fish type in the j th region (μg/g) under an alternat e emissions scenario

Cc ij = current mean methylmercury concentration in the i th fish type in the j th region (μg/g)

Df j = mean annual mercury deposit ion rate in the j th region (μg/m 2 /yr) under an alternate emissions scenario

Dc j = current mean annual mercury deposition rate in the j th region (μg/m 2 /yr) The premise of Equation 2 is that separ ate equilibria exist between the mercury deposition rates in each of the eight regions and the mean methylmercury

26 U.S. EPA (2001d) and EPRI (2003) invoke a similar assumption.62 concentrations in the different types of fish which inhabit these regions. Changes in mercury emissions lead to changes in mercury deposition rates that are specific to each region. Based on assumption 2, in each region the changes in the methylmercury concentrations in each type of fish are proportional to the changes in the mercury deposition rates. Thus, the estimate of alternative mean methylmercury concentration in each type of fish in a region is the pr oduct of the current mean concentration and the

ratio of the alternative to cu rrent mercury deposition rate.

2.1. CHANGES IN MERCURY DEPOSITION For each region, the changes in mercury de position that result from additional control of U.S. power plant mercury emi ssions are based on U.S. EPA’s analysis of the impacts of the CSI (U.S. EPA, 2003a,b). Ba sed on REMSAD simulations, the U.S. EPA estimated mercury deposition rates corres ponding to the following five emissions

scenarios: current emissions , mercury emissions in 2010 ( Baseline 1 ) [this includes projected changes in U.S. me rcury emissions], mercury emissions in 2010 with a 47% reduction in U.S. power plant emissions associated with implementing the CSI ( Scenario 1 ), mercury emissions in 2020 ( Baseline 2 ), and mercury emissions in 2020 with a 69% reduction in U.S. power plant em issions associated with implementing the CSI ( Scenario 2 ) (U.S. EPA, 2003a,b).

For the five freshwater regions, the Atlantic Coas tal Region and the Gulf of Mexico Region, we summed all forms of me rcury predicted to deposit in each 36 x 36 Km grid cell under each of the five emissi ons scenarios (i.e., Current, Baseline 1, Scenario 1, Baseline 2, and Scenario 2). Then, for each grid cell in each region, we developed four ratios of me rcury deposition. The tota l annual mercury deposition63 associated with current emissions, as predi cted by EPA’s REMSAD model, was used as

the denominator in each ratio. The numer ators were the predicted total mercury deposited in the grid cell in Baseline 1, Scenar io 1, Baseline 2, and Scenario 2, based on the relevant REMSAD simulation modeling. Thus, the four ratios are Baseline 1/Current, Scenario 1/Current, Baselin e 2/Current, and Scenario 2/Current. We assume the waters in the Atlantic Coastal and Gulf of Mexico Regions to be well mixed. There are 539 and 511 REMSAD gr id cells in the Atlantic Coastal and the Gulf of Mexico Regions, res pectively. For each of these two regions, we estimated the

mean value of each of the four ratios. These eight me an ratio values were used in subsequent calculations to estimate fish methylmercury concentrations under different

emissions scenarios. For each of the five freshwater regions, using Crystal Ball® (Decisioneering, Inc., Denver, CO), we developed unique distributions of these four ratios (i.e., Baseline 1/Current, Scenario 1/Current, Baseline 2/Cu rrent, and Scenario 2/Current); thus, we developed a total of 20 unique di stributions of these ratios (the product of 5 regions and 4 ratios in each region). A distribution was estimated from the vector of equallyweighted grid cell values. In subsequent calculations, by sampling with replacement,

these distributions are used to estimate the variability of changes in regional freshwater fish methylmercury concentrations asso ciated with mercury emissions reductions. Because U.S. mercury emissions are transported large distances to reach remote areas encompassed by the All Other Waters Region (e.g., Eastern Atlantic Ocean and Pacific Ocean), we assume that U. S. anthropogenic emissions become wellmixed with mercury emitted from other sources in the atmosphere during long-range64 transport. Under the well-mixe d assumption, we assume that the mercury emitted from U.S. power plants deposits in t hese distant areas, at the same rate as the rest of the mercury in the atmosphere. Thus, in the All Other Waters Region, mercury emitted

from U.S. sources is estimated to contri bute to mercury depositi on in proportion to its contribution to global anthropoge nic and natural emissions. (Current mercury emissions from the U.S. utility se ctor are approximatel y 1% of total mercury emitted globally and total U.S. anthropogenic emission s are roughly 3% [Table 1].)

2.2. METHYLMERCURY EXPOSURES THROUGH FISH CONSUMPTION Our model simulates exposures for consum ers of fish from the U.S. commercial seafood market (Section 2.2.1), the most co mmon source in the U.S., and consumers of fish not caught commercially (Section 2.2. 2). The model assumes commercial fish

consumers obtain their fish from a mix of four sources: the coastal Atlantic, the Gulf of Mexico, All Other Waters and aquac ulture (or farm-raised fish). 27 Non-commercial fish

consumers are assumed to eat a mix of fish caught in an individual region and a mix of

commercial fish. The sizes of the fish-cons uming populations are estimated in Section

2.2.3. 28

27 Clearly, there may be regional sources for some co mmercial fish. Under these assumptions, the variability associated with the consum ption of commercial fish from a small region will not be captured in this model. 28 The assumption that non-commercial fish consumer s eat a mix of the most common types of fish caught or targeted for capture reduces the variability in the model. Some consumers may target specific species for consumption (or converse ly avoid certain species); methylmercury intakes may vary markedly from the weighted means calculated for each region.65 2.2.1. Methylmercury Exposures Thr ough Commercial Seafood Consumption. Carrington and Bolger (2002) adjusted their simulations of human methylmercury intake through commercial seafood consumpti on to match the distribution of measured U.S. blood methylmercury levels (Maha ffey et al., 2004, based on NHANES survey data, Table 16). Given that 70%-90% of t he U.S. population consumes commercial fish (Carrington and Bolger, 2002), we assume that the distributi on of blood methylmercury concentrations reported by Mahaffey et al. (2 004) is a consequence of commercial fish consumption. Some individ uals consume no fish. These individuals are represented in the blood methylmercury distribution reported by Mahaffey et al. (2004) . In the model, we assume these individuals (i.e., those that consume no fish) to be in this population.

Table 16. Blood Methylmercur y Concentrations (μg/L) in U.S. Women Aged 16 to 49 Population n Geo. b Mean 5 th c 10 th 25 th 50 th 75 th 90 th 95 th

0 fish and shellfish meal in previous 30 days

a 480 0.39 -- -- -- -- 0.44 1.1 1.6

1-4 fish and shellfish meals in previous 30 days 780 0.7 -- -- -- 0.6 1.29 2.9 4.7

5-8 fish and shellfish meals in previous 30 days 230 1.33 -- -- 0.43 1.29 3.29 6.1 9.9

>8 fish and meals in previous 30 days 153 2.46 -- 0.44 1.15 2.75 5.2 11.1 12.1

Total 1707 0.8 -- -- -- 0.6 1.7 4.44 6.73 Source: Mahaffey et al., 2004

a Fish meal - self-reported number of fish meals in the 30 day period prior to study participation. b Geo. Mean – reported geometric mean.

c 5 th , 10 th , ... 95 th – percentiles of total blood methylmercury concentration66 Based on assumption 1 (see introduction to Section 2), we assume the reported blood methylmercury distributi on in U.S. commercial consum ers to be in equilibrium with the mean methylmercury concentration in co mmercial fish weighted by per capita consumption rates. This imp lies that the fish that comme rcial fish consumers eat are well mixed with respect to the types of fish consumed and the regions in which fish are caught. We use Equation 3 to develop a current weighted mean methylmercury

concentration for commercial fish.

Cmc w x Cc

w i i i 1 24

i i 1 24 = =

=



## ∑ ∑ (Eq. 3)

where: Cmc = current weighted mean me thylmercury concentration for all commercial fish (μg/g)

w i = estimated annual per capita consum ption rate of the ith fish type (kg/person year)

Cc i = current mean methylmercury concent ration of the ith fish type Carrington and Bolger (2002) report current per capita consumption data and mean methylmercury concentrati on data for 24 types of commercial fish (Table 2),

which account for over 90% of the mass of fish in the U.S. commercial market. We assume that the methylmercur y concentrations and the per capita consumption rates of the remaining 10% of the mass of commercial fish to be similar to those reported by Carrington and Bolger. Based on the first two assumptions, the estimated annual per capita consumption

rate of the i th fish type, w i , remains constant over time; thus, Equation 3 can be modified to estimate the weighted mean methylmerc ury concentration for all commercial fish67 under alternative emissions scenarios with corresponding alternative regional mercury deposition rates.

Cmf w x Cf

w i i i 1 24

i i 1 24 = =

=



## ∑ ∑ (Eq. 4)

where: Cmf = weighted mean methylmercury concentration for all commercial fish under alternative em issions scenario (μg/g) w i = estimated annual per capita consumption rate of the i th fish type (kg/person year)

Cf i = mean methylmercury concentration of the i th fish type (μg/g) under alternative emissions scenario (See Eq. 5) In this model, the mean concentration of i th fish type (Cf i) under an alternative deposition scenario depends on the change in mercury deposition in the region. While the regional harvest data can be estimated for individual fish types (NMFS, 2002), the methylmercury concentration data on commercial fish in each region are not available. Only the mean methylmercury concentration data are reported for each type of commercial fish. For an individual type of commercial fish, we used Equation 5 to estimate the mean methylmerc ury concentration in fish under alternative deposition

scenarios.

Cf Cc x Q Q x Df Dc i i j

j j 1 n j j j 1 n

= 

     

    

= = 

## ∑ ∑ (Eq. 5)

where:

Cf i = mean methylmercury concentration of the i th fish type (μg/g) under alternative emissions scenario68 Cc i = current mean concentration of the i th fish type (μg/g)

Q j = quantity of fish har vested annually from the j th region (kg/yr)

Df j = mercury deposition rate in the j th region in which t he fish are captured under alternative emissions scenario (μg/m 2 /yr)

Dc j = current mercury deposition rate in the j th region in which the fish are captured (μg/m 2 /yr) In Equation 5, for each region in whic h commercial fish are captured, we calculate, based on mass, the fr actional contribution of the individual harvest from each regional source to the total annual quantity of the type of fish harvested (Q j / Σ Q j ). Next, the ratio of Df/Dc provides an estimate of the change in the mercury deposition rate in the region. We compute the pr oduct of the fractional contri bution of the fish caught in each region and the ratio of the alternative and current depos ition rates. By summing this product across the regions where fish are caught and multiplying by the current mean fish methylmercury concentration, an alternative mean methylmercury concentration can be estimated for a specific type of fish. In this model, commercial fish are obtained from four r egions, the coastal Atlantic, the Gulf of Mexico, the All Other Waters Region, and aquaculture. We merged the fish raised in aquaculture and those inhabi ting All Other Waters into a single group.

After estimates of Cf are calculated for eac h fish, Equation 4 is used to estimate the change in the weighted mean methylmercury concentration for commercial fish. Next, we determine the quantity of each type of commercial fish caught in the three saltwater regions. We assume that t he methylmercury concentrations of all fish imported into the U.S. are in equilibrium with the mercury deposited in the All Other Waters Region. Because tuna, shark and swordfish are highly migratory and may69 consume prey over broad geogra phic ranges, their methylme rcury concentrations likely are influenced by mercury deposition over broad geographic areas. We assume the methylmercury concentrations in these thre e types of fish to be in equilibrium with mercury deposited to the All Other Waters Region. 29 Methylmercury concentrations in the commercial freshwater species, obtai ned primarily from aquaculture, are also assumed to be in equilibrium with mercury deposited in the All Other Waters Region. 30 We assume that the remaining 19 types of marine fishes that are caught by the U.S. domestic fishing fleet in the Atlantic Ocean or the Gulf of Mexico inhabit and feed on prey exclusively from the Atlantic Coasta l Region or the Gulf of Mexico Region. Thus, in the model, the methylmercury concent rations in these fish are in equilibrium with mercury deposition in these regions. We used commercial fish data compiled by NMFS for year 2001 (summarized in Table 2) to estimate the fraction (based on mass) of each type of fish in the U.S. commercia l market that was caught in the Atlantic Ocean, the Gulf of Mexico, the Pacific Oc ean, the Great Lakes, and imported. The mass of each fish type harvested by the U.S. fishing fleet is reported by NMFS as a round weight (i.e., whole fish weight). NMFS reports imported and exported fish as product weights. To estimate the original whole fish weights of imports and exports, we

29 Some species of tuna and shark do not appear to migrate over broad geographic regions of the oceans. Regional mercury deposition patterns may be im portant sources of methylmercury in these fish. Our model does not incorp orate this possible sour ce of local variability. 30 We note that aquaculture fish are not part of the wild foodchain. Pr otein sources in many farm-raised fish diets include fish meal obtained from wild-caught fish. Meal derived from plant proteins is sometimes mixed or substituted, depending on the type of fish. Methylmercury concentrations in the wild-caught fish used in the fish meal depend on the type of fish used and its location. Because sources of such fish are not completely known, we employed a simplifying a ssumption that the methylmercury concentrations in the fish used in fish meal were in equilibrium with merc ury deposited in all other wa ters. If fed fish meal or fish oils, the methylmercury conc entrations in these feed sources are likely low because fish meal and fish oils are made from low trophic level marine species.70 converted the product weights to round weig hts using product specific conversion factors (provided through pers onal communication by Steve K oplin, NMFS). We then summed the round weights for ea ch product that contained a specific type of fish. There are other product codes that likely co ntain the types of fi sh analyzed here, but, because they do not identify a particular type of fish, they were excluded from the calculations contributing to the imprecision in our estimates. The weights of re-exported fish (i.e., fish imported into the U.S. and then exported) were subtracted from the total import weight of the particular type of fish. Exports of U.S. caught fish are assumed to be proportional to the fraction of the fish type caught by the U.S. fleet in each region. For example, in 2001, 1.7 million pounds of U.S. clams were exported; over 95% of these exported clams were assumed to have

been collected from the Atlantic and the remai nder collected in the Pacific and the Gulf

of Mexico.

We assume that the distribution of U. S. blood methylmercury concentrations in females of reproductive age (Mahaffey et al., 2004 in Table 16) is in equilibrium with the weighted mean methylmercury conc entration for commercial fish. 31 The blood methylmercury concentrations reported in U.S. women of reproductive age are assumed to be representative of the concentrations in this U.S. population. The data of Mahaffey et al. consist of commercial fish-consumers and non-fish consumers and are assumed to be representative of th is population in the model.

31 Implementation of this assumption decreases inter-i ndividual variability in the model results. While some consumers may eat a mix of fish, others may cons ume a subset of the 24 species (e.g., some may consume only tuna or salmon).71 Equation 6 was used to estimate the al ternative distribut ion of U.S. blood methylmercury concentrations among commercia l seafood consumers; this estimate is the product of the ratio of the alternative and the current mean weighted methylmercury concentrations in commercial fish (Cmf/C mc) and the distribution of the current methylmercury blood concentration in the U.S. population. Through an ad hoc procedure, we determined that a gamma distri bution (scale paramet er = 1.28 and shape parameter = 0.65) prov ided a reasonable fit to the centile data reported by Mahaffey et

al.



### Bf = Bc* ( Cmf Cmc ) (Eq. 6)

where: Cmf = weighted mean methylmerc ury concentration in U.S. commercial fish under alternat ive deposition scenario (μg/g)

Cmc = current weighted mean methylme rcury concentration in U.S. commercial fish (μg/g) Bf = blood methylmercury concentra tion under alternative deposition scenario (μg/L) Bc = current blood methylmercury concentration (μg/L) Thus, the projected change in methylmercury concentrations of commercial fish results in proportional shifts in the distribution of blood methylmercury concentrations in the commercial fish consuming populations. Ha ir mercury levels are predicted based on blood methylmercury concentrations (see Equation 1). Because the NHANES data reported by Maha ffey et al. are specific to U.S. females of reproductive age, we estimated ma le methylmercury expo sures by adjusting inputs to the methylmercury one-compartmen t pharmacokinetic model (see Equation 1).72 Using the gamma distribution developed for the blood methylmercury concentrations in U.S. females of reproductive age, male blood methylmercury concentrations were estimated based on a form of the “methylme rcury one-compartment model” (Equation 7) (Ginsberg and Toal, 2000; NRC, 1999; U.S. EPA, 2001c). Between the genders, we

assume that there are no differences in t he mean methylmercury concentrations of thecommercial fish consumed and in methylmercury toxicokinetics. Thus, using the onecompartment model, the differences between the genders’ blood methylmercury levels reduce to differences in fish consumption rates, body weight and blood volume. Average values for these differences are shown in Table 17. We assume the elimination constant, the gastrointestinal absorption factor, and the fraction of the absorbed methylmercury dose entering the blood to be same for both genders (see Equation 1).

C C X E E x Bw Bw V V Bm Bf m f m F

m f = (Eq. 7)

where:

C Bm = Male blood methylmercury concentration (μg/L)

C Bf = Female blood methylmerc ury concentration (μg/L)

E m = Male oral exposure (μg/kg-day -1 )

E f

= Female oral exposure (μg/kg-day -1 )

V m = Male blood volume (L)

V f = Female blood volume (L)

Bw m = Male body weight (kg)

Bw f = Female body weight (kg)73 Using the parameter values reported in Table 17 implie s that mean blood methylmercury concentration is 4.3% less in males than in females.

Table 17. Comparison of Body Weight , Blood Volume and Fish Intake Between U.S. Males and Females

Body Weight a (Kg) Total Blood Volume a (L) Mean Reported Fish Intake (g/person/day) Uncooked Fish Weight b (Age= 15-44 years) Adult Female 60 3.9 0.29118 Adult Male 73 5.3 0.30978

a Source: ICRP, 1975, 2003. b U.S. EPA, 1997b. For general U.S. population.

2.2.2. Methylmercury Exposures Thr ough Consumption of Non-Commercial

Seafood. U.S. residents are also exposed to meth ylmercury through consumption of noncommercial fish. We estimat ed the distribution of daily methylmercury intake for noncommercial fish consumers in each saltwater region and freshwater region separately.

We assumed “recreational” fish statistics to be a reasonable proxy for non-commercial fishing. We assumed that individuals cons umed a mix of non-commercial fish caught in a specific region. 32 We also assume that these non-commercial fish consumers eat a

mix of commercial fish. No individuals ar e assumed to consume non-commercial fish from more than one region. For example, no individuals are assumed to consume non-

32 This assumption, like the similar one employed fo r commercial fish consumers, reduces the interindividual variability captured in this model.74 commercial marine fish and non-commercial fr esh water fish because they would be obtained from two different regions.

2.2.2.1. Non-commercial C onsumers of Marine Fish

― The methylmercury intake for non-commerc ial consumers of marine fish caught

in the Atlantic and Gulf was estimated in a m anner similar to that of the commercial fish intake. Tables 4 and 5 list the mean methylme rcury concentrations reported in Atlantic and Gulf fish as reported in U.S. EPA (2003 c). We assumed these estimates of the mean concentrations to be representative of mean methylmercury concentrations in tissues of fish typically caught non-commerc ially in U.S. saltwaters and consumed. Because the EPA document does not identify the si zes or weights of the sampled fish, it is not possible to evaluate whether the r eported methylmercury co ncentrations for the fish in this database are repr esentative of fish consumed recreationally. Additionally,

there are a small number of sa mples reported for several types of fish. Equations 4 and 5 are used to estimate mean weighted me thylmercury concentrations for current

conditions and under alternative emissions sc enarios. To apply these equations, the w i term is a measure of the quantity of each fish type harvested non-commercially. Using the mean methylmercury concentration data and the estimated mass of the top 10 types of fish caught for consumption by non-commerci al fishers (NMFS, 2002) in Tables 4 and 5, we estimated a current weighted mean methylmercury concentration for noncommercial fish in the At lantic and the Gulf. Non-commercial fish consumption rates for Atlantic and Gulf fish are reported separately in U.S. EPA (1997a) . Equation 8 is used to estimate the current methylmercury intake rates among these consumers. By substituting the mean75 weighted methylmercury concentration under alternative emissions scenarios into Equation 8 (based on results of Equati on 5) methylmercury intake rates (I c ) are

estimated for consumers under these scenarios . This distribution of methylmercury intake rates was converted to mercury hai r concentrations using the one-compartment pharmacokinetic model (see Equation 1). I (CR x Cmc ) / Bw c F nm

= (Eq. 8)

where:

I c = oral intake of methylmercur y from current fish consumption (μg/kg-day) CR F = consumption rate of fish (g/day)

Cmc nm = current weighted mean methylmercury concentration in noncommercial marine fish (μg/g) Bw = body weight (70 kg)

2.2.2.2. Non-commercial C onsumers of Freshwater Fish

― We estimate methylmercury intakes through consumption of non-commercial freshwater fish based on a consumpti on model similar to Equation 8. We used data from the National Listing of Fish and Wildlife Advisories (NLFWA) Mercury Fish Tissue Database (U.S. EPA, 2003d as of October 31, 2003) to estimate methylmercury concentrations in U.S. freshwater fish. We initially identified a total of 57,678 samples in the database that reported methylmerc ury concentrations. We selected sample data that were identified in the database as fillets, skin-on fillets, skinoff fillets, and composite fillets (NLFWA Samp le ID numbers 4, 5, 6, and 7). Based on the fish samples' State of origin, we ca tegorized the sample data by U.S. regions (Figure 4).76 We then subdivided the regional data into nine categories corresponding to the U.S. FWS data describing the types of fish targeted by anglers. 33 Table 18 identifies the U.S. FWS category and examples of the types of fish we grouped under each category.

No steelhead trout were identified in t he NLFWA database; consequently, we did not use that category. We also ignored the U.S. FWS categorie s of “anything” and “other type of fish.” Finally, we assumed that only fish above a certain length were retained for consumption following capture. Samples fr om fish shorter than the minimum length requirements prescribed by the State of Pennsylvania (2003) and samples where fish length was not reported were excluded (Tabl e 18). (Comparison with several other State regulations indicates relatively consist ent size restrictions across States.) We note that minimum length requirements are not identified for certain types of fish in Pennsylvania; we imposed a minimum size of 5 inches for the sample to be considered, because people typically do not consume small fish. We assume t hat the fish tissue methylmercury concentrations reported in t he database are typical of those of fish captured and consumed.

33 The U.S. FWS (2001) national survey asked anglers to identify the type( s) of fish they targeted during their fishing and how much time (in days) they spent ta rgeting particular types of fish. The survey did not evaluate the types of freshwater fish consumed fo llowing capture. They report 12 categories of freshwater fish targeted for capt ure by freshwater anglers (listed in first row of Table 19).77 Table 18. Fish Size Restri ctions Imposed on Model Data U.S. FWS Designation Minimum Length a (inches) Examples of Types of Fish Included from NLFWA Database Crappie 5 b Black and white crappie Panfish 5 b Rock bass, bluegill, sunfishes, perch White Bass, Striped Bass, Striped Bass Hybrids 12 White bass and striped bass Black Bass 12 Largemouth, smallmouth and spotted bass

Catfish, Bullheads 5 b Bullheads, channel catfish, fathead catfish, white catfish, flathead catfish Walleye/Sauger 15/12 Walleye and sauger Northern Pike/Pickerel/ Muskie, Muskie Hybrids 24/15/30 Muskellunge, chain pickerel, and Northern Pike

Trout 7 Rainbow, lake, brook, splake, and brown trout

Salmon 7 Coho salmon, Atlantic Salmon, Lake Whitefish, cisco, Chinook salmon

a State of Pennsylvania (2003). b The State of Pennsylvania has no minimum length requirement fo r crappie, panfish and catfish; we imposed a minimum length of 5 inches for fish to be included in these categories.

Using Crystal Ball ® , we developed unique distributions for each type of fish in each U.S. Region based on the individual methyl mercury concentrations. For a sample size of n, the i th measurement was given a weight of 1/n when developing a distribution. Within each U.S. Region, targeting weight s were developed for each type of fish. For each State in a region, we multiplied the percent days spent fishing in that state by the percent days targeting a specific type of fi sh in each State. For a given type of fish, we summed these products across all the St ates in the Region to develop target weights specific to each ty pe of fish in each region. 34

34 This is parameter T in Equation 9.78Table 19. Percentage of Fishing Days Targeting Selected SpeciesState Crappie PanfishWhite andStripedBassBlack Bass CatfishWalleyeand SaugerNorthernPike, Pickereland MuskieSteelhead Trout Salmon Anything Other SpeciesAL18 16 10 36 11 0 0 0 1 0 5 3AZ5 5 12 24 13 0 0 0 28 0 13 0AR22 9 13 21 22 0 0 0 3 0 5 4CA4 4 10 15 10 0 0 0 35 13 7 3CO5 2 3 7 9 2 4 0 60 3 5 0CN0 10 12 32 2 0 4 0 31 0 8 0DE10 10 15 29 9 0 0 0 8 0 19 0FL13 15 8 27 14 0 0 0 1 0 13 9GA15 15 13 18 23 0 0 0 4 0 7 3ID2 3 2 12 7 0 0 11 54 10 0 0IL15 20 9 15 23 6 0 0 4 0 9 0IN16 27 11 23 16 3 0 0 0 0 4 0IA17 15 9 15 18 15 5 0 3 0 3 0KS19 9 7 21 25 9 0 0 2 0 7 0KY20 13 13 24 19 4 0 0 1 0 6 0LA20 1 9 22 18 0 0 0 2 0 5 5ME0 5 7 24 0 0 9 0 34 16 4 1MD4 8 12 30 13 4 0 0 13 0 7 9MA0 6 12 36 4 0 6 0 23 0 11 2MI7 34 5 14 0 9 9 3 7 3 7 0MN16 20 2 9 1 32 18 0 2 0 1 0MS18 17 9 18 27 0 0 0 2 0 9 0MO21 15 8 24 22 0 0 0 5 0 3 1MT0 5 0 6 2 12 12 0 49 4 9 179Table 19 cont.State Crappie PanfishWhite andStripedBassBlack Bass CatfishWalleyeand SaugerNorthernPike, Pickereland MuskieSteelhead Trout Salmon Anything Other SpeciesNE13 9 9 19 18 15 5 0 2 0 9 2NVND ND ND ND ND ND ND ND ND ND ND NDNH0 8 3 28 0 0 7 0 32 5 14 2NJ0 13 10 36 6 0 7 0 16 0 4 8NM2 3 7 9 11 0 0 0 58 7 3 0NY3 11 3 28 4 10 10 2 19 4 7 0NC19 11 12 24 17 0 0 0 5 0 8 5ND3 16 0 2 2 52 18 0 1 1 5 1OH11 17 7 31 16 11 0 0 3 0 4 1OK20 6 8 31 20 4 0 0 2 0 9 0OR1 2 2 5 3 0 0 14 41 21 6 7PA5 7 10 25 7 7 3 0 26 0 9 0RI0 9 19 26 0 0 0 0 30 0 17 0SC16 15 9 25 22 0 0 0 2 0 8 3SD5 18 2 5 6 42 12 0 3 0 7 0TN17 15 10 27 15 6 0 0 7 0 4 0TX18 5 14 27 30 0 0 0 2 0 4 0UT0 4 3 11 4 1 0 0 68 0 6 3VT0 13 3 10 3 7 7 0 34 9 11 3VA14 11 14 30 14 0 0 0 10 0 7 0WA1 3 2 10 0 0 0 18 39 21 2 4WV7 9 6 28 17 0 0 0 22 0 11 0WI11 29 3 17 1 17 15 0 4 0 3 0WY0 0 0 0 0 1 0 0 78 0 0 3ND= No data reported from State80 U.S. EPA (1997a) repor ts the mean and 95 th percentile fish consumption rates for recreational freshwater anglers to be 8 and 25 g/day, respectively, based on integrating the data repor ted from studies of freshwater anglers fishing in the states of Maine, New York and Michigan (U.S. EPA, 1997a citing Ebert et al., 1993; Connely et al., 1996; West et al., 1989). We assumed t hat these data were lognormally distributed

and calculated the standard deviation of these data to be 10.3 g/day. Some individuals and groups consume lar ge quantities of fish. To estimate exposures among these high-end consumers, we used information from the Columbia

River Inter-Tribal Fish Co mmission Report (1994). The R eport describes consumption rates among a group of Native Americans w ho likely consume fish at the upper end of

the distribution. 35 The mean and 95 th percentile fish consumption rates were 59 and 170 g/day, respectively. We assumed that these data were lognormally distributed and calculated a standard deviation of 64 g/day. We estimated methylmercury exposures to consumers of non-commercial U.S. freshwater fish as the produc t of the distribution of the weighted fish methylmercury

concentrations and the distribution of the freshwater fish cons umption rate (Equation 9). The change in mercury deposition in each region under alternat ive emissions was based on the unique distributions of the four ratios descr ibed in Section 2.1. We implemented a Monte Carlo approach using Crystal Ball ® . Each simulation consisted of 50,000 iterations.

35 While described as a study of “s ubsistence” angler c onsumption (U.S. EPA, 1997b), there is not a quantitative point or percent of total daily caloric intake or protein intake that results in a categorization of subsistence.81 

# [ ] I CR x T xC x

Bw F i ik Df Dc i 1 n L L

= = 

# ∑ (Eq. 9)

where: I = oral intake of methylmercury from fish consumption (μg/kg-day)

CR F = fish consumption rate (g/day)

T i = relative target frequency for i th fish type (unitless)

C ik = methylmercury concentration in the k th randomly selected fish of the i th fish type (μg/g)

Df L = future mercury depos ition rate of the L th randomly selected grid cell (μg/m 2 /yr)

Dc L = current mercury deposition rate of the L th randomly selected grid cell (μg/m 2 /yr) Bw = body weight (kg) n = n different types of non-commercial fr eshwater fish routinely targeted in a region In each iteration, a single REMSAD model out put from a randomly selected grid cell in a region was used to estimate the ratio of t he future to current mercury deposition rate (Df L /Dc L ). This ratio was multiplied by the fish methylmercury concentration of a randomly selected fish. We made no attemp t to estimate the time needed for a change in deposition to result in a change in fi sh concentrations. In each of the 50,000 iterations, each type of fish (n< 10) was selected once and weighted based on the relative target frequency for that fish type. The consumption rate data were also analyzed using the Monte Carlo approach. Thus, each non-commercial freshwater fish consumer is assumed to eat a mix of non-commercial fish caught in a specific r egion. The methylmercury content of each82 fish in the mix is obtained by randomly samp ling fish methylmercury concentrations for each type of fish from a given region. These values are independent of fish consumption rates. Changes in methylmerc ury concentrations of fish are based on randomly sampling from the ratios develope d in Section 2.1. For each of the populations of non-commercial fish consumers, the daily methylmercury intake rates were converted to changes in blood merc ury concentrations us ing a one-compartment methylmercury model (see Equation 1). The changes in mercury concentration in hair were estimated assuming a ratio of hair methylmercury concentration (μg mercury/g hair) to steady-state blood methylmercury co ncentration (μg mercur y/L blood) of 0.25:1 (U.S. EPA, 1997a, 2001a). 2.2.2.3. Commercial Fish Consumpt ion Among Those also Consuming NonCommercial Fish

― Non-commercial fish consumers are also exposed to methylmercury through the consumption of commercial fish . Connelly et al. (1996) devel oped estimates of total fish consumption and non-commercial fish consumpt ion among Lake Ontario anglers. They randomly drew a sample of 2,500 names from 1990-1991 New York fishing license records purchased in six counties bordering La ke Ontario. From the initial sample, 1,202 stated that they int ended to fish Lake Ontario in 1992 and were willing to participate in the study and 853 participated in the final study. Table 20 identifies the quantities of total and non-commercial fish c onsumed. In the Monte Carlo simulation model, non-commercial fish consumers were categorized based on the quantity of noncommercial fish they consumed each day. Tota l commercial fish in take in individuals that consumed less 0.6 g/day was estimated by subtracting the daily fish intake value83 from 8.8 grams/day, the daily intake of total seafood corresponding to the lowest category of recreational fish intake. Indi viduals consuming more than 39.8 grams of non-commercial fish per day were assu med to consume no commercial fish. 36 The remaining individuals in the simulation, who consumed between 0. 6 and 39.8 grams of non-commercial fish per day were categorized into 1 of 5 groups depending on the quantity of recreational fish t hey consumed; the groups were 0.6-2.2, 2.2-6. 6, 6.6-13.2, 13.2-17.9, and 17.9-39.8 (g /day). In these groups, tota l commercial fish intake was estimated by subtracting the non-commercial in take from the average of the total fish

intake values for the same stratum of the populat ion reported by Conno ly. For example, to predict commercial fish consumption am ong individuals that consumed between 0.6 and 2.2 grams per day of non-commercial fi sh, the quantity of non-commercial fish consumed was subtracted from 11.5, the average of 8.8 and 14.1 grams/day.

Table 20. Commercial Fish Intake Rat es Among Consumers of Recreationally Caught Fish

Percentile Source: Conolly et al., 1996 25 50 75 90 95 99 Recreational fish intake g/ day 0.6 2.2 6.6 13.2 17.9 39.8 All fish intake g/day 8. 8 14.1 23.2 34.2 42.3 56.6

2.2.3. Estimates of Population Size.

This section describes the method used to estimate the sizes of the U.S. population consuming non-commercial and commerc ial fish. Both the U.S. FWS and NMFS have developed estimates of the number of recreational fisher s in the Gulf of

36 The value of 39.8 g non-commercial fish/day is the 99 th percentile in the Conolly study.84 Mexico and the Atlantic Ocean (Table 3). We averaged these two estimates, assumed that 80% of the anglers consumed non-commercial fish (U.S. EPA, 1997a), and multiplied this value by 2.5 (U.S. EPA, 1997 a notes that anglers typically share their catch with 1.5 other people, t hus, on average, 2.5 individuals share a catch) to estimate

the number of consumers in the Atlantic Coastal and Gulf of Mexico Regions. In 2001, U.S. FWS estimated that ther e were 28.4 million freshwater anglers, based on their National Survey of Fishing, H unting, and Wildlife-As sociated Recreation.

Because of our interest in the source of the freshwater fish , rather than freshwater fish consumers’ residence state, we estimate th e number of freshwater fish consumers in each Region as the product of the total popul ation predicted to consume recreationallycaught fish and the percentage of days fishing (rela tive to the U.S. total) in each U.S. Region (Table 21). Because roughly 80% of anglers report eating their catch (U.S. EPA, 1997a), we adjusted our initial estimates by a factor of 0.8. Because, on average, anglers share their catch with 1.5 additional people, we multip lied the regional estimates by 2.5. We summed the total number of no n-commercial fish consumers from each of the seven regions and subtracted this from the total U.S. population to estimate the population that consumes no fish or only commercial fish.85 Table 21. Fishing Days by U.S. Region and Estimated Number of Consumers of Fish Caught in Each U.S. Freshwater Region Region Days Fishing Percent of Total Estimated Number of Consumers (thousands of fishers) Northeast 36,685,000 8.7 2,965 Mid-Atlantic 3,053,000 0.7 247 Southeast 109,505,000 25.9 8,852 Midwest 150,895,000 35.7 12,197 West 122,953,000 29.1 9,939 Total 423,091,000 100 34,200

The demographic distribution of each population was assumed to be the same as the general U.S. population. We assume that 22% of the non-commercial fish consumers are women of child-bearing age and that 6.2% of these women bear children in a given year (U.S. Census Bu reau, 2004, based on a 5-year average). The U.S. population above the age of 39 years is at higher risk for adv erse myocardial events than the population below age 39. Based on the 2000 Census data (U.S. Census Bureau, 2004), there were 55,681,000 males and 63,705,000 females above the age of 39 years in the U.S. in 2000; males and females above this age comprise approximately 19.8% and 22.6% of the total U.S. population, re spectively. There is no attempt to adjust this annual estimate to reflect possible demographic changes (e.g., changes in the fraction of women of child-bea ring age or birth rate) ov er time (e.g., 2010 or 2020).

2.3. NEUROTOXICITY Changes in IQ associated with intraut erine methylmercury exposures were estimated using Equation 10. Cohen et al. (submitted) developed a slope estimate of86 -0.7 IQ points per 1 ppm increase in hair me rcury concentration. Cohen et al. used a weighting procedure for the neurol ogical test results from the Seychelle Islands, Faroe Islands, and New Zealand studies. In a re- analysis of the New Zealand cohort study (Kjellstrom et al., 1989), Crump et al. (1998) reported a decreas e of 0.5 IQ points on the Wechsler Intelligence Scale for Children-Revi sed Full Scale IQ for each increase of 1 ppm methylmercury in average maternal hair during pregnancy. We note that in the analysis by Crump et al., the coefficient for the Wechsler Intelligence Scale for ChildrenRevised Full scale IQ was not statistically signif icant. At this time, there is not a single best estimate of the slope of this dose-resp onse function. A range of plausible slope estimates might include values close to 0 (i.e ., essentially no effect) up to values of -1. In this analysis an estimate of -0.6 IQ points per 1 ppm increase in hair mercury

concentration is used. We assume that th is estimate approximates a central tendency value for the slope. Section 1.5.1 descri bes the range of neurodevelopmental delays. While IQ, a rather blunt neurological assessm ent tool, would correspond to some of the domains identified, other neurodevelopmental delays may not be captured through an

IQ assessment. IQ D = -0.6 IQ points x Hg h (Eq. 10)

where:

IQ D = IQ point lost given maternal mercury exposure Hg h = maternal hair mercury concentration (ppm) Next, we discuss the implications of a toxicity threshold for neurodevelopmental effects from methylmercury ex posures. We identify a plausible surrogate value for a population toxicity threshold for methylmercury. The plausible threshold value is used in87 subsequent sections that discuss the human health benefits associated with reducing methylmercury exposures. The risk functions developed by Cohen et al. and Crump et al. did not use a

threshold. This is consistent with a st atement made by EPA in the discussion of the methylmercury oral reference dose (RfD); U.S. EPA (2001a) noted “no evidence of a

threshold arose for meth ylmercury-related neurotox icity within the range of exposures...” of the epidem iologic studies analyzed. 2.3.1. Populati on Toxicity Threshold: Background. A toxicity threshold for a population implie s the existence of a contaminant intake

rate that is without risk of adver se health effects (i.e ., “a safe dose” to which any person could be exposed without deleteri ous effects). The existenc e of a toxicity threshold implies the following functional form for estima ting the risk associated with an oral intake of a contaminant (Equation 11).

If D < T, P(D) = 0 (Eq. 11) D > T, P(D) = m x (D – T)

where: P(D) = Probability of effect at dose D T = Population toxicity threshold (μg /kg-day) D = average dose (μg/kg-day) m(*) = slope of the dose-response functi on observed for the contaminant (per μg/kg-day)We used the same value for m (0.6 IQ points/ppm hair) in both the threshold and nonthreshold analysis. Other slopes coul d be developed using other assumptions regarding the slope of the dose-respons e relationship around the threshold.88 RfDs have served as surrogates for, or approximate values of, toxicity

thresholds. The RfD is defined as “an esti mate (with uncertainty spanning perhaps an order of magnitude) of a da ily oral dose exposure to t he human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime” (U.S. EPA, 1988). Based on th is definition, doses at the RfD are not necessarily without risk. Based on the formul a for estimating the RfD (Equation 12), the

RfD is an estimate of a No-Observed-Adve rse-Effect Level (NOAEL) that would be observed in a well-conducted, chronic epidemiol ogic study in the most sensitive human population (Swartout et al., 1998), assuming t hat other relevant toxicologic data exist; under these conditions, each uncertainty fa ctor (UF) in the RfD equation would be

reduced to 1. 37 Identification of a NOAEL depends on the background incidence rate in the unexposed population, spacing of the do se groups, the number of organisms on study, and study quality including i dentification of a biologically or statistically significant LOAEL in the study. The true value of a popul ation toxicity threshold is likely between a study NOAEL and a LOAEL. A toxicity thres hold must be below a LOAEL and it could be less than a study NOAEL for a sensitive i ndividual (or for a sensitive subpopulation not considered during the development of an RfD).

RfD = NOAEL/UF (Eq. 12)

37 The EPA adjusts NOAELS or Lowest Observed Adverse Effect Levels (LOAELs) identified in animal bioassays by dividing by the product of 5 different uncertainty factors (U F) that account for 1) differences between species, 2) differences within a population, 3) differences between a subchronic and chronic study, 4) the use of a study LOAEL instead of a NO AEL, and 5) an insufficient toxicologic database.89 2.3.2. Population Toxicity Threshold for Methylmercury Exposures. The U.S. EPA’s oral RfD for methylme rcury is based on epidemiologic studies

conducted in the Faroe Islands and New Zealand (U.S. EPA, 2001a; NRC, 2000). EPA

considered the continuous test s employed to evaluate the s ubjects’ neurologic function using a benchmark dose (BMD) analysis. EPA interpreted a child’s neurodevelopmental test response to be abnormal, if the test result was below the value associated with the score achieved by the 5 th percentile of the general population. Doses corresponding to a 5% increase above background in the probability of having a

test result below the 5 th percentile of the general popu lation were identified as the

BMD 05 . For combinations of tests and subgroups within the Faroese cohort, the values reported for BMD 05 ranged from 41 to 393 ppb mercury in blood. A benchmark dose lower limit (BMDL 05 ) (the lower 95% confidence limit of the BMD 05 ) was calculated for each test in the three studies (listed in T able 2 of U.S. EPA, 2001a). The estimates of

the values for the BMDL 05 converged around average methylmercury intakes of approximately 1 μg/kg-day. U.S. EPA’s (2000) draft Benchmark Dose Guidance summarized a series of studies showing the BMDL 05 for reproductive and developmental toxicity data to approximate the NOAEL. EPA applied an intraspecies UF of 10 to the BMDL 05 when developing the methylmercury RfD. The Agency cited in ter-individual toxicokinetic variability

associated with the relations hip between the ingested dose and the cord blood mercury concentrations reported in the Faroes. T he Agency also cited possible toxicodynamic90 variability and uncertainty, noting the hi gh degree of homogeneity in the Faroese population. The EPA oral RfD for methylmercury is 0.1 μg/kg-day. 38,39 In the subsequent analysis, we estima te benefits alternatively assuming a population toxicity threshold for methylmercury does and does not exist. The data are

insufficient to exclude eit her possibility. To date, there does not appear to be substantial toxicologic evidence of a biolog ical population threshol d, but its possibility

cannot be eliminated. Based on the EPA’s BMD analysis, a range of plausible population toxicity thresholds exists. The range of values li kely extends from 0 μg/kg-day (i.e., no apparent threshold) to doses between the BMD 05 and the BMDL 05 . EPA’s RfD (0.1 μg/kg-day) is used as a surrogate based on c onvenience; we note that this value may be a conservative (smaller) estimate of a threshold, if one exists. Methylmercury is a neurodevelopmental to xicant. The threshold applies to maternal methylmercury intake during pregn ancy and for a period of approximately 1.5 to 3 months prior to conception. The hal f-life of methylmercur y in human blood is reported to be 44 days (Clar kson, 1997); thus, exposures prior to conception could affect the developing fetus.

38 Typically, noncancer environmental human health risk as sessments use the hazard quotient (HQ). The HQ compares the dose (D) to the refere nce dose (RfD), but does not quantify risk HQ = D/RfD . RfDs are derived from a NOAEL, LOAEL, or benchmark dose, with uncertainty factors generally applied to reflect data limitations such as uncertainty associated with in ter-individual variability. An HQ of greater than 1 implies that an individual is exposed above a ‘safe’ le vel, but does not predict risk or identify a threshold. 39 One component of the uncertainty factor us ed to estimate the EPA’s RfD was inter-individual toxicokinetic variability associated with the relati onship between the ingested dose and the cord blood mercury concentration. The data of Grandjean et al., (1999) describe the relationship between cord blood methylmercury concentrations and neurological tests. Stern and Smith (2003) published a quantitative analysis of this relationship showing the ratio of me thylmercury concentrations in cord blood to maternal blood to be approximately 1.7. Because the EPA assum ed that this ratio was 1, (i.e., maternal blood and cord blood methylmercury concentrations are the sa me), this study suggests RfD should be reduced by a factor of 1.7, unless the EPA would modify the UF for inter-individual variability.91 2.3.3. Analysis of Risk Assuming Threshold. 2.3.3.1. Estimating Size of Population Above Threshold

― The number of births above the possi ble neurotoxicity threshold in each population was estimated as t he product of the number of bi rths in each population and

the fraction of females of childbearing age in each population above the RfD. We note that Mahaffey et al. (2004) report that roughly 8% of children may be born to women whose methylmercury intake exceeds the threshold of 0.1 μg/kg day. 2.3.3.2. Methylmercury Intake Rate in Population Above RfD

― For the fraction of each population with an intake rate above the possible threshold (i.e., the methylme rcury RfD (0.1 μg/kg day), we estimated the conditional daily mean methylmercury intake rate. Th is rate was converted to a blood mercury concentration (see Equation 1) and then a conditional mean hair methylmercury concentration. Based on Equation 11, the average concentration of methylmercury in

the hair at the RfD was subtracted from the populations’ conditional mean hair methylmercury concentrations. This estima te of incremental (above threshold) concentration was combined with the slope in Equation 10 to estimate the IQ loss for the average individual in each annual birth c ohort. To estimate the population impact, the average IQ changes per child were multiplie d by the number of births occurring in a population to women whose methylmerc ury intake exceeded the threshold.

2.4. DOSE-RESPONSE FOR ACUTE MYO CARDIAL INFARCTION (AMI) AND ALL CAUSE MORTALITY (ACM) The predicted myocardial risks associat ed with methylmercury exposures should be interpreted with caution. Most of the ev idence of such risks is based on observations92 from a single cohort. Additi onally, a great deal of evi dence indicates that fish consumption in general protects individual s from incurring adverse cardiac events. If any of the three cardiotoxic modes of action for methylme rcury that were proposed by Salonen et al. (1995) are true (see Footnote 18), then myocardial risks likely increase with blood mercury concentrations, which in turn are highly influenced by fish consumption. Human studies were not designed to ex amine whether a doseresponse threshold exists for the effects eval uated. In Section 2.4.1, we develop doseresponse functions for increased incidence of myocardial infarction and all cause mortality. We use t he regression coefficients estimated by Salonen et al. (1995) when they modeled risk using the mercury concent ration as a continuous variable with the statistical controls using the Cox Proportional Hazards model (model 2 in Table 12). In Section 2.4.2, we discuss annual backg round rates of myocardial infarctions

and deaths in the U.S. We also discuss t he application of the dose-response models developed in two ways; first, we assume that the models apply only to males that consume non-fatty freshwater fish (similar to those described by Salonen) and, second, we assume that, if any of the three potential mechanisms of action proposed by Salonen are true, they could be generalized to and occur in the adult male and female population as well. Thus, we assume the regr ession coefficients observed in this cohort can be externally generalized to the U.S. population despite diffe rences between these populations and the types of fish consumed. We note that, if there is an antagonistic relationship between methylmercury and poly unsaturated fatty acids then the slope likely would be less steep for consumers of fish containing higher levels of polyunsaturated fatty acids. We also a ssume that the myocar dial events and the93 premature deaths observed in this Finni sh male cohort are related and use the information on the age of myocardial events to estimate the age at which a premature

death occurred. 2.4.1. Estimating the Dose-Response Functions. Using a Cox Proportional Hazards model, Salonen et al. (1995) estimated a relative risk for non-fatal and fatal myocardi al infarctions to be 1.068 per 1 ppm hair mercury. The Cox Proportional Hazards M odel, presented in Equation 13, assumes that changes in levels of the inde pendent variables, such as hair mercury concentrations, will produce proportional, time independent, changes in the hazard

function. 

### h(t) h exp( C ) o h 

### = β (Eq. 13) where: h(t) = hazard function at time t

h o = baseline hazard β = regression coefficient (ppm -1 )

C h = exposure measure hair mercury concentration (ppm)

The relative risk can then be estimated by the following equation:

RR h(t) h exp( C )

0 h = = β (Eq. 14) Rearranging,

ln(RR) ln h(t) h C

0 h =       = β (Eq. 15) Thus, 

### β = ln( ) C

h(t) h

h 0 (Eq. 16)94 When RR = 1.068 and C h = 1 ppm, then β = 0.066 ppm -1

. We substitute R AMI for h o and y for h(t) . For a given level of hair mercury, the incidence rate of acute myocardial infarc tions (AMI) is calculated by the following equation: y R Xexp( C AMI h = ) β (Eq. 17) where: y = incidence rate (yr -1 )

R AMI = AMI background incidence rate (yr -1 )

C h = mean hair mercury concentration (ppm) β = regression coefficient (ppm -1 yr -1 )

We estimate the increased incidence of all cause mortality (ACM) in a similar manner. Using a Cox Proportional Hazards model, Salonen et al. (1995) estimated the relative risk for ACM to be 1.09 per 1 ppm in crease in hair mercur y concentration. Using Equation 16, we estimate a value for β of 0.086 ppm -1 , when C h is 1. 2.4.2. Applying the Dose-Response Functions. In this section, we discuss annual backgr ound rates of myocardial infarctions (R AMI ) and death (R ACM ) in the U.S. Then, we discu ss the application of the doseresponse models. To avoid double-counting deaths, we includ e fatal myocardial infarctions with all cause mortality, and report separately non-fata l myocardial infarctions. In 2001, the AHA (2004) estimated that ther e were 865,000 new and recurr ent myocardial infarctions in the U.S. population. Giv en that these myocardial infa rctions lead to 184,800 deaths in 2001, roughly 21% of all AMI resulted in d eath. Specifically, among males, there were estimated to be 520,000 new and recurr ent myocardial infarctions and these95 resulted in 95,900 deaths (18.44%). Thus, in males, we estimate the non-fatal AMI by reducing the total AMI estimate by 18.44 %. Among females, there were estimated to be 345,000 new and recurrent myocardial in farctions and these resulted in 88,900 deaths (25.77%). Thus, we estimate non-fata l AMIs by reducing the total number of AMI by 25.77%. The average age of a first MI is 65.8 among men and 70.4 among women (AHA, 2004). We make the simplifying assumption that AMI only occur in people above age 39. Based on the 2000 Census data (U.S . Census Bureau, 2004), there were 55,681,000 males and 63,705,000 females above t he age of 39 years in the U.S. in 2000; males and females above this age comp rise approximately 19.8% and 22.6% of the total U.S. population, respectively. If all of the estimated 520,000 non-fatal AMI that occur annually in males occur in this population group, then the annual background rate of AMI in males above 39 years of age is roughly 9 x 10 -3 . If all of the estimated 345,000 non-fatal AMI that o ccur annually in females occur only in this population group, then the annual background rate in fe males above 39 years of age is roughly 5 x

10 -3

. In 2001, life expectancy, conditional on liv ing to age 65 (males) and 70 (females) was 86 years and 81 years for U.S. females and ma les, respectively (Arias et al., 2003). If we assume that the age of death coincide s with the average age of first myocardial infarct, then, on average, wom en and men would lose 16 year s of life due to increased mortality associated with methyl mercury. We emphasize that this is extremely uncertain and conducted as a bounding exercise. We note that the age of the males at the

initiation of the Salonen c ohort was less than 65 years.96 We apply these estimated annual risk coeffi cients in two ways. In the first approach, we limit the applicat ion of the non-fatal AMI a nd all cause mortality risk

coefficients to adult males that consume nort hern pike; this first application is based on

the discussion of the characterist ics of the fish implicated in the diets of Finnish male cohort in the Salonen et al. (1995) study. Li ke the Finnish fish consumed in the study, northern pike are caught in fr eshwaters and, as Figures 5-7 illustrate, relative to other freshwater species, nort hern pike have low levels of DPA, DHA, and total polyunsaturated fatty acids and high concentrati ons of methylmercury. As Figures 5-7 illustrate, other fish may contain low levels of polyunsaturated fatty acids.

Figure 5 DHA and Methylmercury Levels in U.S. Freshwater Fish

drum bass, striped spot pike, northern

walleye

catfish, channel bass, freshwater, mixed species

perch, mixed species pollock, walleye

sunfish, pumpkin seed

trout, mixed species

trout, rainbow, wild

salmon, atlantic wild salmon, coho, wild salmon, sockeye

0 0.1 0.2 0.3 0.4 0.5 0.6

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 DHA (g/100g)Mercury (μg/g)

Source of Mercury data: NLFWA Database, accessed 9/03 Source of DHA data: http://www.nal.u sda.gov/fnic/foodcomp/, accessed 6/0497 Figure 6 EPA and Methylmercury Level s in U.S. Freshwater Fish

drum catfish, channel bass, striped perch, mixed species

salmon, atlantic wild pike, northern

walleye bass, freshwater, mixed species

pollock, walleye

spot sunfish, pumpkin seed

trout, mixed species trout, rainbow, wild

salmon, coho, wild salmon, sockeye

0 0.1 0.2 0.3 0.4 0.5 0.6

0 0.1 0.2 0.3 0.4 0.5 0.6 EPA (g/100g)Mercury (μg/g)

Source of Mercury data: NLFWA Database, accessed 9/03 Source of EPA data: http://www.nal.usda.gov/fnic/foodcomp/, accessed 6/0498 Figure 7 Total Polyunsaturated Fatty Acid and Meth ylmercury Levels in U.S. Freshwater Fish

pike, northern

catfish, channel bass, freshwater, mixed species

bass, striped perch, mixed species

salmon, coho, wild salmon, sockeye drum walleye pollock, walleye

spot sunfish, pumpkin seed

trout, mixed species trout, rainbow, wild

salmon, atlantic wild

0 0.1 0.2 0.3 0.4 0.5 0.6

0 0.5 1 1.5 2 2.5 3 3.5 Total Polyunsaturated Fatty Acid (g/100g)Mercury (μg/g)

Source of Mercury data: NLFWA Database, accessed 9/03 Source of Polyunsaturated Fatty Acid data: ht tp://www.nal.usda.gov/fnic/ foodcomp/, accessed 6/04

Based on the species targeting informati on in U.S. FWS (2001), we estimated

the fraction of the fr eshwater fish consuming population that consumes northern pike in four of the five regions (pike are not listed among the top 10 species sought in the Southeastern Region). We assumed that t he frequency at which pike were targeted corresponded to the share of the non-commerc ial freshwater fish -consuming population that consumes pike. Less than 10% of freshw ater fishers report ta rgeting northern pike in each of the four regions.99 In a separate application, we assume that the myocardial toxicity and premature mortality associated with methylmercury is independent of fish type and polyunsaturated fatty acid levels. We apply the non-fata l AMI and all cause mortality dose response estimates to all males and females above the age of 39 years in separate calculations. Again, the external genera lization of the Salonen data and the resulting application is extremely uncertain.

2.5. VALUING CHANGES IN CHILDREN’S INTELLIGENCE Available economic research provides little empirical data for society’s willingness to pay (WTP) to avoid a decreas e in an infant’s IQ. Models have been developed to address t he monetization of a subset of the effects of decreased IQ. These effects would represent components of society’s WTP to avoid IQ decreases. Employed alone, these monet ized effects should underestimate society’s WTP. This section estimates the impact of small, permanent changes in IQ on lifetime earnings. This section has f our parts. Section 2.5.1 esti mates proportional impact of a 1 point change in IQ on lifetime earnings. Section 2.5.2 quantif ies average lifetime earnings for individuals born in the U.S. in the year 2000. Section 2.5.3 combines the

results of Sections 2.5.1 and 2.5.2 to esti mate the absolute impact of a 1 point change in IQ on lifetime earnings. Finally, Section 2. 5.4 considers the impact of changes in IQ

on costs associated with remedial education. This last cost category reflects the impact of population shifts in IQ on the proportion of individuals in tail categories of the IQ

distribution. We note several caveats. First, our re sults serve as a conservative estimate (i.e., likely lower bound) of the total val ue individuals place on changes in IQ because100 they may also value such changes independent ly of their impact on lifetime earnings. Second, our application of t he results from this analys is to the assessment of intrauterine methylmercury exposures makes t he implicit assumption that the impact of methylmercury on IQ is permanent even t hough epidemiological evidence of such an impact has only been reported in children up to 7 years of age (Gr andjean et al., 1997, 1999). 40 Finally, our estimate does not account for the value of uncompensated labor (e.g., work in the home and volunteer labor), although it is plausible that the value of

labor in these sectors could be si milarly affected by changes in IQ. 2.5.1. Proportional Impact of a One-Point Ch ange in IQ on Lifetime Earnings. We adapt an existing cost-of-illness model (Schwartz, 1994; Salkever, 1995) to examine the influence of small changes in IQ on lifetime earnings. The original model, which was developed as part of an economic assessment of childhood lead exposures, posited that an individual’s lifetime earnings (E) are equal to the product of the average wage rate (W) and the participation rate (P). Differentiating this equation with respect to IQ yields the relationship,

dIQ dP dIQ dW dIQ dP W dIQ dW P dIQ dE + + = (Eq. 18)

(The term, dE/dIQ, refers to the change in lifetime earni ngs with respect to IQ; dW/dIQ refers to the change in annual wage rate with res pect to IQ; dP/dIQ re fers to the change in workforce participation rate with respect to IQ.)

40 We note that the recent report from the Faroe Isla nds cohort presents evidence of neurotoxicity as measured by delays in auditory evoked potentials, but does not re port results of IQ testing among children 14 years of age.101 Dropping the second order term, dIQ dP dIQ dW , which we assume to be small relative

to the remaining terms, and r earranging the remaining terms in Equation (18) yields

         

+ = P dIQ dP

W

dIQ dW E dIQ dE (Eq. 19)

Thus, the change in lifetime earnings equal s the product of the present value of those earnings (E) and the sum of the proportional change in wages associated with the change in IQ and the proportional change in work force participation associated with a change in IQ. As illustrated in Figure 8, this model assumes that in utero methylmercury exposures affect the wage rate and partici pation rates both directly and indirectly (Schwartz, 1994; Rowe et al., 1995; Salkev er, 1995; U.S. EPA, 1997b), via their impact on scholastic attainment and the subsequent impact of scholastic attainment on both wages and labor force participation. Salkever (1995) developed para meters to quantify

these direct and indire ct impacts (Table 22).

Salkever estimated these values from a series of regression analyses using information obtained in the National Longitudin al Survey of Youth (NLSY, 1990) and data provided by Schwartz (1994). T he NLSY data include annual earned income, educational attainment data, and results of in telligence tests that Salkever used as a proxy for IQ. The data are drawn from surveys administe red to a random sample of

U.S. residents.102 Figure 8 Model for Relationship Between IQ a nd Wages and Labor Force Participation

Table 22. Parameter Values Developed by Salkever (1995) Effect Symbol Male Female Direct impact of a 1 IQ point change on: Years of schooling IQ S 0.1007 0.1007 Workforce participation probability IQ P 0.0016 0.0037 Wages (proportional wage change) IQ W 0.0124 0.014 Direct impact of a 1 year of schooling change on: Workforce participation probability S P 0.0035 0.0282 Wages (proportional wage change) S W 0.049 0.10 

## IQ School Wages

Participation IQ s



## IQ p 

## IQ w



## S w



## S p103 Salkever’s model consists of three equatio ns. The first model equation combines

the direct and indirect effects of IQ in order to quantify its tota l proportional impact on

labor force participatio n. In particular, P S P S IQ IQ P dIQ dP + = , where the first term on the

right side of the equation repres ents the direct effect of IQ on labor force participation, and the second term represents its indirect effect (i.e., the effect of IQ on the level of

education attained [ S p ]). Similarly, W S W S IQ IQ W

dIQ dW + = . 41 Substituting the right side

of these two equations into Equation (19) yields 

## ( )( ) 

## [ ] P S P W S W S IQ IQ S IQ IQ E dIQ dE + + + = (Eq. 20)

Substituting the values from Table 22 into Equation 20 yields E × 1.9% for males

and E × 3.2% for females. Using the labor force participation rates for males and females, Salkever (1995) estima ted a weighted average value of E × 2.4%. This adjustment is used to estimate the influence of IQ point changes on lifetime earnings.

2.5.2. Baseline Lifetime Earnings Value (E). This section estimates the value of E to be used in Equation 20 to quantify the impact of changes in IQ on lifetime earnings. Gr osse (Appendix I in Haddix et al., 2003) estimated average lifetime earnings for eac h age and gender group. In his analysis, earnings were comprised of two br oad components: wages/fringe benefits and household production. Wage estimates were based on the Current Population Survey (U.S. Census, 2001 Supplement as cited in Grosse, 2003) and included salary income,

41 Derivative notation is used. For example IQ W refers to the impact of a change in IQ on the proportional change in wages.104 overtime pay, bonus pay, and self-employment earnings. Fringe benefits included

health insurance and retirement pay. Grosse assumed t hat the average person worked 250 days per year. Household production incl uded a number of activities such as cleaning, cooking, and child care, for whic h individuals are typica lly not compensated but are known to be valued; he assumed that household services need to be performed every day. Combining the data for men and women and using a 3% discount rate, Grosse (2003) estimated the present value of labor market earning s over a lifetime of an infant was $692,000 (2000$) (the term 2000$ re fers to the value of a dollar in the year 2000). We use this estimate as a value of E in Equation 20. The discounted present value of an infant’s lifetim e labor market and household production was estimated to total $956,000 (2000$). As there is no evidence to suggest that decreased IQ alters the components of household production (although as previously noted it certainly may), we did not include the househol d production in the estimate. Previously, Rowe et al. (1995) and U.S. EPA (1997b) dev eloped estimates of lifetime earnings.

These estimates differ from those devel oped by Grosse because of apparent differences in discounting and annual earnings estimates. 2.5.3 The Absolute Impact of IQ Changes on Lifetime Earnings. This section combines the findings from Sections 2.5.1 and 2.5.2 to calculate the impact of a 1 point change in IQ on the present value of lifetime earnings (evaluated at the time an individual is born). The pr oduct of the proportional change (2.39%) and baseline lifetime earnings ($691,830) yields a change of $16,500 (2000$).

We note that in their studies of lead’s effect on earnings, Sc hwartz and Salkever included a term in their models representing the impact of that metal’s toxicity on105 educational attainment indepen dent of the impact mediat ed by changes in IQ. We

omitted this term from our model because there is no evidence suggesting that prenatal methylmercury exposures reduce educat ional attainment independent of IQ.

2.5.4. Remedial Education Costs and Shifts in the P opulation IQ Distribution. While changes in IQ affect wages earned in a lifetime, they also change the fraction of the population that is considered to be mentally handicapped (i.e., individuals having an IQ <70). 42 We assume that when individ uals fall below this threshold,

additional educational resources are required. We estimate the cost of a shift in the population IQ distribution as the product of the increment al educational cost and the incremental probability of having an IQ <70. 2.5.4.1. Incremental Probability of IQ <70

― To estimate this incremental probabilit y, we assume that population IQ is normally distributed, and that the change in IQ is the same for all members of the population (i.e., the mean of the distribution shifts but the variance and other properties of the distribution remain unchanged). In general, the probability that a randomly

selected individual has an IQ score less than 70 can be expresse d in terms of the standard normal deviate Z, which is norma lly distributed with mean zero and standard deviation one. In particular,      

− < = < σ μ 70 Z P 70) P(IQ (Eq. 21)

42 A shift in the IQ of a population would also decr ease the number of highly intelligent individuals (e.g., the number of individuals with an IQ above 130). No data were identified that evaluated the economic productivity of this group or compared it to the population with IQ sources between 70 and 129, and so we make no adjustment for the change in number of people with IQs above 130.106 where μ is the mean population IQ score and σ is the corresponding standard deviation. For the baseline conditions, μ = 100 and σ = 15, so that the proportion of individuals with an IQ below 70 is 2.28%. For a 1 point decrease in mean IQ (μ = 99), this proportion increases to 2.66%. Hence, a 1 point decline in mean IQ increases the probability that a randomly se lected individual will have an IQ less than 70 by 0.38%. Similarly, a 1 point increase in mean IQ decreases this probability by 0.33%.

2.5.4.2. Incremental Cost s Associated with IQ <70

― We assume that individuals with IQ scores below 70 require additional educational resources compared to those with IQ scores above 70. We assume further

that these incremental costs are approximately equal to the cost of providing part-time special education for children who remain in the regular classroom . U.S. EPA (1997b) applied the GNP price deflator to the annual cost of this service, as reported by Kakalik (1981) ($3,064, 1978$), to a rrive at an annual cost of $6,318 (1990$). The GNP deflator does not adjust costs based on a ba sket of goods and services, but on the basis of all labor and property supplied by U. S. residents. Using the third quarter 2000 GNP deflator, we estimate the updated annual cost of such services to be $7,800 (2000$) (GNP Deflator 1990 = 87 and GNP Deflator 2000 = 107.08). Assuming that compensatory education begins at age 6 and continues through age 18 and that the annual discount rate is 3%, the present va lue of the compensat ory education is $77,000 (2000$).

2.5.4.3. Cost of a One Point Shift in Mean IQ

― Multiplying the incremental probabili ty that IQ will be less than 70 (0.38%) resulting from a 1 point drop in mean IQ and t he present value at birth for compensatory107 educational costs yields a cost per IQ point of $290. We note that this cost is small (roughly 2% of the total) compared to t he impact on population earnings of $16,500 (2000$) (see Section 2.5.3).

2.6. HEALTH-RELATED QUALITY OF LIFE MEASURES FOR NEUROLOGICAL DECREMENTS Health-related quality of life measures, such as Disability Adjusted Life Years (DALYs) and Quality Adjusted Life Years (QALYs), have been applied to neurological effects from methylmercury ex posures. For example, QALY s are utility functions that may describe individual preferences fo r longevity and health-related quality of life associated with either tempor ary or permanent morbid conditions. The health-related quality of life is assigned a quantitative weight described as a utility weight. The utility weight is typically a value between 0 and 1. A value of 1 repr esents a perfect health state and 0 represents a health st ate equivalent to dead. For an individual’s lifetime, the product of the utility weights and the duration of time an individual experiences these utility weights yields an estimate of an indi vidual’s QALYs. The value of reducing the health decrement associated with lowering in utero methylmercury exposures can be

measured as the difference in QALYs with and without such a reduction in exposures over time (Hammitt, 2002; Pli skin et al., 1980). In Secti ons 2.6.1 and 2.6.2, we discuss the development of a utility weight for the neurologic decrements associated with methylmercury exposures in the New Ze aland and Faroe Islands studies and we describe an exposure-response function for t he health-related quality of life measures.108 2.6.1. Utility Weight for Health-Relat ed Quality of Life Associated with Neurodevelopmental Toxicity. In this section, we examine the appropria te utility weight for neurological changes associated with methylmercury exposures. In the discussion of the New Zealand and Faroe Islands studies, we noted that the inve stigators reported no differences in the exposed and reference groups based on “observ ed” behaviors (i.e., in the absence of sophisticated neurological testing, the groups were indistingu ishable). In these studies of environmentally relevant methylmerc ury exposures, the neurological effects associated with methylmercury exposures, although potentially permanent, appear to be

subtle. These subtle effects are the basis of the RfD. In our lit erature review, we found no data that describe utility weights for the subtle neurologic decrements reported for methylmercury. Several studies examined utility weights a ssociated with cognitive decrements. Ponce et al. (2000) used QALYs to eval uate neurological effects associated with methylmercury exposures. They evaluated the risk of delayed talking using a doseresponse function based on the Iraqi poisoning episode (Marsh et al., 1987). If speech development was delayed, the utility weight a ssigned to that health-related quality of life was a value of 0.9; the health decrement was 0.1. This was based on the work of Kind et al. (1982). In our review of Kind et al., we coul d not identify a description of the relevant neurological condition. Torrance et al. (1996) and Feeney et al. (2002) also developed a series of utility weights for c ognitive decrements. Tables 23 and 24 describe the cognitive decrements associ ated health-related quality of life and the corresponding utility weights. We note that the descriptions of the health-related quality109 of life for the neurocognitive decrements in these two studies suggest impacts that are substantially more severe than those repor ted by the New Zealand and Faroe Islands investigators. There appear to be no survey- based studies that report utility weights for the subtle neurological effects observed in the recent epidemiological literature. Although it is not possible to know the resu lts of a study that has not been conducted, we assume that in such a study the decrem ent in the utility weight for the neurological

effects associated with in utero methylmercury exposures w ould be valued at no more

than 0.01.

Table 23. Description of Cognitive Decr ement and Associated Utility Weight Based on Torrance et al. (1996)

Description of Levels for Health Utilities Index Mark 2: Cognition Multiattribute Function on Dead Healthy Scale 1 Learns and remembers school work normally for age 1.00 2 Learns and remembers school work more slowly as judged by parents and teachers 0.95

3 Learns and remembers very slo wly and usually requires special education 0.88110 Table 24. Description of Cognitive Decr ement and Associated Utility Weight Based on Feeny et al. (2002)

Description of Levels for Health Utilities Index Mark 3: Cognition Multiattribute Function on Dead Healthy Scale 1 Able to remember most things, think clearly and solve day-to-day problems 1.00

2 Able to remember most things, but have a little difficulty when trying to think and solve day-to-day problems 0.92

3 Somewhat forgetful, but able to think clearly and solve day-to-day problems 0.95

4 Somewhat forgetful and have a little difficulty when trying to think and solve day-to-day problems 0.83

2.6.2. Exposure-Response Fun ction for Health-Related Qu ality of Life Weights.

There are no studies that describe an exposure-response relationship appropriate for the health-related quality of life weights. We assumed that all children

born to mothers above the RfD would incur a neurodevelopmental effect and that this health effect would persist throughout their liv es. We assume these IQ losses to cause similar neurodevelopmental effects that woul d be valued at no more than a utility weight

decrement of 0.01. We assu me that the life expectancy of children born is 77.2 years (CDC/NCHS, 2003). Combining these values yields an estimated loss of 0.77 QALYs for each child born to a mother with exposure above the RfD.

2.7. VALUING CHANGES ASSOCIATED WITH ADVERSE MYOCARDIAL EVENTS AND ALL CAUSE MORTALITY

Section 2.7 is divided into three subsec tions. Section 2.7.1 discusses the costs of lost productivity and medical costs a ssociated with a non-fatal and fatal myocardial

infarct. Section 2.7.2 discusses the value of a statistical life, an estimate of a group’s111 willingness-to-pay to reduce the risk of deat h. Section 2.7.3 discusses the utility weights associated with non-fatal and fatal myocardial infarctions. 2.7.1. Cost-Of-Illness Estimates For My ocardial Infarcti ons and All Cause mortality. Then a non-fatal myocardial infarct occurs, individuals and societies incur medical costs for both urgent and continuing treatment and co sts associated with lost productivity while the individual convalesces. Blake et al. (2003) , citing an Appendix in Gold et al. (1996), estimate the lifetime cost s of a myocardial infarction to be between $37,030 and $83,290 depending on the age and sex of the case . These costs, which are discounted at 3%, include ho spitalization, physician co sts, procedural costs, and annual follow-up costs. As an analytic simplific ation, we assume that the myocardial infarct occurs at ages 65 and 70 in males and fe males, respectively. The specific costs for a 65-year-old male and a 70-year-old female were estimated to be $46,000 and $43,000 (2000$), respectively (Blake et al., 2003). Grosse (2003) estimated the value of a lost day to be $150/day (2000$). This estimate included annual earnings and the value of annual household services; he divided these annual estimates by 365 days to estimate loss of a typical day. For simplification, we assume that individuals w ho survive a myocardial infarct are unable to perform these tasks for 6 weeks. Thus, we es timate the lost productivity associated with a non-fatal myocardial infarct to be $6100 (2000$). For cases of nonfatal myocardial infarctions, we sum the m edical and lost productivity costs. As a simplification, we assume that the men and women, who are predicted to die in the analysis due to methylmercur y exposure, die at ages 65 and 70 years,112 respectively. These are the average ages for first myocardial infarct in the U.S. population (AHA, 2004). For men aged 65 y ears and women aged 70 years, Grosse estimated the present value of earnings and household production, di scounted at 3%, to be $274,000 and $151,000 (2000$), respectively. We add the medical costs associated with the nonfatal cases (likely an overesti mate of medical costs) and use these

estimates in our cost-of-illness estimate for males and females.

2.7.2. Value of a Statistical Life. The value of a statistical life (VSL) can also be used to estimate the value of a fatality. The VSL is a measure of an individu al’s willingness to pay to avoid incurring a small change in the risk of dying (Hammitt, 2000). The EPA currently treats the VSL as independent of age, 43 and the societal VSL is t he Agency’s standard approach to estimating a dollar value of mortality benefit s of environmental regulations. In this analysis, we update the VSL to $5.9 million (2000$). This is based on an update of previous EPA estimates of $5.6 million (U .S. EPA, 1997b). Estimates of appropriate values for VSL generally vary between $3 million and $9 million (Mrozek and Taylor, 2002; Viscusi and Aldy, 2003). 2.7.3. Health-Related Qualit y of Life Utility Weights for Myocardial Infarctions. In this section, we discuss estimates of utility weights for individuals incurring non-fatal myocardial infarctions. Blake et al . (2003) present a utility weight of 0.9 for survivors of myocardial infarct. We assu me this decrement of 0.1 to the affected

43 The EPA Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) suggest that adjusting the VSL based on age may be appropriate.113 individuals’ utility persists. Given our assumptions about t he age at which a heart attack occurs and that life expectancy of men aged 65 is 81 years of age and women age 70 is 86 years of age, men and women incurring a non- fatal myocardial infarct are estimated

to lose 1.6 QALYs over their remaining lifetimes. Based on ty pical life expectancy in the U.S., males predicted to die of all cause mortality lose 16 QALYs and females lose 16

QALYs.

2.8. SENSITI VITY ANALYSES Finally, we developed four sens itivity analyses that were limited in scope. In the first analysis, we evaluate the impact of freshwater fish consumption advisories. We assume that fish consumption advisories ar e issued for all bodies of water where fish methylmercury concentrations exceed 0.3 ppm . We assume that freshwater fish consumers are fully compliant with the advisor ies; that is, non-commercial fish having a methylmercury concentration above 0.3 ppm are not consumed by members of this population. In the second, we estimated the distri bution of methylmercury intakes among

non-commercial freshwater fish consumers, assuming that i ndividuals eat only one type

of fish. We note that recr eational anglers may target spec ific species for consumption and may not consume a representative mix of non-commercial species. Thus, some consumers targeting specific types of fi sh may have methylmercury intakes well above the average. In this sens itivity analysis, we assume no correlation between species consumed and quantity of fish consumed. In the third, we evaluate the impact of alternatively doubling or halving the decrease in deposition that wa s predicted in Scenario 1 on methylmercury intakes in114 freshwater fish consumers in a single regi on. The Northeast Region was selected for the example. Doubling the decr ease might account for a response in fish that is greater than linear with respect to mercury deposit ion or possible underprediction of the deposition change associated with the REMSAD modeling. Halving the predicted decrease might account for a less than linea r response in fish to decreased mercury deposition rates or a possible overestimate of the deposition rate change associated

with the REMSAD simulation. In the fourth sensitivity analysis, we re-evaluate the slope of the relationship between neurotoxicity and expos ure in the case where a threshold exists. We estimated a slope of -0.6 IQ points per 1 ppm in crease in mercury in hair. This estimate is based on three epidemiologic studies in which the reported hair mercury concentrations are generally between 0.5 and 30 ppm (These studies are reviewed in Section 1.5.1; see also NRC, 2000). If t here is a threshold below which maternal methylmercury exposures have no effect on the developing fetal nervous system, the slope of the exposure-response function betw een the threshold and the maternal hair mercury concentration where responses are obs erved must be larger than if there is no threshold. To evaluate the sensitivity of our results to this possi bility, we note that a relatively low level of maternal hair me rcury at which effects may be observed is approximately 3 ppm. At this level, the esti mated effect under the no threshold model is a loss of approximately 1.8 IQ points in the affected fetus (i .e., the product of -0.6 IQ points/ppm and 3 ppm). Under an alternative model in which there is a threshold around 1.3 ppm hair methylmercury, the slope of the exposure-response function between 1.3 and 3 ppm hair methylmercury must be -1.1 IQ points per increase of 1115 ppm hair methylmercury in order to achieve the same effect (i.e., -1.8 IQ points per 1.7 ppm increase in hair methylmercury. We use this alternative slope value in the fourth sensitivity analysis.116 3. RESULTS This section has six parts. For each r egion, Section 3.1 describes the predicted current mercury deposition rates and t he predicted changes in annual mercury deposition rates associated with the alternativ e emissions. Section 3.2 quantifies both the current and predicted changes in fish me thylmercury concentrations. Section 3.3

describes both the current and the estima ted changes in human exposures that are predicted to result from the alternative emi ssions scenarios. Section 3.4 quantifies the decreased risks of IQ point loss, as a consequence of the alternative mercury emissions. Cost-of-illness (COI) and QA LY approaches are used to evaluate the

averted cases. Section 3. 5 quantifies the decreased risks of nonfatal AMI and ACM and uses COI, willingness-to-pay, and QALY appr oaches for valuation of the averted outcomes. Finally, Section 3.6 summarizes the results and provides a summary of the limited sensitivity analyses.

3.1. CHANGES IN ANNUAL MERCURY DE POSITION RATES IN THE 8 REGIONS The alternative emissions scenarios were predicted to result in decreased mercury deposition in each Region. For t he All Other Waters Region, the Coastal Atlantic Region, and the Gulf of Mexico Region, the pr edicted changes in annual mercury deposition rates are summarized in Table 25. Table 26 summarizes the decreased deposition rates in the five fres hwater regions. Under the alternative emissions scenarios, the largest decreases in mercury deposition are predicted in the Mid-Atlantic Region (9% and 10% decreases under Scenarios 1 and 2, respectively). Modest decreases are predicted for the Northeast, Midwest, and Southeast Regions (decreases range from 3% to 6%). The predicted decreases in the West, Atlantic117 Coastal and Gulf of Mexico Regions are re latively small (roughly 1%). The reported standard deviations indicate predicted variabilit y in deposition across grid cells within a region. These estimates of the spread of the predicted c hanges in deposition are highest in the Southeast and Mid-Atlantic regions.

Table 25. Predicted Percent Decreases in Mercury Deposition to the Coastal Atlantic Ocean Region, the Gulf of M exico Region, and the All Other Waters Region Under CSI Coastal Atlantic Ocean Gulf of Mexico All Other Waters Current Deposition Rate (μg/m 2 /yr) 22.6 22.1 NA Baseline 1 5.87% 3.52% 0.6% Scenario 1 7.04% 3.89% 1% Baseline 2 6.00% 3.54% 0.6% Scenario 2 7.53% 4.29% 1.2% Notes: Percent decreases ar e based on 2001 deposition levels.118 Table 26. Predicted Percent Decreases in Mercury Deposition in the Five Freshwater Regions Relative to Current Emissions Baseline1 Scenario 1 Baseline 2 Scenario 2 Northeast Current deposition rate: 12.6 μg/m 2 /yr (199 Receptors) Average Decrease 9% 12% 9% 13% Standard deviation 9% 9% 9% 9% MidAtlantic Current deposition rate: 14.1 μg/m 2 /yr (201 Receptors) Average Decrease 22% 31% 24% 34% Standard deviation 12% 12% 12% 12% Southeast Current deposition rate: 10.2 μg/m 2 /yr (661 Receptors) Average Decrease 17% 20% 18% 24% Standard deviation 12% 12% 13% 12% Midwest Current deposition rate: 12.5 μg/m 2 /yr (841 Receptors) Average Decrease 9% 12% 9% 14% Standard deviation 7% 9% 8% 10% West Current deposition rate: 6.5 μg/m 2 /yr (3001 Receptors) Average Decrease 3% 4% 3% 4% Standard deviation 5% 5% 5% 6%

3.2. CHANGES IN FISH METHYLMERCURY CONCENTRATIONS IN THE 8 REGIONS For commercial fish, a weighted mean methylmercury concentration was developed by weighting the m ean methylmercury concentrations in each of the 24 types of fish (Figure 2) by the per capita c onsumption rates (Table 2). We calculated a weighted mean methylmercury concentration of 0.12 μg/g for current U.S. commercial fish. Figure 9 shows the percent contribut ion of each commercial fish type to total methylmercury in the U.S. ma rket. The figure shows tha t, based on the product of the119 methylmercury concentration and the per capita consumption rate, canned tuna, pollock, shrimp, cod, halibut and swordfish have the most influence on the weighted mean and that the remaining types of fish contribute minimally to the current per capita methylmercury intake.1200%5%10%15%20%25%30%35%40% T u n a-can n ed Po llock Sh ri m p Cod Hal i bu t

Sw o r d fish Salmon Catfish Lob s te r - A m eri ca n C r a b s-Blu e Fla t fish

R ockfish S h a rk Crab s -Snow O ysters Lobster-Spiney Scallops C l ams Crabs-Dungeness Sable fish Sardines Per c h -O ce an Crabs-Kin g Cra w fi shFish Type

Percent † of Total Methylmercury in U.S. Market†Estimate based on the product of per capita fish consumption rates and mean methylmercury concentrations of each type of fish (Carrington and Bolger, 2002)Figure 9For "Top 24" Types of Fish in U.S. Commercial Seafood Market, the Percentage ofMethylmercury Contributed by Fish Type121 Table 27 shows the weighted mean methylmercury concentrations in

commercial fish under current emissions and under each of the baselines and the alternative U.S. mercury emissions scenario s. This estimate was based on weighting mean methylmercury concentrations in each of the 24 types of fish by the quantity of the individual types of fish harvest ed from each of the f our source waters, (i.e., the Coastal Atlantic Ocean, the Gulf of Mexico, All Ot her Waters and Aquacul ture) and the predicted change in mercury deposition in each region (Table 25).

Table 27. Weighted Mean Methylmercur y Concentrations in Commercial Fish Commercial Fish Concentration (μg/g) Percent Change Current 0.116 Baseline 1 0.115 0.8% Scenario 1 0.114 1.5% Baseline 2 0.114 1.5% Scenario 2 0.113 2.4%

Decreases of less than 3% are predict ed in the weighted mean methylmercury

concentrations of the commercial fish under the alternative scenarios. Most methylmercury exposures in typical commercial fish consumers (>75%) are a consequence of consuming six types of fish , canned tuna, pollock, sh rimp, cod, halibut, and swordfish. (For example, based on methyl mercury levels in tuna and the per capita consumption rate estimates, tuna accounts for 33% of per capita ex posure.) For these six types of fish, Figure 10 shows the fracti onal contributions of the total catch from

different bodies of water to tota l marketed quantities (NMFS, 2003).1220%10%20%30%40%50%60%70%80%90%100% Tuna-canned ( 1) Pollo ck

S hrimp Cod Ha lib ut Sword f ish (1 ) Sa lmon C at fish (2 ) Lo b ster-A m erica n Cra bs -Blu e Flat fis h Rockfish S har k (1 ) Cra bs-S no w Oyste rs L obste r -Spine y S callo ps C la ms Crabs-Dunge n ess

Sa ble fish S ardi nes

Pe rch- Oc ean Crabs-King Crawfish (2)

Percent of Total Harvest by Water BodyGulfAtlantic(1) Migratory Species. Tuna, shark and swordfish are migratory species. The regions from which these fish were captured wereassumed to notnecessarily serve as a good predictor of change in methylmercury concentration.(2) Aquaculture. Marketed catfish and crawfish are assumed to be raised in aquaculture.Source: NMFS (2002)22Figure 10Percent Contribution of the Atlantic Ocean and Gulf of Mexico Harvests to U.S. Commercial Market by Fish Type123 Mercury concentrations in highly migratory fish, 44 like tuna and swordfish, are likely influenced by U.S. contributions only to the extent that U.S. emissions influence global atmospheric mercury pools, given the at mospheric mixing that likely occurs over

the distance that U.S. merc ury emissions must be transp orted to deposit into such remote food webs. Most pollock are caught in the Pacific Ocean. Given the prevalence of west-to-east atmospheric transport patterns in the U.S., the influence of U.S. power plant mercury emissions on pollock methylmerc ury levels is also likely proportional to the power plants’ contribution to global atmo spheric mercury pool. Shrimp and cod are primarily imported; thus, U.S. sources likely have a small contribution to methylmercury levels in these fish. Halibut, like pollock, ar e typically caught in the Pacific. Roughly

70% of marketed marine fish are imported into the U.S. (NMFS, 2003) and, thus, are

assumed in this model to be harvested fr om the All Other Waters Region. The remaining 30% are harvested prim arily from the U.S. conti nental shelves that extend roughly 200 miles off shore in the Atlantic Ocean, the Pacific Ocean, and the Gulf of Mexico. The small predicted changes in t he methylmercury concentrations of most

commercial fish result from the small in fluence of U.S. emissions sources on the methylmercury concentrations in our model; specifically, the t op five sources of methylmercury in the diets of commercia l seafood consumers per capita are likely influenced very little by U.S. mercury emissi ons. Figure 10 shows t hat, of the top 24 types of fish in the U.S. market, only cl ams and scallops are prim arily harvested from

44 Highly migratory species may consume prey ov er broad geographic ranges; consequently, we assumed that their methylmercury concentrations are influenced by mercury deposition over broad geographic areas and participation in foodwebs located in these different geographic areas. Note that we assumed that changes in mean methylmercury conc entrations in these highly migratory species are based on estimated changes in global emissions as a result of the CSI. See Chapter 2.124 waters that are likely to be heavily influenced by mercury emissions from U.S. sources. Thus, from a policy perspective, changes in U. S. emissions likely exert small changes in deposition rates of mercury at si tes distant from U.S. shores and in the Pacific Ocean. Consequently, U.S. mercur y emissions reductions exert small changes in the methylmercury concentrations of most commer cial fish and, therefore, small changes in U.S. methylmercury exposures. For non-commercial marine fish, we again a ssumed that consum ers ate a mix of

non-commercial marine fish obtained from either the Atlantic or Gulf. The specific mix was weighted by the estimated harvest wei ghts for the top 10 types of fish caught recreationally in the Atlantic and Gulf (Tables 4 and 5, respectively). Table 25 shows the predicted change in deposition in the two regions and T able 28 shows the resulting predicted changes in the weighted mean methylmercury concentrations of noncommercial fish in the Atlantic and Gulf regions. Current fish methylmercury concentrations are predicted to be lower in the Atlantic fish than the Gulf fish, based on data in U.S. EPA (2003c). The predicted decr ease in mercury deposition in the Atlantic Ocean Region (roughly 1.1% and 1.5% under Scenarios 1 and 2, respectively) is greater than the Gulf Regi on (less than 1% under both scenar ios) (see Table 25). The assumption that each consumer eats a mi x of 10 fish species results in an underestimation of the variability in the predicted intake distribution.125 Table 28. Predicted Weighted Mean Non-commercial Fish Methylmercury Concentrations (μg/g) Atlantic Ocean Gulf of Mexico Current Fish Methylmercury concentration (μg/g) 0.28 0.40 Baseline 1 Fish Methylmercury co ncentration (μg/g) 0.26 0.39 Scenario 1 Fish Methylmercury conc entration (μg/g) 0.26 0.38 Baseline 2 Fish Methylmercury co ncentration (μg/g) 0.26 0.39 Scenario 2 Fish Methylmercury conc entration (μg/g) 0.26 0.38

Summary statistics for each of the types of fish from each of the five U.S. freshwater regions are presented in Tables 29 -33, including the number of samples of each type of fish, the mean fillet methylmerc ury concentration and fish length. After adjustment for fish size (i.e ., excluding fish smaller than are usually eaten), the NLFWA fish database is assumed to reasonably depict individual methylme rcury concentrations of fish in different regions. Tables 29-33 also identify the assumed consumption frequency for each type of fish in each regi on; these frequencies were estimated from the U.S. FWS fish targeting data (see note at bottom of Tables 29-33). Given the assumption that consumers eat a mix of freshwater fish, these consumption frequencies were used (factor T in Equation 9) when devel oping the intake mix for individual Monte

Carlo iterations in the simulation. The a ssumption that each consumer eats a mix of eight or nine types of fish results in an underestimate of the likely variability in the predicted intake distribution. Based on the predicted changes in deposition in the five freshwater regions (Table 26), the largest decreases in mercury deposition are predicted in the Mid-Atlantic Region, under the alternative emissions scenarios. There is very little decrease in the West Region (see discussion in Section 3.1).126Table 29. NortheasternFish Consumption Data*Consumption frequency refers to the relativetargeting frequency for region. This was used as a surrogate for consumption frequency.Statistic Crappie PanfishWhite andStripedBassBlackBassCatfishNorthern Pike,Pickerel andMuskieTrout SalmonConsumption Frequency*(unitless)0.02 0.11 0.07 0.33 0.04 0.10 0.29 0.06Sample Size 3 1131 121 755 237 34 173 8Mean MeHg Concentration(ppm)0.38 0.37 0.35 0.57 0.17 0.66 0.31 0.22Standard DeviationMeHg Concentration (ppm)0.18 0.28 0.22 0.35 0.17 0.37 0.24 0.20Mean Length (inches) 10.03 8.0824.61 14.58 9.84 18.42 16.97 12.56Std. Dev. Length (inches) 1.481.78 5.32 1.88 2.12 3.63 5.90 5.57127Table 30. MidAtlantic FishConsumption Summary DataStatistic Crappie PanfishWhite andStripedBassBlackBassCatfishWalleyeandSaugerNorthern Pike,Pickerel andMuskieTroutConsumption Frequency*(unitless)0.07 0.10 0.12 0.32 0.12 0.04 0.03 0.21Sample Size 25 20 190 161 186 5 49 17Mean MeHgConcentration (ppm)0.14 0.14 0.18 0.69 0.14 0.10 0.80 0.22Std. Dev. Concentration(ppm)0.08 0.13 0.21 1.03 0.20 0.02 0.67 0.27Mean Length (inches) 7.68 6.18 25.1914.68 15.78 14.73 18.86 16.84Std. Dev. Length (inches) 1.820.54 6.34 1.80 4.12 2.32 3.12 5.42*Consumption frequency refers to the relativetargeting frequency for region. This was used as a surrogate for consumption frequency.128Table 31. SoutheasternFish Consumption Data*Consumption frequency refers to the relativetargeting frequency for region. This was used as a surrogate for consumption frequency.Statistic Crappie PanfishWhite andStriped BassBlackBassCatfishWalleye andSaugerTroutConsumption Frequency*(unitless)0.19 0.16 0.12 0.28 0.20 0.01 0.04Sample Size 952 1914 99 5431 1335 14 43Mean MeHg Concentration(ppm)0.26 0.25 0.21 0.62 0.29 0.28 0.04Std. Dev. MeHg Concentration(ppm)0.32 0.24 0.17 0.53 0.37 0.22 0.04Mean Length (inches) 11.55 7.9418.64 15.42 15.99 18.48 9.11Std. Dev. Length (inches) 8.90 3.43 4.70 5.18 8.49 2.34 2.48129Table 32. Midwest FishConsumption Summary DataCrappie PanfishWhite andStripedBassBlackBassCatfishWalleyeandSaugerNortherns,Pickerel andMuskieTrout SalmonConsumptionFrequency* (unitless)0.15 0.23 0.07 0.19 0.11 0.14 0.07 0.03 < 0.01Sample Size 905 1824 100 1446 617 5163 2448 1404 322Mean MeHg (ppm) 0.17 0.15 0.25 0.35 0.22 0.45 0.50 0.24 0.10Std. Dev. MeHgconc(ppm)0.12 0.12 0.17 0.21 0.36 0.34 0.37 0.22 0.09Length (inches) 10.76 7.84 13.57 14.58 15.91 19.30 28.17 20.24 20.88Std. Dev. (inches) 17.72 1.51 1.181.84 5.44 3.12 3.37 5.78 4.82*Consumption frequency refers to the relativetargeting frequency for region. This was used as a surrogate for consumption frequency.130Table 33. Western FishConsumption Summary Data*Consumption frequency refers to the relativetargeting frequency for region. This was used as a surrogate for consumption frequency.Crappie PanfishWhite andStripedBassBlackBassCatfishWalleyeandSaugerNorthern Pike,Pickerel andMuskieTrout SalmonConsumption Frequency*(unitless)0.10 0.05 0.08 0.18 0.160.05 0.02 0.30 0.07Sample Size 153 132 105 353 461 122 48 236 338Mean MeHg Concentration(ppm)0.32 0.30 0.41 0.45 0.250.67 0.46 0.21 0.12Standard DeviationMeHg Concentration (ppm)0.24 0.25 0.29 0.38 0.210.59 0.26 0.35 0.22Mean Length (inches) 9.60 7.80 16.2614.54 16.03 20.11 31.06 12.45 22.43Std. Dev. Length (inches) 2.53 1.88 3.91 2.04 4.62 4.34 4.74 4.47 6.31131Table 34. Estimated Population SizesEstimatedAnglerPopulationEstimated Noncommercial FishConsumerPopulationEstimatedWomen ofChild-bearingAgeEstimatedAnnualChildbirthsMen >39Years of AgeWomen >39Years of AgeAtlantic 5,012,000 10,024,000 2,205,000 137,000 1,983,000 2,269,000Gulf 2,897,000 5,793,000 1,274,000 79,000 1,146,000 1,311,000Northeast 2,471,000 4,942,000 1,087,00067,000 978,000 1,119,000Mid-Atlantic 206,000 411,000 90,000 6,000 81,000 93,000Southeast 7,376,000 14,753,000 3,246,000 201,000 2,919,000 3,340,000Midwest 10,164,000 20,329,000 4,472,000 277,000 4,022,000 4,602,000West 8,282,000 16,565,000 3,644,000 226,000 3,277,000 3,750,000GeneralPopulation (TotalPopulation =281,421,906) NA NA45,893,000 2,845,00041,274,000 47,221,000Total 36,409,000 72,817,000 61,913,000 3,839,000 55,681,000 63,705,000NA=not applicable132 3.3. CHANGES IN METHYLMERCURY EXPOSURES AMONG FISH CONSUMERS

IN THE 8 REGIONS

3.3.1. Population Data.

Table 34 summarizes the estimated size s of populations consuming the fish caught in each region. The second column i dentifies the size of the angler population that fishes in each region. The third column identifies the total number of consumers of each type of fish including children. Most individuals in the U.S. consume commercial fish. The size of the population that c onsumes no fish or only commercial fish is estimated by subtracting the non-commercia l fish consuming popul ation (72.8 million) from the total U.S. populat ion (281 million). We a ssume that the population demographics of non-commercial fish consumer s in each region reflect those of the U.S. population. Specifically, we assumed that 22% of the cons umers are women of child-bearing age and that roughl y 6% of these women give birth each year. We also assume that roughly 20% of t he individuals in the populati ons are males over 39 years of age and 23% are females over 39 year s of age (U.S. Census, 2004). 3.3.2. Predicted Exposures in General U.S. Population. The male and female blood methylmercury concentrations that are assumed to

result from commercial fish in take are presented in Table 35. 45 We assumed that the

distribution of U.S. female blood concentrations reported by Mahaffey et al. provides a

reasonable estimate of the distribution of blood methylmercury concentrations in members of the population t hat consume commercial fish or no fish. We noted

45 The distribution was fitted using the NHANES analysis published by Mahaffey et al. (2004) (see second row of Table 35).133 previously that the weighted mean methylmerc ury concentration of the 24 types of fish reported by Carrington and Bolger is 0.116 μ g/g. We also noted that the per capita seafood consumption rate for these 24 types of fish is 13.5 pounds per year, which is roughly 16.8 g/day. Combining the weighted mean fish methylmercury concentration and the daily intake fish rate and dividing by the average body weight yields an average daily intake of 0.03 μ g/kg-day per capita. Based on Equation 1, the corresponding average blood methylmercury concentra tion is estimated to be 1.56 μ g/L. The mean of

the distribution used to fi t the Mahaffey data was 1.64 μ g/L. Because our calculated mean closely approximates the observed U. S. blood methylmercury concentrations reported by Mahaffey et al., it provides supp ort for the use of t he predicted changes in the weighted mean methylmercury concentrati ons of commercial fish to predict the associated changes in the dist ribution of the blood methylme rcury concentration in the U.S. population.134 Table 35. Predicted Tissue Methylmercur y Concentrations in Commercial Fish Consumers

General Population Mean MeHg Blood Conc. (μg/L Blood) Mean MeHg Hair Conc. (μg/g hair) Percent Population Below RfD Conditional Mean MeHg Blood Concentration (μg/L Blood) for those above RfD Current a Female 1.64 0.41 92.1% 8.82 Baseline 1 Female 1.63 0.41 92.2% 8.80 Scenario 1 Female 1.62 0.40 92.4% 8.79 Baseline 2 Female 1.62 0.40 92.4% 8.79 Scenario 2 Female 1.60 0.40 92.5% 8.77 Current Male b 1.56 0.39 Baseline 1 Male 1.55 0.39 Scenario 1 Male 1.54 0.38 Baseline 2 Male 1.54 0.38 Scenario 2 Male 1.53 0.38

a The data in this row results from fitting the female blood methylmercury concentrations reported by Mahaffey et al. (2004). For the total sample population, the 50 th , 75 th , 90 th and 95 th percentile values were 0.6, 1.7, 4.4 and 6.7 μg/L, respectively. See T able 16 for additional details of the Mahaffey data set. b Male blood data are based on fitted distribution for female blood methylmercury concentrations and Equation 7.

Using our distribution, which was based on an approximation of the distribution reported by Mahaffey et al., ti ssue methylmercury levels currently exceed the value that corresponds to the methylmercury RfD in roughl y 8% of U.S. female s of reproductive age. Based on the data in Table 17, cu rrent U.S. male blood methylmercury

concentrations are estimated to be 4.3% lower than current levels in females. Using the135 methylmercury 1-compartment pharmacokinetic model, hair mercury levels were also estimated based on the blood methylmercury concentrations. In our model, decreases in U.S. em issions resulted in small changes in methylmercury exposures. Table 35 show s that, when compared with estimates for Baseline 1 and Baseline 2, female blood meth ylmercury concentrations in Scenario 1 and Scenario 2 are predicted to decrease by 0.01 and 0.02 μg methylmercury/L blood, respectively. The fraction of the populati on exposed above the Rf D was predicted to decrease by less than 0.5% and the c onditional mean blood methylmercury concentration in this group is predicted to decrease by less than 0.05 μg/L. 3.3.3. Predicted Exposures in Consumer s of Non-commercial Marine Fish. Tables 36 and 37 present the results of the exposure models for non-commercial

fish consumers in the Atlantic and Gulf Regions. These estimates are developed for

consumers that eat a mix of non-commercial fish derived from either the Atlantic or the

Gulf and a mix of commercial fish.136 Table 36. Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of Non-Commercial Atlantic Ocean Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Atlantic Current 0.048 0.038 0.123 93.7% 0.144 Atlantic Baseline 1 0.047 0.038 0.119 94.9% 0.147 Atlantic Scenario 1 0.047 0.037 0.118 94.9% 0.146 Atlantic Baseline 2 0.047 0.038 0.119 94.9% 0.147 Atlantic Scenario 2 0.046 0.037 0.117 94.9% 0.145 *This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 37. Predicted Methylmercury Inta ke Rates (μg/kg-day) in Consumers of Non-Commercial Gulf Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Gulf Current 0.065 0.044 0.187 79.7% 0.203 Gulf Baseline 1 0.063 0.043 0.182 80.0% 0.200 Gulf Scenario 1 0.063 0.043 0.181 80.1% 0.199 Gulf Baseline 2 0.063 0.043 0.182 80.0% 0.200 Gulf Scenario 2 0.063 0.043 0.180 80.2% 0.198 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).137 Currently, methylmercury from commercia l fish is estimated to contribute approximately 64% and 55%, respectively, to predicted methylmercury intakes among

consumers of non-commercial Atlantic and Gulf fish. The fractio nal contribution of commercial fish is higher among consumers of non-commercial Atlantic fish because the estimated weighted mean methylmercury levels in these non-commercial fish are

30% lower than those in the Gulf. Current methylmercury intake in the Atl antic and Gulf populations is predicted to exceed the RfD in roughly 6% and 14% of th ese consumers, respectively. The high percentage of the Gulf populat ion that exceeds the RfD is a consequence of both the higher rates of non-commercial fish consumpt ion (relative to non-commercial Atlantic consumers as described in U.S. EPA, 1997a) and the higher concentration of methylmercury in the tissues of non-commercia l Gulf fish (relative to non-commercial Atlantic fish) (see Tables 4 and 5). Some of the exposure variability is not captured based on the assumption that non-commercial ma rine fish consumers eat a mix of noncommercial species. Specifically, c onsumers who primarily eat species with methylmercury levels above the esti mated weighted mean will have higher methylmercury exposures than the consumers that eat the mix of non-commercial fish assumed in the analysis. Conv ersely, others may eat a mix of fish that have a lower methylmercury concentration than the we ighted mean. The assumption that commercial fish consumed by members of these populations contain mean methylmercury concentrations also does not fully capture the variability. Under the alternative mercury emissions modeled, estimated intake in the Atlantic population declines and the fraction of the population that is exposed above the138 RfD also declines. The fractional contribut ion of methylmercury from commercial fish consumption also is predicted to increase un der the alternative emissions. This is a consequence of larger decreases in non-commercial fish methylmercury concentrations

than commercial fish concentrations under t hese alternatives. Table 25 shows that under the emissions cases considered (i.e., the two baselines and the two alternative scenarios), the predicted decreases in mercur y deposition to the Gulf and the Coastal Atlantic exceed the predicted change to the All Other Waters Region. In the Gulf population, estima ted intake and the fraction of the population that is exposed above the RfD also decline, but these changes are small. Because the decrease in methylmercury exposure due to commercial fish consumption is less than

that from non-commercial fish consumption, the fractional contribution of methylmercury from commercial fish consumption also in creases under the alternative emissions. However, the predicted decrease in meth ylmercury intake among non-commercial fish

consumers of Gulf fish is smaller than that among the non-commercial Atlantic fish consuming population. This is a consequence of the relatively smaller decreases in the Gulf non-commercial fish methylmercury conc entrations when compared to those in the Atlantic fish. Also, non-commercial Gulfconsumers eat higher quantities of noncommercial fish than their Atlantic counterparts. 3.3.4. Predicted Exposures in Consumer s of Non-commercial Freshwater Fish. Tables 38-42 present the results of the exposure models for non-commercial fish consumers in the five freshwater regions si mulated. These estimates are developed for

consumers that eat a mix of non-commercial fish derived from the freshwaters of the region and a mix of commercial fish that re flects per capita consumption rates.139 Table 38. Predicted Methylmercury Inta ke Rates (μg/kg-day) in Consumers of Non-Commercial Northeast Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Northeast Current 0.075 0.053 0.196 79.5% 0.173 Northeast Baseline 1 0.069 0.050 0.179 82.3% 0.168 Northeast Scenario 1 0.069 0.049 0.176 82.7% 0.167 Northeast Baseline 2 0.070 0.050 0.182 81.9% 0.169 Northeast Scenario 2 0.068 0.049 0.175 82.9% 0.167 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 39. Predicted Methylmercury Inta ke Rates (μg/kg-day) in Consumers of Non-Commercial Mid-Atlantic Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Mid-Atlantic Current 0.066 0.045 0.171 85.3% 0.186 Mid-Atlantic Baseline 1 0.057 0.040 0.140 89.4% 0.176 Mid-Atlantic Scenario 1 0.053 0.038 0.129 91.1% 0.173 Mid-Atlantic Baseline 2 0.056 0.040 0.139 89.6% 0.175 Mid-Atlantic Scenario 2 0.052 0.037 0.125 91.5% 0.169 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).140 Table 40. Predicted Methylmercury Inta ke Rates (μg/kg-day) in Consumers of Non-Commercial Southeast Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Southeast Current 0.067 0.048 0.170 84.1% 0.171 Southeast Baseline 1 0.059 0.044 0.147 87.6% 0.163 Southeast Scenario 1 0.058 0.043 0.143 88.3% 0.162 Southeast Baseline 2 0.059 0.044 0.146 87.8% 0.163 Southeast Scenario 2 0.057 0.042 0.138 89.0% 0.160 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 41. Predicted Methylmercury Inta ke Rates (μg/kg-day) in Consumers of Non-Commercial Midwest Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Midwest Current 0.057 0.042 0.137 88.4% 0.149 Midwest Baseline 1 0.054 0.041 0.129 89.7% 0.144 Midwest Scenario 1 0.053 0.040 0.125 90.4% 0.143 Midwest Baseline 2 0.054 0.041 0.128 89.8% 0.144 Midwest Scenario 2 0.053 0.039 0.123 90.6% 0.142 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).141 Table 42. Predicted Methylmercury Inta ke Rates (μg/kg-day) in Consumers of Non-Commercial West Fish*

Population Mean Intake (μg/kg-day) 50th % (μg/kg-day) 95th % (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) West Current 0.061 0.044 0.151 86.4% 0.159 West Baseline 1 0.060 0.044 0.147 87.0% 0.158 West Scenario 1 0.060 0.043 0.146 87.1% 0.157 West Baseline 2 0.060 0.044 0.147 86.8% 0.157 West Scenario 2 0.060 0.043 0.146 87.1% 0.157 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Based on the use of the Connely et al. (1996) data on rates of commercial fish consumption among non-commercial fish cons umers, methylmercury from commercial fish is estimated to contribute roughly 50% or more to predicted current methylmercury intakes among these consumers. The fracti onal contribution of commercial fish is higher among consumers of less contaminated freshwater fish (e.g., Midwest or West). When compared to consumers of non-comm ercial Atlantic fish, a greater percentage of non-commercial freshwater cons umers are predicted to exceed the RfD. Currently, roughly 21% of the population that consumes fish from the Northeast is predicted to exceed the RfD. Between 12 and 16% of the other freshwater fish consuming populations are predicted to exce ed the RfD currently. We note that the142 assumption that non-commercial consumers eat a mix of non-commercial fish likely

under estimates the variabilit y in population exposures. Under the alternative mercury emissi ons modeled, estimated methylmercury intakes in each of these five populations dec lines and the fraction of the populations that is exposed above the RfD also declines. T he fractional contribution of methylmercury from commercial fish consumption also incr eases under the alternative emissions. This is a consequence of larger decreases in the methylmercury concentrations of the noncommercial fish, particularly in the Mid-Atl antic and Southeast Regions. Decreases in the Western Regions are relatively small co mpared to the other regions. Again, the predicted decrease in the methylmercury conc entrations of commercial fish is smaller than predicted decreases in the non-commerc ial freshwater fish in each region. For the five freshwater regions, we also evaluated high-end fish consumers. We used the data from the fish consumption surv ey conducted by the Columbia River InterTribal Fish Commission (1994) as a surr ogate for this population. For current exposures and all emissions reduction scenari os considered, meth ylmercury exposures

in most consumers with these rates of fish consumption are predi cted to exceed the RfD (Table 43). For example, our model predicts that over 80% of this population in the Northeast is currently exposed above the RfD. If the CSI is implemented, over 75% of this population will still be exposed above the RfD (see Table 43). We did not estimate the size of this population explicitly, becaus e we found no suitable data to prepare such

an estimate. Additional deta ils are provided in Table 43.143 Table 43. Predicted Methylmercury Intakes Among High-End Freshwater Fish Consumers in the U.S.

Population Mean Intake (μg/kg-day) Percent Population Below RfD Conditional Mean Intake for those above RfD (μg/kg-day) Northeast Current 0.363 18% 0.428 Northeast Baseline 1 0.324 21.6% 0.396 Northeast Scenario 1 0.318 22.1% 0.391 Northeast Baseline 2 0.330 21% 0.401 Northeast Scenario 2 0.316 22.5% 0.389 Mid-Atlantic Current 0.296 35.1% 0.426 Mid-Atlantic Baseline 1 0.230 44.6% 0.374 Mid-Atlantic Scenario 1 0.204 48.9% 0.353 Mid-Atlantic Baseline 2 0.227 45.2% 0.371 Mid-Atlantic Scenario 2 0.195 50.7% 0.345 Southeast Current 0.296 28.8% 0.392 Southeast Baseline 1 0.244 36.2% 0.351 Southeast Scenario 1 0.237 37.4% 0.345 Southeast Baseline 2 0.243 36.3% 0.350 Southeast Scenario 2 0.226 39.1% 0.335 Midwest Current 0.230 33.2% 0.314 Midwest Baseline 1 0.210 37% 0.298 Midwest Scenario 1 0.202 38.6% 0.293 Midwest Baseline 2 0.209 37.2% 0.299 Midwest Scenario 2 0.198 39.6% 0.290 West Current 0.260 30.7% 0.349 West Baseline 1 0.252 31.9% 0.343 West Scenario 1 0.251 32.1% 0.341 West Baseline 2 0.253 31.9% 0.343 West Scenario 2 0.250 32.4% 0.341144 3.4. ESTIMATES OF IN UTERO NEUROTOXICITY RISK S ASSOCIATED WITH MATERNAL CONSUMPTION OF METHYLMERCURY IN FISH In Section 3.4.1, we present the estimated IQ losses associated with in utero methylmercury exposures per annual birth cohor t in the U.S. We then estimate the costs associated with IQ points lost due to methylmercury exposures; these costs are based on a Cost-of-Illness approach. In Secti on 3.4.2, we estimate the averted costs associated with IQ point gains per annual birth cohort if Scenarios 1 and 2 are implemented. In Section 3.4.3, we present estimates of the QALYs currently lost due to

in utero methylmercury exposures and those losse s that may be averted if Scenarios 1 and 2 are implemented. 3.4.1. IQ Points Lost and Associated Costs due to Current Methylmercury Exposures. 3.4.1.1. Current Methylmercury Im pacts Using a No Threshold Model

― Current methylmercury intakes in pr egnant consumers of commercial and noncommercial fish in each regional populati on and in the general population cause IQ decrements (Tables 44-51). Assuming no thres hold, the model predicts that currently, the mean IQ point losses per child range fr om 0.39 to 0.60 among the consumers of non-commercial fish and the mean IQ point loss per child in the population that consumes no fish or only commercial fish is 0.25 (Table 51). When considered as a group and using these modeling assumptions, children born to women consuming only commercial fish lose roughly 59% of t he total IQ point loss attributable to in utero methylmercury exposures in current annual birth cohorts. Figure 11 shows the

fractional contribution of eac h population to the estimated U.S. total IQ point loss.

Tables 44-51 also show the estimated co sts associated with t hese IQ decrements.145 Table 52 shows that the current total cost per annual birth cohort is estimated to be approximately $19.9 billion. This estimate is based on a cost-of-illness estimate. Costof-illness estimates are typically not consid ered valid welfare measures. Willingness-topay measures for IQ losses were not availabl e, but would likely be significantly greater

than the cost-of-illness estimate.

Table 44. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non- Commercial Atlantic Ocean Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per

person Children born per year IQ Loss per Annual birth cohort $ Value IQ point Loss

Atlantic Current 0.649 0.390 137,000 53,300 $894,522,000 Atlantic Baseline 1 0.630 0.378 137,000 51,700 $867,343,000 Atlantic Scenario 1 0.625 0.375 137,000 51,300 $860,587,000 Atlantic Baseline 2 0.629 0.378 137,000 51,600 $866,891,000 Atlantic Scenario 2 0.623 0.374 137,000 51,100 $857,959,000 Benefit Scenario 1 0.003 400 $6,756,000 Benefit Scenario 2 0.004 500 $8,932,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).146 Table 45. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Co mmercial Gulf Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per

person Children born per year IQ Loss per Annual birth cohort $ Value IQ point Loss

Gulf Current 0.866 0. 520 79,000 41,100 $689,416,000 Gulf Baseline 1 0.845 0. 507 79,000 40,000 $672,311,000 Gulf Scenario 1 0.841 0. 505 79,000 39,900 $669,680,000 Gulf Baseline 2 0.845 0. 507 79,000 40,000 $672,286,000 Gulf Scenario 2 0.839 0. 503 79,000 39,800 $667,593,000 Benefit Scenario 1 0.002 100 $2,631,000 Benefit Scenario 2 0.004 200 $4,693,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).147 Table 46. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Co mmercial Northeast Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per

person Children born per year IQ Loss per Annual birth cohort $ Value IQ point Loss

Northeast Current 1.006 0. 604 67,000 40,700 $683,094,000 Northeast Baseline 1 0.932 0.559 67,000 37,700 $632,940,000 Northeast Scenario 1 0.922 0.553 67,000 37,300 $625,877,000 Northeast Baseline 2 0.945 0.567 67,000 38,200 $641,447,000 Northeast Scenario 2 0.916 0.550 67,000 37,100 $622,248,000 Benefit Scenario 1 0.006 400 $7,063,000 Benefit Scenario 2 0.017 1,100 $19,199,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).148 Table 47. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Co mmercial Mid-Atlantic Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per

Person Children Born per Year IQ Loss per Annual Birth Cohort $ Value IQ Point Loss

Mid-Atlantic Current 0.881 0.529 6,000 3,000 $49,817,000 Mid-Atlantic Baseline 1 0.760 0.456 6,000 2,600 $42,948,000 Mid-Atlantic Scenario 1 0.713 0.428 6,000 2,400 $40,303,000 Mid-Atlantic Baseline 2 0.754 0.452 6,000 2,500 $42,597,000 Mid-Atlantic Scenario 2 0.695 0.417 6,000 2,300 $39,289,000 Benefit Scenario 1 0.028 200 $2,645,000 Benefit Scenario 2 0.035 200 $3,308,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).149 Table 48. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Comme rcial Southeast Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per Person Children Born per

Year IQ Loss per Annual

Birth Cohort $ Value IQ Point Loss

Southeast Current 0.892 0. 535 201,000 107,700 $1,808,796,000 Southeast Baseline 1 0.798 0.479 201,000 96,300 $1,617,526,000 Southeast Scenario 1 0.780 0.468 201,000 94,200 $1,581,019,000 Southeast Baseline 2 0.794 0.477 201,000 95,900 $1,609,977,000 Southeast Scenario 2 0.761 0.457 201,000 91,900 $1,542,468,000 Benefit Scenario 1 0.011 2,100 $36,507,000 Benefit Scenario 2 0.020 4,000 $67,509,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).150 Table 49. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Co mmercial Midwest Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per

Person Children Born per

Year IQ Loss per Annual Birth Cohort $ Value IQ Point Loss

Midwest Current 0.767 0.460 277,000 127,500 $2,141,187,000 Midwest Baseline 1 0.730 0.438 277,000 121,400 $2,038,527,000 Midwest Scenario 1 0.714 0.428 277,000 118,800 $1,994,266,000 Midwest Baseline 2 0.729 0.438 277,000 121,300 $2,036,886,000 Midwest Scenario 2 0.705 0.423 277,000 117,400 $1,970,379,000 Benefit Scenario 1 0.010 2,600 $44,261,000 Benefit Scenario 2 0.014 3,900 $66,507,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).151 Table 50. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Co mmercial West Fish* (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per

Person Children Born per

Year IQ Loss per Annual Birth Cohort $ Value IQ Point Loss

West Current 0.824 0. 494 226,000 111,700 $1,875,787,000 West Baseline 1 0.809 0. 485 226,000 109,700 $1,841,484,000 West Scenario 1 0.804 0. 482 226,000 109,000 $1,829,600,000 West Baseline 2 0.809 0. 486 226,000 109,700 $1,842,501,000 West Scenario 2 0.801 0. 481 226,000 108,600 $1,823,060,000 Benefit Scenario 1 0.003 700 $11,884,000 Benefit Scenario 2 0.005 1,100 $19,441,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 51. Predicted Mean Hair Methyl mercury Concentrations, Mean IQ Point Loss, IQ Losses in Annual Birth Cohort, a nd Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Commerc ial Fish and Non-Fish Consumers (Assuming No Neurotoxicity Threshold)

Population Mean MeHg Hair Conc. (μg/g hair) Mean IQ Loss per Person Children Born per

Year IQ Loss per Annual

Birth Cohort $ Value IQ Point Loss

Current Female 0.410 0.246 2,845,000 700,600 $11,763,387,000 Baseline 1 Female 0.407 0. 244 2,845,000 695, 000 $11,668,494,000 Scenario 1 Female 0.404 0. 243 2,845,000 690, 100 $11,586,300,000 Baseline 2 Female 0.404 0. 242 2,845,000 689, 900 $11,583,590,000 Scenario 2 Female 0.401 0. 240 2,845,000 684, 000 $11,484,931,000 Benefit Scenario 1 0.002 4,900 $82,194,000 Benefit Scenario 2 0.002 5,900 $98,659,000152 

### Atlantic

4.5% Gulf 3.4% Northeast 3.4%

Mid-Atlantic 0.3% Southeast 9.1%

Midw est

10.8%

West 9.4% Population consuming commercial fish and no fish 59.1%

Figure 11

Fractional Contribution of Consumers of Non-Commerci al Fish in Each Region and Commercial Fish to Total IQ Point Loss, Assuming No Neurotoxicity Threshold153 Table 52. Summary of IQ Point Losses and Associated Costs per Annual Birth Cohort for the Entire U.S. Population (2000$) IQ Points Lost per Annual Birth Cohort Monetary Value of Lost IQ Points Assuming no Neurotoxicity Threshold Total Population Current 1,185,600 $19,906,000,000 Total Population Baseline 1 1,154,400 $19,382,000,000 Total Population Scenario 1 1,143,000 $19,188,000,000 Total Population Baseline 2 1,149,100 $19,296,000,000 Total Population Scenario 2 1,132,200 $19,008,000,000 Assuming a Neurotoxicity Threshold Total Population Current 187,000 $3,137,000,000 Total Population Baseli ne 1 173,000 $2,897,000,000 Total Population Scenario 1 168,000 $2,821,000,000 Total Population Baseli ne 2 170,000 $2,862,000,000 Total Population Baseli ne 2 163,000 $2,743,000,000154 Table 53. Predicted Incremental IQ Gains per Annual U.S. Birth Cohort and Incremental Estimated Monetary Value of the IQ Gains (Cost-of-Illness) (2000$) IQ Point Gain per Annual Birth Cohort $ Value IQ Point Gain Number Children Born Above RfD Annually QALY Gain per Annual

Birth Cohort Scenario 1 (Assuming No Neurotoxicity Threshold) 11,600 $193,940,000 Scenario 2 (Assuming No Neurotoxicity Threshold) 17,200 $288,248,000 Scenario 1 (Assuming RfD = Neurotoxicity Threshold) 4,500 $75,311,000 7,400 5,700 Scenario 2 (Assuming RfD = Neurotoxicity Threshold) 7,100 $119,002,000 9,600 7,400

3.4.1.2. Current Methylme rcury Impacts Using a Model Threshold of 0.1 μg/kg-day The estimated current methylmercury inta ke rates of some pregnant consumers

of commercial and non-commercial fish in ea ch regional population and in the general population exceed the RfD (Tables 54-61). Thus, if a neurotoxicity threshold of 0.1 μg/kg-day (i.e., the value of the RfD) is assumed, current methylmercury intakes in some pregnant consumers of commercial and non-commercial fish in each regional population and in the populati on that consumes only commercial fish cause IQ decrements. Assuming the RfD value to be a threshold, Tables 54-60 show that the predicted mean IQ point losses per child (i.e., conditional mean) range from 0.36 to 0.83 among the consumers of non-commercial fi sh and Table 61 show s that the mean IQ point loss per child among affected children in the population that consumes only155 commercial fish is 0.52. 46 The mean IQ points lost by affected members of each population is greater under the threshold assumption than under the no threshold assumption because the conditional mean me thylmercury exposures of these children are higher than the mean of t he children at risk under the no threshold assumption. When considered as a group, children born to women in the population that consumes only commercial fish or no fish are estimat ed to lose roughly 62% of the total IQ point

loss attributable to in utero methylmercury exposures in t he current annual birth cohorts.

Tables 54-61 show the estimated costs associ ated with these IQ decre ments. The total cost per current birth cohort is estimated to be approximately $3.1 billion, roughly 16% of the associated cost of $19.9 billion es timated when using the non-threshold model (Table 52). Under the threshold assumpti on, these costs are borne exclusively by approximately 9% of the babies born in each annual birth cohor t. We note that the models employed in the exposure analysis likely underestimate variability in population

intake.

46 This population is assumed to include individuals that consume only commercial fish and those that consume no fish.156 Table 54. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Il lness) (2000$) in Consumers of NonCommercial Atlantic Ocean Fish* (Assu ming RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Atlantic Current 0. 36 8,610 3,060 $51,391,000 Atlantic Baseline 1 0.38 7,000 2,630 $44,127,000 Atlantic Scenario 1 0.37 6,920 2,540 $42,680,000 Atlantic Baseline 2 0.38 6,970 2,620 $43,983,000 Atlantic Scenario 2 0.36 6,920 2,510 $42,092,000 Benefit Scenario 1 80 90 $1,447,000 Benefit Scenario 2 50 110 $1,891,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 55. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Consumers NonCommercial Gulf Fish* (Assuming Rf D is Neurotoxicity Threshold)

Population Average IQ Loss per

Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Gulf Current 0.83 16,040 13,310 $223,413,000 Gulf Baseline 1 0.80 15,770 12,650 $212,369,000 Gulf Scenario 1 0.80 15,710 12,500 $209,904,000 Gulf Baseline 2 0.80 15,770 12,640 $212,296,000 Gulf Scenario 2 0.79 15,660 12,360 $207,561,000 Benefit Scenario 1 60 150 $2,465,000 Benefit Scenario 2 120 280 $4,735,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).157 Table 56. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Il lness) (2000$) in Consumers of NonCommercial Northeast Fish* (Assuming RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Northeast Current 0.59 13,810 8,130 $136,501,000 Northeast Baseline 1 0.54 11,940 6,510 $109,278,000 Northeast Scenario 1 0. 54 11,640 6,310 $105,909,000 Northeast Baseline 2 0.56 12,200 6,790 $114,047,000 Northeast Scenario 2 0. 54 11,550 6,190 $103,933,000 Benefit Scenario 1 300 200 $3,369,000 Benefit Scenario 2 650 600 $10,114,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 57. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Il lness) (2000$) in Consumers of NonCommercial Mid-Atlantic Fish* (Assumi ng RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Mid-Atlantic Current 0.69 820 570 $9,574,000 Mid-Atlantic Baseline 1 0.61 590 360 $6,068,000 Mid-Atlantic Scenario 1 0.58 500 290 $4,900,000 Mid-Atlantic Baseline 2 0.60 590 350 $5,896,000 Mid-Atlantic Scenario 2 0.56 470 260 $4,427,000 Benefit Scenario 1 90 70 $1,168,000 Benefit Scenario 2 110 90 $1,469,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).158 Table 58. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Il lness) (2000$) in Consumers of NonCommercial Southeast Fish* (Assuming RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Southeast Current 0.57 32,070 18,280 $306,955,000 Southeast Baseline 1 0.51 24,880 12,680 $212,816,000 Southeast Scenario 1 0.50 23,590 11,770 $197,686,000 Southeast Baseline 2 0.51 24,630 12,480 $209,572,000 Southeast Scenario 2 0.49 22,140 10,740 $180,298,000 Benefit Scenario 1 1,290 900 $15,130,000 Benefit Scenario 2 2,500 1,740 $29,273,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). Table 59. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Il lness) (2000$) in Consumers of NonCommercial Midwest Fish* (Assuming RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Midwest Current 0.39 32,250 12,640 $212,145,000 Midwest Baseline 1 0.36 28,590 10,150 $170,484,000 Midwest Scenario 1 0.35 26,700 9,300 $156,139,000 Midwest Baseline 2 0.36 28,390 10,130 $170,154,000 Midwest Scenario 2 0.34 25,940 8,730 $146,517,000 Benefit Scenario 1 1,900 850 $14,345,000 Benefit Scenario 2 2,450 1,400 $23,637,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).159 Table 60. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of-Il lness) (2000$) in Consumers of NonCommercial West Fish* (Assuming RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

West Current 0.48 30,750 14,640 $245,866,000 West Baseline 1 0.47 29,420 13,700 $229,987,000 West Scenario 1 0.46 29,230 13,510 $226,851,000 West Baseline 2 0.46 29,770 13,720 $230,393,000 West Scenario 2 0.46 29,130 13,300 $223,274,000 Benefit Scenario 1 190 190 $3,136,000 Benefit Scenario 2 650 420 $7,120,000 * This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3).

Table 61. Predicted Mean IQ point lo ss, IQ Losses per Annual Birth Cohort, and Estimated Monetary Value (Cost-of- Illness) (2000$) in Commercial Fish Consumers* (Assuming RfD is Neurotoxicity Threshold)

Population Average IQ Loss per Person Number Children Born Above RfD per Annual Birth Cohort IQ Loss per Annual Birth Cohort Value IQ Point Loss

Current 0.52 224,330 116,220 $1,951,400,000 Baseline 1 0.52 220, 860 113,840 $1,911,439,000 Scenario 1 0.51 217, 330 111,800 $1,877,187,000 Baseline 2 0.51 217, 220 111,740 $1,876,063,000 Scenario 2 0.51 214, 090 109,310 $1,835,300,000 Benefit Scenario 1 3,530 2,040 $34,251,000 Benefit Scenario 2 3,130 2,430 $40,763,000 * This population also is assumed to include individuals that consume no fish.160 3.4.2. Averted Costs Asso ciated with IQ Point Ga ins per Birth Cohort Under Alternative Emissions Scenarios 1 and 2.

3.4.2.1. Results of No Threshold Model

― In this section, the results of Scenario 1 are compared to those of Baseline 1 and

the results of Scenario 2 ar e compared to those of Baseline 2. Based on the model results, under Scenarios 1 and 2 methylmercur y intakes in pregnant consumers of commercial and non-commercial fish in each regional population and in the general population will decline but continue to cause IQ decrements in children (Tables 44-51). When compared to Baseline 1 and Baseline 2, the mean IQ point losses per child across the entire population under Scenarios 1 and 2 are predicted to decrease by roughly 0.003 and 0.004 IQ points, respective ly. They decrease from 0.301 to 0.298

between Baseline 1 and Scenario 1 and from 0.299 to 0.295 betw een Baseline 2 and Scenario 2. The decrease in IQ points lo st is predicted to be highest among noncommercial fish consumers (except in the West) and lowest among commercial

consumers. Table 52 summarizes the estima ted IQ points lost per annual birth cohort in the entire population for eac h emissions scenario. Rows 2 and 3 of Table 53 show that the predicted increment al gain in average IQ betw een Baseline1 and Scenario1 is

associated with a societal benef it of approximately $194 million and the societal benefit associated with the predicted incremental gain in aver age IQ between Baseline 2 and Scenario 2 is $288 million. (See last two ro ws in Tables 44-51 for regional benefits summaries of Scenarios 1 and 2.) Most of the cost decrease is associated with the decreased IQ point loss in the commercial fish consuming population. Among the non-161 commercial consumers, most of the predict ed benefit is predicted to occur in the populations that consume Southeast or Midwestern non-commercial fish.

3.4.2.2. Averted Costs A ssociated with IQ Point Ga ins per Birth Cohort Under

Alternative Emissions Scenarios 1 and 2 Using Model Threshold of 0.1 μg/kg day The estimated current methylmercury inta ke rates of some pregnant consumers

of commercial and non-commercial fish in each regional population and in the population that consumes only commercial fish or no fish are predicted to exceed the RfD. Based on the model results, under Sc enarios 1 and 2 methylmercury intakes in pregnant consumers of commercial and non- commercial fish in each regional population and in the population that consumes only commercial fish or no fish will decline resulting in fewer affected i ndividuals in each population; however, methylmercury exposures will continue to c ause IQ decrements in children (Tables 54- 61) (see summary in Table 52). When compared to Baseline 1 and Baseline 2, the mean IQ point gains per child

under Scenarios 1 and 2 ar e predicted to be roughly 0.001 and 0.006 IQ points, respectively. When compared to the current estimate, under Scenario 2 the mean IQ point losses per child are predicted to dec rease roughly 0.015 IQ points. Table 53 shows that the incremental gain in m ean IQ in each annual birth cohort between

Baseline1 and Scenario 1 is associated with a societal benefit of approximately $75

million. The societal benefit associat ed with mean IQ gain between Baseline 2 and Scenario 2 is $119 million. Mo st of the societal benefit is associated with the decreased IQ point loss in the population that cons umes only commercial fish. Among the non-162 commercial consumers, most of the benefit is predicted to occur in the populations that

consume Southeast or Midwestern non-commercial fish. 3.4.3. IQ Results Considering QALYs. We estimated QALYs lost by assuming that each child born to a mother whose

intake exceeded the RfD lost a utility weight of 0.01 (see Section 2.6), and that this effect would persist throughout the child’s life (life expectancy at birth is estimated to be 77.2 years) for a loss of 0.77 QALYs. We noted that both the utility weight and the exposure-response function are quite simple. The results are similar to those in Sections 3.4.2.2. Currently , children born to commercial fish consumers (i.e., the population that eats only commerc ial fish or no fish) are predi cted to lose most of the

QALYs lost due to in utero methylmercury exposures (Table 53). When compared to Baseline 1, under Scenario 1 the loss of roughly 5700 QALYs is averted. When compared to Baseline 2, under Scenario 2 the loss of roughly 7400 QALYs is averted.

3.5. COSTS ASSOCIATED WITH ACUT E MYOCARDIAL INFARCTIONS AND ALL CAUSE MORTALITY

3.5.1. Pike Consumers. If it is appropriate to generalize the Salonen et al. data, then it is most plausible that the regression coefficient s also apply to consumption of non-fatty fish with high methylmercury levels such as pike. The Salonen data are based on a male cohort. In this report, initially we apply the predicted rate increases for acute myocardial infarction and all cause mortality to the male pike consumers in the U.S.163 To estimate the size of this cohort, we assu me that those targeting pike for catch are the only consumers. 47 Table 62 lists the predicted mean daily methylmercury intakes (μg/kg-day) and the estimated sizes of the male pike-consum ing populations in each of four freshwater regions. 48 In this part of the analysis we assum ed that only males that consumed pike were affected by the cardiovascular risk. The mean daily current methylmercury intakes range from approximatel y 0.05 to 0.07 μg/kg-day. The blood and hair concentrations are estimated using the methylmercur y one compartment model (Equation 1).

The last two columns in Table 62 list t he predicted annual changes in the number of cases of nonfatal acute myocardial infa rctions and premature deaths due to all cause mortality, when comparing Baseline 1 to Scenar io 1 and Baseline 2 to Scenario 2. The

incremental decreases are the result of the change in daily methylmercury intake and the size of the affected population. Giv en the small sizes of the populations and the modest predicted changes in mean methylme rcury intakes, annually, 4.5 cases of nonfatal acute myocardial infarctions and eight premature deaths ar e predicted to be avoided through implementing Scenario 1 in this population. Under Scenario 2, annually, we predict eight cases of non-fata l acute myocardial infarctions and 14.6 premature deaths to be avoided. Table 63 lists the annual avoi ded costs associated

with these outcomes. If the VSL estimate is used for prem ature death, then the annual avoided costs are $49 million and $87 million under Scenarios 1 and 2, respectively.

47 If methylmercury antagonizes the effects of n-3 fatty acids, then other fish with low to moderate n-3 concentrations and high methylmercury concentrations ma y cause cardiovascular effects as well. Thus, a larger population may be affected. However, they may be affected less severely than the Salonen cohort, because they will likely consume higher levels of n-3 fatty acids than the members of the Salonen cohort. 48 Pike are not among the species routinely target ed by anglers fishing in the Southeastern Region.164 Most of the benefit is realized by Midwest pike consumers. Annually, we predict 140 and 250 QALYs are gained under Scenarios 1 and 2. Most QALYs gained are the

result of decreases in premature mortality.165Table 62. Distribution ofPredicted Annual Cases of Acute MyocardialInfarction (AMI) and Premature Deaths(ACM) in Male Northern Pike ConsumersMeanDailyIntakeMean BloodConcentrationMean HairConcentrationIncidenceRate AMIIncidenceRate ACMPopulationNonFatal AMICasesPrematureDeathsNon-FatalAMIAvoidedPrematureDeathsAvoidedNortheastCurrent0.075 4.05 1.01 9.983E-031.123E-02 98,000 796 1,098NortheastBaseline1 0.067 3.61 0.90 9.911E-03 1.112E-02 98,000 790 1,088NortheastScenario 1 0.066 3.55 0.89 9.902E-03 1.111E-02 98,000 790 1,086 0.7 1.3NortheastBaseline20.069 3.69 0.92 9.924E-03 1.114E-02 98,000 791 1,089NortheastScenario 20.066 3.53 0.88 9.898E-03 1.110E-02 98,000 789 1,086 2.0 3.7MidAtlanticCurrent0.092 4.93 1.23 1.013E-021.144E-02 2,000 17 23MidAtlanticBaseline 10.071 3.83 0.96 9.948E-03 1.118E-02 2,000 17 23MidAtlanticScenario 10.063 3.40 0.85 9.877E-03 1.107E-02 2,000 16 23 0.1 0.2MidAtlanticBaseline 20.070 3.78 0.95 9.939E-031.116E-02 2,000 17 23MidAtlanticScenario 20.060 3.25 0.81 9.852E-03 1.104E-02 2,000 16 23 0.1 0.3MidwestCurrent0.057 3.05 0.76 9.820E-03 1.099E-02 292,000 2,340 3,211MidwestBaseline 10.052 2.78 0.70 9.777E-03 1.093E-02 292,000 2,330 3,193166Table 62 cont.MeanDailyIntakeMean BloodConcentrationMean HairConcentrationIncidenceRate AMIIncidenceRate ACMPopulationNonFatal AMICasesPrematureDeathsNon-FatalAMIAvoidedPrematureDeathsAvoidedMidwestScenario 1 0.050 2.69 0.67 9.762E-031.090E-02 292,000 2,326 3,186 3.6 6.4MidwestBaseline 2 0.052 2.78 0.69 9.776E-03 1.093E-02 292,000 2,330 3,192MidwestScenario 2 0.049 2.63 0.66 9.752E-031.089E-02 292,000 2,324 3,182 5.7 10.2WestCurrent 0.052 2.80 0.70 9.779E-03 1.093E-02 54,000 427 586WestBaseline 1 0.050 2.71 0.68 9.766E-03 1.091E-02 54,000 427 585WestScenario 1 0.050 2.69 0.67 9.763E-03 1.091E-02 54,000 427 584 0.1 0.2WestBaseline 2 0.050 2.71 0.68 9.766E-03 1.091E-02 54,000 427 585WestScenario 2 0.050 2.68 0.67 9.761E-03 1.090E-02 54,000 427 584 0.2 0.4Cases Avoided Scenario 14.5 8.2Cases Avoided Scenario 28.1 14.6167Table 63. Using a Cost-of-Illness Approach and VSL, Annual Costs Associated with Cases of Non-Fatal AMI andPremature Death In Male Northern Pike Consumers (2000$)PredictedAnnualdecrease incases ofnon-fatalAMIAnnualAvoided Costsdue toreduction innon-fatal casesof AMIPredictedAnnualdecrease incases ofACMAnnual AvoidedCosts (COI) dueto reduction incases of ACMAnnual AvoidedCosts (VSL)due to reductionin cases ofACMAnnual TotalAvoided Costs(COI) due toreduction in casesof AMI and ACMAnnual Total AvoidedCosts (COI for AMI andVSL for ACM) due toreduction in cases ofAMI and ACMChangeScenario 14.5 $235,000 8.2 $2,624,000 $48,380,000 $2,858,000 $48,614,000ChangeScenario 28.1 $423,000 14.6 $4,671,000 $86,140,000 $5,094,000 $86,563,000168 3.5.2. Entire U.S. Population. In the second case, we apply the regressi on coefficients from the Salonen et al. data to consumption of all fish. We apply them to males and females. We note that this is an extremely uncertain extrapolation, because of potential differences between the subjects in the Salonon et al. study and the U.S. population and the potential differences in the types of fish consumed. Tables 64-71 list the predicted cases of male non-fatal AMI and ACM by region. Currently, most cases are predicted to occu r in the population that consumes only commercial fish or no fish. Among the non- commercial consumers, most cases are predicted to occur among Midwestern and West ern consumers. Table 72 summarizes

the benefits of Scenarios 1 and 2 in males. Under Scenario 1, annually, roughly 140 cases of non-fatal acute myocardial infarc tions and 260 premature deaths are predicted to be avoided. Under Scenario 2, annually , roughly 210 cases of non-fatal acute myocardial infarctions and 380 premature deat hs are predicted to be avoided. Based on the VSL estimate for premature death, th e annual avoided costs are predicted to be $1.5 billion and $2.3 bi llion under Scenarios 1 and 2, respec tively. Among males, most of the benefit is realized by the population that consumes co mmercial fish. Annually, we predict 4300 and 6500 QALYs are gained under Scenarios 1 and 2, respectively. Nearly 95% of QALYs gained under both scenarios are the result of decreases in premature mortality.169Table 64. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Atlantic Ocean Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Atlantic Current 15,768 21,585 $823,048,000 $6,906,000,000 $127,353,000,000Atlantic Baseline 115,747 21,549 $821,981,000 $6,894,000,000 $127,136,000,000Atlantic Scenario 1 15,742 21,539 $821,716,000 $6,892,000,000 $127,083,000,000Atlantic Baseline 215,747 21,548 $821,963,000 $6,894,000,000 $127,133,000,000Atlantic Scenario 2 15,740 21,536 $821,612,000 $6,890,000,000 $127,062,000,000Benefit Scenario 1 5 10 $265,000 $2,914,000 $53,728,000Benefit Scenario 2 7 12 $351,000 $3,852,000 $71,026,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).170Table 65. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Gulf Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Gulf Current 9,243 12,710 $482,484,000 $4,066,000,000 $74,987,000,000Gulf Baseline 1 9,230 12,686 $481,802,000 $4,059,000,000 $74,848,000,000Gulf Scenario 1 9,228 12,682 $481,697,000 $4,058,000,000 $74,827,000,000Gulf Baseline 2 9,230 12,686 $481,801,000 $4,059,000,000 $74,848,000,000Gulf Scenario 2 9,227 12,680 $481,614,000 $4,057,000,000 $74,810,000,000Benefit Scenario 1 2 4 $105,000 $1,156,000 $21,311,000Benefit Scenario 2 3 6 $187,000 $2,062,000 $38,018,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).171Table 66. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Northeast Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Northeast Current 7,959 10,974 $415,430,000 $3,511,000,000 $64,749,000,000Northeast Baseline 1 7,920 10,905 $413,416,000 $3,489,000,000$64,339,000,000Northeast Scenario 1 7,915 10,895 $413,134,000 $3,486,000,000$64,281,000,000Northeast Baseline 2 7,927 10,917 $413,757,000 $3,493,000,000$64,408,000,000Northeast Scenario 2 7,912 10,890 $412,988,000 $3,484,000,000$64,251,000,000Benefit Scenario 1 5 10 $283,000 $3,126,000 $57,642,000Benefit Scenario 2 15 27 $769,000 $8,499,000 $156,718,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).172Table 67. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Mid-Atlantic Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MortalityDeath (COI)Costs MaleMortality (VSL)Mid-Atlantic Current 657 904$34,291,000 $289,000,000 $5,331,000,000Mid-Atlantic Baseline 1 652 894$34,018,000 $286,000,000 $5,276,000,000Mid-Atlantic Scenario 1 650 891$33,913,000 $285,000,000 $5,254,000,000Mid-Atlantic Baseline 2 651 894$34,004,000 $286,000,000 $5,273,000,000Mid-Atlantic Scenario 2 649 889$33,873,000 $284,000,000 $5,246,000,000Benefit Scenario 1 2 3$105,000 $1,151,000 $21,232,000Benefit Scenario 2 2 5$131,000 $1,439,000 $26,528,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).173Table 68. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Southeastern Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Southeast Current 23,580 32,440 $1,230,830,000 $10,379,000,000$191,395,000,000Southeast Baseline 1 23,434 32,177 $1,223,213,000 $10,295,000,000$189,845,000,000Southeast Scenario 1 23,406 32,127 $1,221,765,000 $10,279,000,000$189,551,000,000Southeast Baseline 2 23,428 32,167 $1,222,914,000 $10,292,000,000$189,784,000,000Southeast Scenario 2 23,377 32,075 $1,220,237,000 $10,262,000,000$189,240,000,000Benefit Scenario 1 28 50 $1,448,000 $15,965,000 $294,402,000Benefit Scenario 2 51 92 $2,676,000 $29,494,000 $543,875,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).174Table 69. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Midwestern Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Midwest Current 32,225 44,219 $1,682,077,000 $14,148,000,000$260,895,000,000Midwest Baseline 1 32,147 44,080 $1,678,015,000 $14,103,000,000$260,070,000,000Midwest Scenario 1 32,114 44,019 $1,676,267,000 $14,084,000,000$259,715,000,000Midwest Baseline 2 32,146 44,077 $1,677,950,000 $14,103,000,000$260,057,000,000Midwest Scenario 2 32,096 43,987 $1,675,324,000 $14,074,000,000$259,523,000,000Benefit Scenario 1 33 61 $1,748,000 $19,245,000 $354,876,000Benefit Scenario 2 50 90 $2,626,000 $28,906,000 $533,031,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).175Table 70. Predicted Annual AMI and ACM Incidenceand Valuation (2000$) in Male Consumers of NonCommercial Western Fish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)West Current 26,357 36,210 $1,375,801,000 $11,586,000,000$213,641,000,000West Baseline 1 26,331 36,163 $1,374,438,000 $11,571,000,000$213,364,000,000West Scenario 1 26,322 36,147 $1,373,966,000 $11,565,000,000$213,268,000,000West Baseline 2 26,332 36,165 $1,374,478,000 $11,571,000,000$213,372,000,000West Scenario 2 26,317 36,138 $1,373,706,000 $11,562,000,000$213,215,000,000Benefit Scenario 1 9 16 $472,000 $5,205,000 $95,979,000Benefit Scenario 2 15 27 $772,000 $8,514,000 $156,997,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).176Table 71. Predicted Annual AMI and ACM Incidence andValuation (2000$) in Male Consumers of CommercialFish*PopulationMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Current 322,603 439,302$16,839,216,000 $140,555,000,000 $2,591,880,000,000Baseline 1 322,536 439,182 $16,835,723,000 $140,517,000,000$2,591,176,000,000Scenario 1 322,478 439,079$16,832,699,000 $140,484,000,000 $2,590,566,000,000Baseline 2 322,476 439,076 $16,832,599,000 $140,483,000,000$2,590,546,000,000Scenario 2 322,406 438,952$16,828,969,000 $140,443,000,000 $2,589,814,000,000Benefit Scenario 1 58 103 $3,025,000 $33,069,000 $609,806,000Benefit Scenario 2 70 124 $3,630,000 $39,683,000 $731,772,000* This population also is assumed to include individuals that consume no fish.177Table 72. Predicted Annual Decreased AMI and ACMIncidence and Annual Benefit (2000$) in MalesMale Non-FatalAMI CasesMale TotalACM CasesMale Total AMICOSTSCosts MaleMortality (COI)Costs Male Mortality(VSL)Scenario 1 140 260 $7,451,000$81,830,000 $1,508,976,000Scenario 2 210 380 $11,141,000$122,447,000 $2,257,964,000178 Tables 73-80 list the predicted cases of female non-fatal AMI and ACM by region. Currently, most case s are predicted to occur among commercial consumers in the general population. Am ong the non-commercial consum ers, most cases are predicted to occur among Midwestern consum ers. Table 81 summarizes the benefits of

Scenarios 1 and 2 in females. Under Sc enario 1, annually, roughly 150 cases of nonfatal acute myocardial infarctions and 300 premature deaths are avoided. Under Scenario 2, annually, roughly 230 cases of n on-fatal acute myocardial infarctions and 450 premature deaths are avoide d. Based on the VSL estimate for premature death,

the annual avoided costs are $1.8 billion and $2.6 billio n under Scenarios 1 and 2, respectively. Most of the benef it is realized by commercial fish consumers. Annually, we predict 5000 and 7500 QALYs are gained under Scenarios 1 and 2, respectively. Over 95% of QALYs gained un der both scenarios are the result of decreases in premature mortality. Table 82 summarizes the predicted annual benefits of Scenarios 1 and 2 in both males and females. Under Scenario 1, annually, roughly 300 cases of non-fatal acute myocardial infarctions and 550 premature deat hs are predicted to be avoided. Under Scenario 2, annually, roughly 440 cases of n on-fatal acute myocardial infarctions and 830 premature deaths are predicted to be av oided. Based on the VSL estimate for premature death, the annual avoided costs are estimate d to be $3.3 billion and $4.9 billion under Scenarios 1 and 2, respectively. Most of the benefit is realized by the population that consumes commercial fi sh. Annually, we predict 9300 and 13,900 QALYs are gained under Scenarios 1 and 2, re spectively (Table 83). Over 95% of QALYs gained under both scenarios are the result of decreases in premature mortality.179Table 73. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Atlantic Ocean Fish*PopulationFemale Non-Fatal AMI CasesFemale TotalACM CasesFemale Total AMICOSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 16,419 24,696 $813,199,000 $4,796,000,000 $145,704,000,000Baseline 1 16,397 24,654 $812,144,000$4,788,000,000 $145,456,000,000Scenario 1 16,392 24,643$811,882,000 $4,786,000,000 $145,395,000,000Baseline 2 16,397 24,653 $812,127,000$4,788,000,000 $145,452,000,000Scenario 2 16,390 24,639$811,780,000 $4,785,000,000 $145,371,000,000Benefit Scenario 1 5 11 $262,000 $2,024,000 $61,471,000Benefit Scenario 2 7 14 $346,000 $2,675,000 $81,260,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).180Table 74. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Gulf Fish*PopulationFemale Non-Fatal AMI CasesFemale TotalACM CasesFemale Total AMICOSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 9,625 14,541 $476,710,000 $2,824,000,000 $85,792,000,000Baseline 1 9,611 14,514 $476,036,000$2,819,000,000 $85,633,000,000Scenario 1 9,609 14,510$475,933,000 $2,818,000,000 $85,609,000,000Baseline 2 9,611 14,514 $476,035,000$2,819,000,000 $85,633,000,000Scenario 2 9,608 14,507$475,851,000 $2,818,000,000 $85,590,000,000Benefit Scenario 1 24 $103,000 $803,000 $24,382,000Benefit Scenario 2 3 7 $185,000 $1,432,000 $43,496,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).181Table 75. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Northeast Fish*PopulationFemale Non-Fatal AMI CasesFemale TotalACM CasesFemale Total AMICOSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 8,287 12,556 $410,458,000$2,439,000,000 $74,080,000,000Baseline 1 8,247 12,476 $408,469,000 $2,423,000,000 $73,610,000,000Scenario 1 8,241 12,465 $408,190,000 $2,421,000,000 $73,544,000,000Baseline 2 8,254 12,490 $408,806,000 $2,426,000,000 $73,689,000,000Scenario 2 8,239 12,459 $408,046,000 $2,420,000,000 $73,510,000,000Benefit Scenario 1 611 $279,000 $2,171,000 $65,948,000Benefit Scenario 2 1531 $760,000 $5,902,000 $179,301,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).182Table 76. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Mid-Atlantic Fish*PopulationFemale Non-Fatal AMI CasesFemale TotalACM CasesFemale Total AMICOSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 684 1,034 $33,881,000 $201,000,000 $6,099,000,000Baseline 1 679 1,023 $33,611,000 $199,000,000 $6,036,000,000Scenario 1 677 1,019 $33,508,000 $198,000,000 $6,011,000,000Baseline 2 678 1,022 $33,597,000 $199,000,000 $6,033,000,000Scenario 2 676 1,017 $33,468,000 $198,000,000 $6,002,000,000Benefit Scenario 1 2 4 $103,000 $800,000 $24,292,000Benefit Scenario 2 2 5 $129,000 $999,000 $30,351,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).183Table 77. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Southeast Fish*PopulationFemale Non-FatalAMI CasesFemale TotalACM CasesFemale TotalAMI COSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 24,553 37,114 $1,216,101,000 $7,208,000,000 $218,975,000,000Baseline 1 24,401 36,814$1,208,575,000 $7,150,000,000 $217,202,000,000Scenario 1 24,372 36,757 $1,207,144,000$7,139,000,000 $216,865,000,000Baseline 2 24,395 36,802$1,208,279,000 $7,148,000,000 $217,132,000,000Scenario 2 24,342 36,697$1,205,635,000 $7,127,000,000 $216,510,000,000Benefit Scenario 1 29 57 $1,431,000 $11,088,000 $336,825,000Benefit Scenario 2 53 105 $2,644,000 $20,484,000 $622,247,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).184Table 78. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Midwest Fish*PopulationFemale Non-Fatal AMI CasesFemale TotalACM CasesFemale TotalAMI COSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 33,555 50,591 $1,661,948,000 $9,826,000,000 $298,489,000,000Baseline 1 33,474 50,431 $1,657,935,000 $9,795,000,000 $297,545,000,000Scenario 1 33,439 50,363 $1,656,207,000 $9,782,000,000 $297,139,000,000Baseline 2 33,473 50,429 $1,657,870,000 $9,794,000,000 $297,530,000,000Scenario 2 33,420 50,326 $1,655,276,000 $9,774,000,000 $296,921,000,000Benefit Scenario 1 35 68$1,727,000 $13,366,000 $406,013,000Benefit Scenario 2 53 103$2,595,000 $20,075,000 $609,840,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).185Table 79. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial West Fish*PopulationFemale Non-FatalAMI CasesFemale TotalACM CasesFemale TotalAMI COSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Current 27,445 41,428 $1,359,337,000 $8,046,000,000 $244,427,000,000Baseline 1 27,418 41,374 $1,357,990,000 $8,036,000,000 $244,109,000,000Scenario 1 27,409 41,356 $1,357,524,000 $8,032,000,000 $244,000,000,000Baseline 2 27,419 41,376 $1,358,030,000 $8,036,000,000 $244,119,000,000Scenario 2 27,403 41,346 $1,357,267,000 $8,030,000,000 $243,939,000,000Benefit Scenario 1 918 $466,000 $3,615,000 $109,810,000Benefit Scenario 2 1630 $763,000 $5,913,000 $179,620,000* This population also is assumed to consumea mix of commercial fish (see Section 2.2.2.3).186Table 80. Predicted Annual AMI and ACM Incidence andValuation (2000$) in Female Consumers of CommercialFish*PopulationFemale NonFatal AMICasesFemaleTotal ACMCasesFemale Total AMICOSTSCosts FemaleMortality (COI)Costs Female Mortality(VSL)Current 336,350 503,450 $16,659,069,000 $97,781,000,000 $2,970,356,000,000Baseline 1 336,277 503,307 $16,655,441,000 $97,753,000,000 $2,969,509,000,000Scenario 1 336,213 503,182 $16,652,299,000 $97,729,000,000 $2,968,775,000,000Baseline 2 336,211 503,178 $16,652,196,000 $97,728,000,000 $2,968,751,000,000Scenario 2 336,135 503,029 $16,648,426,000 $97,699,000,000 $2,967,870,000,000Benefit Scenario 1 64 125 $3,142,000 $24,153,000 $733,710,000Benefit Scenario 2 76 149 $3,770,000 $28,983,000 $880,447,000* This population also is assumed to include individuals that consume no fish.187Table 81. Predicted Annual Decreased AMI and ACMIncidence and Annual Benefit (2000$) in FemalesFemale Non-FatalAMI CasesFemale TotalACM CasesFemale Total AMICOSTSCosts FemaleMortality (COI)Costs FemaleMortality (VSL)Scenario 1 150 300 $7,515,000$58,018,000 $1,762,450,000Scenario 2 230 450 $11,192,000$86,464,000 $2,626,562,000Table 82. Predicted Annual DecreasedAMI and ACM Incidence and Annual Benefit (2000$) in Males and FemalesNon-Fatal AMICasesTotal ACMCasesTotal AMICOSTSCosts Mortality(COI)Costs Mortality(VSL)Scenario 1 300 600 $14,965,000 $139,849,000 $3,271,425,000Scenario 2 400 800 $22,333,000 $208,911,000 $4,884,526,000188Table 83. Predicted Annual QALYIncrease Resulting from Decreased AMI and ACM Incidence in Males andFemalesQALYs Non-Fatal AMI Cases QALYs Total ACM Cases Total QALYsScenario 1 470 8,900 9,300Scenario 2 700 13,200 13,900189 3.6. RESULTS SUMMARY Tables 84 and 85 present a summary of th e results. Table 84 summarizes the results of Scenario 1 and Scenario 2 in rows 2 and 3. In rows 4 and 5, Table 84 integrates the avoided costs associated wit h a reduction in the neurological effects (assuming no threshold), non-fatal cardiova scular events, and pr emature fatalities related to methylmercury exposures. Based on the results of this analysis, the potential

value of the estimated human health benefits va ries across a broad range depending on the health effects included in the analysis. If benefits are limited to COI estimates for persistent IQ deficits in ch ildren exposed above the RfD in utero , then the annual benefits of mercury control ar e predicted to be roughly $75 m illion and $119 million, in Scenarios 1 and 2, respectively. If these IQ deficits persist and occur in all children exposed to methylmercury (i.e ., there is no threshold) then the annual benefits of mercury control are predicted to be roughly $194 million and $288 million, in Scenarios 1 and 2, respectively. If the cardiovascula r effects associated with methylmercury are

limited to males that consume non-fatty freshwater fish, such as pike, then the annual benefits of mercury control are predicted to be roughly $48 million and $86 million, in Scenarios 1 and 2, respectively. Finally, if a ll individuals are at ri sk for experiencing the

cardiovascular effects associated with me thylmercury, then the annual benefits of mercury control are predicted to be roughly $3.3 billion and $4 .9 billion, in Scenarios 1 and 2, respectively. If the IQ benefits (no th reshold) and the cardiovascular benefits for the entire population are comb ined, then, the annual benefit s of mercury control are predicted to be roughly $3.5 billion and $5.2 bil lion, in Scenarios 1 and 2, respectively.190Table 84. Summary of Cost-of-Illness and Value-of-Statistical Life Approaches for Neurotoxicity andCardiovascular ToxicityNeurotoxicityThresholdNoNeurotoxicityThresholdCosts AMI+ ACM(VSL) Male PikeConsumersCosts AMI+ACM (COI)Costs AMI+ ACM(VSL)Scenario 1 $75,311,000 $193,940,000 $48,436,000 $154,814,000 $3,286,000,000Scenario 2 $119,002,000 $288,247,000 $86,713,000 $231,244,000 $4,907,000,000Scenario 1Summary of neurotoxicitycosts and cardiovasculartoxicity costs (no threshold)$242,376,000 $348,754,000 $3,480,000,000Scenario 2Summary neurotoxicitycosts and cardiovasculartoxicity costs (no threshold)$374,959,000 $519,491,000 $5,195,000,000191 Table 85. Summary of Estimates of QALY Gains due to Reductions in Neurotoxicity and Cardiovascular Toxicity Neurotoxicity QALY Nonfatal AMI QALY ACM Total

Scenario 1 5,700 470 8,900 15,000 Scenario 2 7,400 700 13,200 21,300

Table 85 summarizes QALY gains associ ated with the reductions in the health effects due to reductions in mercury emissi ons. Less than half of these predicted gains

are associated with decreased neur ological effects. Figure 12 shows that our degree of

confidence in these event estimates differs . The neurological e ffects associated with in

utero methylmercury exposu res are well documented and have been thoroughly evaluated by a number research and advisory groups (e.g., NRC, 2000). On the other hand, while the studies that have evaluated the association of adult methylmercury exposures with cardiovascular events and premat ure mortality appear to be scientifically sound and the individual study results appear to be credible, they have not been subjected to a rigorous scientific analysis as a group.192 Figure 12 Spectrum of Certainty of Causal Associ ation of Health Effect with Mercury Exposure with Estimated Benefit Overlay in Millions ($M) and Billions ($B) of Dollars (2000$)

Finally, we estimate the annual benefits (2000$) per ton of power plant mercury emissions removed. Annually, 104.7 tons of mercury are emitted in the U.S. under the

conditions of Baseline 1. Under Scenario 1, 19.1 tons of power plant mercury

emissions are removed and annual U.S. anthropogenic mercury emissions are estimated to be 85.6 tons. Annually, 105.7 t ons of mercury are emitted in the U.S.

under the conditions of Baseline 2. Under Sc enario 2, 26.7 tons of power plant mercury

emissions are removed and annual U.S. anthropogenic mercury emissions are

estimated to be 79 tons. 

# Spectrum of Health Effect Certainty

Persistent IQ deficits from fetal exposures above MeHg RfD Persistent IQ deficits in all children from fetal MeHg exposures Cardiovascular effects and premature mortality in male consumers of non - fatty freshwater fish with high MeHg levels Cardiovascular effects and premature mortality in male fish consumers Cardiovascular effects and premature mortality in all fish consumers



### Decreasing Certainty Increasing Benefit Scenario 1 $75M $194M $48M $1.5B $ 3.3B Scenario 2 $119M $288M $86M $2.3B $ 4.9 B193 We use the data in Table 84 to estimate the ratio in terms of dollar benefits per ton. If we assume a neurotoxicity threshol d and assume that only neurological benefits result, we estimate the annual benefits to be $3.9 million/ton removed and $4.5 million/ton removed in Scenarios 1 and 2, res pectively. If we limit the benefits to neurotoxicity but assume that there is no threshold for neurotoxicity, then we estimate the annual benefits to be $10.2 million/ton removed and $10.8 million/ton removed under Scenarios 1 and 2, respectively. If we limit the benefits anal ysis to male pike consumers and neurotoxicity assu ming no neurotoxicity threshol d, (i.e., a conservative external generalization of the Salonen data), we estimate the annual monetized benefits associated with Scenario 1 to be approxim ately $242 million and $375 million under Scenario 1 and Scenario 2, respectively (see column 4 in Table 84). These annual benefits are estimated to be $12.7 million /ton removed and $14 million/ton removed under Scenarios 1 and 2, respectively. Final ly, if we externally generalize the Salonen

data to the entire U. S. population (benefits presented in column 6 of Table 84) and include neurotoxicity assu ming no neurotoxicity threshold, the annual benefits are estimated to be $182 million/ton removed and $194.5 million/ton removed under Scenarios 1 and 2, respectively. We note t hat there are likely to be co-benefits of controlling power plant mercury emissions; t hese include reduced em issions of primary PM and precursors of secondary PM. T he human health benefits of reducing these emissions are not quantified in this analysis. If we use the QALY results in Table 85, we

estimate benefits of 300 QALYs/ton re moved and 280 QALYs/ton removed based on neurotoxicity only in Scenarios 1 and 2, respec tively. In scenarios 1 and 2, we estimate194 benefits to be 790 and 800 QALY s per ton removed, respectively, based on reduced neurotoxicity, non-fatal acute myocardial infarctions, and premature mortality.

3.7. RESULTS OF LIMITE D SENSITIVITY ANALYSES Sensitivity analyses of risk and economic models are typically conducted to quantify the impacts of variability and uncerta inty in model parameters on the final model outputs. We did not undertake a compr ehensive model sensitivity analysis in this report. We plan to conduct such an analysis. We conducted four limited sensitivity analyses to investigate of several key assumptions. In the first limited sensitivity analysis, we investigated methylmercury intakes, if freshwater fishing was confined to bodies of water where fish methylmercury levels were less than 0.3 ppm. Under conditions where the U.S. EPA's methylmercury water criterion of 0.3 ppm methylmercury in fish is met, the methylmercury intakes still exceed

the RfD in more than half of the high-end consumer populati ons in each region. This result suggests that methylmercury ex posures in populations consuming large quantities of freshwater fish will still ex ceed a level considered by some to be acceptable. We note that intakes in the average freshwater fish consuming population do not exceed the RfD, if the criterion is met.

The results of the second limited sens itivity analysis, in which individual

freshwater fish consumers at e only one type of fish, were indistinguishable from the results of the primary analysis. The same fraction of the populations is predicted to

exceed the RfD in each freshwat er region in this sensitiv ity analysis and in the primary analysis.195 The third sensitivity analysis was c onducted to evaluate the impact of an overprediction or an underpredict ion of the change in deposit ion associated with the CSI in the Northeast Region. This region was c hosen only as an example to investigate the impact of changes in deposition levels in on e geographical region. Table 86 compares the predicted IQ point losses per annual birth c ohort if deposition rates in the Northeast decreased by two times more than predicted in Scenarios 1. If the decrease is doubled, roughly 950 additional IQ point s are gained. If the decrease is halved, then roughly 300 fewer IQ points are gained in Scenario 1. T he benefits estimate vari es from $1.4 million (if the estimated deposition rate decrease is halved) to$22.7 million (if the estimated deposition rate decrease is doubled). Cons umers of non-commercial fish were

assumed to eat commercial fi sh; methylmercury concentrations in the commercial fish change vary little across the different merc ury emissions cases used in the analysis.

Table 86. Predicted IQ Point Loss per Annual Birth Cohort in the Northeast Region and the Associated Cost-of-Illn ess Estimate (2000$), if Deposition Rates are Doubled or Halved IQ Loss per Annual Birth Cohort Value IQ point Loss ($)

Scenario 1 Double 1350 $22,661,000

Scenario 1 Unc hanged 400 $7,063,000 Scenario 1 Halved 90 $1,434,000

In the fourth sensitivity analysis, we developed an alternativ e estimate of the slope of the exposure-response function, if there is a neurotoxicity threshold below

which the risk is 0. The results of the four th limited sensitivity are presented in Table196 87. The results suggest that substituting the value of -1.1 for the slope of the exposureresponse relationship in the case where a th reshold exists increases the IQ point gain by roughly a factor of 2. Under a thres hold assumption, the slope value used in the sensitivity analysis results in the highest es timate of IQ points lost per annual cohort currently. The average IQ poi nts lost per affected individual ranges from 0.6 to 1.4 IQ points across the groups of fish consumer s. The estimated benefits of scenario 1 increase from an estimated 4,500 IQ points per annual birth cohort to 8,100 IQ points, in

the case where a threshold exists. The es timated benefits of scenario 2 increase from 7,100 IQ points per annual birth cohort to 12,800 IQ points.

Table 87. Comparison of Predicted Incr emental IQ Gains per Annual U.S. Birth Cohort and Incremental Estimated Mone tary Value of th e IQ Gains (Cost-ofIllness) (2000$) for 3 Neurotoxicity Models IQ Point Gain per Annual Birth Cohort $ Value IQ Point Gain

Scenario 1 (No Neurotoxicity Threshold) 11,600 $193,940,000 Scenario 2 (No Neurotoxicity Threshold) 17,200 $288,248,000 Scenario 1 (Threshold; slope -0.6 IQ points per ppm) 4,500 $75,311,000 Scenario 2 (Threshold; slope -0.6 IQ points per ppm) 7,100 $119,002,000 Scenario 1 (Threshold; slope -1.1 IQ points per ppm) 8100 $135,560,000 Scenario 2 (Threshold; slope -1.1 IQ points per ppm) 12,800 $214,203,000

In summary, the results of the firs t sensitivity analysis indicate that methylmercury exposures in most consumer s of freshwater fish will not exceed the methylmercury RfD, if they consume fish that have a methylmercury concentration above the methylmercury criterion. People who frequently consume large quantities of197 fish may experience exposures to methylme rcury above the methylmercury RfD. The second sensitivity analysis high lights a limitation of the appr oach; specifically, we did not identify data that indi cated the mix of non-commercial fish that individuals consumed. The alternative assumpti on used changed neither the predicted mean intakes or the fraction of the population pr edicted whose methylmercury intake rate

exceeds the RfD. The thir d sensitivity analysis highli ghts the potential impacts of changes in the air modeling results on the benefits predicted for a given region. We have noted the uncertainties in these models, particularly in the prediction of dry deposition rates. This analysis also highlights the potent ial importance of methylmercury exposures from commercial fi sh consumption to total methylmercury intake. Finally, the fourth sensitivity analysis suggests that, if there is a threshold for the neurotoxicity exposure-response function, t hen the impacts on those fetuses exposed above the RfD (we again note that the RfD is used as a surrogate for a neurotoxicity threshold) may be quite large (e.g., some populations may lose 1.4 IQ points on average).198 4. DISCUSSION This appears to be the first mercury analysi s that attempts to develop multiple model compartments for marine fish and freshwat er fish on a national level for the U.S. This model is used to estimate the impacts of mercury emissions control policies on methylmercury intake in different U.S. adult fish-consuming populations and their offspring. The assumption that there is a pr oportional relationship between fish methylmercury concentrations and deposition rates has been used in previous analyses to predict changes in methylmercury concent rations in freshwater fish (U.S. EPA, 2001d, 2003b; EPRI, 2003) and marine fish (EPRI, 2003). Limited evidence from field studies suggests that decreases in depositio n may result in proportional decreases in fish methylmercury concentrations in fres hwater systems (U.S. EPA, 2003b). It is not known whether studies of changes in me rcury deposition to salt water bodies will support this assumption or not. Methylmerc ury concentrations in yellowfin tuna caught between 1971 and 1998 do not appear to have c hanged over time despite significant increases in surface water mercury concentrations in the area where these fish were caught (Kraepiel et al., 2003). It is not know n whether these data reflect a general trend for marine species or not. Clearly, if these data reflect a general trend our estimates of changes in methylmercury intake rates in the general population may be biased upward and the possibility exists that there may be no change in marine fish methylmercury concentrations as a result of mercury emissions controls. Alternatively, the cycling of mercury in ocean waters may be significantly slower than such cycles in freshwater199 systems. We note that ther e are no data that can be com pared with the Kraepiel data

set. We discuss several additional exposure model assumptions and indicate the likely direction of bias. 1. Although we assume all mercury in fi sh to be methylmercury, in reality, methylmercury is half of the total me rcury typically measured in mollusks and 95% of the total mercury measured in piscivorous species. Thus, we may overestimate the percent decrease in me thylmercury intake in commercial fish consumers because all of the mercury repor ted in the fish is not methylated and does not contribute to the methylmercury measured in blood. It is unlikely that we overestimate methylmercury intake among non-commercial fish consumers; however, because this component of the analysi s was limited to finfish intake of large freshwater and marine fish that were likely piscivorous and primarily contaminated with methylmercury. 2. In the global emissions analysis that was used to estimate changes in mercury deposition in the All Other Waters Region, we did not reduce the input of U.S. power plant emissions to account for depos ition to the Atlantic Coastal Region, the Gulf of Mexico Region and the five freshwater regions. Consequently, we overestimate the contribution of power plants to deposition in the All Other Waters Region by approximately 12%. In effect, the fraction of the emitted mercury depositing in these other regi ons is double counted in the All Other Waters Region. Given the small changes predicted in the All Other Waters Region, this is unlikely to change the estimate significantly. 3. We assume that mercury deposition is t he only source of methylmercury in water bodies. Because there ar e other potential sources of cont amination around water bodies, this approach may overestima te reductions in fish methylmercury concentrations that may follow emission decreases (U.S. EPA, 2001d); U.S. EPA (2001d) identifies several potential source s of mercury to freshwater bodies. Also, natural processes such as ocean currents and volatilization of deposited mercury from the ocean back into the atmosphere may also transport mercury into coastal ocean habitats that can later be methylated. If this is the case, this approach may overestimate impacts of decreased mercury emissions in the coastal Atlantic and Gulf regions. If terrestrial and riverine mercury inputs to coastal oceans substantially influenc e the quantity of me thylmercury that accumulates in such fish, then decreases in deposition at distant inland points that influence riverine concentrations may contribute substantially to decreased mercury inputs to coastal marine habitats and this assumption regarding changes in deposition to the ocean waters may underestimate changes in fish concentrations, because the predicted decreases in mercury deposition rates200 associated with power plant emissions r eductions are generally predicted to be larger in terrestrial environment s than in marine environments. 4. Based on the REMSAD model results, merc ury emitted to the ai r in the U.S., in general, flows from west to east. Consequently, U. S. power plant mercury emissions influence mercury deposition into freshwater regions and the Atlantic Coastal region and the Gulf of Mexico regi on. We may underestimate impacts of mercury emissions reductions in the coas tal Pacific region, because we assume that fish methylmercury concentrations in the coastal Pacific Ocean are not directly affected by decreases in U.S. pow er plant emissions. If changes in the contributions of riverine mercury to the coastal Pacific Ocean change methylmercury levels in the fish along the coastal Pacific, then this assumption may result in a significant underestima tion of impacts of mercury emissions decreases. Overall, the impact of not evaluating changes in this region is believed to have little effect on the results. 5. If changes in fish methylmercury levels are not linear and proportional to changes in mercury deposition, but, instead if t he changes in fish concentrations are greater than linear and proportional , then the primary analysis may underestimate impacts of emissions decreases. Conversely, if this relationship is less than linear and proportional , then it may overesti mate the impacts of emissions control. 6. This analysis does not account for the impact of fish consumption advisories on choices of bodies of water fished or choice s of sources of fish for consumption. Some anglers do not consume fish or cons ume fewer fish from bodies of water under fish consumption advisories. T hus, the model likely overestimates the number of freshwater fish consumers above the RfD. The first sensitivity analysis showed that, if the methylmercury cr iterion of 0.3 ppm in fish is met only populations of high-end freshwater fish consumers are exposed above the RfD. 7. In the analysis (with the exception of the analysis of pike consumption and the second sensitivity analysis) we did not address exposures to individuals that consumed specific commercial and non-co mmercial fish. Our assumption of consumers eating a mix of different types of fish is a simplification that reduces predicted exposure variability. The non- commercial freshwater and marine fish in the analysis, in general, have higher methylmercury concentrations than the commercial fish. There are several exc eptions including commercial shark or swordfish.

In addition to the uncertainties associat ed with our exposure model assumptions,

other uncertainties are associated with model outputs and other data utilized in the analysis. The accuracy of the results of the REMSAD atmospheric model is uncertain.201 Despite the advances in scientific research into the chemistry and fate of emitted mercury, the atmospheric chemistry and t he deposition of mercury, particularly dry deposition of Hg II , remain uncertain. If atmospher ic transport and deposition processes differ substantially from those modeled with REMSAD, then the results of this modeling analysis are likely incorrect for the freshwat er regions and the Atlantic Coastal and Gulf regions. For example, the atmospheric modeling conducted fo r the EPRI (2003) analysis of the CSI predicts s ubstantially smaller decreases in mercury deposition than

the REMSAD model results of EPA. Our assumption that the REMSAD output s are, in general, correct given the limited comparisons with deposit ion data led to the structur e of the Regions in the analysis. EPRI (2003) modeled mercury deposit ion to U.S. freshwaters as a single compartment (i.e., the cont inental U.S. was one compar tment in the EPRI model); however, U.S. EPA (2003b) chose to model deposition at the i ndividual watershed level.

While we did not want to ob scure potential regional differenc es in mercury deposition,

we also did not want to base the model on changes in deposition at relatively small spatial scales (e.g., the watershed level); thus, to strike a balance, simulation of exposure at a regional level was chosen for this analysis. We also wanted to evaluate possible impacts of decreased deposition al ong the Coast of the Atlantic Ocean and Gulf; thus, we included these regions. Factors that influence the aquatic c hemistry of deposited mercury and the bioaccumulation of methylmerc ury in different aquatic environments are similarly not well understood (see footnote 7). This analysis employed a simple linear relationship; in reality, the relationships between deposited me rcury and fish methyl mercury levels may202 vary based on a number of chemical and physi cal factors. Between saltwaters and freshwaters, the relationship between chan ges in deposited mercury and changes in fish methylmercury concentration s may differ. Although not a focus of this analysis, the time scales of mercury cycling in marine environments may differ substantially from those of freshwater systems (Lamborg et al., 2003; Kraepiel et al., 2003). The fish methylmercury concentrations in the NLFWA database are voluntarily reported by the States to t he U.S. EPA's Office of Water. The data quality criteria and sampling approaches may vary among the St ates. While non-commercial freshwater fish that were unlikely to be eaten were excluded, biases in the fish sampling approaches used by the States may render this database inconsistent with the types of fish caught and consumed by freshwater anglers. Also, the types of fr eshwater fish that anglers report targeting for capture, used as a surrogate for the types of fish consumed, may differ substantially from those ac tually caught and consumed. These data influence weighted mean methylmercury concent rations in freshwater fish and the estimates of the number of fetuses predicted to be exposed to methylmercury above the

RfD. Exposures to high-end consumers of non-commercial freshwater fish were simulated using a fish consumption rate distribution based on data from the Columbia

River Inter-Tribal Fish Co mmission Report. We assum ed that these individuals consumed no commercial fish. Methylmercury intakes in most of these individuals are predicted to exceed the RfD (see Table 43). Due to a lack of data, we developed no estimates of the size of this populati on. We assumed that the non-commercial freshwater fish consumpti on rate distribution developed for populations of typical non-203 commercial fish consumers would include these high level consumers. We note that in this distribution, the fish consum ption rate was 59 g/day at the 99.4 th percentile; thus, roughly 0.5% of all non-commercial consumers of freshwater fish consume levels of fish above the mean value reported in the Columbia River In ter-Tribal Fish Commission Report in our model. To date, there hav e been no studies that adequately quantify the sizes of high fish consuming populations in the U.S.

There are additional uncertainties associ ated with the non-commercial saltwater fish data. When compared to commercial fi sh, there are fewer me thylmercury samples

of saltwater fishes intended for non-commercia l consumption. There are no indicators of fish length or weight so the relevance of the samples compiled by the EPA (2003d) in the Mercury in Marine Life database to the non-commercial fish caught and consumed

in the Atlantic and the Gulf is not known. Also, the Me rcury in Marine Life Database does not provide information on the dispersi on of the data around the central tendency estimates provided. Finally, intake of meth ylmercury from shellfis h consumption is not

evaluated. Currently, the simulation results predict that roughly 359,000 ch ildren are born to women with blood methylmercury levels ab ove 5.8 μg/L blood, the value corresponding

to the EPA's oral RfD. Our estimate that roughly 9% of the populat ion of U.S. females of child-bearing age have blood levels above this value is slightly greater than those developed by Mahaffey et al. (2003) based on earlier NHANES data and with recent CDC estimates (CDC, 2004). We developed an approximate distribution of the blood methylmercury concentration data reported by Mahaffey et al., (2003) and used this distribution as an exposure measure for t he population that consumes only commercial204 fish or no fish. We note t hat the distribution developed did not precisely match that reported by Mahaffey et al. Our estimate of median female blood concentration in the general population is 0.9 μg/L, which is higher than the median values reported by Mahaffey et al. (0.6 μg/L) and by CDC (2004) (0. 86 μg/L). Similarly, our estimate of the

current 95 th percentile blood concentration in this population is 7 μg/L; Mahaffey et al. report a 95 th percentile value of 6.7 μg/L and CDC ( 2004) reports a value of 6 μg/L. Using our assumptions, we currently estimate that 63% of the women predicted to have

blood concentrations that exceed the RfD consume commercial fish only (and do not consume non-commercial fish); the rema inder consume some fish caught noncommercially and may also consume fish caught commercially. Under the decreased mercury emissions sc enarios, the predicted decreases in methylmercury intake and in the number of fish consumers above the RfD are modest. For the commercial fish consuming population, this small change is caused by the large fraction (approximately 70%) of the U.S. co mmercial seafood that is imported and the relatively small contribution of U.S. coal-f ired power plant mercury emissions to global (natural and anthropogenic) emis sions under the assumptions used in this model. We assumed that the decrease in methylmercur y concentrations in tuna, the most heavily

consumed saltwater fish in the U.S. comme rcial market, was linear and proportional to the fractional decrease in global emissions. If the methylmercury concentrations in the

tuna in the U.S. commercial market reflec t changes in mercury deposition off the coast of North America, then we predict a slight ly greater decrease (of less than 1%) in the weighted mean methylmercury concentration of commercial fish than the baseline value. Also, the analysis assumed that t he methylmercury levels in freshwater205 commercial fish were in equilibrium with the All Other Waters Region. If the decreased mercury deposition from other freshwater regions was subs tituted, it made almost no difference in the predicted weighted mean methylmercury concentration predicted in commercial fish. The use of mean methylme rcury concentrations in commercial fish and the use of a mean weighted average r educed the variability in the predicted

exposures. The predicted decreases in the methylmerc ury concentrations of fish caught in the Atlantic Coastal, Gulf, and the freshwater regions are larger than those in the All Other Waters Region. Thus, the decreases in methylmercury intakes in individuals consuming fish from these waters are lar ger than in those consuming commercial fish exclusively. Based on our model results, on a per cons umer basis, the policies that control mercury emissions from U.S. power plant s will likely have the largest impact on

freshwater fish consumers and non-commerc ial saltwater fish consumers. This

simulation indicates that some consumers of these fish have methyl mercury intake rates above the RfD. Thus, as a result of these po licies, fewer individuals, including those in the sensitive population, will be exposed above the RfD. Other anthropogenic sources including some outside the U.S. contribute to deposition of mercury in the U.S.; thus, the impact of reducing mercury emissions from a single source or a single country is limited. The effectiveness of U.S. policies fo r reducing exposures in the majority of the population that consumes commercial fish is limited because imported fish serve as the primary source of fish to the commercial seafood mark et and the relatively small contribution of U.S. power plant mercur y emissions to global mercury emissions.206 Thus, reduction in U.S. methylmercury exposures may best be accomplished by pursuit of both domestic mercury emissions reductions and international reductions in

emissions. Global emissions reductions could have a greater impact on U.S. commercial fish methylmercury levels and likely reduce methylmercury intake among non-U.S. fish consumers globally. We not e that a substantial body of evidence (summarized in U.S. EPA, 1997c; Mason et al., 1994; Lamborg et al., 2002; Mason and Scheu, 2002) indicates that significant changes in the global mercury cycle have occurred as a consequence of historical and current anthropogenic mercury emissions. From the mid-eighteenth cent ury until the present, mercur y concentrations in the biosphere have increased; for example, t he mercury concentration in the atmospheric compartment is predicted to have increased by a factor of 3 over this period of time (Mason et al., 1994; Lamborg et al., 2002; Mason and Sc heu, 2002). The modeling results of Lamborg et al. ( 2002) indicate that mercury concentrations in the biosphere continue to increase; for example, their model predicts annual mercury concentration increases of 1.4%, 1.5%, and 0.4% in the gl obal atmosphere, mixed layer of the ocean, and ocean thermocline region, respectively. Ot her data, notably the work of Schuster et al. (2002) and Slemr et al. (2003) suggests t hat global atmospheric levels of Hg have been declining since about 1990. If the quantit y of mercury in the biosphere influences the quantities in fish globally, then there is additional reason to reduce emissions globally. Finally, there are a number of significant research need s related to the exposure assessment, that, if met, would reduce the uncertainty in future model results. 1. Long-term monitoring studies that collect mercury concentrations in air, water, sediment and fish are needed across the U.S. and around the world. Such207 efforts could lead to long-term data records that can be compared to model predictions. Without such studies, it will be difficult to quantify the actual environmental responses to a change in U. S. mercury emissions and to evaluate the time needed for such responses to manifest. 2. Additional development of atmospheric fate model s for mercury is needed, including analyses of the chemical reacti ons that control many of the processes in the atmosphere. Additional resear ch is needed to improve the understanding of the fate of atmospheric mercury em issions from coal-fired power plants on local, regional, and global scales. 3. Additional development of models for evaluating the fa te of aquatic mercury and bioaccumulation by fish is needed. Studi es are needed that evaluate the factors influencing the response time between chan ges in deposition and changes in fish methylmercury concentrations; these studies should include water bodies of different sizes and characte ristics (see footnote 7). 4. Simulation models need to be developed t hat account for factors that might lead to regional differences in mercury cycling. 5. Fish methylmercury sampling strategies in freshwaters and saltwaters that are designed to collect the types and sizes of fish that non-commercial fish consumers eat need to be implemented. These fish need to be collected from the source waters that are fished.

6. Additional studies should be undertak en that identify short- term (peak) and longterm fish consumption rates among hi gh-end commercial and non-commercial fish consumers. These studies shoul d examine the types of fish these individuals are consuming and the sources of these fish. These studies should evaluate the number of individuals in t hese groups. Additional studies should be undertaken to evaluate consumption rate s in pregnant females, females of reproductive age, and men.

7. Studies should be undertak en to identify the locations of populations that exhibit elevated non-commercial fish consumption rates. (If many of these populations are located in areas of the U.S. that would experience high reductions in mercury deposition as a consequence of mercury co ntrol policies, t hen the benefits of mercury emissions control would be greater for this population).

8. Additional studies are needed to im prove the understanding of the rates and types of commercial fish intake among non-commercial fish consumers. 9. The next NHANES survey should colle ct blood methylmercury concentrations and detailed exposure information about source s of fish for consumption and the quantities of fish consumed.208 The analysis of health effects and the associated economic burden has

numerous limitations and uncertainties. Ther e may be additional neurotoxic effects associated with intrauterine methylmercury exposures that are not captured by IQ measures. These were not quantified. We did not evaluate ecological benefits of reducing methylmercury in terrestrial, fres hwater, estuarine or marine ecosystems. Methylmercury appears to be neurotoxic fo r most chordates, thus reductions in methylmercury levels may improve ecosystem health. We did not evaluate the benefits of mercury emission reductions on recreational fisheries in the U.S. The control technologies that would be employed to r educe mercury emissions from coal-fired power plants would likely reduce other emissi ons such as fine particulate matter and its precursors (oxides of nitr ogen and sulfur dioxide). T hese co-benefits were not evaluated. We also noted that our degree of confidence in these estimates differs

between the neurotoxic and cardiotoxic effects. Although there are differences in the results of the three primary epidemiologic ne urotoxicity studies, the neurological effects

associated with in utero methylmercury exposures have been thoroughly evaluated by a number of research and advisory groups (e .g., NRC, 2000). As U.S. EPA noted, there is no evidence of a threshold for neurotoxicity associated with in utero methylmercury exposures; thus, it is plausible that there are neurotoxicity benefits associated with reducing methylmercury exposures below the RfD. On the other hand, as Figure 12 attempts to illustrate, the studies evaluating the

association of cardiovascular events with adult methylmercury exposures have, as a group, not been as thoroughly ev aluated. While high doses of methylmercury are clearly associated with neurological decrem ents, they have not been repeatedly shown209 to be associated with adverse cardiac events; in fact, fish consumption, which implies some methylmercury exposure, is recommended as prot ective of cardiovascular disease. Thus, we urge caution in interpre ting the results related to monetized benefits associated with the cardiovascular endpoint. Interpretations of these results should include a full explanation of the assumpti ons used to develop the integrated benefit

estimates. The QALY gains and cost-of-illness estimates associated with myocardial infarctions and all cause mortality are hi ghly dependent on our assumptions of when these events occurred during the course of an individual’s life. For example, if we assume that the myocardial infarctions occurred 5 years earlier, then our present estimates of earnings and household producti on lost would be roughly 45% larger than the estimates we currently use for males and females. The methylmercury-associated mortality events, if valid for the entire U.S. population and valued using the VSL estimate, are likely the most important economic consequence of methylmercury exposure. We note that r eported estimates for the VSL vary by a wide range. Mrozek and Taylor (2002) suggest a VSL range of between $1.5 and $2.5 million (1998$). Viscusi and Aldy (2003) suggest a range between $4 and $9 million. Variability in these measures would influence the predicted range of the benefits analysis if cardiovascular and prematur e mortality effects are included in the

benefits assessment. Given the uncertainty surrounding this heal th effect and the values involved, further research is needed to clarify the relationship between adult methylmercury exposures and adverse cardiovascular event s and premature mortality. Additional210 research is needed to determine whether in utero methylmercury ex posures alter heart rate variability. If meth ylmercury exposures change hear t rate variability and these changes lead to increased incidence of adverse myocardial events, then benefits of mercury control are likely substantia lly underestimated in this analysis.

If the cardiovascular effects are limited to male consumers of northern pike and similar species, then avoided costs asso ciated with IQ are comparable to those associated with avoided myocardial infa rctions and premature deaths when valued using a VSL approach. The size of the populat ion that is potentially affected by the possible cardiovascular toxici ty of methylmercury is an important uncertainty. The number of QALYs gained due to IQ increases contributes roughly one third of the total predicted QALYs gained in each ofthe two scenarios. Reductions in nonfatal myocardial infarctions and premature deaths account for the remain der of the total. We note the uncertainty in the utility wei ght associated with the neurological decrement (see Sections 2.6 and 2.6.1). If this substantially over or under estimates the actual utility weight, then the results of this comparison would change. While it is unlikely that the utility weight associated wit h such neurological decrements is much larger than 0.03 (a factor of 3 above our current estimate) given the data review ed in Section 2.6.1, it is possible that the utility weight could be much lower than current value (e.g., factor of 10). (We note that there may be neurological effects a ssociated with methylmercury exposures that are not captured by measures such as IQ and these would increase the benefits associated with mercury emissions control). We al so note the uncertainty in the identification of when the my ocardial infarction or death occurs in the lifespan of an211 individual could have a large impact on t he number of QALYs gained due to reductions in methylmercury exposures. There are a number of significant research needs related to this risk and benefits assessment, that, if met, would reduce the uncertainty in future model results. 1. Additional analysis is needed of the persistence and magnitude of neurological decrements reported in children in the Ne w Zealand and Faroe Islands studies. Additional epidemiologic in vestigations need to evaluate the relationship between in utero methylmercury exposures and neur otoxicity, including potential confounders and modifying factors that may influence the relationship. 2. Willingness-to-pay studies are needed on neurological decrements, such as IQ point loss in children, to improve t he economic assessment of benefits of decreased methylmercury exposures. 3. Additional epidemiologic studies are needed on the relationship between methylmercury exposures in adults and the development of cardiovascular effects.

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