Economic Valuation of Human 

Health Benefits of Controlling 

Mercury Emissions from U.S. 

Coal-Fired Power Plants 

February 2005 
ii

Members of Northeast States for Coordinated Air Use Management 

Anne Gobin, Bureau Chief 

Connecticut Department of Environmental 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 Environmental Protection, Bureau of Waste Prevention 

Robert Scott, Director 

New Hampshire Department of Environmental Services, Air Resources Division 

William O’Sullivan, Director 

New Jersey Department of Environmental Protection, Office of Air Quality Management 

David Shaw, Director 

New York Department of Environmental Conservation, Division of Air Resources 

Stephen Majkut, Chief 

Rhode Island Department of Environmental Management, Office of Air Resources 

Richard A. Valentinetti, Director 

Vermont Department of Environmental Conservation, Air Pollution Control Division 

Arthur Marin, Executive Director 

Northeast States for Coordinated Air Use Management 
iii

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.org
iv
v

TABLE OF CONTENTS

Page

LIST OF TABLES.......................................................................................................... viii 

LIST OF FIGURES.........................................................................................................xv 

EXECUTIVE SUMMARY...............................................................................................xvi 

ACKNOWLEDGMENTS.................................................................................................xx 

1. INTRODUCTION AND BACKGROUND............................................................... 1 

 1.1. ATMOSPHERIC MERCURY ..................................................................... 4 

 1.2. BIOACCUMULATION OF METHYLMERCURY ...................................... 10 

 1.3. HUMAN EXPOSURE TO METHYLMERCURY ....................................... 12 

 1.4. TOXICOKINETICS .................................................................................. 22 

 1.5. HUMAN HEALTH EFFECTS ................................................................... 24 

 1.5.1. Neurological Decrements Associated with Intraurine 

 Methylmercury Exposures............................................................. 26 

 1.5.2. Myocardial Effects Associated with Adult Methylmercury 

 Exposures ..................................................................................... 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 POWER PLANTS......................................................................... 54 

 1.7. PURPOSE OF REPORT ......................................................................... 55 

 1.8. OVERVIEW OF ANALYSIS..................................................................... 55 

2. METHODS.......................................................................................................... 57 

 2.1. CHANGES IN MERCURY DEPOSITION ................................................ 62 

 2.2. METHYLMERCURY EXPOSURES THROUGH FISH 

 CONSUMPTION...................................................................................... 64 

 2.2.1. Methylmercury Exposures Through Commercial Seafood 

 Consumption................................................................................. 65 

2.2.2. Methylmercury Exposures Through Consumption of 

 Non-Commercial Seafood............................................................. 73 

 2.2.3. Estimates of Population Size ........................................................ 83 

 2.3. NEUROTOXICITY ................................................................................... 85 

 2.3.1. Population Toxicity Threshold: Background .................................. 87 

 2.3.2. Population Toxicity Threshold for Methylmercury Exposures........ 89 

 2.3.3. Analysis of Risk Assuming Threshold ........................................... 91 

 2.4. DOSE-RESPONSE FOR ACUTE MYOCARDIAL INFARCTION 

 AND ALL CAUSE MORTALITY ............................................................... 91 

 2.4.1. Estimating the Dose-Response Functions .................................... 93 

 2.4.2. Applying the Dose-Response Functions ....................................... 94 
vi

TABLE OF CONTENTS cont. 

Page

 2.5. VALUING CHANGES IN CHILDREN’S INTELLIGENCE......................... 99 

 2.5.1. Proportional Impact of a One-Point Change in IQ on Lifetime 

 Earnings...................................................................................... 100 

 2.5.2. Baseline Lifetime Earnings Value (E).......................................... 103 

 2.5.3. The Absolute Impact of IQ Changes on Lifetime Earnings.......... 104 

 2.5.4. Remedial Education Costs and Shifts in the Population IQ 

 Distribution .................................................................................. 105 

 2.6. HEALTH-RELATED QUALITY OF LIFE MEASURES FOR 

 NEUROLOGICAL DECREMENTS ........................................................ 107 

 2.6.1. Utility Weight for Health-Related Quality of Life Associated 

 with Neurodevelopmental Toxicity............................................... 108 

 2.6.2. Exposure-Response Function for Health-Related Quality 

 of Life Weights ............................................................................ 110 

 2.7. VALUING CHANGES ASSOCIATED WITH ADVERSE MYOCARDIAL 

 EVENTS AND ALL CAUSE MORTALITY.............................................. 110 

 2.7.1. Cost-of-Illness Estimates for Myocardial Infarctions and All 

 Cause Mortality ........................................................................... 111 

 2.7.2. Value of a Statistical Life............................................................. 112 

 2.7.3. Health-Related Quality of Life Utility Weights for Myocardial 

 Infarctions ................................................................................... 112 

 2.8. SENSITIVITY ANALYSES..................................................................... 113 

3. RESULTS......................................................................................................... 116 

 3.1. CHANGES IN ANNUAL MERCURY DEPOSITION RATES IN THE 

 8 REGIONS ........................................................................................... 116 

 3.2. CHANGES IN FISH METHYLMERCURY CONCENTRATIONS IN 

 THE 8 REGIONS ................................................................................... 118 

 3.3. CHANGES IN METHYLMERCURY EXPOSURES AMONG FISH 

 CONSUMERS IN THE 8 REGIONS ...................................................... 132 

 3.3.1. Population Data........................................................................... 132 

 3.3.2. Predicted Exposures in General U.S. Population........................ 132 

 3.3.3. Predicted Exposures in Consumers of Non-commercial 

 Marine Fish ................................................................................. 135 

 3.3.4. Predicted Exposures in Consumers of Non-commercial 

 Freshwater 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 

 Methylmercury Exposures........................................................... 144 

 3.4.2. Averted Costs Associated with IQ Point Gains per Birth Cohort 

 Under Alternative Emissions Scenarios 1 and 2 ......................... 160 

 3.4.3. IQ Results Considering QALYs................................................... 162 
vii

TABLE OF CONTENTS cont. 

Page

 3.5. COSTS ASSOCIATED WITH ACUTE MYOCARDIAL INFARCTIONS 

 AND ALL CAUSE MORTALITY ............................................................. 162 

 3.5.1. Pike Consumers.......................................................................... 162 

 3.5.2. Entire U.S. Population................................................................. 168 

 3.6. RESULTS SUMMARY........................................................................... 189 

 3.7. RESULTS OF LIMITED SENSITIVITY ANALYSES .............................. 194 

4. DISCUSSION ................................................................................................... 198 

5. REFERENCES................................................................................................. 212 
viii

LIST OF TABLES 

Table Page

1 Mercury Emissions Sources ................................................................................. 2 

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............................................. 17 

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........... 18 

4 Median, Mean, and Maximum Methylmercury Concentrations (μg/g) Reported 

Fish Species Harvested via Recreational Angling in the Atlantic Ocean ............ 20 

5 Median, Mean, and Maximum Methylmercury 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 Studies.................................................................................................. 29 

7 Regression Coefficients and 95% Confidence Intervals for Hair Mercury 

Concentrations (ppm) Calculated by Crump et al. (1998)................................... 30 

8 Tests Employed in the Seychelles Islands Child Development Study ................ 32 

9 Tests Employed in Studies of Faroese Children................................................. 36 

10 Coefficients for Logarithmic Transformation of Cord Blood Mercury 

Concentrations on Selected Neuropsychological Tests...................................... 37 

11 Measures of Cohort Methylmercury Intake Rates Reported in Salonen 

 et al. (1995) ........................................................................................................ 40 

12 Results of Cox Proportional Hazards 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 Hypertension in the Control Group 

from the City of Ine ............................................................................................. 52 
ix

LIST OF TABLES cont. 

Table Page

15 Comparison of Hypertensive Diagnoses Between Those with and Without 

Minamata Disease in Two Different Age Categories .......................................... 52 

16 Blood Methylmercury Concentrations (μg/L) in U.S. Women Aged 16 to 49 ...... 65 

17 Comparison of Body Weight, Blood 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 Targeting Selected Species................................... 78 

20 Commercial Fish Intake Rates Among Consumers of Recreationally 

 Caught Fish ........................................................................................................ 83 

21 Fishing Days by U.S. Region and Estimated Number of Consumers of Fish 

Caught in Each U.S. Freshwater Region............................................................ 85 

22 Parameter Values Developed by Salkever (1995)............................................ 102 

23 Description of Cognitive Decrement and Associated Utility Weight Based 

 on Torrance et al. (1996) .................................................................................. 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 Mercury Deposition in the Five Freshwater 

Regions Relative to Current Emissions ............................................................ 118 

27 Weighted Mean Methylmercury Concentrations in Commercial Fish ............... 121 

28 Predicted Weighted Mean Non-commercial Fish Methylmercury 

 Concentrations (μg/g)....................................................................................... 125 

29 Northeastern Fish Consumption Data .............................................................. 126 

30 Mid Atlantic Fish Consumption Summary Data ................................................ 127 
x

LIST OF TABLES cont. 

Table Page

31 Southeastern Fish Consumption Data.............................................................. 128 

32 Midwest Fish Consumption Summary Data...................................................... 129 

33 Western Fish Consumption Summary Data...................................................... 130 

34 Estimated Population Sizes.............................................................................. 131 

35 Predicted Tissue Methylmercury Concentrations in Commercial Fish 

 Consumers ....................................................................................................... 134 

36 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial Atlantic Ocean Fish............................................................... 136 

37 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial Gulf Fish ............................................................................... 136 

38 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial Northeast Fish ...................................................................... 139 

39 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial Mid-Atlantic Fish ................................................................... 139 

40 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial Southeast Fish...................................................................... 140 

41 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial Midwest Fish......................................................................... 140 

42 Predicted Methylmercury Intake Rates (μg/kg-day) in Consumers of 

 Non-Commercial West Fish.............................................................................. 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 Non-Commercial Atlantic Ocean Fish .......... 145 
xi

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 Fish........................... 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 Northeast 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 Non-Commercial Mid-Atlantic 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 Southeast Fish ................. 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 Midwest Fish .................... 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 Methylmercury Concentrations, Mean IQ Point 

 Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value 

 (Cost-of-Illness) (2000$) in Consumers of Commercial Fish and 

 Non-Fish Consumers........................................................................................ 151 

52 Summary of IQ Point Losses and Associated Costs per Annual Birth 

 Cohort for the Entire U.S. Population ............................................................... 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 Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial Atlantic Ocean Fish............................ 156 

55 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial Gulf Fish............................................. 156 
xii

LIST OF TABLES cont. 

Table Page

56 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial Northeast Fish.................................... 157 

57 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial Mid-Atlantic Fish ................................ 157 

58 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial Southeast Fish ................................... 158 

59 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial Midwest Fish ...................................... 158 

60 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of 

 Commercial Fish and Non-Commercial West Fish........................................... 159 

61 Predicted Mean IQ Point Loss, IQ Losses per Annual Birth Cohort, and 

 Estimated Monetary Value (Cost-of-Illness) (2000$) in Commercial Fish 

 Consumers ....................................................................................................... 159 

62 Distribution of Predicted Annual Cases 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 Premature Death in Male Northern Pike 

Consumers ....................................................................................................... 167 

64 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male 

 Consumers of Commercial Fish and Non-Commercial Atlantic Ocean Fish..... 169 

65 Predicted Annual AMI and ACM Incidence 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 and Non-Commercial Northeast Fish ............ 171 

67 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male 

 Consumers of Commercial Fish and Non-Commercial Mid-Atlantic Fish ......... 172 
xiii

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 and Non-Commercial Western Fish............... 175 

71 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male 

 Consumers of Commercial Fish ....................................................................... 176 

72 Predicted Annual Decreased AMI and ACM 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 Non-Commercial Atlantic Ocean Fish..... 179 

74 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in 

 Female Consumers of Commercial Fish and Non-Commercial Gulf Fish ........ 180 

75 Predicted Annual AMI and ACM Incidence 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 Non-Commercial Mid-Atlantic Fish ......... 182 

77 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female 

 Consumers of Commercial Fish and Non-Commercial Southeast Fish............ 183 

78 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female 

 Consumers of Commercial Fish and Non-Commercial Midwest Fish............... 184 

79 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female 

 Consumers of Commercial Fish and Non-Commercial West Fish.................... 185 

80 Predicted Annual AMI and ACM Incidence and Valuation (2000$) in 

 Female Consumers of Commercial Fish .......................................................... 186 

81 Predicted Annual Decreased AMI and ACM Incidence and Annual Benefit 

 (2000$) in Females........................................................................................... 187 
xiv

LIST OF TABLES cont. 

Table Page

82 Predicted Annual Decreased AMI and ACM Incidence and Annual Benefit 

 (2000$) in Males and Females ......................................................................... 187 

83 Predicted Annual QALY Increase Resulting from Decreased AMI and ACM 

 Incidence in Males and Females ...................................................................... 188 

84 Summary of Cost-of-Illness and Value-of-Statistical Life Approaches for 

 Neurotoxicity and Cardiovascular Toxicity........................................................ 190 

85 Summary of Estimates of QALY Gains due to Reductions in 

 Neurotoxicity and Cardiovascular Toxicity........................................................ 191 

86 Predicted IQ Point Loss per Annual Birth Cohort in the Northeast Region 

and the Associated Cost-of-Illness Estimate (2000$), if Deposition Rates 

are Doubled or Halved...................................................................................... 195 

87 Comparison of Predicted Incremental IQ Gains per Annual U.S. Birth 

Cohort and Incremental Estimated Monetary Value of the IQ Gains 

(Cost-of-Illness) (2000$) for 3 Neurotoxicity Models......................................... 196 
xv

LIST OF FIGURES 

Page

1 Global Mercury Cycle ........................................................................................... 4 

2 Average Methylmercury Concentrations for "Top 24" Types of Fish 

 Consumed in the U.S. Commercial Seafood Market .......................................... 16 

3 Conceptual Model of Human Mercury Exposures .............................................. 59 

4 Regions Considered in Model............................................................................. 60 

5 DHA and Methylmercury Levels in U.S. Freshwater Fish................................... 96 

6 EPA and Methylmercury Levels in U.S. Freshwater Fish ................................... 97 

7 Total Polyunsaturated Fatty Acid and Methylmercury Levels in U.S. 

 Freshwater Fish.................................................................................................. 98 

8 Model for Relationship Between IQ and Wages and Labor Force 

 Participation...................................................................................................... 102 

9 For "Top 24" Types of Fish in U.S. Commercial Seafood Market, the 

Percentage of Methymercury Contributed by Fish Type................................... 120 

10 Percent Contribution of the Atlantic Ocean and Gulf of Mexico Harvests 

 to U.S. Commercial Market by Fish Type ......................................................... 122 

11 Fractional Contribution of Consumers of Non-Commercial Fish in Each 

 Region and Commercial Fish to Total IQ Point Loss, Assuming No 

 Neurotoxicity Threshold.................................................................................... 152 

12 Spectrum of Certainty of Causal Association of Health Effect with Mercury 

Exposure with Estimated Benefit Overlay in Millions ($M) and Billions 

($B) of Dollars (2000$) ..................................................................................... 192 
xvi

EXECUTIVE SUMMARY 

This report describes the results of a comprehensive study to estimate the health 

benefits of reducing mercury emissions 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 fish. Fish consumption is the primary 

pathway of human exposure to methylmercury, which is a human neurotoxicant and 

possibly a cardiovascular toxicant. Some babies born to U.S. residents, a population 

sensitive to the neurotoxic effects of methylmercury, 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 methylmercury concentrations and between fish 

methylmercury concentrations and methylmercury exposures to individuals who 

consume these fish. Changes in the quantity of mercury deposited are assumed to lead 

to linear and proportional changes in fish methylmercury concentrations, assuming no 

other factors change. In the model, the waters from which fish are caught for human 

consumption are divided into eight regions. Based on broad regional patterns of 

mercury deposition rates, the continental 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 Mexico, 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 associated with reductions in power plant 

mercury emissions are based on regional deposition modeling results from the EPA's 

analysis of the Clear Skies Initiative. In its analysis, the EPA simulated current mercury 

deposition rates and the changes in these rates that would result if power plants 

reduced their mercury emissions from the current rate of 49 tons per year to either 26 or 

15 tons per year. We used these predictions to estimate changes in deposition rates for 

the freshwater regions, the Atlantic Coastal Region, and the Gulf of Mexico. Estimated 

decreases range from approximately 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 emissions 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. Food 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 predicted deposition rate changes in the Atlantic 
xvii

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 freshwater region, the mix of species that 

is consumed is based on regional data on the frequencies at which individual species 

are targeted. For the Atlantic 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 Service (NMFS). The regional populations 

of non-commercial fish consuming populations 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 decreases in intrauterine methylmercury exposures capture some 

of the neurodevelopmental delays reported in positive epidemiologic studies. We use a 

recent estimate of the dose-response function that integrates the results of the three 

primary methylmercury epidemiologic studies (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 (year 2000 dollars). We note that the body 

of scientific evidence that has evaluated the relationship between intrauterine 

methylmercury exposures and childhood neurodevelopmental delays has been 

thoroughly reviewed by the National Academy of Sciences. 

Credible epidemiological studies have reported an association between 

methylmercury exposures in males and increased risks of myocardial infarction and 

premature mortality. The 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 relationships for premature mortality and nonfatal myocardial infarction. We apply these 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 relationship between adult methylmercury exposures 

and increases in cases of premature mortality and non-fatal myocardial infarction has 

not been thoroughly reviewed. Furthermore, this relationship is potentially confounded 

with the cardio-protective effect of fish consumption. Based on the available data, 

independent scientific bodies evaluating the weight of evidence for fish cardio-protective 

effects have recommended that adults increase their fish consumption. Whether there is 

an increased cardiovascular risk associated with methylmercury exposures is not clear 

at this time. Thus, we recommend that the predicted benefits 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 face annual mercury emissions caps of 26 tons and 15 tons, 

respectively. While the benefits estimates increase from left to right in this figure, our 

confidence that the U.S. population experiences these effects decreases. For Scenario 

1, the predicted annual benefit associated with IQ increases in the annual birth cohort 

ranges from $75 million (assuming a neurotoxicity 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 consume non-fatty freshwater fish, then the 

monetized annual benefits are $48 million and $86 million in Scenarios 1 and 2, 

respectively. If these cardiovascular effects are experienced by the whole U.S. 

population, then the monetized annual 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 important. Long-term monitoring studies that measure mercury 

concentrations in environmental media and biota over time are needed to determine the 

extent to which reductions in mercury emissions 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 consumption rates in the U.S. population; these studies need to 

identify the types of fish consumed and their sources. Additional studies are needed to 

evaluate the relationship between methylmercury exposures and the development of 

cardiovascular effects; these should include studies of human physiological responses 

to fish fatty acids (the component of fish thought to be cardio-protective) and to 

methylmercury. 
xix

Figure ES-1 

Spectrum of Certainty of Causal Association 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 B
xx

ACKNOWLEDGMENTS 

We thank Steve Koplin, National Oceanic and Atmospheric Administration, for the 

information he provided regarding the sources of commercial fish. 

We thank Thomas Braverman, U.S. Environmental Protection Agency, Michael Troyer, 

U.S. Environmental Protection Agency, and Tara Maddock, U.S. Environmental 

Protection Agency, for their help with the REMSAD modeling results. 

We gratefully acknowledge the following individuals for their helpful discussions 

regarding cognitive and cardiovascular health effects: 

Steve Daniels, Cincinnati Children's Hospital Medical Center 

Mark H. Deis, Pediatric Associates 

Kim N. Dietrich, University of Cincinnati College of Medicine. 

We are grateful to the following individuals for their discussions regarding the 

environmental fate of mercury and U.S. methylmercury exposures: 

Robert Ambrose, U.S. Environmental Protection Agency 

Rona Birnbaum, U.S. Environmental Protection Agency 

Ellen Brown, U.S. Environmental 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. Environmental Protection Agency 

Carl Lamborg, University of Connecticut 

Debora Martin, U.S. Environmental Protection Agency (Retired) 

Mary Rothermich, U.S. Environmental Protection Agency 

Tamara Saltman, U.S. Environmental Protection Agency 

Jeff Swartout, U.S. Environmental Protection Agency 

Finally, we thank members of our external peer-reviewer panel. Their thoughtful and 

extensive comments and suggestions were extremely valuable and greatly improved 

this report. 

Michael Aucott, New Jersey Department of Environmental Protection 

Myrick Freeman III, Bowdoin College 

David P. Krabbenhoft, U.S. Geological Survey 

Jacqueline Moya, U.S. Environmental Protection Agency 

Rita Schoeny, U.S. Environmental Protection Agency 

Joel Schwartz, Harvard University 

C. Mark Smith, Massachusetts Department of Environmental Protection 

Alan H. Stern, New Jersey Department of Environmental Protection 
 

1

1. INTRODUCTION AND BACKGROUND 

In recent years, the U.S. Environmental Protection Agency (EPA) issued final 

regulations mandating the reduction of mercury emissions from municipal waste 

combustors (1995), medical waste incinerators (1997), and hazardous waste 

incinerators (1999). The impetus for these 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; Mahaffey et al., 2004; U.S. NCHS, 

2003; U.S. EPA, 2001a; NRC, 2000; CDC, 2004). New federal and state legislation, 

regulation and policies designed to reduce mercury emissions 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 uncertainty 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 effects 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 2850a

900 

Mason et al., 2002; 

Lamborg et al., 2002 

Anthropogenic Northern Hemispherea 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 released to the atmosphere from the ocean re deposits into ocean. b

 The percentage of total U.S. anthropogenic emissions as simulated 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 total quantity emitted globally (4900 tons). 

Annually, 900 tons of mercury are emitted from aquatic systems and 1100 tons are 

emitted from terrestrial systems; these emissions consist of both re-emitted 

anthropogenic mercury and mercury naturally present in these environmental media. 

Deposition of atmospheric mercury to land accounts for slightly more than 50% of the 

total deposition; the remainder of the atmospheric mercury deposits to water bodies. In 

this model, concentrations in compartments of the biosphere (e.g., atmosphere and 

upper levels of the oceans) are predicted to be increasing approximately 1% per year.2

 

Estimating the human-health benefits of reductions in mercury emissions from 

U.S. power plants requires accounting for the atmospheric transport and deposition of 

mercury, methylation, uptake and bioconcentration in fish, human consumption, the 

relationships between human exposure to methylmercury and health effects, and the 

valuation of those effects. The remainder of this section provides background 

information on these topics. 

 

2

 Seigneur et al. (2004) compare four global mercury simulation 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 associated with other models. See Table 1. 
 

4

Figure 1 

Global Mercury Cycle 

1.1. ATMOSPHERIC MERCURY 

Understanding the transport and deposition of inorganic mercury emitted to the 

atmosphere is fundamental to analyzing the changes in methylmercury exposures that 

may be associated with future mandated mercury emissions reductions. In the 

environment, mercury exists in two general forms: inorganic forms, which include 

elemental mercury, mercury oxides and mercury 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-gassing 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/yr
 

5

environment is dominated by transport during its atmospheric phase. Although the 

atmospheric chemistry of mercury is extremely complex and incompletely understood, 

most experts agree that mercury emissions can deposit potentially causing problems 

both locally (i.e., close to emissions sources), regionally (i.e., within a few hundred 

kilometres of a source) and globally. Thus, policy makers are concerned about the 

impacts of local and long-range transport of atmospheric mercury. 

Three different forms of inorganic mercury emissions are typically modeled in 

atmospheric transport models. These are elemental mercury (Hg0), gas phase divalent 

mercury (HgII) (also referred to as reactive gaseous mercury), and particulate-bound 

divalent mercury (Hgp). Based on the current understanding of the fate of atmospheric 

mercury (e.g., Petersen et al., 1995; Cohen et al., 2004; Seigneur et al., 2004), HgII and 

Hgp are deposited to the surface of the earth relatively rapidly through wet and dry 

deposition processes.3

 Thus, when a mix of Hg0, HgII and Hgp is released from an 

anthropogenic source, HgII and Hgp are thought to deposit locally and regionally around 

the source. Due to the difficulties in measuring the dry deposition of HgII, 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-fired power plant was emitted 

as HgII and Hgp,

4

 although the two forms comprised only 50% of the total emitted. 

 

3

 HgII and Hgp are readily scavenged by atmospheric precipitation and deposited to the earth through wet 

deposition. Hgp is dry deposited due to gravitational settling. Gaseous HgII also can dry deposit. HgII dry 

deposition velocities appear to depend on both ground cover and weather conditions; a typical deposition 

velocity for HgII is estimated to be 2.9 cm/s (U.S. EPA, 1997a, 1998a). While the wet deposition of 

mercury and dry deposition of Hgp can be measured in a relatively direct manner, measuring the 

quantities of gaseous HgII that dry deposit is complex and estimates 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 these 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, Hg0 is volatile and much less soluble than HgII. Its estimated 

atmospheric half-life is approximately 1.5 years. Given this long half-life, emitted Hg0 is 

unlikely to deposit locally; rather, it enters regional and global atmospheric mercury 

pools. In these regional and global pools, Hg0 slowly converts to HgII 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 plants deposits in the contiguous 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 the 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 Lagrangian or trajectory 

model to estimate the fate of emitted mercury 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 Hgp is emitted by power plants; however, roughly 20% of the measured mercury in power 

plant emissions plumes is Hgp (U.S. EPA, 1997b). When the U.S. EPA simulated the atmospheric fate of 

mercury, they assumed that 20% was bound to particulate matter based on measurements in the 

emission plume. 

5

 Other groups have also developed regional atmospheric 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 deposition fluxes of atmospheric mercury over large geographic 

domains 

In 2002, the U.S. EPA proposed the Clear Skies Initiative (CSI) to reduce power 

plant emissions of mercury through the application of a market-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 Aerosols and Deposition (REMSAD) model to 

predict potential changes in mercury deposition 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., the western Atlantic Ocean and the Gulf of 

Mexico. 

The EPA’s CSI simulation included predictions 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 mercury emitted annually by U.S. 

anthropogenic sources was attributed to power plants.7

 Separate simulations were 

developed to compare predicted changes in mercury deposition under the CSI at two 

different points in time. The mercury emissions 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 with 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. anthropogenic sources annually. (www.epa.gov/ttn/chief/net (See 1999 NEI data). 
 

8

 predicts U.S. anthropogenic mercury emissions to be 104.7 tons; if the CSI is 

implemented, total U.S. anthropogenic 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 subject 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 their simulations, the EPA included differences in 

the quantities and species of mercury emitted from power plants using a model that 

predicts the U.S. power sector’s response (including fossil fuel consumption) to 

regulatory and economic changes. If the CSI is fully implemented, EPA predicts slightly 

more than a 10% decrease in mercury deposition 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 chemistry of mercury over the North American 

continent using 100 x 100 Km grids. Changes in coal consumption patterns and 

mercury emissions by the electric power industry under the CSI were modeled. EPRI 

(2003) predicted that current mercury emissions from power plants account for less than 

 

8

 The “safety valve” is a provision of the cap and trade 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 the U.S. The impact of mercury emissions 

changes was analyzed using “transfer coefficients” estimated from the model. Transfer 

coefficients are model parameters 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. Midwest influence mercury deposition at the New 

York receptor more than changes in emissions from the South (Figure 2-2 in the EPRI 

report). Many of the transfer coefficients were estimated for sites monitored by the 

National Atmospheric Deposition Program Mercury Deposition Network (NADP-MDN), 

although this network was designed to measure background mercury concentrations 

and not contributions from local anthropogenic sources. Based on their analysis of 

transfer coefficients at 19 receptor sites, EPRI (2003) estimates that, with CSI 

regulations in place, there 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 rate decreases vary between the two 

models, the TEAM and REMSAD simulations predict similar geographic patterns of 

mercury deposition. For example, 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 model results suggest significantly smaller 

contributions to overall mercury deposition from power plants than do the EPA models. 

The EPRI report does not detail changes in deposition within the 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, inorganic mercury enters bodies of water where sulfatereducing micro organisms in the water column 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 the 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 result, 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 accumulation 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 fish and its position in the food web. 

 Understanding the fate of mercury entering a water body is fundamental to 

predicting the changes in methylmercury concentrations in fish that may be associated 

with reductions in mercury deposition. In an analysis of the applications of the primary 

models used to estimate methylmercury 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 methylmercury concentrations in fish were 

proportional to changes in mercury entering the water body under steady-state 

assumptions. If the atmospheric mercury 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-atmospheric sources, such as mines and relic accumulations 

in soil and sediments, are significant sources of inorganic mercury to a body of water, 

then reducing atmospheric deposition of mercury 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 including 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 piscivorous 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 Maps 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 methylmercury concentrations reported in the National 

Listing of Fish and Wildlife Advisories (NLFWA) database (U.S. EPA, 2003d) and 

simulation results from atmospheric mercury 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 anthropogenic mercury emissions were reduced. 

The EPRI (2003) analysis also evaluated the impact of reducing power plant 

mercury emissions on fish methylmercury concentrations. The analysis assumed that a 

linear relationship existed between reductions 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 fish 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 resulted from decreased power plant emissions 

were assumed to result in linear and proportional reductions in fish methylmercury 

concentrations. In the EPRI analysis of the CSI, U.S. exposures to methylmercury from 

fish consumption were predicted to change very little from current exposures. 

1.3. HUMAN EXPOSURE TO METHYLMERCURY

Humans are exposed to methylmercury primarily through fish consumption (i.e., 

eating the contaminated fish muscle). Schober et al. (2003) and Mahaffey et al. (2004) 
 

13

showed that individuals reporting some fish consumption have higher levels of blood 

mercury and blood methylmercury, respectively, than those who report no fish 

consumption. Self-reported number of fish meals is a strong predictor of blood mercury 

(Schober et al., 2003) and methylmercury concentrations (Mahaffey et al., 2004). The 

magnitude of human methylmercury exposures 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., family members or friends). 

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

consumes seafood from the commercial market (Carrington and Bolger, 2002). In the 

U.S., this market is comprised of farmed fish 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 marketed fish (Carrington and Bolger, 2002). 

Based on Carrington and Bolger’s input data, 22 of the 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 unaware 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, nutrient 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 methylmercury concentrations in the food webs 

of the commercial seafood stocks living in these areas of the ocean. The continental 

shelf adjacent to the North American continent under the Atlantic Ocean is one such 

area of concern; the Gulf of Mexico is another. 

In the U.S., the Food and Drug Administration (FDA) monitors methylmercury 

levels in commercial seafood. Methylmercury concentrations are typically highest in the 

wild finfish such as shark and swordfish (Figure 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 changes in fish consumption patterns. Table 2 lists per 

capita consumption rates and mean methylmercury concentrations for 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 fish raised in aquaculture (fish-eating species 

raised in aquaculture include salmon and cod). 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 many marine species only inhabit and feed in 

one location, three important commercial fish, tuna, shark and swordfish, are migratory. 

Thus, the methylmercury concentrations of these 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 fisheries statistics, the mean trophic level of fish 

caught has declined from 1950. The decline is a consequence 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. 
16

0

0.80.60.40.2

1

1.2

Swo dr fishSha kr

Lobster Americ - anHalibut Sable fish Rockfish Tuna-canned Crabs D- ungenessPollock C ar bs-Snow C ar bs-Bue l Lobster-SpineyCodFlatfish Crabs-King Pe cr h-OceanShrimp Salmon Oysters C awfi r shCatfish Scallops Sa din r esCams l

Fish Type

Methylmercury Concentration (ppm)

Figure 2 

Average Methylmercury Concentrations for "Top 24" Types of Fish Consumed in the U.S. Commercial Seafood 

Market 
 

17

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 0.0 100.0 

LobsterAmerican 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 40.6 

CrabsDungeness 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 commercial 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 methylmercury (for example, due to elevated 

mercury deposition rates associated with emissions 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) 10,024,000 
 

19

Non-commercial fish caught in marine and estuarine waters along the U.S. 

coasts are monitored through the annual National Marine Recreational Fisheries 

Statistics Survey (NMFS, 2003). Their surveys 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 consumption by recreational anglers in the 

Atlantic Ocean and in the Gulf of Mexico. The estimated total catch weights of these 10 

types of fish comprise roughly 70% of the estimated total catch 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 concentration 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 Advisories (NLFWA) Mercury Fish Tissue Database 

(U.S. EPA, 2003d). States voluntarily report information to this database, which 

contains approximately 58,000 samples reporting methylmercury concentrations. In 

addition to the methylmercury concentration, many of the entries 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 surveys 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 Recreational 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/Mackerelsb 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 category of other tunas and 

cero mackerels evenly between the two types of fish. 
 

21

Table 5. Median, Mean, and Maximum Methylmercury Concentrations (μg/g) 

Reported Fish Species Harvested via Recreational 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. The FDA has evaluated commercial fish 

consumption rates (Carrington and Bolger, 2002). Table 2 reports per capita 

consumption rates for selected types of commercial fish based on FDA dietary surveys 

of the U.S. population. Based on an extensive analysis of NMFS survey data, U.S. EPA 

(1997a) estimated the distribution of finfish consumption rates for Atlantic and Gulf 

recreational anglers. U.S. EPA (1997a) also reports the distribution of freshwater fish 

consumption rates for U.S. recreational anglers. These values 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 large quantities of non-commercial fish. For 
 

22

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 consume high quantities of commercial and non-commercial fish 

or consume contaminated fish. 

1.4. TOXICOKINETICS 

Once ingested, roughly 95% of the methylmercury entering the gastrointestinal 

tract is absorbed. This compound passes through the lining of the gut to the liver and 

enters the blood stream where it primarily binds red blood cell proteins, quickly 

distributing from blood to the 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 acid carrier protein (Aschner, 1989); entry 

into the brain tissues can lead to adult neurotoxicity. In pregnant females, 

methylmercury can also bind and pass through the placenta tissues; it may also enter 

placental tissues through an active transport process via an amino acid carrier protein. 

Stern and Smith (2003) report 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 exposed to methylmercury. The possible 

 

13 While described as a study of “subsistence” angler consumption (U.S. EPA, 1997b), there is not a 

specific intake quantity or percent of total daily caloric intake or protein intake that results in a 

categorization of “subsistence.” 
 

23

cardiovascular effects associated with increased methylmercury exposures may result 

from the oxidative properties 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 demethylated 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 methylmercury (90%) is eventually eliminated as 

inorganic mercury through the feces via the bile and exfoliated gastrointestinal 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 elimination pathway from the body. 

While some researchers have used blood sera and toenails to analyze mercury 

exposure, measures of mercury concentrations 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 “methylmercury 1-compartment model.” This 

model provides reasonable approximations 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 constant (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 effects14

observed in children that result from intrauterine methylmercury exposures and adult 

myocardial effects associated with methylmercury exposures through fish consumption. 

The risks of incurring these two effects are quantified in Section 3 of this report and they 

likely account for a large fraction of the total damage to humans that is associated with 

 

14 In laboratory animal studies, ingested methylmercury 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 cystiene. Methylmercury causes diffuse damage throughout 

the entire developing brain. When compared to normal 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 discuss 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 pressure does not appear to persist and the 

clinical significance of changes in heart rate variability of otherwise healthy children is 

not known. 

The following reported effects associated with intrauterine methylmercury 

exposures and several reported effects associated 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 years 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 exposures 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 visuospatial organization, in children ages 

5 to 7 years was associated with intrauterine 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 possibly associated with methylmercury 

exposures through fish consumption have been reported. These include 

reduced motor control (Mergler and Dolbec, 1998) and reduced function 

on tests of fine motor speed and dexterity (Yokoo et al., 2003) and on 

tests of verbal memory (Yokoo et al., 2003), difficulty with accuracy and 
 

26

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 Associated with Intrauterine Methylmercury 

Exposures. 

The need for increased scientific understanding of the relationship between 

exposures to organic forms of mercury and human neurological decrements 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 consumption of treated seed grain in Iraq 

(summarized in Clarkson, 2002). Exposures to these high levels of mercury led to 

severe neurological effects in adults, children and fetuses; however, the neurotoxicity 

that was associated with intrauterine exposures (i.e., severe cerebral palsy, delayed 

walking and talking) occurred at lower doses 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 exposures to environmental levels of 

methylmercury in fish continue to be the subject of intensive scientific investigation and 

there have been additional epidemiologic studies of such exposures (U.S. EPA, 2001b), 

epidemiologic studies conducted in New Zealand, the Seychelle Islands and the Faroe 

Islands have been the subject of most recent scientific interest. All three studies 
 

27

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 developmental stages. The National Research 

Council (NRC, 2000), which evaluated all three 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 reported such associations. At present, no 

adequate explanation has been offered 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 development of children who had elevated 

intrauterine methylmercury exposures. From the 10,930 mother-child pairs enrolled in 

the cohort, 935 mothers who reported eating more than three fish meals per week while 

pregnant were identified. The fetal exposure measure was the mercury level in 

maternal hair over the entire pregnancy. The 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 reference groups were 8.8 ppm and 1.9 ppm, 

respectively. At 4 years of age, the children were administered a group of tests (Table 

6). Based on the Denver Developmental Screening Tests (DDST) results, a statistically 

significant difference existed between the neurological functions of the high-exposure 
 

28

and reference group. Developmental delays (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 additional 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 pregnancies. The average maternal hair mercury 

concentrations during pregnancy in members of the two control groups that reported 

frequent fish consumption were less than 3 ppm and between 3 and 6 ppm. A 

statistically significant association was reported between high maternal mercury 

exposure and decreased test scores on the McCarthy 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-gross 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 Kjellstrom et al. (1989) study conducting 
 

30

Multiple regression analyses on the reported average maternal hair mercury 

concentrations during pregnancy as a continuous variable (i.e., unlike Kjellstrom et al. 

they used the reported values of hair mercury concentrations in their analyses rather 

than as a categorical variable as Kjellstrom had done). They did not identify a 

statistically significant relationship between maternal hair mercury levels and the 

neuropsychological test scores unless they omitted the single subject whose mother 

had the highest average maternal hair mercury concentration in the cohort, which was 

over four times higher than the second highest measurement. The maternal hair 

mercury variable was a statistically 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 for 

ChildrenRevised 

Performance

Wechsler 

Intelligence 

Scale for 

ChildrenRevised Full 

scale IQ 

McCarthy 

Scales 

Perceptual 

McCarthy 

Scales 

Motoric 

1st

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)

2nd

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, alcohol intake, social class, birth weight, maternal age, breastfeeding, gender, ethnicity, 

residence, residence time in New Zealand, and other siblings. c

 Statistically controlled for age of child at testing and parental education levels 
 

31

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 evaluated 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 statistically significant associations between 

environmental prenatal methylmercury exposures (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 tests, this association could have been by 

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

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

in utero exposures to environmental concentrations of methylmercury through fish 

consumption are not associated 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 statistically 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 Seychelles Islands Child Development Study 

Developmental Age of Child (months) 

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 CBCL 

Learningachievement  WoodcockJohnson 

 

16 The NAS report states “power analyses that are based on total sample size can be misleading if 

adverse effects occur primarily among 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

Developmental Age of Child (months) 

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 CBCL 

Learningachievement  WoodcockJohnson 

Letter and Word 

Recognition, 

Applied 

Problems 

Myers et al. (2003)

Globalcognitive  WISC-FSIQ 

Speechlanguage  BNT 

Memory visual 

memory 

subtest of 

the widerange 

assessment 

of memory 

and learning 

Sustained 

Attention 

 Connors 

Continuous 

Performance 

Test 

Behavioral Connors 

Teacher 
 

34

Developmental Age of Child (months) 

Domain 6.5 19 29 66 168 

rating scale, 

parent-child 

behavior 

checklist 

Learningachievement  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 Index; 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 methylmercury exposure through seafood 

consumption during pregnancy (including episodic pilot whale consumption) and 

neurodevelopment in the offspring (Grandjean 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 period 
 

35

between 1986 and 1987. The exposure 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), the 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 was 4.27 μg/g (interquartile range 2.6-7.7 

μg/g). No mercury-related abnormalities were identified during clinical examination of 

the cohort. Intrauterine methylmercury exposures 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. Grandjean 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 neuropsychological tests appear in Table 10; the 

coefficients were calculated for those children 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 evidence from two of the three studies, low 

dose in utero exposures to methylmercury likely lead to subtle but measurable 

neurological effects in children. Because the three studies used different test batteries, 

it is difficult to directly compare their results. In the future, there may be opportunities to 

evaluate confidence intervals associated with significant and non-significant test results 

at the level of developmental domains across the three studies (see first column in 

Tables 6, 8, and 9 for listing of the domains evaluated in the three studies). This type of 
 

36

evaluation may provide alternative measures of consistencies between the three 

studies. Additional evaluations of potential confounding effects 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 

potentials 
 

37

Table 10. Coefficients for Logarithmic Transformation of Cord Blood Mercury 

Concentrations on Selected Neuropsychological Tests (only 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 medical risk factors, 

smoking, alcohol intake, parental education levels, father's employment status, 

current residence, child's computer acquaintance, day care, and other siblings. NRC 

(2000) presents a summary table (Table 7-1) that provides estimates of the 

regression coefficients for all of the subjects. 

1.5.2. Myocardial Effects Associated with 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 neurologic studies as a group, similar panels 

have not evaluated the group of studies that have examined the relationship between 

adult methylmercury intakes and myocardial effects. Studies conducted by Salonen et 

al. (1995, 2000), Rissanen et al. (2000), and Guallar et al. (2002) suggest that 

methylmercury exposures may be a risk factor for myocardial events and premature 

deaths. Studies by Tamashiro et al. (1986), Ahlqwist et al. (1999) and Yoshizawa et al. 

(2002) did not observe such a relationship, indicating that it may not exist. We note that 

cardiovascular diseases have been associated with many other behavioral, genetic, and 
 

38

dietary risk factors. Typically, in adult populations, the primary source of methylmercury 

exposure is consumption of contaminated fish and, in general, the consumption of fish 

and fish oils has been consistently associated with reduced risk of myocardial events 

(e.g., Kromhaut et al., 1985; Daviglus 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 over 30 years, showed that, when compared to those 

reporting no fish consumption, individuals who 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 substantial body of evidence on the cardiovascular 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 (Curb 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 cardiovascular benefits of fish intake. That is, one 

explanation for the fish consumption studies that show no fish consumption-associated 

cardiovascular benefits or even adverse cardiovascular effects could be that the 

cardiotoxic effect of methylmercury offsets or exceeds the cardioprotective effect of the 

polyunsaturated fatty acids in fish. Of course, there may be other contaminants and 

other factors in these studies that lead to these observations. We note that these 

positive epidemiologic studies appear to be well conducted 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 relationships between adult methylmercury 

exposures, as measured by hair mercury 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 significant 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 via 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, stroke and claudication. Mercury exposures 

were measured using hair and urine mercury concentrations. Hair mercury 

concentrations reflect long-term average mercury exposures (months) and urinary 

mercury concentrations are influenced strongly 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 excretion rate was 1.18 μg/day. Table 11 

provides additional details of these measures. In this study, the mean and maximum 

follow-up periods were 5 years and 7.75 years for cardiac events and 6 years and 8.75 

years for deaths, respectively. 

 Salonen et al. used Cox proportional hazards models to evaluate the relationship 

between hair mercury levels and AMI and premature death. 

 

17 All cause mortality (ACM) is an epidemiologic term that 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 factors 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 myocardial events) and are more likely to 

estimate the isolated effects of methylmercury 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 increased 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 observed increase in AMI risks is not statistically significant 

(p=0.17). 

Table 11. Measures of Cohort Methylmercury 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 5 

 
 

41

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 CHD history, smoking, 

systolic blood pressure, diabetes, socioeconomic status, residence, dietary iron 

intake, serum apolipoprotein B, HDL2 cholesterol, and ferritin concentrations 

The Cox proportional hazards model is described in Equation 6 in Section 2.5.1. 

 Salonen et al. also reported an analysis that compared risks to subjects with hair 

mercury concentrations of 2.0 ppm or more with those that had less than 2.0 ppm. In 

the group with hair mercury concentrations of 2.0 ppm or more, the authors reported a 

69% (p=0.038) greater risk of fatal or nonfatal AMI and a 93% greater (p=0.007) risk of 

premature death from all causes. 

Based on an additional multivariate analysis of a small subset (n=187), the 

authors reported that mercury levels in the hair and urine were statistically significantly 

associated with immune complexes containing oxidized low density lipoprotein (LDL). 

These complexes are believed to be related to adverse myocardial events. The authors 
 

42

posited that the increased incidence of myocardial events may be caused by oxidation 

of serum lipids. Of the variables 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 progression of arteriosclerosis, as measured by 

changes in carotid artery thickness over a 4-year period, and hair mercury 

concentration. Based on the results of a multivariate 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 relationship 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 associations were observed between a third 

fish-derived fatty acid, eicosapentaenoic acid, and the risk of acute coronary events. 

The mean hair mercury concentration increased 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 groups, it could reduce the antioxidative capacity of the 

plasma by binding to proteins such as glutathiols. 

3. When bound to mercury, selenium does not function 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 all three compounds. 

 To analyze the relationship between the two fatty acids and acute coronary 

events, the cohort was divided into quintiles 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 interval 11%, 65%; p=0.014) when compared to 

subjects in the lowest quintile. To evaluate whether mercury exposure might modify the 

relationship between fish-derived serum acids and myocardial events, the data were 

stratified based on hair mercury content and a dichotomous analysis was developed 

that compared risks to subjects that had hair 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 mercury 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 higher risks of acute 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 Composition, 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, ischemic exercise ECG, maximal 

oxygen uptake, family CHD history, smoking, systolic blood pressure, diabetes, body 

mass index, socioeconomic status, serum insulin, ADP-induced platelet aggregation, 

residence, dietary iron intake, dietary energy 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 tissue 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 more than 200. The countries from which cases and 

controls were obtained included Spain, Norway, Russia, Germany, Israel, the Netherlands, Finland, 

Switzerland and the United Kingdom. 
 

45

higher than those of the controls 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 adjusting 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 interval 0.3, 1.19; the trend 

test was statistically significant). 

In a follow-up study of the Japanese population 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 lower 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 recruitment who were followed for up to 25 

years. Serum mercury concentrations, which disproportionately reflect inorganic 

mercury exposures, were measured at cohort initiation. No association between serum 

mercury concentrations and myocardial infarctions was found. 

In a prospective nested case-control study, Hallgren et al. (2001) examined the 

relationship between first time 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 erythrocyte mercury concentrations and plasma 

polyunsaturated fatty acid concentrations. 

Using a nested case-control design, Yoshizawa et al. (2002) studied the 

association between toenail mercury concentrations and risks of coronary heart disease 

in male health professionals aged 40 to 75 years. They excluded from the study 

subjects reporting myocardial infarction, angina, coronary artery by-pass surgery, 

stroke, diagnosis of cancer or having a very high or very low daily caloric intake. Based 

on several different analyses (e.g., odds ratios, multivariate regression), there was no 

statistically significant difference between the mercury levels of cases and controls. A 

separate multivariate analysis that excluded dentists (due to occupational exposure to 

inorganic mercury) compared the highest and lowest 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 effects of fish consumption, in general, is 

the strongest. The questions are whether co-exposure to methylmercury attenuates the 

cardiovascular benefit of fish consumption and, if so, under what conditions and by how 

much. We have noted that some epidemiologic studies that evaluated the relationship 

between fish consumption and cardiovascular effects did not report a positive effect. 

When compared to the body of epidemiologic data indicating that fish consumption may 
 

47

reduce the risk of myocardial events (e.g., Daviglus et al., 1997), the epidemiologic 

studies showing an association between methylmercury 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. The exposure measure used in this study may be inappropriate 

for inferring whether there is an association between methylmercury intake and fish 

consumption. Serum levels of mercury, which 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 statistically significant association, evaluated 

methylmercury poisoning, and the effects of the large methylmercury doses in these 

individuals may differ substantially from those exposed to typical environmental levels. 

Finally, the Yoshizawa et al. data showed a positive but not statistically significant 

association between toenail mercury concentrations and cardiovascular effects, if 

dentists were not included. 

 In Section 2, we use the regression coefficients of Salonen et al. to estimate 

methylmercury-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 exposures and adult cardiovascular effects have, 

to date, not been subjected as a group to such 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. The neurotoxicity studies have 
 

48

been independently evaluated as a group. The studies reporting the cardiovascular 

benefits of fish consumption have also been subjected as a group to thorough reevaluation. 

1.5.3. Elevated Childhood 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 discuss human blood pressure reports from the 

methylmercury poisoning in Minamata, Japan and several relevant rodent bioassays. 

Although blood pressure increases in children 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 association 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 years 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 (Grandjean et al., 1995), Sorensen et al. (1999) 

reported statistically significant associations 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 used 
 

49

to obtain pregnancy and pertinent maternal information. A single measure of blood 

pressures was made for each 7-year-old child in the cohort standardized conditions. 

A series of multiple regression analyses were conducted for maternal and child 

risk factors and elevated blood pressure. As anticipated, both increasing 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 hypertension, the authors report that DBP 

increased on average by 13.9 mmHg (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 was 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 blood 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 associated with an average increase 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 were 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 potential child risk factors such as breastfeeding duration and parity did not appear to be significant predictors of the subjects’ 

blood pressures. The authors also evaluated birth weight, placenta weight, and 

gestational age simultaneously and found that these coefficients were not statistically 

significant predictors for DBP or SBP. The authors then divided current body weight, 

birth weight and placenta weight into quartiles and found no statistically significant 

effects. Assessment of mercury exposure using a biomarker such as cord blood 

mercury levels is indicative of fetal exposures over a relatively short time period 

(between 1 and 2 months). Hair mercury concentrations integrate a broader period of 

exposure than cord blood measures (Grandjean et al., 1999). 

Blood pressure data were collected again in this cohort at 14 years of age 

(Grandjean et al., 2004). At that age, no effect of intrauterine mercury exposure was 

observed on systolic or diastolic blood pressure. Shifts in childhood blood pressure, 

while potentially important, do not appear to be as consequential as those that persist 

into adulthood. 

Other epidemiologic data describe associations between methylmercury 

exposures and hypertension. Tsubaki and Irukayama (1977) describe a retrospective 

epidemiological study designed to estimate the prevalence of Minamata disease, which 

was caused by exposures to high levels of methylmercury, among residents of three 
 

51

Japanese cities around Minamata Bay. From 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. Fish in the Minamata area were the most 

heavily contaminated by methylmercury; those in Goshonoura were considered 

moderately contaminated, and those 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 examined 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 Minamata disease and those in the control group 

were diagnosed as hypertensive. In this study, the criteria for a diagnosis of 

hypertension were a SBP greater 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 disease (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 interesting, 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 Minamata Disease Diagnoses and Diagnosis of 

Hypertension and the Occurrence of Hypertension 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 Disease 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 congenital Minamata Cases 

Table 15. Comparison of Hypertensive Diagnoses Between Those with and 

Without Minamata Disease in Two Different Age Categories 

Minamata Disease Status/City/Age category 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 Years old 107/378 (28.3%) 

Disease-Free/Ine/<39 Years old 2/230 (0.9%) 

In the section of Minamata that had the 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 which the cause of death was 

listed as hypertension or hypertensive heart disease did not differ significantly from their 

comparison population that was not exposed to elevated mercury levels. As noted 

previously, the frequencies of heart disease and cerebral infarction in the study 
 

53

population were lower than those of the general Japanese population but the frequency 

of cerebral hemorrhage, a sequela 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 incidence of hypertension (e.g., 

Fukuda et al., 1999).21

1.5.3.2. Childhood Cardiac Rhythms ― 

Sorensen et al. (1999) also reported that 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 decreased (i.e., variability of cardiac electrical 

impulses was decreased).22 At age 14 years, the heart rate 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) administered 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 females was significantly higher during weeks 3 and 5 of the 5 week 

experiment. The blood pressures in the treated males 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 after day 60. 

After this point the SBP of all treated rats were statically 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 higher than the controls. Small numbers of experimental 

animals, short study durations, high doses, and inadequate 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 report 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 methylmercury may be associated with blood pressure 

increases, but neither body of data directly address the issue of fetal methylmercury exposures. 22 Each heartbeat is a discrete event affected by both sympathetic and parasympathetic influences. 

When the heart beats, electrical impulses are discharged. 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 activities decreased with increasing intrauterine 

exposures in a statistically significant manner. Heart rate variability also declined with 

increasing exposures in a statistically significant 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 significance of such decreases in apparently 

healthy children remains an active research area 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 death 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. Senate (S. 485), would create a mandatory 

program that would reduce power plant emissions 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 plants would be capped under a national marketbased “cap and trade” approach. The U.S. power plants subjected 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 introduced 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 reintroduced in the U.S. Congress in 2005 with a major 

change in mercury control requirements: the first-phase cap in 2010 now is 34 TPY instead of 26 TPY, 
 

55

1.7. PURPOSE OF REPORT 

Previous studies that evaluated the impact of reducing mercury emissions from 

power plants either did not evaluate the potentially resultant changes in methylmercury 

exposure in the U.S. population (U.S. EPA, 2003b) or, if exposure was evaluated, were 

unable to adequately evaluate differences in sources of fish in U.S. diets (EPRI, 2003). 

Our goals were to characterize the impact that reductions in mercury emissions from 

power plants would have on methylmercury 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 emissions scenarios. The first scenario is 

based on current mercury emissions, 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 the quantity of fish from each region that is 

consumed, noting the current population exposures 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 mercury 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 and 
 

56

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 estimate 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 methylmercury through consumption of U.S. 

commercial and non-commercial fish. The monetary estimates are primarily based on 

human capital approaches. We also estimate 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 effects of the type that have been observed 

in epidemiologic studies of pregnant women and their children. We note that this is the 

primary health concern associated with methylmercury intake from fish (Grandjean et 

al., 1997; Kjellstrom et al., 1986, 1989). We also estimate cases of adult myocardial 

infarction and premature death. These effects 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 impacts that might result from decreased 

mercury emissions from power plants is presented 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, divalent particulate mercury, and gaseous divalent 

mercury at a 36 Km x 36 Km grid resolution for 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 methylmercury 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). Mercury emissions from all 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 humans (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 atmospheric compartment. There are five 

freshwater regions in the contiguous U.S.; these are depicted 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 States 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 States of Kentucky and Missouri and the 

States to their North including Ohio and Minnesota, and 

5. West Region includes the States of Texas, Oklahoma, Kansas, Nebraska, 

Iowa and the Dakotas and States to the west in the conterminous U.S. 

 

25 While there is evidence that global mercury emission and deposition rates are decreasing (Schuster et 

al., 2002; Slemr et al., 2003), the model results of Lamborg 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 eastern 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 south of Florida extending 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.
59

Figure 3 

Conceptual Model of Human Mercury Exposures 

Freshwater Fish 

Gulf of Mexico Fish

Regional Models of Atmospheric Hg Deposition 

Global Model for Deposition of Atmospheric Hg 

Other Marine Fish 

US Power Plant Hg Emissions 

Other Hg Natural and Anthropogenic Emissions Sources

Consumers of Non-Commercial Freshwater Fish 

Consumers of Commercial Fish 

Consumers of Non-CommercialAtlantic Fish 

Atlantic CoastalFish 

Consumers of Non-Commercial Gulf Fish 
60

Figure 4 

Regions Considered in Model
 

61

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 individuals who consume these fish. 

2. Changes in the total quantity of mercury deposited lead to proportional changes 

in fish methylmercury concentrations26, assuming no other factors change (i.e., 

ceteris paribus). 

As a consequence of these two assumptions, changes in mercury deposition 

rates result in proportional changes in human methylmercury intakes, because there are 

no changes in other factors including 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 commercial and non-commercial fish included in the 

model. 

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

where: 

Cfij = mean methylmercury concentration in the ith fish type in the jth region 

(μg/g) under an alternate emissions scenario 

Ccij = current mean methylmercury concentration in the ith fish type in the jth

region (μg/g) 

Dfj = mean annual mercury deposition rate in the jth region (μg/m2/yr) under an 

alternate emissions scenario 

Dcj = current mean annual mercury deposition rate in the jth region (μg/m2

/yr) 

The premise of Equation 2 is that separate 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 product of the current mean concentration and the 

ratio of the alternative to current mercury deposition rate. 

2.1. CHANGES IN MERCURY DEPOSITION 

For each region, the changes in mercury deposition that result from additional 

control of U.S. power plant mercury emissions are based on U.S. EPA’s analysis of the 

impacts of the CSI (U.S. EPA, 2003a,b). Based on REMSAD simulations, the U.S. EPA 

estimated mercury deposition rates corresponding to the following five emissions 

scenarios: current emissions, mercury emissions in 2010 (Baseline 1) [this includes 

projected changes in U.S. mercury 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 emissions associated with implementing the 

CSI (Scenario 2) (U.S. EPA, 2003a,b). 

For the five freshwater regions, the Atlantic Coastal Region and the Gulf of 

Mexico Region, we summed all forms of mercury predicted to deposit in each 36 x 36 

Km grid cell under each of the five emissions 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 mercury deposition. The total annual mercury deposition 
 

63

associated with current emissions, as predicted by EPA’s REMSAD model, was used as 

the denominator in each ratio. The numerators were the predicted total mercury 

deposited in the grid cell in Baseline 1, Scenario 1, Baseline 2, and Scenario 2, based 

on the relevant REMSAD simulation modeling. Thus, the four ratios are Baseline 

1/Current, Scenario 1/Current, Baseline 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 grid cells in the Atlantic Coastal and the 

Gulf of Mexico Regions, respectively. For each of these two regions, we estimated the 

mean value of each of the four ratios. These eight mean 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/Current, and Scenario 2/Current); thus, we 

developed a total of 20 unique distributions 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 associated 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-range 
 

64

transport. Under the well-mixed assumption, we assume that the mercury emitted from 

U.S. power plants deposits in these 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 contribute to mercury deposition in proportion to its 

contribution to global anthropogenic and natural emissions. (Current mercury emissions 

from the U.S. utility sector are approximately 1% of total mercury emitted globally and 

total U.S. anthropogenic emissions are roughly 3% [Table 1].) 

2.2. METHYLMERCURY EXPOSURES THROUGH FISH CONSUMPTION 

Our model simulates exposures for consumers of fish from the U.S. commercial 

seafood market (Section 2.2.1), the most common 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 aquaculture (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-consuming populations are estimated in Section 

2.2.3.28

 

27 Clearly, there may be regional sources for some commercial fish. Under these assumptions, the 

variability associated with the consumption of commercial fish from a small region will not be captured in 

this model. 

28 The assumption that non-commercial fish consumers 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 conversely avoid certain species); methylmercury intakes may vary markedly 

from the weighted means calculated for each region. 
 

65

2.2.1. Methylmercury Exposures Through Commercial Seafood Consumption.

Carrington and Bolger (2002) adjusted their simulations of human methylmercury 

intake through commercial seafood consumption to match the distribution of measured 

U.S. blood methylmercury levels (Mahaffey et al., 2004, based on NHANES survey 

data, Table 16). Given that 70%-90% of the U.S. population consumes commercial fish 

(Carrington and Bolger, 2002), we assume that the distribution of blood methylmercury 

concentrations reported by Mahaffey et al. (2004) is a consequence of commercial fish 

consumption. Some individuals 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 Methylmercury Concentrations (μg/L) in U.S. Women Aged 16 to 

49 

Population n Geo.b

Mean 5th c 10th 25th 50th 75th 90th 95th

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 5th, 10th, ... 95th – percentiles of total blood methylmercury concentration 
 

66

Based on assumption 1 (see introduction to Section 2), we assume the reported 

blood methylmercury distribution in U.S. commercial consumers to be in equilibrium with 

the mean methylmercury concentration in commercial fish weighted by per capita 

consumption rates. This implies that the fish that commercial 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 methylmercury concentration for all 

commercial fish (μg/g) 

wi = estimated annual per capita consumption rate of the ith fish type 

(kg/person year) 

Cci = current mean methylmercury concentration of the ith fish type 

Carrington and Bolger (2002) report current per capita consumption data and 

mean methylmercury concentration 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 methylmercury 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 ith fish type, wi, remains constant over time; thus, Equation 3 can be modified 

to estimate the weighted mean methylmercury concentration for all commercial fish 
 

67

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 emissions scenario (μg/g) 

wi = estimated annual per capita consumption rate of the ith fish type 

(kg/person year) 

Cfi = mean methylmercury concentration of the ith fish type (μg/g) 

under alternative emissions scenario (See Eq. 5) 

In this model, the mean concentration of ith fish type (Cfi) 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 methylmercury concentration in fish under alternative deposition 

scenarios. 

 Cf Cc x

Q

Q

x

Df

Dc i i

j

j

j 1

n

j

j 1 j

n

=























 =

=

∑

∑ (Eq. 5) 

where: 

Cfi = mean methylmercury concentration of the ith fish type (μg/g) under 

alternative emissions scenario 
 

68

Cci = current mean concentration of the ith fish type (μg/g) 

Qj = quantity of fish harvested annually from the jth region (kg/yr) 

Dfj = mercury deposition rate in the jth region in which the fish are captured 

under alternative emissions scenario (μg/m2

/yr) 

Dcj = current mercury deposition rate in the jth region in which the fish are 

captured (μg/m2/yr) 

In Equation 5, for each region in which commercial fish are captured, we 

calculate, based on mass, the fractional contribution of the individual harvest from each 

regional source to the total annual quantity of the type of fish harvested (Qj/ ΣQj). Next, 

the ratio of Df/Dc provides an estimate of the change in the mercury deposition rate in 

the region. We compute the product of the fractional contribution of the fish caught in 

each region and the ratio of the alternative and current deposition 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 regions, the coastal 

Atlantic, the Gulf of Mexico, the All Other Waters Region, and aquaculture. We merged 

the fish raised in aquaculture and those inhabiting All Other Waters into a single group. 

After estimates of Cf are calculated for each 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 the 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 may 
 

69

consume prey over broad geographic ranges, their methylmercury concentrations likely 

are influenced by mercury deposition over broad geographic areas. We assume the 

methylmercury concentrations in these three types of fish to be in equilibrium with 

mercury deposited to the All Other Waters Region.29 Methylmercury concentrations in 

the commercial freshwater species, obtained 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 Coastal Region or the Gulf of Mexico Region. 

Thus, in the model, the methylmercury concentrations 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. commercial market that was caught in the Atlantic 

Ocean, the Gulf of Mexico, the Pacific Ocean, 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 important sources of methylmercury in these fish. 

Our model does not incorporate this possible source of local variability. 30 We note that aquaculture fish are not part of the wild foodchain. Protein 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 assumption that the methylmercury concentrations in 

the fish used in fish meal were in equilibrium with mercury deposited in all other waters. If fed fish meal or 

fish oils, the methylmercury concentrations 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 weights using product specific conversion 

factors (provided through personal communication by Steve Koplin, NMFS). We then 

summed the round weights for each product that contained a specific type of fish. 

There are other product codes that likely contain the types of fish 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 remainder 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 concentration 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 this population in the model. 

 

31 Implementation of this assumption decreases inter-individual variability in the model results. While 

some consumers may eat a mix of fish, others may consume a subset of the 24 species (e.g., some may 

consume only tuna or salmon). 
 

71

Equation 6 was used to estimate the alternative distribution of U.S. blood 

methylmercury concentrations among commercial seafood consumers; this estimate is 

the product of the ratio of the alternative and the current mean weighted methylmercury 

concentrations in commercial fish (Cmf/Cmc) and the distribution of the current 

methylmercury blood concentration in the U.S. population. Through an ad hoc

procedure, we determined that a gamma distribution (scale parameter = 1.28 and shape 

parameter = 0.65) provided a reasonable fit to the centile data reported by Mahaffey et 

al. 

Bf = Bc* ( Cmf

Cmc ) (Eq. 6) 

where: 

Cmf = weighted mean methylmercury concentration in U.S. 

commercial fish under alternative deposition scenario (μg/g) 

Cmc = current weighted mean methylmercury concentration in U.S. 

commercial fish (μg/g) 

Bf = blood methylmercury concentration 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. Hair mercury levels are predicted based on 

blood methylmercury concentrations (see Equation 1). 

Because the NHANES data reported by Mahaffey et al. are specific to U.S. 

females of reproductive age, we estimated male methylmercury exposures by adjusting 

inputs to the methylmercury one-compartment 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 “methylmercury 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 the mean methylmercury concentrations of the 

commercial 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: 

CBm = Male blood methylmercury concentration (μg/L) 

CBf = Female blood methylmercury concentration (μg/L) 

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

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

Vm = Male blood volume (L) 

Vf = Female blood volume (L) 

Bwm = Male body weight (kg) 

Bwf = Female body weight (kg) 
 

73

Using the parameter values reported in Table 17 implies 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 

Weighta (Kg)

Total Blood 

Volumea (L) 

Mean Reported Fish Intake 

(g/person/day) 

Uncooked Fish Weightb

(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 Through Consumption of Non-Commercial 

Seafood. 

U.S. residents are also exposed to methylmercury through consumption of noncommercial fish. We estimated 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 consumed 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 are 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 for commercial fish consumers, reduces the interindividual variability captured in this model. 
 

74

commercial marine fish and non-commercial fresh water fish because they would be 

obtained from two different regions. 

2.2.2.1. Non-commercial Consumers of Marine Fish ― 

The methylmercury intake for non-commercial consumers of marine fish caught 

in the Atlantic and Gulf was estimated in a manner similar to that of the commercial fish 

intake. Tables 4 and 5 list the mean methylmercury concentrations reported in Atlantic 

and Gulf fish as reported in U.S. EPA (2003c). We assumed these estimates of the 

mean concentrations to be representative of mean methylmercury concentrations in 

tissues of fish typically caught non-commercially in U.S. saltwaters and consumed. 

Because the EPA document does not identify the sizes or weights of the sampled fish, it 

is not possible to evaluate whether the reported methylmercury concentrations for the 

fish in this database are representative of fish consumed recreationally. Additionally, 

there are a small number of samples reported for several types of fish. Equations 4 and 

5 are used to estimate mean weighted methylmercury concentrations for current 

conditions and under alternative emissions scenarios. To apply these equations, the wi

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-commercial fishers (NMFS, 2002) in Tables 4 and 

5, we estimated a current weighted mean methylmercury concentration for noncommercial fish in the Atlantic 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 mean 
 

75

weighted methylmercury concentration under alternative emissions scenarios into 

Equation 8 (based on results of Equation 5) methylmercury intake rates (Ic) are 

estimated for consumers under these scenarios. This distribution of methylmercury 

intake rates was converted to mercury hair concentrations using the one-compartment 

pharmacokinetic model (see Equation 1). 

I (CR x Cmc ) / Bw c F nm = (Eq. 8) 

where: 

Ic = oral intake of methylmercury from current fish consumption 

(μg/kg-day) 

CRF = consumption rate of fish (g/day) 

Cmcnm = current weighted mean methylmercury concentration in noncommercial marine fish (μg/g) 

Bw = body weight (70 kg) 

2.2.2.2. Non-commercial Consumers of Freshwater Fish ― 

We estimate methylmercury intakes through consumption of non-commercial 

freshwater fish based on a consumption 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 methylmercury concentrations. We 

selected sample data that were identified in the database as fillets, skin-on fillets, skinoff fillets, and composite fillets (NLFWA Sample ID numbers 4, 5, 6, and 7). Based on 

the fish samples' State of origin, we categorized 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 the NLFWA database; consequently, we did not 

use that category. We also ignored the U.S. FWS categories of “anything” and “other 

type of fish.” Finally, we assumed that only fish above a certain length were retained for 

consumption following capture. Samples from fish shorter than the minimum length 

requirements prescribed by the State of Pennsylvania (2003) and samples where fish 

length was not reported were excluded (Table 18). (Comparison with several other 

State regulations indicates relatively consistent 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 that the fish tissue 

methylmercury concentrations reported in the 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 targeting particular types of fish. The survey did not

evaluate the types of freshwater fish consumed following capture. They report 12 categories of 

freshwater fish targeted for capture by freshwater anglers (listed in first row of Table 19). 
 

77

Table 18. Fish Size Restrictions Imposed on Model Data 

U.S. FWS Designation Minimum Lengtha

(inches) 

Examples of Types of Fish Included from 

NLFWA Database 

Crappie 5b Black and white crappie 

Panfish 5b 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 5b 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 for 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 methylmercury concentrations. For a sample 

size of n, the ith measurement was given a weight of 1/n when developing a distribution. 

Within each U.S. Region, targeting weights 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 fish in each State. For a given type of fish, 

we summed these products across all the States in the Region to develop target 

weights specific to each type of fish in each region.34 

 

34 This is parameter T in Equation 9.
78

Table 19. Percentage of Fishing Days Targeting Selected Species 

State Crappie Panfish 

White and 

Striped 

Bass 

Black Bass Catfish Walleye and Sauger Northern Pike, Pickerel and Muskie Steelhead Trout Salmon Anything Other Species 

AL 18 16 10 36 11 0 0 0 1 0 5 3 AZ 5 5 12 24 13 0 0 0 28 0 13 0 AR 22 9 13 21 22 0 0 0 3 0 5 4 CA 4 4 10 15 10 0 0 0 35 13 7 3 CO 5 2 3 7 9 2 4 0 60 3 5 0 CN 0 10 12 32 2 0 4 0 31 0 8 0 DE 10 10 15 29 9 0 0 0 8 0 19 0 FL 13 15 8 27 14 0 0 0 1 0 13 9 GA 15 15 13 18 23 0 0 0 4 0 7 3 ID 2 3 2 12 7 0 0 11 54 10 0 0 IL 15 20 9 15 23 6 0 0 4 0 9 0 IN 16 27 11 23 16 3 0 0 0 0 4 0 IA 17 15 9 15 18 15 5 0 3 0 3 0 KS 19 9 7 21 25 9 0 0 2 0 7 0 KY 20 13 13 24 19 4 0 0 1 0 6 0 LA 20 1 9 22 18 0 0 0 2 0 5 5 ME 0 5 7 24 0 0 9 0 34 16 4 1 MD 4 8 12 30 13 4 0 0 13 0 7 9 MA 0 6 12 36 4 0 6 0 23 0 11 2 MI 7 34 5 14 0 9 9 3 7 3 7 0 MN 16 20 2 9 1 32 18 0 2 0 1 0 MS 18 17 9 18 27 0 0 0 2 0 9 0 MO 21 15 8 24 22 0 0 0 5 0 3 1 MT 0 5 0 6 2 12 12 0 49 4 9 1 
79

Table 19 cont.

State Crappie Panfish 

White and 

Striped 

Bass 

Black Bass Catfish Walleye and Sauger Northern Pike, Pickerel and Muskie Steelhead Trout Salmon Anything Other Species 

NE 13 9 9 19 18 15 5 0 2 0 9 2 NV ND ND ND ND ND ND ND ND ND ND ND ND NH 0 8 3 28 0 0 7 0 32 5 14 2 NJ 0 13 10 36 6 0 7 0 16 0 4 8 NM 2 3 7 9 11 0 0 0 58 7 3 0 NY 3 11 3 28 4 10 10 2 19 4 7 0 NC 19 11 12 24 17 0 0 0 5 0 8 5 ND 3 16 0 2 2 52 18 0 1 1 5 1 OH 11 17 7 31 16 11 0 0 3 0 4 1 OK 20 6 8 31 20 4 0 0 2 0 9 0 OR 1 2 2 5 3 0 0 14 41 21 6 7 PA 5 7 10 25 7 7 3 0 26 0 9 0 RI 0 9 19 26 0 0 0 0 30 0 17 0 SC 16 15 9 25 22 0 0 0 2 0 8 3 SD 5 18 2 5 6 42 12 0 3 0 7 0 TN 17 15 10 27 15 6 0 0 7 0 4 0 TX 18 5 14 27 30 0 0 0 2 0 4 0 UT 0 4 3 11 4 1 0 0 68 0 6 3 VT 0 13 3 10 3 7 7 0 34 9 11 3 VA 14 11 14 30 14 0 0 0 10 0 7 0 WA 1 3 2 10 0 0 0 18 39 21 2 4 WV 7 9 6 28 17 0 0 0 22 0 11 0 WI 11 29 3 17 1 17 15 0 4 0 3 0 WY 0 0 0 0 0 1 0 0 78 0 0 3 

ND= No data reported from State
 

80

U.S. EPA (1997a) reports the mean and 95th percentile fish consumption rates 

for recreational freshwater anglers to be 8 and 25 g/day, respectively, based on 

integrating the data reported 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 that these data were lognormally distributed 

and calculated the standard deviation of these data to be 10.3 g/day. 

Some individuals and groups consume large quantities of fish. To estimate 

exposures among these high-end consumers, we used information from the Columbia 

River Inter-Tribal Fish Commission Report (1994). The Report describes consumption 

rates among a group of Native Americans who likely consume fish at the upper end of 

the distribution.35 The mean and 95th 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 product of the distribution of the weighted fish methylmercury 

concentrations and the distribution of the freshwater fish consumption rate (Equation 9). 

The change in mercury deposition in each region under alternative emissions was 

based on the unique distributions of the four ratios described 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 “subsistence” angler consumption (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) 

CRF = fish consumption rate (g/day) 

Ti = relative target frequency for ith fish type (unitless) 

Cik = methylmercury concentration in the kth randomly selected fish of the ith

fish type (μg/g) 

DfL = future mercury deposition rate of the Lth randomly selected grid cell 

(μg/m2/yr) 

DcL = current mercury deposition rate of the Lth randomly selected grid cell 

(μg/m2/yr) 

Bw = body weight (kg) 

n = n different types of non-commercial freshwater fish routinely targeted in 

a region 

In each iteration, a single REMSAD model output from a randomly selected grid cell in a 

region was used to estimate the ratio of the future to current mercury deposition rate 

(DfL/DcL). This ratio was multiplied by the fish methylmercury concentration of a 

randomly selected fish. We made no attempt to estimate the time needed for a change 

in deposition to result in a change in fish 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 region. The methylmercury content of each 
 

82

fish in the mix is obtained by randomly sampling fish methylmercury concentrations for 

each type of fish from a given region. These values are independent of fish 

consumption rates. Changes in methylmercury concentrations of fish are based on 

randomly sampling from the ratios developed 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 mercury concentrations using 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 concentration (μg mercury/L blood) of 0.25:1 

(U.S. EPA, 1997a, 2001a). 

2.2.2.3. Commercial Fish Consumption 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) developed estimates of total fish 

consumption and non-commercial fish consumption 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 Lake Ontario. From the initial sample, 

1,202 stated that they intended 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 consumed. In the Monte Carlo simulation 

model, non-commercial fish consumers were categorized based on the quantity of noncommercial fish they consumed each day. Total commercial fish intake in individuals 

that consumed less 0.6 g/day was estimated by subtracting the daily fish intake value 
 

83

from 8.8 grams/day, the daily intake of total seafood corresponding to the lowest 

category of recreational fish intake. Individuals consuming more than 39.8 grams of 

non-commercial fish per day were assumed 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 they 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, total commercial fish intake was 

estimated by subtracting the non-commercial intake from the average of the total fish 

intake values for the same stratum of the population reported by Connoly. For example, 

to predict commercial fish consumption among individuals that consumed between 0.6 

and 2.2 grams per day of non-commercial fish, 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 Rates 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 commercial fish. Both the U.S. FWS and 

NMFS have developed estimates of the number of recreational fishers in the Gulf of 

 

36 The value of 39.8 g non-commercial fish/day is the 99th 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, 1997a notes that anglers typically share their 

catch with 1.5 other people, thus, 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 there were 28.4 million freshwater anglers, 

based on their National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. 

Because of our interest in the source of the freshwater fish, rather than freshwater fish 

consumers’ residence state, we estimate the number of freshwater fish consumers in 

each Region as the product of the total population predicted to consume recreationallycaught fish and the percentage of days fishing (relative 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 multiplied the regional estimates 

by 2.5. We summed the total number of non-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 Bureau, 2004, based on a 5-year average). The 

U.S. population above the age of 39 years is at higher risk for adverse 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, respectively. There is no 

attempt to adjust this annual estimate to reflect possible demographic changes (e.g., 

changes in the fraction of women of child-bearing age or birth rate) over time (e.g., 2010 

or 2020). 

2.3. NEUROTOXICITY 

Changes in IQ associated with intrauterine methylmercury exposures were 

estimated using Equation 10. Cohen et al. (submitted) developed a slope estimate of 
 

86

-0.7 IQ points per 1 ppm increase in hair mercury concentration. Cohen et al. used a 

weighting procedure for the neurological 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 decrease of 0.5 IQ points on the 

Wechsler Intelligence Scale for Children-Revised 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 significant. At this time, there is not a single 

best estimate of the slope of this dose-response 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 this estimate approximates a central tendency 

value for the slope. Section 1.5.1 describes the range of neurodevelopmental delays. 

While IQ, a rather blunt neurological assessment tool, would correspond to some of the 

domains identified, other neurodevelopmental delays may not be captured through an 

IQ assessment. 

 IQD = -0.6 IQ points x Hgh (Eq. 10) 

where: 

IQD = IQ point lost given maternal mercury exposure 

Hgh = maternal hair mercury concentration (ppm) 

Next, we discuss the implications of a toxicity threshold for neurodevelopmental 

effects from methylmercury exposures. We identify a plausible surrogate value for a 

population toxicity threshold for methylmercury. The plausible threshold value is used in 
 

87

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 statement 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 methylmercury-related neurotoxicity within the range of 

exposures...” of the epidemiologic studies analyzed. 

2.3.1. Population Toxicity Threshold: Background. 

A toxicity threshold for a population implies the existence of a contaminant intake 

rate that is without risk of adverse health effects (i.e., “a safe dose” to which any person 

could be exposed without deleterious effects). The existence of a toxicity threshold 

implies the following functional form for estimating 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 function 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 could be developed using other assumptions 

regarding the slope of the dose-response relationship around the threshold. 
 

88

RfDs have served as surrogates for, or approximate values of, toxicity 

thresholds. The RfD is defined as “an estimate (with uncertainty spanning perhaps an 

order of magnitude) of a daily oral dose exposure to the 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 this definition, doses at the RfD are not 

necessarily without risk. Based on the formula for estimating the RfD (Equation 12), the 

RfD is an estimate of a No-Observed-Adverse-Effect Level (NOAEL) that would be 

observed in a well-conducted, chronic epidemiologic study in the most sensitive human 

population (Swartout et al., 1998), assuming that other relevant toxicologic data exist; 

under these conditions, each uncertainty factor (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 dose groups, the number of organisms on 

study, and study quality including identification of a biologically or statistically significant 

LOAEL in the study. The true value of a population toxicity threshold is likely between a 

study NOAEL and a LOAEL. A toxicity threshold must be below a LOAEL and it could 

be less than a study NOAEL for a sensitive individual (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 (UF) 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 NOAEL, and 5) an insufficient toxicologic database. 
 

89

2.3.2. Population Toxicity Threshold for Methylmercury Exposures. 

The U.S. EPA’s oral RfD for methylmercury is based on epidemiologic studies 

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

considered the continuous tests employed to evaluate the subjects’ 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 5th percentile of the general population. 

Doses corresponding to a 5% increase above background in the probability of having a 

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

BMD05. For combinations of tests and subgroups within the Faroese cohort, the values 

reported for BMD05 ranged from 41 to 393 ppb mercury in blood. A benchmark dose 

lower limit (BMDL05) (the lower 95% confidence limit of the BMD05) was calculated for 

each test in the three studies (listed in Table 2 of U.S. EPA, 2001a). The estimates of 

the values for the BMDL05 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 BMDL05 for reproductive and 

developmental toxicity data to approximate the NOAEL. 

EPA applied an intraspecies UF of 10 to the BMDL05 when developing the 

methylmercury RfD. The Agency cited inter-individual toxicokinetic variability 

associated with the relationship between the ingested dose and the cord blood mercury 

concentrations reported in the Faroes. The Agency also cited possible toxicodynamic 
 

90

variability and uncertainty, noting the high 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 estimate benefits alternatively assuming a 

population toxicity threshold for methylmercury does and does not exist. The data are 

insufficient to exclude either possibility. To date, there does not appear to be 

substantial toxicologic evidence of a biological population threshold, 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 likely extends from 0 μg/kg-day (i.e., no 

apparent threshold) to doses between the BMD05 and the BMDL05. EPA’s RfD (0.1 

μg/kg-day) is used as a surrogate based on convenience; we note that this value may 

be a conservative (smaller) estimate of a threshold, if one exists. 

Methylmercury is a neurodevelopmental toxicant. The threshold applies to 

maternal methylmercury intake during pregnancy and for a period of approximately 1.5 

to 3 months prior to conception. The half-life of methylmercury in human blood is 

reported to be 44 days (Clarkson, 1997); thus, exposures prior to conception could 

affect the developing fetus. 

 

38 Typically, noncancer environmental human health risk assessments use the hazard quotient (HQ). The 

HQ compares the dose (D) to the reference 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 inter-individual variability. An HQ of greater than 1 

implies that an individual is exposed above a ‘safe’ level, but does not predict risk or identify a threshold. 39 One component of the uncertainty factor used to estimate the EPA’s RfD was inter-individual 

toxicokinetic variability associated with the relationship 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 methylmercury concentrations in cord blood to maternal 

blood to be approximately 1.7. Because the EPA assumed that this ratio was 1, (i.e., maternal blood and 

cord blood methylmercury concentrations are the same), 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 possible neurotoxicity threshold in each 

population was estimated as the product of the number of births 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 methylmercury RfD (0.1 μg/kg day), we estimated the conditional 

daily mean methylmercury intake rate. This 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 estimate 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 cohort. To estimate the population impact, 

the average IQ changes per child were multiplied by the number of births occurring in a 

population to women whose methylmercury intake exceeded the threshold. 

2.4. DOSE-RESPONSE FOR ACUTE MYOCARDIAL INFARCTION (AMI) AND ALL 

CAUSE MORTALITY (ACM) 

The predicted myocardial risks associated with methylmercury exposures should 

be interpreted with caution. Most of the evidence of such risks is based on observations 
 

92

from a single cohort. Additionally, a great deal of evidence indicates that fish 

consumption in general protects individuals from incurring adverse cardiac events. 

If any of the three cardiotoxic modes of action for methylmercury 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 examine whether a doseresponse threshold exists for the effects evaluated. In Section 2.4.1, we develop doseresponse functions for increased incidence of myocardial infarction and all cause 

mortality. We use the regression coefficients estimated by Salonen et al. (1995) when 

they modeled risk using the mercury concentration 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 background rates of myocardial infarctions 

and deaths in the U.S. We also discuss the 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 regression coefficients observed in this cohort 

can be externally generalized to the U.S. population despite differences between these 

populations and the types of fish consumed. We note that, if there is an antagonistic 

relationship between methylmercury and polyunsaturated fatty acids then the slope 

likely would be less steep for consumers of fish containing higher levels of 

polyunsaturated fatty acids. We also assume that the myocardial events and the 
 

93

premature deaths observed in this Finnish 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 myocardial infarctions to be 1.068 per 1 ppm hair 

mercury. The Cox Proportional Hazards Model, presented in Equation 13, assumes 

that changes in levels of the independent 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 

ho = baseline hazard 

β = regression coefficient (ppm-1) 

Ch = exposure measure hair mercury concentration (ppm) 

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

RR h(t)

h exp( C )

0

= = β h (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 Ch = 1 ppm, then β = 0.066 ppm-1. 

We substitute RAMI for ho and y for h(t). For a given level of hair mercury, the 

incidence rate of acute myocardial infarctions (AMI) is calculated by the following 

equation: 

y R Xexp( C AMI h = β ) (Eq. 17) 

where: 

y = incidence rate (yr-1) 

RAMI = AMI background incidence rate (yr-1) 

Ch = 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 increase in hair mercury concentration. 

Using Equation 16, we estimate a value for β of 0.086 ppm-1, when Ch is 1. 

2.4.2. Applying the Dose-Response Functions. 

In this section, we discuss annual background rates of myocardial infarctions 

(RAMI) and death (RACM) in the U.S. Then, we discuss the application of the doseresponse models. 

To avoid double-counting deaths, we include fatal myocardial infarctions with all 

cause mortality, and report separately non-fatal myocardial infarctions. In 2001, the 

AHA (2004) estimated that there were 865,000 new and recurrent myocardial infarctions 

in the U.S. population. Given that these myocardial infarctions lead to 184,800 deaths 

in 2001, roughly 21% of all AMI resulted in death. Specifically, among males, there 

were estimated to be 520,000 new and recurrent myocardial infarctions and these 
 

95

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 infarctions and these resulted in 88,900 

deaths (25.77%). Thus, we estimate non-fatal 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 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, 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 occur annually in females occur only in this population 

group, then the annual background rate in females above 39 years of age is roughly 5 x 

10-3. 

In 2001, life expectancy, conditional on living to age 65 (males) and 70 (females) 

was 86 years and 81 years for U.S. females and males, respectively (Arias et al., 2003). 

If we assume that the age of death coincides with the average age of first myocardial 

infarct, then, on average, women and men would lose 16 years of life due to increased 

mortality associated with methylmercury. 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 cohort was less than 65 years. 
 

96

We apply these estimated annual risk coefficients in two ways. In the first 

approach, we limit the application of the non-fatal AMI and all cause mortality risk 

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

the discussion of the characteristics of the fish implicated in the diets of Finnish male 

cohort in the Salonen et al. (1995) study. Like the Finnish fish consumed in the study, 

northern pike are caught in freshwaters and, as Figures 5-7 illustrate, relative to other 

freshwater species, northern pike have low levels of DPA, DHA, and total 

polyunsaturated fatty acids and high concentrations 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.usda.gov/fnic/foodcomp/, accessed 6/04
 

97

Figure 6 

EPA and Methylmercury Levels 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 6/04
 

98

Figure 7 

Total Polyunsaturated Fatty Acid and Methylmercury 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

pollock, walleye 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: http://www.nal.usda.gov/fnic/foodcomp/, accessed 6/04

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

the fraction of the freshwater 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 the frequency at which pike were targeted 

corresponded to the share of the non-commercial freshwater fish-consuming population 

that consumes pike. Less than 10% of freshwater fishers report targeting 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-fatal 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 generalization 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 decrease in an infant’s IQ. Models have been 

developed to address the 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 monetized effects should underestimate society’s WTP. 

This section estimates the impact of small, permanent changes in IQ on lifetime 

earnings. This section has four parts. Section 2.5.1 estimates proportional impact of a 

1 point change in IQ on lifetime earnings. Section 2.5.2 quantifies 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 estimate 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 results serve as a conservative estimate 

(i.e., likely lower bound) of the total value individuals place on changes in IQ because 
 

100

they may also value such changes independently of their impact on lifetime earnings. 

Second, our application of the results from this analysis to the assessment of 

intrauterine methylmercury exposures makes the implicit assumption that the impact of 

methylmercury on IQ is permanent even though epidemiological evidence of such an 

impact has only been reported in children up to 7 years of age (Grandjean 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 similarly affected by changes in IQ. 

2.5.1. Proportional Impact of a One-Point Change 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 earnings with respect to IQ; dW/dIQ 

refers to the change in annual wage rate with respect to IQ; dP/dIQ refers to the change 

in workforce participation rate with respect to IQ.) 

 

40 We note that the recent report from the Faroe Islands cohort presents evidence of neurotoxicity as 

measured by delays in auditory evoked potentials, but does not report 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 rearranging the remaining terms in Equation (18) yields 

 



















= +

P

dIQ

dP

W

dIQ

dW

E

dIQ

dE (Eq. 19) 

Thus, the change in lifetime earnings equals 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 workforce 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 participation rates both directly and indirectly 

(Schwartz, 1994; Rowe et al., 1995; Salkever, 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 parameters to quantify 

these direct and indirect impacts (Table 22). 

Salkever estimated these values from a series of regression analyses using 

information obtained in the National Longitudinal Survey of Youth (NLSY, 1990) and 

data provided by Schwartz (1994). The NLSY data include annual earned income, 

educational attainment data, and results of intelligence tests that Salkever used as a 

proxy for IQ. The data are drawn from surveys administered to a random sample of 

U.S. residents. 
 

102

Figure 8 

Model for Relationship Between IQ and 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 IQS 0.1007 0.1007 

Workforce participation probability IQP 0.0016 0.0037 

Wages (proportional wage change) IQW 0.0124 0.014 

Direct impact of a 1 year of schooling change on: 

Workforce participation probability SP 0.0035 0.0282 

Wages (proportional wage change) SW 0.049 0.10 

IQ School 

Wages 

Participation 

IQs 

IQp

IQw

Sw

Sp
 

103

Salkever’s model consists of three equations. The first model equation combines 

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

labor force participation. In particular, P SSP IQ IQ P

dIQ

dP

= + , where the first term on the 

right side of the equation represents 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 [Sp]). Similarly, IQW IQSSW W

dIQ

dW

= + .

41 Substituting the right side 

of these two equations into Equation (19) yields 

 [ ] ( )( ) W S W P SSP E IQ IQ S IQ IQ 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) estimated 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. Grosse (Appendix I in Haddix et al., 2003) 

estimated average lifetime earnings for each age and gender group. In his analysis, 

earnings were comprised of two broad 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 IQW 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 that the average person worked 

250 days per year. Household production included a number of activities such as 

cleaning, cooking, and child care, for which individuals are typically 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 earnings over a lifetime of 

an infant was $692,000 (2000$) (the term 2000$ refers 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 lifetime 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 household production in the estimate. Previously, 

Rowe et al. (1995) and U.S. EPA (1997b) developed estimates of lifetime earnings. 

These estimates differ from those developed 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 product 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, Schwartz and Salkever 

included a term in their models representing the impact of that metal’s toxicity on 
 

105

educational attainment independent of the impact mediated by changes in IQ. We 

omitted this term from our model because there is no evidence suggesting that prenatal 

methylmercury exposures reduce educational attainment independent of IQ. 

2.5.4. Remedial Education Costs and Shifts in the Population 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 individuals 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 incremental educational cost and the 

incremental probability of having an IQ <70. 

2.5.4.1. Incremental Probability of IQ <70 ― 

To estimate this incremental probability, 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 expressed in terms of the 

standard normal deviate Z, which is normally distributed with mean zero and standard 

deviation one. In particular, 





 



 − < = <

σ

70 μ P(IQ 70) P Z (Eq. 21) 

 

42 A shift in the IQ of a population would also decrease 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 selected 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 Costs 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 arrive at an annual cost of $6,318 (1990$). The GNP 

deflator does not adjust costs based on a basket 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 value of the compensatory education is $77,000 

(2000$).

2.5.4.3. Cost of a One Point Shift in Mean IQ ― 

Multiplying the incremental probability that IQ will be less than 70 (0.38%) 

resulting from a 1 point drop in mean IQ and the present value at birth for compensatory 
 

107

educational costs yields a cost per IQ point of $290. We note that this cost is small 

(roughly 2% of the total) compared to the 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 exposures. For example, QALYs are utility functions that 

may describe individual preferences for longevity and health-related quality of life 

associated with either temporary 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 represents a perfect health 

state and 0 represents a health state 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 individual’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; Pliskin et al., 1980). In Sections 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 Zealand and Faroe Islands studies and we 

describe an exposure-response function for the health-related quality of life measures. 
 

108

2.6.1. Utility Weight for Health-Related Quality of Life Associated with 

Neurodevelopmental Toxicity. 

In this section, we examine the appropriate utility weight for neurological changes 

associated with methylmercury exposures. In the discussion of the New Zealand and 

Faroe Islands studies, we noted that the investigators reported no differences in the 

exposed and reference groups based on “observed” behaviors (i.e., in the absence of 

sophisticated neurological testing, the groups were indistinguishable). In these studies 

of environmentally relevant methylmercury 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 literature review, we found 

no data that describe utility weights for the subtle neurologic decrements reported for 

methylmercury. 

Several studies examined utility weights associated with cognitive decrements. 

Ponce et al. (2000) used QALYs to evaluate 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 assigned 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 could 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 cognitive decrements. Tables 23 and 24 

describe the cognitive decrements associated health-related quality of life and the 

corresponding utility weights. We note that the descriptions of the health-related quality 
 

109

of life for the neurocognitive decrements in these two studies suggest impacts that are 

substantially more severe than those reported 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 results of a study that has not been conducted, 

we assume that in such a study the decrement in the utility weight for the neurological 

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

than 0.01. 

Table 23. Description of Cognitive Decrement 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 slowly and usually requires special 

education 

0.88 
 

110

Table 24. Description of Cognitive Decrement 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 Function for Health-Related Quality 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 lives. We assume these IQ losses to cause 

similar neurodevelopmental effects that would be valued at no more than a utility weight 

decrement of 0.01. We assume 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 subsections. Section 2.7.1 discusses the costs 

of lost productivity and medical costs associated with a non-fatal and fatal myocardial 

infarct. Section 2.7.2 discusses the value of a statistical life, an estimate of a group’s 
 

111

willingness-to-pay to reduce the risk of death. Section 2.7.3 discusses the utility 

weights associated with non-fatal and fatal myocardial infarctions. 

2.7.1. Cost-Of-Illness Estimates For Myocardial Infarctions and All Cause 

mortality. 

 Then a non-fatal myocardial infarct occurs, individuals and societies incur 

medical costs for both urgent and continuing treatment and costs associated with lost 

productivity while the individual convalesces. Blake et al. (2003), citing an Appendix in 

Gold et al. (1996), estimate the lifetime costs 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 hospitalization, physician costs, procedural costs, and 

annual follow-up costs. As an analytic simplification, we assume that the myocardial 

infarct occurs at ages 65 and 70 in males and females, 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 who survive a myocardial infarct are unable to 

perform these tasks for 6 weeks. Thus, we estimate the lost productivity associated 

with a non-fatal myocardial infarct to be $6100 (2000$). For cases of nonfatal 

myocardial infarctions, we sum the medical and lost productivity costs. 

 As a simplification, we assume that the men and women, who are predicted to 

die in the analysis due to methylmercury 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 years and women aged 70 years, Grosse 

estimated the present value of earnings and household production, discounted at 3%, to 

be $274,000 and $151,000 (2000$), respectively. We add the medical costs associated 

with the nonfatal cases (likely an overestimate 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 individual’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 the Agency’s standard approach to 

estimating a dollar value of mortality benefits 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 Quality 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 assume this decrement of 0.1 to the affected 

 

43The 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 the 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 typical life expectancy in the 

U.S., males predicted to die of all cause mortality lose 16 QALYs and females lose 16 

QALYs. 

2.8. SENSITIVITY ANALYSES 

Finally, we developed four sensitivity 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 are 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 advisories; 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 distribution of methylmercury intakes among 

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

of fish. We note that recreational anglers may target specific species for consumption 

and may not consume a representative mix of non-commercial species. Thus, some 

consumers targeting specific types of fish may have methylmercury intakes well above 

the average. In this sensitivity 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 was predicted in Scenario 1 on methylmercury intakes in 
 

114

freshwater fish consumers in a single region. The Northeast Region was selected for 

the example. Doubling the decrease might account for a response in fish that is greater 

than linear with respect to mercury deposition or possible underprediction of the 

deposition change associated with the REMSAD modeling. Halving the predicted 

decrease might account for a less than linear 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 exposure in the case where a threshold exists. We 

estimated a slope of -0.6 IQ points per 1 ppm increase 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 there is a threshold below which maternal 

methylmercury exposures have no effect on the developing fetal nervous system, the 

slope of the exposure-response function between the threshold and the maternal hair 

mercury concentration where responses are observed must be larger than if there is no 

threshold. To evaluate the sensitivity of our results to this possibility, we note that a 

relatively low level of maternal hair mercury at which effects may be observed is 

approximately 3 ppm. At this level, the estimated 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 1 
 

115

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 region, Section 3.1 describes the predicted 

current mercury deposition rates and the predicted changes in annual mercury 

deposition rates associated with the alternative emissions. Section 3.2 quantifies both 

the current and predicted changes in fish methylmercury concentrations. Section 3.3 

describes both the current and the estimated changes in human exposures that are 

predicted to result from the alternative emissions 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 QALY 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 approaches 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 DEPOSITION RATES IN THE 8 REGIONS

The alternative emissions scenarios were predicted to result in decreased 

mercury deposition in each Region. For the All Other Waters Region, the Coastal 

Atlantic Region, and the Gulf of Mexico Region, the predicted changes in annual 

mercury deposition rates are summarized in Table 25. Table 26 summarizes the 

decreased deposition rates in the five freshwater 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, Atlantic 
 

117

Coastal and Gulf of Mexico Regions are relatively small (roughly 1%). The reported 

standard deviations indicate predicted variability in deposition across grid cells within a 

region. These estimates of the spread of the predicted changes 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 Mexico Region, and the All Other Waters 

Region Under CSI 

Coastal Atlantic 

Ocean Gulf of Mexico All Other Waters 

Current Deposition 

Rate (μg/m2/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 are 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/m2/yr (199 Receptors) 

Average Decrease 9% 12% 9% 13% 

Standard deviation 9% 9% 9% 9% 

MidAtlantic Current deposition rate: 14.1 μg/m2/yr (201 Receptors) 

Average Decrease 22% 31% 24% 34% 

Standard deviation 12% 12% 12% 12% 

Southeast Current deposition rate: 10.2 μg/m2/yr (661 Receptors) 

Average Decrease 17% 20% 18% 24% 

Standard deviation 12% 12% 13% 12% 

Midwest Current deposition rate: 12.5 μg/m2/yr (841 Receptors) 

Average Decrease 9% 12% 9% 14% 

Standard deviation 7% 9% 8% 10% 

West Current deposition rate: 6.5 μg/m2/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 mean methylmercury concentrations in each of the 24 types 

of fish (Figure 2) by the per capita consumption 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 contribution of each commercial fish type to total 

methylmercury in the U.S. market. The figure shows that, based on the product of the 
 

119

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.
120

5%0%

40%35%30%25%20%15%10%

Tuna-canned Pollock Sh mri pCod Hal bu i t Swo dr fish Salmon Catfish Lobster A- merican C ar bs-Blue Flatfish RockfishShark Crabs-Snow Oysters Lobster-Spiney Scallops C ams l Crabs-Dungeness Sable fish Sardines Perch-Ocean Crabs-King Crawfish

Fish 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 9 

For "Top 24" Types of Fish in U.S. Commercial Seafood Market, the Percentage of 

Methylmercury Contributed by Fish Type 
 

121

 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 scenarios. 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 harvested from each of the four source waters, (i.e., the Coastal 

Atlantic Ocean, the Gulf of Mexico, All Other Waters and Aquaculture) and the predicted 

change in mercury deposition in each region (Table 25). 

Table 27. Weighted Mean Methylmercury 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 predicted 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, shrimp, cod, halibut, 

and swordfish. (For example, based on methylmercury levels in tuna and the per capita 

consumption rate estimates, tuna accounts for 33% of per capita exposure.) For these 

six types of fish, Figure 10 shows the fractional contributions of the total catch from 

different bodies of water to total marketed quantities (NMFS, 2003).
122

0%

90%80%70%60%50%40%30%20%10%

100%

Tuna-canned 1) (Pollock ShrimpCod Halibut Swordfish (1) Salmon Catfish (2) Lobster-American Crabs-Blue Flatfish Rockfish Shark (1) Crabs-Snow Oysters Lobster-Spiney ScallopsClams Crabs-Dungeness Sable fish Sardines Perch-Ocean Crabs-King Crawfish (2)

Percent of Total Harvest by Water Body Gulf Atlantic

(1) Migratory Species. Tuna, shark and swordfish are migratory species. The regions from which these fish were captured were assumed to not necessarily 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)

2

2

Figure 10 

Percent Contribution of the Atlantic Ocean and Gulf of Mexico Harvests to U.S. Commercial Market by Fish Type 
 

123

 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 atmospheric mixing that likely occurs over 

the distance that U.S. mercury emissions must be transported 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 methylmercury levels is also likely proportional to 

the power plants’ contribution to global atmospheric 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, are 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 from the All Other Waters Region. The 

remaining 30% are harvested primarily from the U.S. continental 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 the methylmercury concentrations of most 

commercial fish result from the small influence of U.S. emissions sources on the 

methylmercury concentrations in our model; specifically, the top five sources of 

methylmercury in the diets of commercial seafood consumers per capita are likely 

influenced very little by U.S. mercury emissions. Figure 10 shows that, of the top 24 

types of fish in the U.S. market, only clams and scallops are primarily harvested from 

 

44 Highly migratory species may consume prey over 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 concentrations 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 sites distant from U.S. shores and in the Pacific Ocean. 

Consequently, U.S. mercury emissions reductions exert small changes in the 

methylmercury concentrations of most commercial fish and, therefore, small changes in 

U.S. methylmercury exposures. 

For non-commercial marine fish, we again assumed that consumers ate a mix of 

non-commercial marine fish obtained from either the Atlantic or Gulf. The specific mix 

was weighted by the estimated harvest weights 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 Table 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 decrease 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 Region (less than 1% under both scenarios) (see Table 25). The 

assumption that each consumer eats a mix 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 concentration (μg/g) 0.26 0.39 

Scenario 1 Fish Methylmercury concentration (μg/g) 0.26 0.38 

Baseline 2 Fish Methylmercury concentration (μg/g) 0.26 0.39 

Scenario 2 Fish Methylmercury concentration (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 methylmercury 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 methylmercury concentrations 

of fish in different regions. Tables 29-33 also identify the assumed consumption 

frequency for each type of fish in each region; 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 developing the intake mix for individual Monte 

Carlo iterations in the simulation. The assumption 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).
126

Table 29. Northeastern Fish Consumption Data *Consumption frequency refers to the relative targeting frequency for region. This was used as a surrogate for consumption frequency. 

Statistic Crappie Panfish 

White and 

Striped 

Bass 

Black 

Bass Catfish 

Northern Pike, 

Pickerel and 

Muskie 

Trout Salmon

Consumption Frequency* (unitless) 0.02 0.11 0.07 0.33 0.04 0.10 0.29 0.06 Sample Size 3 1131 121 755 237 34 173 8 Mean MeHg Concentration (ppm) 0.38 0.37 0.35 0.57 0.17 0.66 0.31 0.22 Standard Deviation MeHg Concentration (ppm) 0.18 0.28 0.22 0.35 0.17 0.37 0.24 0.20 Mean Length (inches) 10.03 8.08 24.61 14.58 9.84 18.42 16.97 12.56 Std. Dev. Length (inches) 1.48 1.78 5.32 1.88 2.12 3.63 5.90 5.57 
127

Table 30. MidAtlantic Fish Consumption Summary Data 

Statistic Crappie Panfish 

White and 

Striped 

Bass 

Black 

Bass Catfish 

Walleye 

and 

Sauger 

Northern Pike, 

Pickerel and 

Muskie 

Trout 

Consumption Frequency* (unitless) 0.07 0.10 0.12 0.32 0.12 0.04 0.03 0.21 Sample Size 25 20 190 161 186 5 49 17 Mean MeHg Concentration (ppm) 0.14 0.14 0.18 0.69 0.14 0.10 0.80 0.22 Std. Dev. Concentration (ppm) 0.08 0.13 0.21 1.03 0.20 0.02 0.67 0.27 Mean Length (inches) 7.68 6.18 25.19 14.68 15.78 14.73 18.86 16.84 Std. Dev. Length (inches) 1.82 0.54 6.34 1.80 4.12 2.32 3.12 5.42 

*Consumption frequency refers to the relative targeting frequency for region. This was used as a surrogate for consumption frequency. 
128

Table 31. Southeastern Fish Consumption Data *Consumption frequency refers to the relative targeting frequency for region. This was used as a surrogate for consumption frequency. 

Statistic Crappie Panfish White and Striped Bass Black Bass Catfish Walleye and Sauger Trout 

Consumption Frequency* (unitless) 0.19 0.16 0.12 0.28 0.20 0.01 0.04 Sample Size 952 1914 99 5431 1335 14 43 Mean MeHg Concentration (ppm) 0.26 0.25 0.21 0.62 0.29 0.28 0.04 Std. Dev. MeHg Concentration (ppm) 0.32 0.24 0.17 0.53 0.37 0.22 0.04 Mean Length (inches) 11.55 7.94 18.64 15.42 15.99 18.48 9.11 Std. Dev. Length (inches) 8.90 3.43 4.70 5.18 8.49 2.34 2.48 
129

Table 32. Midwest Fish Consumption Summary Data 

 Crappie Panfish

White and 

Striped 

Bass 

Black 

Bass Catfish 

Walleye

and 

Sauger 

Northerns, 

Pickerel and 

Muskie 

Trout Salmon

Consumption Frequency* (unitless) 0.15 0.23 0.07 0.19 0.11 0.14 0.07 0.03 < 0.01 

Sample Size 905 1824 100 1446 617 5163 2448 1404 322 

Mean MeHg (ppm) 0.17 0.15 0.25 0.35 0.22 0.45 0.50 0.24 0.10 Std. Dev. MeHg conc(ppm) 0.12 0.12 0.17 0.21 0.36 0.34 0.37 0.22 0.09 Length (inches) 10.76 7.84 13.57 14.58 15.91 19.30 28.17 20.24 20.88 Std. Dev. (inches) 17.72 1.51 1.18 1.84 5.44 3.12 3.37 5.78 4.82 

*Consumption frequency refers to the relative targeting frequency for region. This was used as a surrogate for consumption frequency. 
130

Table 33. Western Fish Consumption Summary Data 

*Consumption frequency refers to the relative targeting frequency for region. This was used as a surrogate for consumption frequency. 

 Crappie Panfish

White and 

Striped 

Bass 

Black 

Bass Catfish 

Walleye 

and 

Sauger 

Northern Pike, 

Pickerel and 

Muskie 

Trout Salmon

 Consumption Frequency*  (unitless) 0.10 0.05 0.08 0.18 0.16 0.05 0.02 0.30 0.07  Sample Size 153 132 105 353 461 122 48 236 338  Mean MeHg Concentration  (ppm) 0.32 0.30 0.41 0.45 0.25 0.67 0.46 0.21 0.12  Standard Deviation  MeHg Concentration (ppm) 0.24 0.25 0.29 0.38 0.21 0.59 0.26 0.35 0.22  Mean Length (inches) 9.60 7.80 16.26 14.54 16.03 20.11 31.06 12.45 22.43  Std. Dev. Length (inches) 2.53 1.88 3.91 2.04 4.62 4.34 4.74 4.47 6.31 
131

Table 34. Estimated Population Sizes 

Estimated 

Angler 

Population 

Estimated Noncommercial Fish 

Consumer 

Population 

Estimated 

Women of 

Child-bearing 

Age 

Estimated 

Annual 

Childbirths 

Men >39 

Years of Age 

Women >39 

Years of Age 

Atlantic 5,012,000 10,024,000 2,205,000 137,000 1,983,000 2,269,000 Gulf 2,897,000 5,793,000 1,274,000 79,000 1,146,000 1,311,000 Northeast 2,471,000 4,942,000 1,087,000 67,000 978,000 1,119,000 Mid-Atlantic 206,000 411,000 90,000 6,000 81,000 93,000 Southeast 7,376,000 14,753,000 3,246,000 201,000 2,919,000 3,340,000 Midwest 10,164,000 20,329,000 4,472,000 277,000 4,022,000 4,602,000 West 8,282,000 16,565,000 3,644,000 226,000 3,277,000 3,750,000 General Population (Total Population = 281,421,906) NA NA 45,893,000 2,845,000 41,274,000 47,221,000 Total 36,409,000 72,817,000 61,913,000 3,839,000 55,681,000 63,705,000 

NA=not applicable 
 

132

3.3. CHANGES IN METHYLMERCURY EXPOSURES AMONG FISH CONSUMERS 

IN THE 8 REGIONS 

3.3.1. Population Data. 

Table 34 summarizes the estimated sizes of populations consuming the fish 

caught in each region. The second column identifies 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 consumes no fish or only commercial fish is 

estimated by subtracting the non-commercial fish consuming population (72.8 million) 

from the total U.S. population (281 million). We assume that the population 

demographics of non-commercial fish consumers in each region reflect those of the 

U.S. population. Specifically, we assumed that 22% of the consumers are women of 

child-bearing age and that roughly 6% of these women give birth each year. We also 

assume that roughly 20% of the individuals in the populations are males over 39 years 

of age and 23% are females over 39 years 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 intake 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 that 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 methylmercury concentration of the 24 types of fish 

reported by Carrington and Bolger is 0.116 μg/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 μg/kg-day per capita. Based on Equation 1, the corresponding 

average blood methylmercury concentration is estimated to be 1.56 μg/L. The mean of 

the distribution used to fit the Mahaffey data was 1.64 μg/L. Because our calculated 

mean closely approximates the observed U.S. blood methylmercury concentrations 

reported by Mahaffey et al., it provides support for the use of the predicted changes in 

the weighted mean methylmercury concentrations of commercial fish to predict the 

associated changes in the distribution of the blood methylmercury concentration in the 

U.S. population. 
 

134

Table 35. Predicted Tissue Methylmercury 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 Maleb 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 50th, 75th, 90th and 95th percentile values were 

0.6, 1.7, 4.4 and 6.7 μg/L, respectively. See Table 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., tissue methylmercury levels currently exceed the value that 

corresponds to the methylmercury RfD in roughly 8% of U.S. females of reproductive 

age. Based on the data in Table 17, current U.S. male blood methylmercury 

concentrations are estimated to be 4.3% lower than current levels in females. Using the 
 

135

methylmercury 1-compartment pharmacokinetic model, hair mercury levels were also 

estimated based on the blood methylmercury concentrations. 

In our model, decreases in U.S. emissions resulted in small changes in 

methylmercury exposures. Table 35 shows that, when compared with estimates for 

Baseline 1 and Baseline 2, female blood methylmercury 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 population exposed above the RfD was predicted to 

decrease by less than 0.5% and the conditional mean blood methylmercury 

concentration in this group is predicted to decrease by less than 0.05 μg/L. 

3.3.3. Predicted Exposures in Consumers 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 Intake 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 commercial fish is estimated to contribute 

approximately 64% and 55%, respectively, to predicted methylmercury intakes among 

consumers of non-commercial Atlantic and Gulf fish. The fractional 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 Atlantic and Gulf populations is predicted to 

exceed the RfD in roughly 6% and 14% of these consumers, respectively. The high 

percentage of the Gulf population that exceeds the RfD is a consequence of both the 

higher rates of non-commercial fish consumption (relative to non-commercial Atlantic 

consumers as described in U.S. EPA, 1997a) and the higher concentration of 

methylmercury in the tissues of non-commercial 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 marine fish consumers eat a mix of noncommercial species. Specifically, consumers who primarily eat species with 

methylmercury levels above the estimated weighted mean will have higher 

methylmercury exposures than the consumers that eat the mix of non-commercial fish 

assumed in the analysis. Conversely, others may eat a mix of fish that have a lower 

methylmercury concentration than the weighted 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 the 
 

138

RfD also declines. The fractional contribution of methylmercury from commercial fish 

consumption also is predicted to increase under the alternative emissions. This is a 

consequence of larger decreases in non-commercial fish methylmercury concentrations 

than commercial fish concentrations under these alternatives. Table 25 shows that 

under the emissions cases considered (i.e., the two baselines and the two alternative 

scenarios), the predicted decreases in mercury deposition to the Gulf and the Coastal 

Atlantic exceed the predicted change to the All Other Waters Region. 

In the Gulf population, estimated 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 increases under the alternative emissions. 

However, the predicted decrease in methylmercury 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 concentrations when compared to those in the 

Atlantic fish. Also, non-commercial Gulf consumers eat higher quantities of noncommercial fish than their Atlantic counterparts. 

3.3.4. Predicted Exposures in Consumers 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 simulated. 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 reflects per capita consumption rates. 
 

139

Table 38. Predicted Methylmercury Intake 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 Intake 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 Intake 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 Intake 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 Intake 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 consumers, methylmercury from commercial 

fish is estimated to contribute roughly 50% or more to predicted current methylmercury 

intakes among these consumers. The fractional contribution of commercial fish is 

higher among consumers of less contaminated freshwater fish (e.g., Midwest or West). 

When compared to consumers of non-commercial Atlantic fish, a greater 

percentage of non-commercial freshwater consumers 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 exceed the RfD currently. We note that the 
 

142

assumption that non-commercial consumers eat a mix of non-commercial fish likely 

under estimates the variability in population exposures. 

Under the alternative mercury emissions modeled, estimated methylmercury 

intakes in each of these five populations declines and the fraction of the populations that 

is exposed above the RfD also declines. The fractional contribution of methylmercury 

from commercial fish consumption also increases under the alternative emissions. This 

is a consequence of larger decreases in the methylmercury concentrations of the noncommercial fish, particularly in the Mid-Atlantic and Southeast Regions. Decreases in 

the Western Regions are relatively small compared to the other regions. Again, the 

predicted decrease in the methylmercury concentrations of commercial fish is smaller 

than predicted decreases in the non-commercial 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 survey conducted by the Columbia River InterTribal Fish Commission (1994) as a surrogate for this population. For current 

exposures and all emissions reduction scenarios considered, methylmercury exposures 

in most consumers with these rates of fish consumption are predicted 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, because we found no suitable data to prepare such 

an estimate. Additional details 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% 0.341 
 

144

3.4. ESTIMATES OF IN UTERO NEUROTOXICITY RISKS 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 cohort 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 Section 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 losses 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 Impacts Using a No Threshold Model ― 

Current methylmercury intakes in pregnant consumers of commercial and noncommercial fish in each regional population and in the general population cause IQ 

decrements (Tables 44-51). Assuming no threshold, the model predicts that currently, 

the mean IQ point losses per child range from 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 the total IQ point loss attributable to in utero

methylmercury exposures in current annual birth cohorts. Figure 11 shows the 

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

Tables 44-51 also show the estimated costs associated with these 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 considered valid welfare measures. Willingness-topay measures for IQ losses were not available, but would likely be significantly greater 

than the cost-of-illness estimate. 

Table 44. Predicted Mean Hair Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and 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 Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Commercial 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 Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Commercial 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 Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Commercial 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 Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Commercial 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 Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Commercial 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 Methylmercury Concentrations, Mean IQ point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Non-Commercial 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 Methylmercury Concentrations, Mean IQ Point 

Loss, IQ Losses in Annual Birth Cohort, and Estimated Monetary Value (Cost-ofIllness) (2000$) in Consumers of Commercial 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 $98,659,000
 

152

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-Commercial Fish in Each Region 

and Commercial Fish to Total IQ Point Loss, Assuming No Neurotoxicity 

Threshold 
 

153

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 Baseline 1 173,000 $2,897,000,000 

Total Population Scenario 1 168,000 $2,821,000,000 

Total Population Baseline 2 170,000 $2,862,000,000 

Total Population Baseline 2 163,000 $2,743,000,000 
 

154

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 Methylmercury Impacts Using a Model Threshold of 0.1 μg/kg-day

The estimated current methylmercury intake rates of some pregnant consumers 

of commercial and non-commercial fish in each 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 population 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 fish and Table 61 shows that the mean IQ 

point loss per child among affected children in the population that consumes only 
 

155

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 methylmercury exposures of these children 

are higher than the mean of the 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 estimated to lose roughly 62% of the total IQ point 

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

Tables 54-61 show the estimated costs associated with these IQ decrements. 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 estimated when using the non-threshold model 

(Table 52). Under the threshold assumption, these costs are borne exclusively by 

approximately 9% of the babies born in each annual birth cohort. 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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of NonCommercial Atlantic Ocean 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 

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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers NonCommercial Gulf 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 

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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (2000$) in Consumers of NonCommercial Mid-Atlantic 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 

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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (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 loss, IQ Losses per Annual Birth Cohort, and 

Estimated Monetary Value (Cost-of-Illness) (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 loss, 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 Associated with IQ Point Gains 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 are compared to those of Baseline 2. Based on the model 

results, under Scenarios 1 and 2 methylmercury 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, respectively. They decrease from 0.301 to 0.298 

between Baseline 1 and Scenario 1 and from 0.299 to 0.295 between Baseline 2 and 

Scenario 2. The decrease in IQ points lost is predicted to be highest among noncommercial fish consumers (except in the West) and lowest among commercial 

consumers. Table 52 summarizes the estimated IQ points lost per annual birth cohort 

in the entire population for each emissions scenario. Rows 2 and 3 of Table 53 show 

that the predicted incremental gain in average IQ between Baseline1 and Scenario1 is 

associated with a societal benefit of approximately $194 million and the societal benefit 

associated with the predicted incremental gain in average IQ between Baseline 2 and 

Scenario 2 is $288 million. (See last two rows 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 predicted benefit is predicted to occur in the 

populations that consume Southeast or Midwestern non-commercial fish. 

3.4.2.2. Averted Costs Associated with IQ Point Gains per Birth Cohort Under 

Alternative Emissions Scenarios 1 and 2 Using Model Threshold of 0.1 μg/kg day 

The estimated current methylmercury intake 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 Scenarios 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 individuals in each population; however, 

methylmercury exposures will continue to cause 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 are 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 decrease roughly 0.015 IQ points. Table 53 

shows that the incremental gain in mean IQ in each annual birth cohort between 

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

million. The societal benefit associated with mean IQ gain between Baseline 2 and 

Scenario 2 is $119 million. Most of the societal benefit is associated with the decreased 

IQ point loss in the population that consumes 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 commercial fish or no fish) are predicted 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 ACUTE 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 coefficients 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 assume 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-consuming populations in each of four freshwater 

regions.48 In this part of the analysis we assumed that only males that consumed pike 

were affected by the cardiovascular risk. The mean daily current methylmercury intakes 

range from approximately 0.05 to 0.07 μg/kg-day. The blood and hair concentrations 

are estimated using the methylmercury one compartment model (Equation 1). 

The last two columns in Table 62 list the predicted annual changes in the number 

of cases of nonfatal acute myocardial infarctions and premature deaths due to all cause 

mortality, when comparing Baseline 1 to Scenario 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. Given the small sizes of the populations and the 

modest predicted changes in mean methylmercury intakes, annually, 4.5 cases of nonfatal acute myocardial infarctions and eight premature deaths are predicted to be 

avoided through implementing Scenario 1 in this population. Under Scenario 2, 

annually, we predict eight cases of non-fatal acute myocardial infarctions and 14.6 

premature deaths to be avoided. Table 63 lists the annual avoided costs associated 

with these outcomes. If the VSL estimate is used for premature 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 may 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 targeted 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.
165

Table 62. Distribution of Predicted Annual Cases of Acute Myocardial Infarction (AMI) and Premature Deaths 

(ACM) in Male Northern Pike Consumers 

Mean 

Daily 

Intake 

Mean Blood 

Concentration 

Mean Hair 

Concentration

Incidence 

Rate AMI 

Incidence 

Rate ACM Population

NonFatal AMI 

Cases 

Premature

Deaths 

Non-Fatal 

AMI 

Avoided 

Premature

Deaths 

Avoided 

Northeast Current 0.075 4.05 1.01 9.983E-03 1.123E-02 98,000 796 1,098 Northeast Baseline1 0.067 3.61 0.90 9.911E-03 1.112E-02 98,000 790 1,088 Northeast Scenario 1 0.066 3.55 0.89 9.902E-03 1.111E-02 98,000 790 1,086 0.7 1.3 Northeast Baseline2 0.069 3.69 0.92 9.924E-03 1.114E-02 98,000 791 1,089 Northeast Scenario 2 0.066 3.53 0.88 9.898E-03 1.110E-02 98,000 789 1,086 2.0 3.7 MidAtlantic Current 0.092 4.93 1.23 1.013E-02 1.144E-02 2,000 17 23 MidAtlantic Baseline 1 0.071 3.83 0.96 9.948E-03 1.118E-02 2,000 17 23 MidAtlantic Scenario 1 0.063 3.40 0.85 9.877E-03 1.107E-02 2,000 16 23 0.1 0.2 MidAtlantic Baseline 2 0.070 3.78 0.95 9.939E-03 1.116E-02 2,000 17 23 MidAtlantic Scenario 2 0.060 3.25 0.81 9.852E-03 1.104E-02 2,000 16 23 0.1 0.3 Midwest Current 0.057 3.05 0.76 9.820E-03 1.099E-02 292,000 2,340 3,211 Midwest Baseline 1 0.052 2.78 0.70 9.777E-03 1.093E-02 292,000 2,330 3,193 
166

Table 62 cont.

Mean 

Daily 

Intake 

Mean Blood 

Concentration 

Mean Hair 

Concentration

Incidence 

Rate AMI 

Incidence 

Rate ACM Population

NonFatal AMI 

Cases 

Premature

Deaths 

Non-Fatal 

AMI 

Avoided 

Premature

Deaths 

Avoided 

Midwest Scenario 1 0.050 2.69 0.67 9.762E-03 1.090E-02 292,000 2,326 3,186 3.6 6.4 Midwest Baseline 2 0.052 2.78 0.69 9.776E-03 1.093E-02 292,000 2,330 3,192 Midwest Scenario 2 0.049 2.63 0.66 9.752E-03 1.089E-02 292,000 2,324 3,182 5.7 10.2 West Current 0.052 2.80 0.70 9.779E-03 1.093E-02 54,000 427 586 West Baseline 1 0.050 2.71 0.68 9.766E-03 1.091E-02 54,000 427 585 West Scenario 1 0.050 2.69 0.67 9.763E-03 1.091E-02 54,000 427 584 0.1 0.2 West Baseline 2 0.050 2.71 0.68 9.766E-03 1.091E-02 54,000 427 585 West Scenario 2 0.050 2.68 0.67 9.761E-03 1.090E-02 54,000 427 584 0.2 0.4 Cases Avoided Scenario 1 4.5 8.2 Cases Avoided Scenario 2 8.1 14.6 
167

Table 63. Using a Cost-of-Illness Approach and VSL, Annual Costs Associated with Cases of Non-Fatal AMI and 

Premature Death In Male Northern Pike Consumers (2000$) 

Predicted 

Annual 

decrease in 

cases of 

non-fatal 

AMI 

Annual 

Avoided Costs 

due to 

reduction in 

non-fatal cases 

of AMI 

Predicted 

Annual 

decrease in 

cases of 

ACM 

Annual Avoided 

Costs (COI) due 

to reduction in 

cases of ACM 

Annual Avoided 

Costs (VSL) 

due to reduction 

in cases of 

ACM 

Annual Total 

Avoided Costs 

(COI) due to 

reduction in cases 

of AMI and ACM 

Annual Total Avoided 

Costs (COI for AMI and 

VSL for ACM) due to 

reduction in cases of 

AMI and ACM 

Change Scenario 1 4.5 $235,000 8.2 $2,624,000 $48,380,000 $2,858,000 $48,614,000 Change Scenario 2 8.1 $423,000 14.6 $4,671,000 $86,140,000 $5,094,000 $86,563,000 
 

168

3.5.2. Entire U.S. Population. 

In the second case, we apply the regression 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 occur 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 Western 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 infarctions 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 deaths are predicted to be avoided. Based 

on the VSL estimate for premature death, the annual avoided costs are predicted to be 

$1.5 billion and $2.3 billion under Scenarios 1 and 2, respectively. Among males, most 

of the benefit is realized by the population that consumes commercial 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.
169

Table 64. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Atlantic Ocean Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

Atlantic Current 15,768 21,585 $823,048,000 $6,906,000,000 $127,353,000,000 Atlantic Baseline 1 15,747 21,549 $821,981,000 $6,894,000,000 $127,136,000,000 Atlantic Scenario 1 15,742 21,539 $821,716,000 $6,892,000,000 $127,083,000,000 Atlantic Baseline 2 15,747 21,548 $821,963,000 $6,894,000,000 $127,133,000,000 Atlantic Scenario 2 15,740 21,536 $821,612,000 $6,890,000,000 $127,062,000,000 Benefit Scenario 1 5 10 $265,000 $2,914,000 $53,728,000 Benefit Scenario 2 7 12 $351,000 $3,852,000 $71,026,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
170

Table 65. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Gulf Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

Gulf Current 9,243 12,710 $482,484,000 $4,066,000,000 $74,987,000,000 Gulf Baseline 1 9,230 12,686 $481,802,000 $4,059,000,000 $74,848,000,000 Gulf Scenario 1 9,228 12,682 $481,697,000 $4,058,000,000 $74,827,000,000 Gulf Baseline 2 9,230 12,686 $481,801,000 $4,059,000,000 $74,848,000,000 Gulf Scenario 2 9,227 12,680 $481,614,000 $4,057,000,000 $74,810,000,000 Benefit Scenario 1 2 4 $105,000 $1,156,000 $21,311,000 Benefit Scenario 2 3 6 $187,000 $2,062,000 $38,018,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
171

Table 66. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Northeast Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

Northeast Current 7,959 10,974 $415,430,000 $3,511,000,000 $64,749,000,000 Northeast Baseline 1 7,920 10,905 $413,416,000 $3,489,000,000 $64,339,000,000 Northeast Scenario 1 7,915 10,895 $413,134,000 $3,486,000,000 $64,281,000,000 Northeast Baseline 2 7,927 10,917 $413,757,000 $3,493,000,000 $64,408,000,000 Northeast Scenario 2 7,912 10,890 $412,988,000 $3,484,000,000 $64,251,000,000 Benefit Scenario 1 5 10 $283,000 $3,126,000 $57,642,000 Benefit Scenario 2 15 27 $769,000 $8,499,000 $156,718,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
172

Table 67. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Mid-Atlantic Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Mortality Death (COI) Costs Male Mortality (VSL) 

Mid-Atlantic Current 657 904 $34,291,000 $289,000,000 $5,331,000,000 Mid-Atlantic Baseline 1 652 894 $34,018,000 $286,000,000 $5,276,000,000 Mid-Atlantic Scenario 1 650 891 $33,913,000 $285,000,000 $5,254,000,000 Mid-Atlantic Baseline 2 651 894 $34,004,000 $286,000,000 $5,273,000,000 Mid-Atlantic Scenario 2 649 889 $33,873,000 $284,000,000 $5,246,000,000 Benefit Scenario 1 2 3 $105,000 $1,151,000 $21,232,000 Benefit Scenario 2 2 5 $131,000 $1,439,000 $26,528,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
173

Table 68. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Southeastern Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

Southeast Current 23,580 32,440 $1,230,830,000 $10,379,000,000 $191,395,000,000 Southeast Baseline 1 23,434 32,177 $1,223,213,000 $10,295,000,000 $189,845,000,000 Southeast Scenario 1 23,406 32,127 $1,221,765,000 $10,279,000,000 $189,551,000,000 Southeast Baseline 2 23,428 32,167 $1,222,914,000 $10,292,000,000 $189,784,000,000 Southeast Scenario 2 23,377 32,075 $1,220,237,000 $10,262,000,000 $189,240,000,000 Benefit Scenario 1 28 50 $1,448,000 $15,965,000 $294,402,000 Benefit Scenario 2 51 92 $2,676,000 $29,494,000 $543,875,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
174

Table 69. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Midwestern Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

Midwest Current 32,225 44,219 $1,682,077,000 $14,148,000,000 $260,895,000,000 Midwest Baseline 1 32,147 44,080 $1,678,015,000 $14,103,000,000 $260,070,000,000 Midwest Scenario 1 32,114 44,019 $1,676,267,000 $14,084,000,000 $259,715,000,000 Midwest Baseline 2 32,146 44,077 $1,677,950,000 $14,103,000,000 $260,057,000,000 Midwest Scenario 2 32,096 43,987 $1,675,324,000 $14,074,000,000 $259,523,000,000 Benefit Scenario 1 33 61 $1,748,000 $19,245,000 $354,876,000 Benefit Scenario 2 50 90 $2,626,000 $28,906,000 $533,031,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
175

Table 70. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of NonCommercial Western Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

West Current 26,357 36,210 $1,375,801,000 $11,586,000,000 $213,641,000,000 West Baseline 1 26,331 36,163 $1,374,438,000 $11,571,000,000 $213,364,000,000 West Scenario 1 26,322 36,147 $1,373,966,000 $11,565,000,000 $213,268,000,000 West Baseline 2 26,332 36,165 $1,374,478,000 $11,571,000,000 $213,372,000,000 West Scenario 2 26,317 36,138 $1,373,706,000 $11,562,000,000 $213,215,000,000 Benefit Scenario 1 9 16 $472,000 $5,205,000 $95,979,000 Benefit Scenario 2 15 27 $772,000 $8,514,000 $156,997,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
176

Table 71. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Male Consumers of Commercial Fish* 

Population Male Non-Fatal AMI Cases Male Total ACM Cases Male Total AMI COSTS Costs Male Mortality (COI) Costs Male Mortality (VSL) 

Current 322,603 439,302 $16,839,216,000 $140,555,000,000 $2,591,880,000,000 Baseline 1 322,536 439,182 $16,835,723,000 $140,517,000,000 $2,591,176,000,000 Scenario 1 322,478 439,079 $16,832,699,000 $140,484,000,000 $2,590,566,000,000 Baseline 2 322,476 439,076 $16,832,599,000 $140,483,000,000 $2,590,546,000,000 Scenario 2 322,406 438,952 $16,828,969,000 $140,443,000,000 $2,589,814,000,000 Benefit Scenario 1 58 103 $3,025,000 $33,069,000 $609,806,000 Benefit 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. 
177

Table 72. Predicted Annual Decreased AMI and ACM Incidence and Annual Benefit (2000$) in Males 

Male Non-Fatal 

AMI Cases 

Male Total 

ACM Cases 

Male Total AMI 

COSTS 

Costs Male 

Mortality (COI) 

Costs Male Mortality 

(VSL) 

Scenario 1 140 260 $7,451,000 $81,830,000 $1,508,976,000 Scenario 2 210 380 $11,141,000 $122,447,000 $2,257,964,000 
 

178

 Tables 73-80 list the predicted cases of female non-fatal AMI and ACM by 

region. Currently, most cases are predicted to occur among commercial consumers in 

the general population. Among the non-commercial consumers, most cases are 

predicted to occur among Midwestern consumers. Table 81 summarizes the benefits of 

Scenarios 1 and 2 in females. Under Scenario 1, annually, roughly 150 cases of nonfatal acute myocardial infarctions and 300 premature deaths are avoided. Under 

Scenario 2, annually, roughly 230 cases of non-fatal acute myocardial infarctions and 

450 premature deaths are avoided. Based on the VSL estimate for premature death, 

the annual avoided costs are $1.8 billion and $2.6 billion under Scenarios 1 and 2, 

respectively. Most of the benefit 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 under 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 deaths are predicted to be avoided. Under 

Scenario 2, annually, roughly 440 cases of non-fatal acute myocardial infarctions and 

830 premature deaths are predicted to be avoided. Based on the VSL estimate for 

premature death, the annual avoided costs are estimated 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 fish. Annually, we predict 9300 and 13,900 

QALYs are gained under Scenarios 1 and 2, respectively (Table 83). Over 95% of 

QALYs gained under both scenarios are the result of decreases in premature mortality.
179

Table 73. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Atlantic Ocean Fish* 

Population Female Non- Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 16,419 24,696 $813,199,000 $4,796,000,000 $145,704,000,000 Baseline 1 16,397 24,654 $812,144,000 $4,788,000,000 $145,456,000,000 Scenario 1 16,392 24,643 $811,882,000 $4,786,000,000 $145,395,000,000 Baseline 2 16,397 24,653 $812,127,000 $4,788,000,000 $145,452,000,000 Scenario 2 16,390 24,639 $811,780,000 $4,785,000,000 $145,371,000,000 Benefit Scenario 1 5 11 $262,000 $2,024,000 $61,471,000 Benefit Scenario 2 7 14 $346,000 $2,675,000 $81,260,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
180

Table 74. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Gulf Fish* 

Population Female Non- Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 9,625 14,541 $476,710,000 $2,824,000,000 $85,792,000,000 Baseline 1 9,611 14,514 $476,036,000 $2,819,000,000 $85,633,000,000 Scenario 1 9,609 14,510 $475,933,000 $2,818,000,000 $85,609,000,000 Baseline 2 9,611 14,514 $476,035,000 $2,819,000,000 $85,633,000,000 Scenario 2 9,608 14,507 $475,851,000 $2,818,000,000 $85,590,000,000 Benefit Scenario 1 2 4 $103,000 $803,000 $24,382,000 Benefit Scenario 2 3 7 $185,000 $1,432,000 $43,496,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
181

Table 75. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Northeast Fish* 

Population Female Non- Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 8,287 12,556 $410,458,000 $2,439,000,000 $74,080,000,000 Baseline 1 8,247 12,476 $408,469,000 $2,423,000,000 $73,610,000,000 Scenario 1 8,241 12,465 $408,190,000 $2,421,000,000 $73,544,000,000 Baseline 2 8,254 12,490 $408,806,000 $2,426,000,000 $73,689,000,000 Scenario 2 8,239 12,459 $408,046,000 $2,420,000,000 $73,510,000,000 Benefit Scenario 1 6 11 $279,000 $2,171,000 $65,948,000 Benefit Scenario 2 15 31 $760,000 $5,902,000 $179,301,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
182

Table 76. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Mid-Atlantic Fish* 

Population Female Non- Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 684 1,034 $33,881,000 $201,000,000 $6,099,000,000 Baseline 1 679 1,023 $33,611,000 $199,000,000 $6,036,000,000 Scenario 1 677 1,019 $33,508,000 $198,000,000 $6,011,000,000 Baseline 2 678 1,022 $33,597,000 $199,000,000 $6,033,000,000 Scenario 2 676 1,017 $33,468,000 $198,000,000 $6,002,000,000 Benefit Scenario 1 2 4 $103,000 $800,000 $24,292,000 Benefit Scenario 2 2 5 $129,000 $999,000 $30,351,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
183

Table 77. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Southeast Fish* 

Population Female Non-Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 24,553 37,114 $1,216,101,000 $7,208,000,000 $218,975,000,000 Baseline 1 24,401 36,814 $1,208,575,000 $7,150,000,000 $217,202,000,000 Scenario 1 24,372 36,757 $1,207,144,000 $7,139,000,000 $216,865,000,000 Baseline 2 24,395 36,802 $1,208,279,000 $7,148,000,000 $217,132,000,000 Scenario 2 24,342 36,697 $1,205,635,000 $7,127,000,000 $216,510,000,000 Benefit Scenario 1 29 57 $1,431,000 $11,088,000 $336,825,000 Benefit Scenario 2 53 105 $2,644,000 $20,484,000 $622,247,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
184

Table 78. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial Midwest Fish* 

Population Female Non- Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 33,555 50,591 $1,661,948,000 $9,826,000,000 $298,489,000,000 Baseline 1 33,474 50,431 $1,657,935,000 $9,795,000,000 $297,545,000,000 Scenario 1 33,439 50,363 $1,656,207,000 $9,782,000,000 $297,139,000,000 Baseline 2 33,473 50,429 $1,657,870,000 $9,794,000,000 $297,530,000,000 Scenario 2 33,420 50,326 $1,655,276,000 $9,774,000,000 $296,921,000,000 Benefit Scenario 1 35 68 $1,727,000 $13,366,000 $406,013,000 Benefit Scenario 2 53 103 $2,595,000 $20,075,000 $609,840,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
185

Table 79. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of NonCommercial West Fish* 

Population Female Non-Fatal AMI Cases Female Total ACM Cases Female Total AMI COSTS Costs Female Mortality (COI) Costs Female Mortality (VSL) 

Current 27,445 41,428 $1,359,337,000 $8,046,000,000 $244,427,000,000 Baseline 1 27,418 41,374 $1,357,990,000 $8,036,000,000 $244,109,000,000 Scenario 1 27,409 41,356 $1,357,524,000 $8,032,000,000 $244,000,000,000 Baseline 2 27,419 41,376 $1,358,030,000 $8,036,000,000 $244,119,000,000 Scenario 2 27,403 41,346 $1,357,267,000 $8,030,000,000 $243,939,000,000 Benefit Scenario 1 9 18 $466,000 $3,615,000 $109,810,000 Benefit Scenario 2 16 30 $763,000 $5,913,000 $179,620,000 

* This population also is assumed to consume a mix of commercial fish (see Section 2.2.2.3). 
186

Table 80. Predicted Annual AMI and ACM Incidence and Valuation (2000$) in Female Consumers of Commercial Fish* 

Population 

Female NonFatal AMI 

Cases 

Female 

Total ACM 

Cases 

Female Total AMI 

COSTS 

Costs Female 

Mortality (COI) 

Costs Female Mortality 

(VSL) 

Current 336,350 503,450 $16,659,069,000 $97,781,000,000 $2,970,356,000,000 Baseline 1 336,277 503,307 $16,655,441,000 $97,753,000,000 $2,969,509,000,000 Scenario 1 336,213 503,182 $16,652,299,000 $97,729,000,000 $2,968,775,000,000 Baseline 2 336,211 503,178 $16,652,196,000 $97,728,000,000 $2,968,751,000,000 Scenario 2 336,135 503,029 $16,648,426,000 $97,699,000,000 $2,967,870,000,000 Benefit Scenario 1 64 125 $3,142,000 $24,153,000 $733,710,000 Benefit 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. 
187

Table 81. Predicted Annual Decreased AMI and ACM Incidence and Annual Benefit (2000$) in Females 

Female Non-Fatal 

AMI Cases 

Female Total 

ACM Cases 

Female Total AMI 

COSTS 

Costs Female 

Mortality (COI) 

Costs Female 

Mortality (VSL) 

Scenario 1 150 300 $7,515,000 $58,018,000 $1,762,450,000 Scenario 2 230 450 $11,192,000 $86,464,000 $2,626,562,000 

Table 82. Predicted Annual Decreased AMI and ACM Incidence and Annual Benefit (2000$) in Males and Females 

Non-Fatal AMI 

Cases 

Total ACM 

Cases 

Total AMI 

COSTS 

Costs Mortality 

(COI) 

Costs Mortality 

(VSL) 

Scenario 1 300 600 $14,965,000 $139,849,000 $3,271,425,000 Scenario 2 400 800 $22,333,000 $208,911,000 $4,884,526,000 
188

Table 83. Predicted Annual QALY Increase Resulting from Decreased AMI and ACM Incidence in Males and 

Females 

 QALYs Non-Fatal AMI Cases QALYs Total ACM Cases Total QALYs 

Scenario 1 470 8,900 9,300 Scenario 2 700 13,200 13,900 
 

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3.6. RESULTS SUMMARY 

Tables 84 and 85 present a summary of the 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 with a reduction in the neurological effects 

(assuming no threshold), non-fatal cardiovascular events, and premature fatalities 

related to methylmercury exposures. Based on the results of this analysis, the potential 

value of the estimated human health benefits varies 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 children exposed above the RfD in utero, then the annual 

benefits of mercury control are predicted to be roughly $75 million 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 cardiovascular 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 all individuals are at risk for experiencing the 

cardiovascular effects associated with methylmercury, 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 threshold) and the cardiovascular benefits for 

the entire population are combined, then, the annual benefits of mercury control are 

predicted to be roughly $3.5 billion and $5.2 billion, in Scenarios 1 and 2, respectively.
190

Table 84. Summary of Cost-of-Illness and Value-of-Statistical Life Approaches for Neurotoxicity and Cardiovascular Toxicity 

Neurotoxicity 

Threshold 

No 

Neurotoxicity 

Threshold 

Costs AMI+ ACM 

(VSL) Male Pike 

Consumers 

Costs AMI+ 

ACM (COI) 

Costs AMI+ ACM 

(VSL) 

Scenario 1 $75,311,000 $193,940,000 $48,436,000 $154,814,000 $3,286,000,000 Scenario 2 $119,002,000 $288,247,000 $86,713,000 $231,244,000 $4,907,000,000 Scenario 1 Summary of neurotoxicity costs and cardiovascular toxicity costs (no threshold) $242,376,000 $348,754,000 $3,480,000,000 Scenario 2 Summary neurotoxicity costs and cardiovascular toxicity costs (no threshold) $374,959,000 $519,491,000 $5,195,000,000 
 

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 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 associated with the reductions in the health 

effects due to reductions in mercury emissions. Less than half of these predicted gains 

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

confidence in these event estimates differs. The neurological effects associated with in 

utero methylmercury exposures 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 premature 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. 
 

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Figure 12 

Spectrum of Certainty of Causal Association 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 tons of mercury are emitted in the U.S. 

under the conditions of Baseline 2. Under Scenario 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 B
 

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We use the data in Table 84 to estimate the ratio in terms of dollar benefits per 

ton. If we assume a neurotoxicity threshold 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, respectively. 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 analysis to male pike 

consumers and neurotoxicity assuming no neurotoxicity threshold, (i.e., a conservative 

external generalization of the Salonen data), we estimate the annual monetized benefits 

associated with Scenario 1 to be approximately $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. Finally, if we externally generalize the Salonen 

data to the entire U.S. population (benefits presented in column 6 of Table 84) and 

include neurotoxicity assuming 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 that there are likely to be co-benefits of 

controlling power plant mercury emissions; these include reduced emissions of primary 

PM and precursors of secondary PM. The 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 removed and 280 QALYs/ton removed based on 

neurotoxicity only in Scenarios 1 and 2, respectively. In scenarios 1 and 2, we estimate 
 

194

benefits to be 790 and 800 QALYs per ton removed, respectively, based on reduced 

neurotoxicity, non-fatal acute myocardial infarctions, and premature mortality. 

3.7. RESULTS OF LIMITED SENSITIVITY ANALYSES 

Sensitivity analyses of risk and economic models are typically conducted to 

quantify the impacts of variability and uncertainty in model parameters on the final 

model outputs. We did not undertake a comprehensive 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 populations in each region. This 

result suggests that methylmercury exposures in populations consuming large 

quantities of freshwater fish will still exceed 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 sensitivity analysis, in which individual 

freshwater fish consumers ate 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 freshwater region in this sensitivity analysis and in the primary 

analysis. 
 

195

The third sensitivity analysis was conducted to evaluate the impact of an 

overprediction or an underprediction of the change in deposition associated with the CSI 

in the Northeast Region. This region was chosen only as an example to investigate the 

impact of changes in deposition levels in one geographical region. Table 86 compares 

the predicted IQ point losses per annual birth cohort 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 points are gained. If the decrease is halved, then roughly 300 

fewer IQ points are gained in Scenario 1. The benefits estimate varies from $1.4 million 

(if the estimated deposition rate decrease is halved) to$22.7 million (if the estimated 

deposition rate decrease is doubled). Consumers of non-commercial fish were 

assumed to eat commercial fish; methylmercury concentrations in the commercial fish 

change vary little across the different mercury 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-Illness 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 Unchanged 400 $7,063,000 

Scenario 1 Halved 90 $1,434,000 

In the fourth sensitivity analysis, we developed an alternative 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 fourth limited sensitivity are presented in Table 
 

196

87. The results suggest that substituting the value of -1.1 for the slope of the exposureresponse relationship in the case where a threshold exists increases the IQ point gain 

by roughly a factor of 2. Under a threshold assumption, the slope value used in the 

sensitivity analysis results in the highest estimate of IQ points lost per annual cohort 

currently. The average IQ points lost per affected individual ranges from 0.6 to 1.4 IQ 

points across the groups of fish consumers. 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 estimated benefits of scenario 2 increase from 

7,100 IQ points per annual birth cohort to 12,800 IQ points. 

Table 87. Comparison of Predicted Incremental IQ Gains per Annual U.S. Birth 

Cohort and Incremental Estimated Monetary Value of the 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 first sensitivity analysis indicate that 

methylmercury exposures in most consumers 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 of 
 

197

fish may experience exposures to methylmercury above the methylmercury RfD. The 

second sensitivity analysis highlights a limitation of the approach; specifically, we did 

not identify data that indicated the mix of non-commercial fish that individuals 

consumed. The alternative assumption used changed neither the predicted mean 

intakes or the fraction of the population predicted whose methylmercury intake rate 

exceeds the RfD. The third sensitivity analysis highlights 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 potential importance of 

methylmercury exposures from commercial fish consumption to total methylmercury 

intake. Finally, the fourth sensitivity analysis suggests that, if there is a threshold for the 

neurotoxicity exposure-response function, then 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). 
 

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4. DISCUSSION

This appears to be the first mercury analysis that attempts to develop multiple 

model compartments for marine fish and freshwater 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 proportional relationship between fish 

methylmercury concentrations and deposition rates has been used in previous analyses 

to predict changes in methylmercury concentrations in freshwater fish (U.S. EPA, 

2001d, 2003b; EPRI, 2003) and marine fish (EPRI, 2003). Limited evidence from field 

studies suggests that decreases in deposition may result in proportional decreases in 

fish methylmercury concentrations in freshwater systems (U.S. EPA, 2003b). It is not 

known whether studies of changes in mercury deposition to salt water bodies will 

support this assumption or not. Methylmercury concentrations in yellowfin tuna caught 

between 1971 and 1998 do not appear to have changed 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 known 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 freshwater 
 

199

systems. We note that there are no data that can be compared 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 fish to be methylmercury, in reality, 

methylmercury is half of the total mercury typically measured in mollusks and 

95% of the total mercury measured in piscivorous species. Thus, we may 

overestimate the percent decrease in methylmercury intake in commercial fish 

consumers because all of the mercury reported 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 analysis 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 deposition 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 regions 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 the only source of methylmercury in water 

bodies. Because there are other potential sources of contamination around 

water bodies, this approach may overestimate reductions in fish methylmercury 

concentrations that may follow emission decreases (U.S. EPA, 2001d); U.S. EPA 

(2001d) identifies several potential sources 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 influence the quantity of methylmercury 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 rates 
 

200

associated with power plant emissions reductions are generally predicted to be 

larger in terrestrial environments than in marine environments. 

4. Based on the REMSAD model results, mercury emitted to the air 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 region. We may underestimate impacts of 

mercury emissions reductions in the coastal Pacific region, because we assume 

that fish methylmercury concentrations in the coastal Pacific Ocean are not 

directly affected by decreases in U.S. power 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 underestimation 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 the 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 overestimate 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 choices of sources of fish for consumption. 

Some anglers do not consume fish or consume fewer fish from bodies of water 

under fish consumption advisories. Thus, the model likely overestimates the 

number of freshwater fish consumers above the RfD. The first sensitivity 

analysis showed that, if the methylmercury criterion 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-commercial 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 exceptions including commercial shark or 

swordfish. 

In addition to the uncertainties associated 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 the deposition of mercury, particularly dry 

deposition of HgII, remain uncertain. If atmospheric transport and deposition processes 

differ substantially from those modeled with REMSAD, then the results of this modeling 

analysis are likely incorrect for the freshwater regions and the Atlantic Coastal and Gulf 

regions. For example, the atmospheric modeling conducted for the EPRI (2003) 

analysis of the CSI predicts substantially smaller decreases in mercury deposition than 

the REMSAD model results of EPA. 

Our assumption that the REMSAD outputs are, in general, correct given the 

limited comparisons with deposition data led to the structure of the Regions in the 

analysis. EPRI (2003) modeled mercury deposition to U.S. freshwaters as a single 

compartment (i.e., the continental U.S. was one compartment in the EPRI model); 

however, U.S. EPA (2003b) chose to model deposition at the individual watershed level. 

While we did not want to obscure potential regional differences 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 along the Coast of the Atlantic Ocean and 

Gulf; thus, we included these regions. 

Factors that influence the aquatic chemistry of deposited mercury and the 

bioaccumulation of methylmercury 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 mercury and fish methylmercury levels may 
 

202

vary based on a number of chemical and physical factors. Between saltwaters and 

freshwaters, the relationship between changes in deposited mercury and changes in 

fish methylmercury concentrations 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 the U.S. EPA's Office of Water. The data quality criteria and 

sampling approaches may vary among the States. 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 freshwater fish that 

anglers report targeting for capture, used as a surrogate for the types of fish consumed, 

may differ substantially from those actually caught and consumed. These data 

influence weighted mean methylmercury concentrations 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 Commission Report. We assumed 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 population. We assumed that the non-commercial 

freshwater fish consumption 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 consumption rate was 59 g/day at the 99.4th 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 Inter-Tribal Fish Commission 

Report in our model. To date, there have been no studies that adequately quantify the 

sizes of high fish consuming populations in the U.S. 

There are additional uncertainties associated with the non-commercial saltwater 

fish data. When compared to commercial fish, there are fewer methylmercury samples 

of saltwater fishes intended for non-commercial 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 Mercury in Marine Life Database 

does not provide information on the dispersion of the data around the central tendency 

estimates provided. Finally, intake of methylmercury from shellfish consumption is not 

evaluated. 

Currently, the simulation results predict that roughly 359,000 children are born to 

women with blood methylmercury levels above 5.8 μg/L blood, the value corresponding 

to the EPA's oral RfD. Our estimate that roughly 9% of the population 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 the population that consumes only commercial 
 

204

fish or no fish. We note that 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 95th percentile blood concentration in this population is 7 μg/L; Mahaffey et al. 

report a 95th 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 remainder consume some fish caught noncommercially and may also consume fish caught commercially. 

Under the decreased mercury emissions scenarios, 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. commercial seafood that is imported and the 

relatively small contribution of U.S. coal-fired power plant mercury emissions to global 

(natural and anthropogenic) emissions under the assumptions used in this model. We 

assumed that the decrease in methylmercury concentrations in tuna, the most heavily 

consumed saltwater fish in the U.S. commercial 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 reflect changes in mercury deposition off the coast 

of North America, then we predict a slightly greater decrease (of less than 1%) in the 

weighted mean methylmercury concentration of commercial fish than the baseline 

value. Also, the analysis assumed that the methylmercury levels in freshwater 
 

205

commercial fish were in equilibrium with the All Other Waters Region. If the decreased 

mercury deposition from other freshwater regions was substituted, it made almost no 

difference in the predicted weighted mean methylmercury concentration predicted in 

commercial fish. The use of mean methylmercury concentrations in commercial fish 

and the use of a mean weighted average reduced the variability in the predicted 

exposures. 

The predicted decreases in the methylmercury 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 larger than in those consuming commercial fish 

exclusively. 

Based on our model results, on a per consumer basis, the policies that control 

mercury emissions from U.S. power plants will likely have the largest impact on 

freshwater fish consumers and non-commercial saltwater fish consumers. This 

simulation indicates that some consumers of these fish have methylmercury intake rates 

above the RfD. Thus, as a result of these policies, 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 for 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 market and the relatively small 

contribution of U.S. power plant mercury 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 note 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 century until the present, mercury concentrations in the 

biosphere have increased; for example, the 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 Scheu, 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 global atmosphere, mixed layer of the ocean, 

and ocean thermocline region, respectively. Other data, notably the work of Schuster et 

al. (2002) and Slemr et al. (2003) suggests that global atmospheric levels of Hg have 

been declining since about 1990. If the quantity 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 needs 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. Such 
 

207

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 models for mercury is needed, 

including analyses of the chemical reactions that control many of the processes 

in the atmosphere. Additional research is needed to improve the understanding 

of the fate of atmospheric mercury emissions from coal-fired power plants on 

local, regional, and global scales. 

3. Additional development of models for evaluating the fate of aquatic mercury and 

bioaccumulation by fish is needed. Studies are needed that evaluate the factors 

influencing the response time between changes in deposition and changes in fish 

methylmercury concentrations; these studies should include water bodies of 

different sizes and characteristics (see footnote 7). 

4. Simulation models need to be developed that 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 undertaken that identify short-term (peak) and longterm fish consumption rates among high-end commercial and non-commercial 

fish consumers. These studies should examine the types of fish these 

individuals are consuming and the sources of these fish. These studies should 

evaluate the number of individuals in these groups. Additional studies should be 

undertaken to evaluate consumption rates in pregnant females, females of 

reproductive age, and men. 

7. Studies should be undertaken 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 control policies, then the benefits of 

mercury emissions control would be greater for this population). 

8. Additional studies are needed to improve the understanding of the rates and 

types of commercial fish intake among non-commercial fish consumers. 

9. The next NHANES survey should collect blood methylmercury concentrations 

and detailed exposure information about sources 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. There 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, freshwater, estuarine or marine ecosystems. 

Methylmercury appears to be neurotoxic for 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 reduce mercury emissions from coal-fired 

power plants would likely reduce other emissions such as fine particulate matter and its 

precursors (oxides of nitrogen and sulfur dioxide). These 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 neurotoxicity 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 evaluated. While high doses of methylmercury are 

clearly associated with neurological decrements, they have not been repeatedly shown 
 

209

to be associated with adverse cardiac events; in fact, fish consumption, which implies 

some methylmercury exposure, is recommended as protective of cardiovascular 

disease. Thus, we urge caution in interpreting the results related to monetized benefits 

associated with the cardiovascular endpoint. Interpretations of these results should 

include a full explanation of the assumptions used to develop the integrated benefit 

estimates. 

The QALY gains and cost-of-illness estimates associated with myocardial 

infarctions and all cause mortality are highly 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 production 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 reported 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 premature mortality effects are included in the 

benefits assessment. 

Given the uncertainty surrounding this health effect and the values involved, 

further research is needed to clarify the relationship between adult methylmercury 

exposures and adverse cardiovascular events and premature mortality. Additional 
 

210

research is needed to determine whether in utero methylmercury exposures alter heart 

rate variability. If methylmercury exposures change heart rate variability and these 

changes lead to increased incidence of adverse myocardial events, then benefits of 

mercury control are likely substantially underestimated in this analysis. 

If the cardiovascular effects are limited to male consumers of northern pike and 

similar species, then avoided costs associated with IQ are comparable to those 

associated with avoided myocardial infarctions and premature deaths when valued 

using a VSL approach. The size of the population that is potentially affected by the 

possible cardiovascular toxicity 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 of the two scenarios. Reductions in nonfatal myocardial infarctions and premature deaths account for the remainder of the total. 

We note the uncertainty in the utility weight 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 with such neurological decrements is much larger than 0.03 

(a factor of 3 above our current estimate) given the data reviewed 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 associated with methylmercury 

exposures that are not captured by measures such as IQ and these would increase the 

benefits associated with mercury emissions control). We also note the uncertainty in 

the identification of when the myocardial infarction or death occurs in the lifespan of an 
 

211

individual could have a large impact on the 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 New Zealand and Faroe Islands studies. 

Additional epidemiologic investigations need to evaluate the relationship between 

in utero methylmercury exposures and neurotoxicity, 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 the 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. 

4. If increased cardiovascular effects are observed, then additional studies are 

needed on the physiological responses to fish fatty acid and methylmercury 

exposures. 

5. Finally, detailed sensitivity analyses are needed on methylmercury benefits 

studies, such as this one, to identify the effects of factors that are most uncertain. 
 

212

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