Revised Technical Support Document: 

National-Scale Assessment of Mercury Risk 

to Populations with High Consumption of 

Self-caught Freshwater Fish 

In Support of the Appropriate and Necessary 

Finding for Coal- and Oil-Fired Electric 

Generating Units 
ii 

EPA-452/R-11-009 

December 2011 

Revised Technical Support Document: National-Scale Assessment of 

Mercury Risk to Populations with High Consumption of Self-caught 

Freshwater Fish 

In Support of the Appropriate and Necessary Finding for Coal- and OilFired Electric Generating Units 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, North Carolina 
iii 

DISCLAIMER

 This document has been prepared by staff from the Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or 

recommendations are those of the authors and do not necessarily reflect the views of the EPA. 

Questions related to this document should be addressed to Dr. Zachary Pekar, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, 

Research Triangle Park, North Carolina 27711 (email: pekar.zachary@epa.gov). 
iv 

Table of Contents: 

Executive Summary............................................................................................................viii 

1 Review of Analysis Approach ................................................................................................ 1

1.1 Summary of Significant SAB Peer Review Panel Recommendations and Revisions 

Reflected in this Revised Mercury Risk TSD ............................................................................. 2

1.2 Purpose and Scope of Analysis ........................................................................................ 5

1.3 Overview of Risk Metrics and the Risk Characterization Framework .......................... 10

1.4 Overview of Analytical Approach ................................................................................. 13

1.4.1 Specifying the spatial scale of watersheds .............................................................. 16

1.4.2 Characterizing measured fish tissue Hg concentrations at the watershed level ...... 17

1.4.2.1 Projecting 75th percentile fish tissue Hg concentrations for the 2016 scenario . 31

1.4.3 Defining subsistence fisher scenarios to include in the analysis ............................ 31

Identify subsistence fisher populations ..................................................................................... 32

Assess where the subsistence fisher populations might be active ............................................ 33

1.4.4 Estimating total fish consumption-related MeHg exposure (2016 scenario) .......... 41

1.4.5 Estimating risk (RfD-based hazard quotient) (2016 scenario) ............................... 43

1.4.6 Estimation of U.S. EGU-attributable risk (2016 scenario) ..................................... 43

1.4.6.1 Mercury Maps analysis ....................................................................................... 44

1.4.6.2 Additional research supporting the proportionality assumption and examining the 

issue of temporal response .................................................................................................... 46

1.4.6.3 CMAQ mercury deposition modeling ................................................................. 47

1.5 Differences between the 2005 Section 112(n) Revision Rule analysis and the current 

analysis in support of the Appropriate and Necessary Finding ................................................ 48

1.6 Detailed Example Calculation (watershed-level risk HQ) ............................................. 50

2 Discussion of Analytical Results .......................................................................................... 53

2.1 Key design elements to consider when reviewing the risk assessment results .............. 53

2.2 Mercury Deposition from U.S. EGUs as Modeled Using CMAQ ................................. 54

2.3 Fish Tissue Mercury Concentrations .............................................................................. 65

2.4 Comparing Patterns of Hg Deposition with Hg Fish Tissue Data for the 3,141 

Watersheds Included in the Risk Assessment ........................................................................... 74

2.5 Overview of Risk Estimates ........................................................................................... 80

2.5.1 Overview of percentile risk estimates (2016 scenario) ........................................... 80

2.5.2 Overview of number and percentage of watersheds with populations potentially atrisk due to U.S. EGU mercury emissions (2016 scenario) ................................................... 83

2.6 Sensitivity Analyses ....................................................................................................... 87

2.7 Discussion of key sources of variability and uncertainty ............................................... 91

3 Summary of Key Observations ........................................................................................... 110

Appendices: Additional Technical Detail on Modeling Elements and Presentation of 

Supplemental Risk Estimates ...................................................................................................... 117

Appendix A. Technical Approach Used in Modeling IQ Loss ............................................... 117

Appendix B. Supplemental Risk Estimates (IQ loss estimates) ............................................. 118

Appendix C. SAB Mercury Panel Peer Review Letter: Review of EPA's Draft National-Scale 

Mercury Risk Assessment ........................................................................................................ 120
v 

List of Tables 

Table 1-1 Summary Statistics for the 2011 MFT HUC-level Data ............................................. 25

Table 1-2 Summary of 75th Percentile Hg Concentrations in Fish Tissue Samples by Number of 

Observations and Number of Sites per HUC ................................................................................ 27

Table 1-3 Summary of 50th Percentile Hg Concentrations in Fish Tissue Samples by Number of 

Observations and Number of Sites per HUC ................................................................................ 29

Table 1-4 Comparison of HUC-Level Fish Tissue Hg Statistics for (a) Fish Tissue Dataset with 

Fish >7 Inches and (b) Dataset with All fish Lengths Included ................................................... 31

Table 1-5 Spatial Extent and Number of HUCs Reflected in Risk Modeling for the Female 

subsistence fish consumer Scenarios Included in the Risk Assessment ....................................... 32

Table 1-6 Fish consumption rates and additional behavior-related information for subsistence 

populations included in the analysis ............................................................................................. 38

Table 2-1 Comparison of total and U.S. EGU-attributable mercury deposition (μg/m2) for the 

2005 and 2016 scenarios.* ............................................................................................................ 64

Table 2-2 Comparison of percent of total mercury deposition attributable to U.S. EGUs for 2005 

and 2016.* ..................................................................................................................................... 64

Table 2-3 Comparison of percent reduction of total mercury deposition, and U.S. EGUattributable deposition, based on comparing the 2016 scenario against the 2005 scenario.* ....... 64

Table 2-4 Comparison of HUC-Level Fish Tissue Mercury Concentrations Across Datasets 

Used in the March Version and Current Version of the Risk Assessment ................................... 73

Table 2-5 Comparison of total and U.S. EGU-attributable Hg fish tissue concentrations 

(including % change) for the original fish tissue dataset and 2016 scenarios* ............................ 73

Table 2-6 Percentile risk estimates for the full set of female subsistence fish consumer scenarios 

included in the analysis (2016 scenario) (for both total and U.S. EGU incremental RfD-based 

HQ)* ............................................................................................................................................. 81

Table 2-8 Watersheds with potentially at-risk populations based on consideration for risk based 

on U.S. EGU mercury deposition and resulting exposure considered alone, without taking into 

account other sources of mercury deposition (2016 scenario) ...................................................... 85

Table 2-9 Combination of watersheds with potentially at-risk populations based on either 

consideration for (a) U.S. EGU percent contribution to total risk OR (b) risk when U.S. EGU 

mercury deposition is considered alone, without taking into account deposition from other 

sources (2016 scenario) ................................................................................................................. 85

Table 2-10 Reflecting the March version of the risk assessment - combination of watersheds 

with potentially at-risk populations based on either consideration for (a) U.S. EGU percent 

contribution to total risk OR (b) risk when U.S. EGU mercury deposition is considered alone, 

without taking into account deposition from other sources (2016 scenario) ................................ 86

Table 2-11 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on U.S. EGUs making a specified contribution to total risk (2016 scenario) 88

Table 2-12 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on consideration for U.S. EGU-attributable HQ risk (2016 scenario) (risk 

considering U.S. EGU Hg deposition before considering other sources of Hg deposition) ........ 89

Table 2-13 Sensitivity analysis results presented as: Combination of watersheds with potentially 

at-risk populations based on either consideration for (a) U.S. EGU percent contribution to total 

risk OR (b) risk when U.S. EGU mercury deposition is considered alone, without taking into 

account deposition from other sources (2016 scenario) ............................................................... 90
vi 

Table 2-14 Key sources of variability associated with the analysis and degree to which they are 

reflected in the design of the analysis ........................................................................................... 93

Table 2-15 Key sources of uncertainty associated with the analysis, the nature of their potential 

impact on risk estimates, and degree to which they are characterized as part of the analysis ...... 96

List of Figures 

Figure 1-1. 2-Stage Risk Characterization Framework ............................................................... 13

Figure 1-2 Flow Diagram of Risk Analysis (for the 2016 air quality scenario) Including Major 

Analytical Steps and Associated Modeling Elements .................................................................. 14

Figure 1-3 Diagram Illustrating Step-wise Procedure Used to Develop 2010 Mercury Fish 

Tissue (MFT) Dataset Used in the 2010 National-Scale Mercury Risk Assessment ................... 21

Figure 1-4 Diagram Illustrating Step-wise Procedure Used to Develop the Augmentation 

Mercury Fish Tissue (MFT) Dataset............................................................................................. 22

Figure 1-5 Diagram Illustrating Step-wise Procedure Used to Combine the 2010 MFT and 

Augmentation MFT Datasets ........................................................................................................ 23

Figure 1-6 Diagram Illustrating Number of HUC12s with Fish Tissue Mercury Data (for 2010 

MFT, Augmentation MFT and the Combined 2011 MFT Datasets) ............................................ 26

Figure 1-7 Histogram Characterizing Frequency of Sample Sizes Across HUCs Included in the 

Risk Assessment (illustrates fraction of HUCs with small sample size of 1-2) ........................... 28

Figure 1-8 LOESS (locally-weighted scatter plot smoothing) Least-Square Regression of 75th

Percentile HUC-level Fish tissue Hg Levels Against HUC-level Sample Size............................ 29

Figure 1-9 Sample Calculation for watershed-level Risk HQ ..................................................... 52

Figure 2-1 Total Mercury Deposition by HUC (μg/m2) for the 2005 Scenario ........................... 56

Figure 2-2 Total Mercury Deposition by HUC (μg/m2) for the 2016 Scenario ........................... 57

Figure 2-3 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2005 Scenario

....................................................................................................................................................... 58

Figure 2-4 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2016 Scenario 59

Figure 2-5 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario ............ 60

Figure 2-6 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario ............. 61

Figure 2-7 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario ............. 62

Figure 2-8 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario ............. 63

Figure 2-9 Set of 3,141 HUC12s with Fish Tissue Mercury Data Used in the Risk Assessment*

....................................................................................................................................................... 67

Figure 2-10 Fish Tissue Measurement Sampling Frequency for HUCs with Fish Tissue Data 

Included in the Risk Assessment .................................................................................................. 68

Figure 2-11 Total Fish Tissue Mercury Concentrations for 2005 Scenario (HUC12-level 75th

percentile values, ppm) ................................................................................................................. 69

Figure 2-12 Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12-

level 75th percentile values, ppm)* ............................................................................................... 70

Figure 2-13 EGU-Attributable Fish Tissue Mercury Concentrations for 2005 Scenario ............. 71

Figure 2-14 EGU-Attributable Fish Tissue Mercury Concentrations Projected for 2016 Scenario

....................................................................................................................................................... 72
vii 

Figure 2-15 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of 

U.S. EGU-attributable Hg Deposition (2005 scenario) ................................................................ 76

Figure 2-16 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of 

U.S. EGU-attributable Hg Deposition (2016 scenario) ................................................................ 77

Figure 2-17 For the 2005 Scenario, Scatter Plot of Hg Fish Tissue Concentrations Versus Total 

Hg Deposition for the 3,141 Watersheds Included in the Risk Assessment ................................. 78

Figure 2-18 Cumulative distribution plots of U.S. EGU-attributable Hg deposition over the 

3,141 watersheds used in modeling the high-end female consumer population as contrasted with 

all 88,000 watersheds (2016 Scenario). ........................................................................................ 78
viii 

Executive Summary 

The Technical Support Document is a revised version of the TSD for EPA’s Nationalscale Mercury Risk Assessment completed in March 2011. EPA used the previous version of 

this report as support for the 2011 finding that it is appropriate and necessary to regulate coal and 

oil-fired electric utility steam generating units in the United States (U.S. EGUs), pursuant to 

Section 112(n)(1)(A) of the Clean Air Act. 

EPA commissioned a formal peer review of this assessment during the public comment 

period for the regulation through the EPA Science Advisory Board (SAB), which provides 

independent advice and peer review to EPA's Administrator on the scientific and technical 

aspects of environmental issues. The SAB established a 22-member panel with representation 

from academic institutions, industry, federal agencies, and state governments. The SAB 

supported the overall design of the risk assessment, confirmed EPA’s analytical assumptions, 

and concluded that the risk assessment should provide “an objective, reasonable, and credible 

determination” of the potential public health hazard. The SAB made many recommendations for 

improving this TSD, which SAB organized into three general themes: (1) improve clarify of the 

document regarding methods and presentation of results, (2) expand discussion of sources of 

variability and uncertainty, and (3) de-emphasize IQ loss as an endpoint. EPA has responded to 

the peer review by substantially revising this TSD. 

Purpose and Scope of Analysis 

The goal of this assessment was to determine whether mercury emitted from U.S. EGUs 

poses a potential public health hazard. Therefore, we have designed this risk assessment as a 

screening analysis focused on identifying watersheds where there is a public health hazard 

attributable to U.S. EGU mercury deposition. Mercury emitted from U.S. EGUs, depending on 

the form of mercury emitted and other factors, can deposit locally and regionally in U.S. 

waterbodies, as well as contribute to the global pool of mercury, where it can be transported and 

eventually deposited around the world. This deposited mercury is transformed into 

methylmercury (MeHg) by microorganisms and then bioaccumulates as MeHg in fish. The 

primary pathway of concern from a public health standpoint is consumption of mercurycontaminated fish by women of child bearing age (since mercury can stay in the system for some 

time, both women who are pregnant or about to be pregnant are of concern.) Depending on the 

level of prenatal exposure, children born to those women may then experience a range of 

neurodevelopmental effects including decrements on a number of neuropsychological measures. 

We focused on consumption from inland freshwater waterbodies in the U.S rather than 

estuarine or marine waterbodies because the U.S. EGU-attributable mercury deposition is a 

larger fraction of total mercury deposition, particularly for waterbodies with elevated U.S. EGUattributable mercury deposition such as the Ohio River Valley. We focused on subsistence 

fishing populations because they typically have substantially higher consumption rates of selfcaught fish than recreational fishers and therefore, experience higher risk. Because we do not 

have data available on the distribution of subsistence fishing populations in all watersheds in the 

U.S., we modeled a hypothetical female subsistence consumer at those watersheds where we 

have fish tissue data and where we believe subsistence fishing activity has the potential to occur. 
ix 

Risk Assessment Methods 

We followed a 10-step process to estimate risk to female subsistence consumers. 

1. Model total and U.S. EGU-attributable mercury deposition for the continental U.S. 

2. Interpolate mercury deposition to watersheds 

3. Estimate 75th percentile fish tissue mercury concentrations at watersheds with fish tissue 

data 

4. Project 75th percentile fish tissue mercury concentrations for the 2016 scenario, which 

reflects projected Hg deposition without regulation of mercury emissions from U.S. 

EGUs 

5. Define female subsistence consumer scenarios 

6. Identify watersheds with subsistence fisher population activity 

7. Define self-caught fish consumption rates for the subsistence scenarios 

8. Estimate total fish consumption-related MeHg exposure 

9. Estimate total MeHg risk 

10. Estimate U.S. EGU-attributable risk 

We assume a proportional relationship between changes in mercury deposition and 

changes in fish tissue mercury concentrations. This assumption, supported by the Mercury Maps 

assessment and other scientific literature, is used to estimate the fraction of U.S. EGUattributable MeHg in fish and to project fish tissue data to 2016. We use the 75th percentile fish 

tissue sample because we believe it is likely that subsistence fishers may target larger fish (with 

somewhat higher MeHg levels) to supplement family meals. Although we can only calculate risk 

for the 4% (3,100) of the 88,000 watersheds in the continental U.S. for which we have fish tissue 

mercury data, these data cover 48 states including many states with high levels of mercury 

deposition and fish tissue mercury concentrations. We excluded watersheds with potentially 

significant non-air sources of mercury. 

In estimating risk for the most comprehensive scenario reflecting exposures for women of 

childbearing age with subsistence level consumption of freshwater self-caught fish, we apply the 

scenario to all watersheds with fish tissue mercury data, reflecting the potential for these 

populations to obtain and consume fish from any watershed. To estimate this risk, we use the 

EPA’s MeHg reference dose (MeHg RfD), which identifies a level of exposure above which 

there is the risk of adverse health effects. We compare modeled MeHg exposure levels against 

the RfD to generate a hazard quotient (HQ). An HQ above one represents a potential exposure 

above the MeHg RfD and EPA, supported by the SAB, considers such an exposure to represent a 

public health hazard. We use two risk metrics at the watershed level: (a) number and percent of 

watersheds modeled where the total mercury-based HQ>1 and where U.S. EGU-attributable 

mercury deposition contributes at least 5% of the risk and (b) number and percent of watersheds 

modeled where U.S. EGU-sourced mercury considered alone, without taking account deposition 

from other sources, results in an HQ>1. The combination of these two estimates provides the 
x 

total number and percent of watersheds where female subsistence consumers are potentially atrisk. 

We have also modeled an additional set of female subsistence consumer scenarios 

focused on different socioeconomic (SES) groups (e.g., low income Blacks in the Southeast, low 

income Hispanics, Vietnamese). Each of these female subsistence consumer scenarios is 

supported by scientific data on SES specific high-end subsistence-level fish consumption rates 

for self-caught fish from inland freshwater bodies. We model watersheds for each of these 

scenarios if there are at least 25 individuals from the SES group in close proximity to the 

watershed. 

Response to SAB Recommendations 

In response to the SAB review, we have substantially modified this TSD. Key 

modifications include: (a) clarifying that the analysis is watershed-focused and designed to 

assess risk for female subsistence consumers (and not a representative characterization of risk for 

recreational anglers), (b) refining and improving the technical presentation in general, including 

addition of a sample calculation figure clarifying how the watershed-level risk estimates are 

generated, (c) expanding the discussion of how the fish tissue mercury database was developed, 

(d) incorporating additional fish tissue mercury data for states with high levels of U.S. EGU 

mercury deposition, (e) verifying the linkages between type of fish tissue mercury measured, 

type of fish consumption rates used and application of a cooking/preparation adjustment factor, 

(f) inclusion of sensitivity analysis involving median watershed-level fish tissue mercury levels, 

(g) expanded discussion of key sources of variability and uncertainty, and (h) de-emphasized the 

IQ loss estimates and moved those estimates to an appendix In response to public comments, we 

have also moved the previously titled “Hotspot Analysis” to a separate TSD titled “Potential for 

Excess Local Deposition of Mercury in Areas near U.S. EGUs”. 

. 

Key Observations 

 Based on a combination of the two risk metrics, and reflecting consumption rates ranging 

from the 90th percentile to the 99th percentile, we estimate that 22% to 29% of the 

watersheds modeled have populations that are potentially at-risk due to U.S. EGU Hg 

emissions in 2016. 

o Based on U.S. EGU-attributable deposition considered alone, without taking into 

account other sources of deposition, 10% of the modeled watersheds have 

populations that are potentially at-risk in 2016 (based on the 99th percentile 

consumption rate) (2016 is the year when regulations under Section 112 of the 

Clean Air Act would need to be implemented for EGUs). 

o In 2016, 24% of the watersheds have populations that are potentially at-risk due to 

total deposition with at least 5% of that total deposition attributable to U.S. EGUs 

(based on the 99th% percentile consumption rate). 

 These risk results reflect U.S. EGU-attributable deposition. In 2016, we estimate that for 

watersheds modeled in the risk assessment, U.S EGUs contribute up to 16% of total 

mercury deposition and related fish tissue mercury concentrations. On average, for these 
xi 

modeled watersheds, U.S. EGUs contribute 3% of total mercury deposition and related 

fish tissue mercury concentrations. 

 Reducing U.S. EGU-attributable mercury will reduce the magnitude of the risk from total 

mercury exposure. However, a large fraction of modeled watersheds would still have 

populations at-risk due to deposition from global sources of mercury, although the degree 

of risk would be diminished. 
1 

1 Review of Analysis Approach 

1.1 Introduction 

The EPA completed a national-scale risk assessment for mercury (Hg) that informed the 

March 2011 finding that it is appropriate and necessary to regulate electric utility steam 

generating units in the United States (U.S. EGUs), pursuant to Section 112(n)(1)(A) of the Clean 

Air Act (CAA). That-risk assessment is documented in the Technical Support Document: 

National-Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for 

Coal and Oil-Fired Electric Generating Units (U.S.EPA, 2011a), hereafter referred to as the 

“Mercury Risk TSD (March publication)” or “March TSD”. In making that finding, EPA 

determined that this assessment should be peer-reviewed, and that the results of the peer review 

and any EPA response to them would be published before the final rule. EPA conducted a 

formal peer review through the EPA Science Advisory Board, which provides independent 

advice and peer review to EPA's Administrator on the scientific and technical aspects of 

environmental issues. The SAB established a 22-member peer review panel with representation 

from academic institutions, industry, federal agencies, and state governments. The peer review 

charge questions and results of the peer review provided in letter form are publically available on 

the EPA Science Advisory Board website,1

 and are included in this document (see Appendix C). 

EPA reviewed the SAB comments and recommendations for strengthening the nationalscale risk assessment and developed a revised version of the risk assessment reflecting 

implementation of a number of the recommendations made by the SAB. This document 

(hereafter referred to as the “Mercury Risk TSD (revised)” or “Revised TSD” describes the 

technical approach used in completing the revised version of the risk assessment. While the 

underlying technical approach used in assessing risk remains essentially the same, some 

elements of the risk model, including some data inputs, have been modified and the presentation 

and discussion of methods and risk estimates has been significantly revised in accordance with 

SAB recommendations. 

The remainder of this revised TSD is organized as follows. We begin this section with a 

summary of the significant recommendations provided by the SAB peer review panel, and an 

overview of the changes to the technical approach made in response to specific SAB 

recommendations (section 1.1). We then provide a description of the purpose and scope of the 

analysis (section 1.2). Next, we provide an overview of the risk metrics generated and the 2-

stage risk characterization framework used to help interpret the risk estimates in a policyrelevant context (section 1.3). In section 1.4, we provide an overview of the analytical approach 

used in the analysis, with subsections addressing specific elements of the analysis. Section 1.5 

describes differences between the current risk assessment and the assessment completed in 2005 

(the 2005 Section 112(n) Revision Rule analysis). Section 1.6 presents a sample calculation that 

 

1

 Link to the SAB letter (which includes the charge questions as an appendix): 

http://yosemite.epa.gov/sab/sabproduct.nsf/02ad90b136fc21ef85256eba00436459/BCA23C5B7917F5BF8525791A

0072CCA1/$File/EPA-SAB-11-017-unsigned.pdf
2 

walks the reader through the process used to generate risk estimates in the analysis. This detailed 

example calculation should help the reader to see how the various inputs and intermediate 

modeling calculations come together in generating watershed-level hazard quotient (HQ)-based 

risk estimates. Section 2 describes analytical results of the risk assessment including both 

intermediate modeling results and the risk estimates that are generated (a detailed overview of 

the subsections in section 2 is presented at the beginning of that section). Section 3 presents a 

summary of key observations from the analysis. 

1.1 Summary of Significant SAB Peer Review Panel Recommendations and 

Revisions Reflected in this Revised Mercury Risk TSD 

In response to the comments and recommendations by the SAB as part of their review of 

the March TSD, EPA revised the technical approach and documentation for the national-scale 

Hg risk assessment. These enhancements to the technical approach are briefly described below, 

along with a summary of the original SAB comment in italics.2

 

 The watershed-focus of the risk assessment should be clearly stated. We revised the 

technical approach section to more clearly state that the analysis is focused on assessing 

risk for subsistence-level consumers of self-caught fish evaluated at the watershed-level.3

Specifically, we are modeling risk for a set of female subsistence fish consumer scenarios 

at those waterbodies where (a) we have measured fish tissue mercury (Hg) data and (b) it 

is reasonable to assume that high-end fishing activity could occur. We emphasize the 

point that the analysis is not a representative population-weighted assessment of risk. 

Rather, it is based on evaluating exposure scenarios. 

 Because IQ does not fully capture the range of neurodevelopmental effects associated 

with Hg exposure, this endpoint should be deemphasized (and covered in an appendix) 

and primary focus should be placed on the MeHg RfD-based hazard quotient metric. We 

modified the structure of the Revised TSD accordingly, including discussion of 

alternative neurodevelopemental endpoints (besides IQ) reflected in the MeHg RfD (see 

section 1.3). 

 Clarify the rationale for using an HQ at or above 1.5 as the basis for selecting potentially 

impacted watersheds. We revised this discussion accordingly (see section 1.4.5). 

 Additional detail should be provided on the characteristics of the fish tissue Hg dataset, 

including its derivation and the distribution of specific attributes across the dataset (e.g., 

number of fish tissue samples and number of different waterbodies reflected in the 

 

2

 We focused this section on those SAB comments that resulted in more significant revisions to the TSD or the risk 

assessment itself. Those comments that are more editorial in nature, or address less substantial technical elements of 

the analysis, are not covered here, although we addressed those comments within the Revised TSD. The charge 

questions for the peer review together with the SAB letter response provided as part of the peer review are included 

in Appendix C (the SAB letter includes the original EPA charge questions as an attachment). 

3

 The assessment focuses on women of child-bearing age who consume subsistence-levels of fish that they catch or 

are caught by relatives/acquaintances and shared with them (hereafter referred to as female subsistence fish 

consumers). As discussed in section 1.4 and 1.4.3, we model a number of female subsistence fish consumer 

scenarios that are differentiated to provide coverage for different socio-economic status (SES) differentiated groups. 

In this context, “subsistence” refers to individuals who rely on noncommercial fish as a major source of protein in 

their diet (U.S. EPA, 2000). For purposes of this risk assessment, we have interpreted this as representing selfcaught fish consumption ranging from a fish meal (8 ounce) every few days to a large fish meal (12 ounces or more) 

every day. 
3 

percentiles provided for a given hydrologic unit code (HUC)4

, number of species 

reflected across HUCs). We included additional figures and tables describing the 

derivation of the HUC-level fish tissue Hg dataset including the filtering steps to derive 

the fish tissue dataset and to generate the HUC-level percentile estimates (see section 

1.4.2). In addition, we included tables summarizing the distribution of key attributes 

within that dataset highlighted by the SAB (e.g., distribution of fish tissue sample size 

and number of species across the HUC-level estimates). We also provided a table that 

contrasts fish tissue Hg concentrations for fish greater than 7 inches in length with 

concentrations based on all fish, without filtering on size (i.e., the dataset used in the risk 

assessment, versus all fish combined). 

 Determine whether there is additional (more recent) fish tissue data for key states 

including Pennsylvania, New Jersey, Kentucky and Illinois where U.S. EGUs Hg 

deposition may be more significant. We expanded the fish tissue dataset by incorporating 

additional (newer) fish tissue data from the National Listing of Fish Advisory (NLFA) 

(which included additional data for four states including MI, NJ, PA, and MN). We also 

obtained additional data for Wisconsin (see section 1.4.2). 

 Regarding the fish tissue dataset used in the risk assessment, clarify which species of Hg 

is reflected in the underlying samples and discuss the implications of differences across 

states in sampling protocols in introducing bias into the analysis. We clarified that in 

most cases, the fish tissue is measured for total Hg. Furthermore, based on the literature, 

it is reasonable to assume that more than 90% of fish tissue Hg is methylmercury 

(MeHg). Therefore, we incorporated a Hg conversion factor of 0.95 into our exposure 

calculations to account for the fraction of total Hg that is MeHg in fish (see section 

1.4.4). We also expanded the discussion of uncertainty (see Table 2-15 entry B in section 

2.7) to include the potential for different sampling protocols across states to introduce 

bias into the risk assessment. 

 Expand the uncertainty discussion related to the screening out of HUCs from the risk 

assessment to address (a) uncertainty in characterizing significant non-air sources of Hg 

loading reflected in the Toxics Release Inventory (TRI) and (b) failure to consider release 

of Hg from larger urban areas (e.g., sanitary sewer discharges) in screening out HUCs. 

We expanded the uncertainty discussion presented in Table 2-15 entry D of section 2.7 to 

address these two sources of uncertainty related to the exclusion criteria for HUCs. 

 While there is support for the use of the 75th percentile fish tissue Hg value in the risk 

assessment, there is concern that the low sampling rates reflected across the HUCs may 

low-bias the 75th percentile estimates. As noted above, we provided additional 

description of the fish tissue dataset including distribution of sample sizes and fish 

species across the HUCs, which includes an improved discussion of uncertainty and 

potential bias related to the derivation and use of the 75th percentile fish tissue levels (see 

Table 2-15 entry C in section 2.7, as well as the discussion in section 1.4.2). 

 Include a sensitivity analysis based on use of the median fish tissue Hg value (as 

contrasted with the 75th percentile value) in generating risk estimates: We also included a 

sensitivity analysis that used the 50th percentile watershed-level fish tissue Hg 

 

4

 For the assessment we have selected Hydrologic Unit Code 12 (HUC12) watersheds as the spatial unit of analysis 

(see section 1.4.1). Throughout this revised TSD, unless otherwise stated, the terms “watershed” and “HUC” are 

used interchangeably to refer to HUC12’s. 
4 

concentration, in addition to the 75th percentile value used in the core analysis (see 

section 2.6) 

 The SAB was generally supportive of the consumption rates used, but recommended that 

EPA expand its discussion of caveats associated with the fish consumption rates used in 

the analysis (e.g., high-end consumption rates for South Carolina reflect small sample 

sizes, the consumption surveys underlying the studies are older and behavior may have 

changed, consumption rates may reflect seasonality even if they are expressed in annual 

terms). We expanded the discussion of uncertainty related to the fish consumption rates 

to address the caveats identified by the SAB (see Table 2-15 entries F and G in section 

2.7). 

 The EPA needs to verify whether the consumption rates are daily values expressed as 

annual averages and whether they are “as caught” or “as prepared” (these factors have 

important implications for the exposure calculations). As noted in section 1.4.4, we 

carefully reviewed the studies underlying the fish consumption rates used in the risk 

assessment and verified that the rates are annual-average daily consumption rates and that 

they represent as prepared estimates. We also expanded the explanation of the exposure 

calculations to more completely describe the exposure factors and equation used to 

generate the average daily MeHg intake estimates for the female subsistence fish 

consumer scenarios (see section 1.4.4). 

 Need to explain exclusion of fish smaller than 7 inches in length from analysis. We 

described the rationale for the 7-inch cutoff for edible fish used in the risk analysis (see 

section 1.4.2). 

 The SAB was generally supportive of the approach used for identifying HUCs with the 

potential for subsistence activity; however, they did recommend that we identify the 

number of HUCs excluded from the analysis due to this criterion. We added a table to 

illustrate the number of HUCs with fish tissue Hg data used to model risk for each of the 

female subsistence fish consumer scenarios (see section 1.4.3, Table 1-5). 

 Enhance the discussion of the proportionality assumption linking Hg deposition and fish 

tissue Hg concentrations, including more recent studies supporting the proportional 

relationship between changes in Hg deposition and changes in MeHg fish tissue levels. 

We expanded our discussion of uncertainty on the proportionality assumption and added 

citations for the more recent studies supporting the proportionality between changes in 

Hg deposition and changes in fish tissue Hg concentrations (see Table 2-15 entries I 

through M in section 2.7 for additional uncertainty discussion and section 1.4.6 for 

discussion of newer literature supporting the proportionality assumption). 

 Given that there are published studies comparing measurements of wet deposition with 

CMAQ-based estimates, the presentation of Hg deposition estimates should include maps 

of wet and dry Hg deposition in addition to the total Hg deposition results used in the risk 

assessment.: We expanded the presentation of Hg modeling results to include maps of 

wet and dry deposition for both the 2005 and 2016 scenarios (see section 2.2). 

 The discussion of the concentration-response (C-R) function used in modeling IQ loss, 

should be expanded to include coverage for the potential masking effect of MeHg-related 

IQ loss due to nutrients (polyunsaturated fatty acids - PUFAs) in fish. This is particularly 

relevant if IQ loss functions derived from saltwater fish consumption are used to model 

risk associated with freshwater fish consumption. We expanded the discussion of 
5 

uncertainty on modeling IQ loss to address this issue (see Table B-2 entry B in Appendix 

B). 

 Additional sources of variability should be discussed in terms of the degree to which they 

are reflected in the design of the risk assessment and the impact that they might have on 

risk estimates. These include: 

o The geographic patterns of populations of female subsistence fish consumers, 

including how this factor interacts with the limited coverage we have for 

watersheds with our fish tissue Hg data. 

o The protocols used by states in collecting fish tissue Hg data. 

o Fisher body weights and the impact that this might have on exposure estimates. 

o Preparation and cooking methods which effects the conversion of fish tissue Hg 

concentrations (as measured) into “as consumed” values. 

We expanded the discussion of sources of variability presented in Table 2-14 (in section 

2.7) to more fully address these sources of variability as requested. 

 Additional sources of uncertainty should be discussed in terms of their potential impact 

on risk estimates. These include: 

o Emissions inventory (including the non-EGU segment) used in projecting total 

and U.S. EGU-attributable Hg deposition. This includes the projection of 

reduction in U.S. EGU emission for the 2016 scenario. 

o Air quality modeling with CMAQ including the prediction of future air quality 

scenarios. 

o Ability of the Mercury Maps-based approach for relating Hg deposition to MeHg 

in fish to capture Hg hotspots. 

o The limited coverage that we have with fish tissue Hg data for watersheds in the 

U.S. and implications for the risk assessment. 

o The preparation factor used to estimate “as consumed” fish tissue Hg 

concentrations. 

o Proportionality assumption used to relate changes in Hg deposition to changes in 

fish tissue Hg concentrations at the watershed-level. 

o Characterizing the spatial location of female subsistence fish consumer 

populations (including degree to which these provide coverage for highconsuming recreational fishers). 

o Application of the RfD to low SES populations and concerns that this could lowbias the risk estimates. 

We expanded the discussion of sources of uncertainty presented in Table 2-15 (in section 

2.7) to more fully address these sources of uncertainty and the potential impact on risk 

estimates. 

1.2 Purpose and Scope of Analysis 

This document is a revision to the March TSD, and provides a revised description of the 

national-scale risk assessment for Hg that was completed to inform the finding that it is 

appropriate and necessary to regulate electric utility steam generating units in the United States 

(U.S. EGUs), pursuant to Section 112(n)(1)(A) of the Clean Air Act (CAA). The appropriate and 

necessary finding is based, in part, on an assessment of the potential public health hazard 
6 

associated with Hg emitted from U.S. EGUs (see Section III of the preamble to the proposed 

Mercury and Air Toxics Standard (MATS) for U.S. EGUs).5

 Consequently, the risk assessment 

was designed to assess whether a potential public health hazard is associated with Hg emitted 

from U.S. EGUs. Because the focus of the analysis is to determine whether a potential public 

health hazard exists and not to characterize the full range of risk associated with exposure to Hg 

emitted from U.S. EGUs, this risk assessment can be viewed as a public health hazard screening 

analysis. As such, the primary objective is to determine whether individuals exposed to Hg 

emitted from U.S. EGUs through high-end consumption of freshwater self-caught fish have the 

potential to experience significant risk. Conversely, the risk assessment is not intended to 

represent a comprehensive assessment of risk for all segments of the population potentially 

exposed to Hg emitted from U.S. EGUs. It is beyond the scope of this risk assessment to assess 

the residual risk remaining after accounting for emissions reductions anticipated from MATS. 

Given the purpose for the analysis, the following policy-relevant questions were 

developed to guide the design of the risk assessment: 

(a) What is the nature and magnitude the potential risk to public health in the 

United States for individuals experiencing reasonable high-end exposure to Hg 

in freshwater, self-caught fish that is attributable to current U.S. EGU Hg 

emissions? 

(b) What is the nature and magnitude of the potential risk for this same group of 

individuals based on projected U.S. EGU Hg emissions in 2016 considering 

potential reductions in EGU Hg emissions attributable to CAA requirements?6

and 

(c) How is total risk from Hg exposure as estimated for both the current and future 

scenarios apportioned between the U.S. EGUs and other sources of Hg? The 

last policy-relevant question reflects the fact that Hg emitted from U.S. EGUs 

does not result in a distinct and isolated exposure pathway, but rather is 

combined with Hg emitted from other sources (domestic and international) in 

contaminating fish. Therefore, to consider U.S. EGU contributions to exposure 

and risk associated with the consumption of fish containing Hg, we determine 

what share of total Hg exposure is attributable to U.S. EGUs. 

In addition to the above policy-relevant questions, the overall design and scope of the risk 

assessment reflects consideration of a number of important technical factors related to airsourced Hg, including, in particular, Hg released from U.S. EGUs: 

 While Hg exposure and risk can occur through a variety of pathways, the dominant 

human exposure pathway associated with ambient air emissions is through the 

consumption of fish that have bioaccumulated Hg originally deposited to watersheds 

following atmospheric release and transport. Deposition of Hg to watersheds includes Hg 

originating from local/regional sources, combined with Hg that has been transported 

 

5

 76 FR 24976 

6

 For purposes of this analysis, we focus on 2016 as this is the year when compliance with mercury standards would 

be required to occur. 
7 

regionally and globally from global anthropogenic and natural sources. Generally 

oxidized (divalent) and particle-bound Hg will deposit relatively closer to the release 

source, while elemental Hg will travel much further, often becoming part of the global 

pool, before being deposited.7

 The scientific literature on mercury health effects provides the strongest support for 

quantifying neurological deficits in children who were exposed to MeHg in utero through 

maternal fish consumption.8

 

 Mercury emitted by U.S. EGUs is likely to make a small contribution to MeHg in 

foreign-sourced commercial fish consumed in the U.S. and in commercial fish caught 

further off the U.S. coast. Therefore, this risk assessment, while acknowledging these 

sources of exposure to U.S. EGU-sourced Hg, does not quantify these risks since the U.S. 

EGU-attributable portion of these risks is likely to be small and any quantitative 

estimates of U.S. EGU-attributable risk would be highly uncertain.9

 While areas closer to the U.S. coast (including estuarine areas) as well as the Great Lakes 

may have elevated U.S. EGU deposition in some cases, because of uncertainty in 

modeling the linkage between U.S. EGU-attributable deposition and Hg in fish, we have 

not included this commercial consumption pathway in the quantitative risk assessment.10 

 The type of fish consumption likely to result in exposures to Hg with the greatest 

contributions from U.S. EGU Hg emissions is associated with fishing activity at inland 

freshwater rivers and lakes located in regions experiencing relatively elevated U.S. EGU 

Hg deposition. Some watersheds had U.S. EGU contributions ranging up to 11% and 

 

7

 Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants in three forms: Gaseous 

elemental Hg (Hg0), oxidized Hg compounds (Hg+2), and particle-bound Hg (HgP). Elemental Hg does not quickly 

deposit or chemically react in the atmosphere, resulting in residence times that are long enough to contribute to 

global scale deposition. Hg(2+) and Hg(p) deposit quickly from the atmosphere impacting local and regional areas 

in proximity to sources. 

8

 The EPA’s health benchmark for methylmercury exposure (the reference dose or RfD) is based on three 

epidemiological studies. These studies relate hair mercury levels in mothers (a surrogate for exposure in utero) or 

mercury in cord blood (a direct measure of fetal exposure) to deficits in children’s performance on a range of neurocognitive tests (see section 1.4.5). 

9

 While mercury emitted from U.S. EGUs does contribute to contamination of foreign-sourced commercial fish, the 

fraction contributed by U.S. EGUs is small. Current estimates of U.S. EGU mercury emissions are ~29 tons per year 

(see section 2.3), compared with global anthropogenic mercury emissions (for 2005), excluding biomass burning, 

estimated at approximately 2,320 tons (Pirrone et al., 2010; UNEP, 2010). Since the mercury in commercially 

caught foreign-sourced fish also includes contributions by natural sources and re-emitted natural and anthropogenic 

sources, estimated to be as high as 5,207 metric tons/year (Pironne et al, 2010; UNEP, 2010), the fraction 

contributed by U.S. EGUs is quite small. Therefore, particularly in the context of estimating individual risk, U.S. 

EGU contributions to risk that residents in the US experience through consumption of commercially caught foreignsourced fish, at present, is expected to be too small to characterize, given the uncertainties in determining the 

fraction of mercury that is from U.S. EGUs. This observation would also likely hold for the U.S. EGU contribution 

to commercial fish sourced from further off the U.S. coast, where total mercury loading is likely to also be 

dominated by non-U.S. anthropogenic emissions which are globally transported. 10 While air quality modeling does suggest that some near coastal areas (e.g. the Chesapeake Bay) and portions of 

the Great Lakes may have elevated U.S. EGU deposition relative to the average levels in the continental U.S., 

several factors make modeling how changes in mercury deposition affect fish tissue Hg concentrations in these nearcoastal areas and the Great Lakes challenging and uncertain. Specifically, the size of these waterbodies relative to 

inland lakes and rivers and the potential for fish to have larger habitats makes it difficult to quantify the EGU 

contribution to fish tissue Hg concentrations in these locations. Due to the greater uncertainty associated with 

modeling near coastal watersheds, we have elected not to simulate this pathway in the risk assessment. Because we 

have not assessed this pathway, this risk assessment may underestimate the number of at-risk watersheds in the U.S. 
8 

higher in 2016 (see Section 2.4). Therefore, efforts to identify areas with high U.S. EGUattributable MeHg exposures and risk are focused on assessing risk for those areas that 

have (a) relatively elevated fish tissue Hg concentrations and (b) relatively elevated levels 

of U.S. EGU Hg deposition. 

Reflecting consideration of the policy questions and technical factors discussed above, 

the scope of the national-scale Hg risk assessment is as follows. 

 Hg deposition was modeled for two years, 2005 and 2016: The 2005 deposition is used 

to represent deposition that is reflected in the sampled fish tissue MeHg levels for 2000 – 

2009. The projected 2016 deposition reflects emissions after imposition of CAA 

requirements. The available emissions data (see Section 2.3) suggest that current 2010 

U.S. EGU emissions are closer to levels reflected in the projected 2016 Scenario and 

substantially lower than levels reflected in the 2005 Scenario. As a result, the 2016 

Scenario analysis is most relevant for this rulemaking. Further modeling of future 

emissions indicates that in the absence of binding federal regulations U.S. EGU 

emissions are not likely to be substantially reduced between 2010 and 2016 (although the 

CAA requires the Agency to consider only federal CAA requirements in estimating 

future HAP emissions and attendant risks associated with EGUs). The 2016 Scenario is 

thus used to estimate risks related to both current emissions and emissions after 

implementation of the CAA requirements and risks are not generated for the 2005 

scenario.11 The estimated Hg emissions for U.S. EGUs in 2016, used in the deposition 

modeling for this TSD, is 29 tons. 

 Focus on assessing risk for female subsistence fish consumers of self-caught fish obtained 

from inland freshwater watersheds: Given the goal of determining whether a public 

health hazard is associated with U.S. EGU emissions, we have assessed risk for those 

individuals likely to experience the greatest exposure and risk from consuming fish 

impacted by Hg emitted from U.S. EGUs. This translates into a focus on women of childbearing age who consume subsistence-levels of fish caught from inland freshwater 

waterbodies.12 By focusing on inland waterbodies, we are focusing on those locations 

with the greatest U.S. EGU-attributable Hg deposition (see Figure 2-4) and consequently 

the greatest U.S. EGU-attributable fish tissue Hg concentrations. By focusing on 

subsistence-level fish consumption scenarios, we focus on those self-caught fish 

consumers with the highest intake rates and therefore, those who will experience the 

greatest Hg exposures at a given watershed. In defining the high-end fisher populations 

to include in the analysis, we have used peer-reviewed study data to characterize 

consumption rates for a variety of high-consuming fisher populations that reflect different 

SES groups and are active in different regions of the country (e.g., Laotians, Great Lakes 

Tribal populations, Black and White anglers active in the Southeast - see Section 1.4.3). 

As noted earlier in section 1.1 for the analysis, we focus on women of child-bearing age 

 

11 Deposition estimates for the 2005 scenario are used in scaling fish tissue Hg concentrations to represent future 

fish tissue levels in 2016, which are then used in turn to model total risk for the 2016 scenario (see section 1.4.6). 12 While subsistence-level consumption can reflect consumption by individuals whose SES status compels them to 

supplement their diet with self-caught fish, elevated levels of inland freshwater self-caught fish consumption can 

also be experienced by recreational anglers who fish often and consume a large amount of the fish they catch. 
9 

since the MeHg RfD used in assessing risk is based on neurodevelopmental effects in 

children exposed to mercury in utero.

 Watershed-level assessment based on modeling female subsistence fish consumer risk at 

watersheds where there is the potential for this type of high fish-consuming activity and 

where we have fish tissue Hg data: The risk assessment is conducted at the watershed 

level, focusing on the subset of inland watersheds in the U.S. where we have fish tissue 

Hg data and where we believe high fish-consuming subsistence fisher populations could 

be active.13 Specifically, we generate a set of risk estimates for each watershed reflecting 

the type of subsistence fisher activity that could potentially exist at that watershed. 

Because it is not possible to enumerate these high-end fisher populations, the risk 

estimates that are generated are not population-weighted and instead are given a uniform 

weight for each watershed-level risk estimate generated.14 This focus on subsistence 

fishing activity and associated consumption reflects the fact that this is a screening-level 

analysis for risk associated with mercury emitted from U.S. EGUs and is not intended to 

provide a comprehensive picture of the distribution of risk across all types of fishers 

potentially active at watersheds where we have Hg fish tissue data. 

 Exclude commercial fish consumption from the quantitative risk analysis. Although risk 

associated with commercial fish consumption may be a potential public health concern 

under certain circumstances, the relatively low contribution of U.S. EGU Hg to this 

source of dietary fish (relative to non-US Hg emissions), leads us to exclude this 

consumption pathway from the risk assessment and to focus instead, on high-end selfcaught fish consumers. In the specific case of commercial fish sourced from near the U.S. 

coast (e.g. Chesapeake Bay) and the Great Lakes, while there is the potential for U.S. 

EGUs to have a greater role in affecting Hg levels in these fish, as noted earlier, 

uncertainty associated with modeling the linkage between U.S. EGU Hg deposition and 

Hg exposure and risk for this dietary pathway precludes us from including this pathway 

in the risk assessment. We do not expect that exclusion of these scenarios will result in 

significant downward bias in our overall estimates of the proportion of watersheds with 

populations potentially at-risk due to U.S. EGU emissions of Hg, although the absolute 

number of watersheds will be understated. 

 

13 With the exception of the typical female subsistence fish consumer scenario (which is assessed across all 

watersheds with fish tissue Hg data), the potential for the other SES-differentiated female subsistence fish consumer 

scenarios to be active at a given watersheds is based on determining whether a group of similar SES-differentiated 

individuals (referred to as a source population) lives in the vicinity of the watershed (see section 1.4.3). 14 In order to enumerate risk estimates generated for the female high-end consumer scenario used in this risk 

assessment, we would need to have the following types of specific information: (a) the fraction of anglers who 

consume at the subsistence-levels modeled for this population specifically at inland freshwater waterbodies, (b) for 

this population, the fraction that focus their activity at individual watersheds, and target somewhat larger fish to 

supplement their diet, and (c) for this subgroup, the fraction of female consumers of childbearing age who either fish 

themselves and consume at this level, or obtain and regularly consume fish provided by fishers who focus their 

fishing efforts at the individual watershed.. However, currently available information does not allow us to estimate 

each of these subgroups of high-consuming fishers. Specifically, while we have data on the frequency of 

recreational angling within the U.S., this covers general recreational fishing and not subsistence fishing. In this 

analysis, we have focused on a subset of female subsistence-level fish consumers that we believe (a) could 

potentially exist at a subset of watersheds evaluated in this analysis and (b) are likely to experience higher risk due 

to their behavior (i.e., favor larger fish as a dietary source, likely to consume fish obtained primarily from individual 

watersheds, consume larger amounts of self-caught fish). While we believe it is reasonable to assume that a subset 

of high-end fishers would have these attributes, it is not possible at this point to definitively state at which 

waterbodies they are active or to enumerate them for purposes of generating population-weighted risk distributions. 
10 

 Include estimates of total risk from all Hg deposition sources, as well as the U.S. EGU 

incremental contribution to total risk. As discussed below (Section 1.2), we focus on two 

aspects of MeHg-related risk: (a) total Hg risk, with an estimate of the percent of that 

total risk contributed by U.S. EGUs (i.e., the fraction of total risk associated with U.S. 

EGUs) and (b) risk when deposition from U.S. EGUs is considered alone, before taking 

into account deposition and exposures resulting from other sources of Hg. These two risk 

metrics reflect the cumulative burden of Hg exposures and incremental contribution that 

the U.S. EGU-attributable deposition makes to overall exposures to MeHg.15 

1.3 Overview of Risk Metrics and the Risk Characterization Framework 

The risk assessment generates hazard quotient (HQ) estimates by comparing estimates of 

modeled potential exposure for subsistence fisher populations to the MeHg RfD. In addition to 

the HQ estimates, Appendix A provides estimates of IQ loss in children born to mothers from 

these high fish-consuming subsistence fishing populations. Because of concerns expressed both 

by the SAB and by EPA staff that IQ loss may not fully capture the full range of 

neurodevelopmental deficits in children exposed to MeHg in utero, we focus on the HQ 

estimates in making our determination of the public health hazard associated with U.S. EGU Hg 

emissions.16 

As mentioned above in section 1.2 and discussed in greater detail in sections 1.4 and 

1.4.6, we generate risk estimates at the watershed-level for the subset of watersheds in the U.S. 

where we have Hg fish tissue data and where we believe the potential exists for high-end fish 

consumption due to the presence of subsistence fishers. As noted earlier, limitations in 

quantifying the number of high-consuming fishers active across the set of modeled watersheds 

prevents us from generating population-weighted risk distributions. However, we do use the 

watershed-level risk estimates (with uniform weighting across watersheds) to generate risk 

 

15 When exposures are to be compared to the EPA’s reference dose (RfD) for MeHg in order to generate a hazard 

quotient (HQ), we must first consider total MeHg exposure given the definition of the RfD, which is intended to be 

compared against total exposure to a given hazardous air pollutant. Once an HQ reflecting total exposure is 

calculated, we can then consider the U.S. EGU incremental contribution to that total risk. However, U.S. EGU 

incremental risk in the form of an HQ should not be considered in isolation without considering total risk associated 

with MeHg in consumed fish. 

16 Concerns have been raised in the literature that if mercury affects a set of specific neurological functions, then use 

of full-scale IQ as the modeled health endpoint, could underestimate the neurodevelopmental impacts on other 

targeted functions (Axelrad et al., 2007). In addition, two of the most sensitive endpoints in the Faroe Islands study 

were the Boston Naming Test and California Verbal Learning Test, both of which can represent a significant 

educational risk depending on severity, and those tests are not directly assessed as part of measuring IQ in children. 

In addition, IQ does not cover other neurologic domains such as motor skills and attention/behavior and therefore, 

risk estimates based on IQ will not cover these additional endpoints and could further underestimate overall 

neurodevelopmental impacts (Axelrad et al., 2007). The wide range of neuropsychological effects potentially 

associated with Hg exposure has also been highlighted by Grandjean et al., (1997) who described developmental 

delays in verbal skills, learning and short-term memory, and more recently by Wijngaarden et al., (2006), who 

provided benchmark dose calculations for 26 endpoints including a number of neuropsychological measures. These 

studies highlight the range of neurodevelopmental effects in children potentially associated with Hg exposure. In 

contrast to the IQ loss metric, the HQ metric based on the RfD reflects consideration for a wider array of 

neurodevelopmental effects in children (e.g., Boston Naming Test, Continuous Performance Test, California Verbal 

Learning Test, McCarthy Perceived Performance, McCarthy Motor Test, finger tap – U.S. EPA’s Integrated Risk 

Information System in 2001 - http://www.epa.gov/iris/subst/0073.htm ). 
11 

distributions from which we can identify specific risk percentiles, (e.g. the 95th percentile of risk 

across watersheds). In this context, the distribution of modeled subsistence fisher risk estimates 

represents the range of risk across modeled watersheds based on assessing the same subsistence 

fisher scenario at each watershed (without population-weighting). Percentile risk estimates 

obtained from this distribution are for the population of watersheds, rather than the population of 

subsistence fishers. 

Reflecting the goals of the analysis presented in Section 1.2, the HQ risk metrics are 

calculated for both total risk and U.S. EGU-attributable risk. In considering U.S. EGUattributable risk, we generate two types of risk estimates: 

 The percent or fraction of total risk attributable to U.S. EGUs at watersheds where total 

risk is considered to pose a potential public health hazard: We consider the magnitude of 

total HQ risk, identifying those watersheds with total MeHg exposure exceeding the RfD 

(i.e., an HQ>1) and then estimate the fraction (or percent) of that total risk that is 

attributable to U.S. EGUs. 

 Risk focusing on U.S. EGU deposition and excluding other non-U.S. EGU sources: For 

this metric, we estimate risk based on the U.S. EGU incremental contribution to total 

exposure. Specifically, for HQ, we compare U.S. EGU-attributable exposure against the 

MeHg RfD.

In assessing the potential public health significance of HQ estimates, we considered total 

MeHg exposures above the RfD to represent a potential public health hazard.17 

Risk Characterization Framework

 We have developed a 2-stage framework for using the risk metrics described above to 

address the policy-relevant questions outlined in section 1.2. This 2-stage framework is 

illustrated in Figure 1-1 and each of the stages is also described below (throughout this 

document, this will be referred to as the “risk characterization framework”): 

Stage 1 – Identify watersheds with populations potentially at-risk due to U.S. EGU Hg based 

on application of the two risk metrics: 

a) The percent or fraction of total risk attributable to U.S. EGUs at watersheds 

where total risk is considered to pose a potential public health hazard : Here we 

identify watersheds with populations potentially at-risk due to U.S. EGU Hg by 

identifying (a) those watersheds where total risk meets or exceeds levels 

 

17 EPA’s interpretation for this assessment is that any exposures to MeHg above the RfD are of concern given the 

nature of the data available for Hg that is not available for many other chemicals, where exposures have often had to 

be significantly above the RfD before they might be considered as causing a hazard to public health. The scientific 

basis for the mercury RfD includes extensive human data and extensive data on sensitive subpopulations including 

children exposed in utero ; therefore, the RfD does not include extrapolations from animals to humans, and for 

database deficiencies. In addition, there is no evidence for a biological threshold observed for critical effect of 

neurological deficits in children studied in the principal studies of the IRIS assessment for MeHg. This additional 

confidence in the basis for the RfD suggests that all potential exposures above the RfD can be interpreted with more 

confidence as representing a hazard to public health. 
12 

considered to represent a potential public health hazard (i.e., HQ > 1); and (b) 

U.S. EGUs contribute to total risk at this subset of watersheds with elevated risk 

(we have considered various increments of U.S. EGU contribution ranging from 5 

to 15%). Any contribution of Hg emissions from U.S. EGUs to watersheds where 

potential exposures from total Hg deposition exceed the RfD is a hazard to public 

health, but for purposes of our analyses we evaluated only those watersheds 

where we determined U.S. EGUs contributed 5 percent or more to Hg deposition 

in the watershed. EPA believes this is a conservative approach given the 

increasing risks associated with incremental exposures above the RfD. 

b) Risk focusing on U.S. EGU deposition and excluding other non-U.S. EGU 

sources:: Here we identify watersheds with populations potentially at-risk due to 

U.S. EGU-attributable risk (prior to considering Hg contributed by other sources). 

Although this metric focuses on U.S. EGU exposure, it is important to keep this 

incremental exposure in perspective with regard to total MeHg exposure in which 

non-U.S. EGU sources of deposition typically dominate the U.S. EGU increment 

across watersheds. 

Stage 2 – calculate the combined (total) number of watersheds and percentage of watersheds 

where populations may be at-risk from U.S. EGU-attributable Hg: Here we combine 

estimates from Stages 1a and 1b to consider watersheds where populations may be atrisk due to (a) U.S. EGUs contributing to exposures at watersheds where total risk 

potentially poses a potential public health hazard or (b) U.S. EGUs making an 

incremental contribution to total Hg exposure which, when considered separate from 

total Hg exposure, represents a potential public health hazard. 

This framework allows us to identify watersheds where U.S. EGU-related exposure 

considered separately, or as a portion of total risk, represents a potential public health hazard. 

More specifically, it allows us to estimate the number and percentage of watersheds where 

populations may be at-risk due to U.S. EGU-related Hg emissions. 
13 

Figure 1-1. 2-Stage Risk Characterization Framework 

1.4 Overview of Analytical Approach 

This section provides an overview of the analytical approach for this risk assessment, 

which we illustrate in Figure 1-2. In this figure, we identify the subsections of the TSD with 

more detailed technical information on each of the analytical steps. We also provide a detailed 

example calculation of the total HQ and U.S. EGU incremental HQ at the watershed-level in 

section 1.6. 

Stage 1a: watersheds where  total  

MeHg exposure exceeds the MeHg

RfD and where US EGUs make a

substantial contribution (e.g., 5%) to

that exposure.

Stage 1b: watershedswhere  U.S.

EGUdeposition, if considered in

isolation, could result in MeHg

exposure  that exceedsthe MeHg RfD.

Stage 2: total numberof watershedswith

populations potentially at‐risk due to Stage 1a

or Stage 1b analyses(i.e., union of A and B) :

Stage 2 = A + B ‐ C

Somewatersheds are identified in

both stages(thesearenot double‐

counted in the totalStage2

number).

A C B
14 

Figure 1-2 Flow Diagram of Risk Analysis (for the 2016 air quality scenario) Including 

Major Analytical Steps and Associated Modeling Elements 

The risk assessment assesses risk to female subsistence fish consumers from potential 

exposure to MeHg from consuming fish caught in U.S. watersheds where we have measured fish 

tissue Hg concentration data and where we have determined that subsistence fishing activity 

could occur. As noted earlier in section 1.2, risks are only estimated for the 2016 scenario since 

Specifying 

spatial 

scale of 

watersheds

(section 

1.4.1) Characterizing 

measured fish 

tissue Hg 

concentrations at 

the watershed level

(section 1.4.2) Characterize female subsistence 

consumer scenarios to model

Estimating total fish consumptionrelated MeHg exposure for fishers/ 

consumers active at each watershed

(section 1.4.4)

Studies 

characterizing fish 

consumption rates 

for freshwater 

subsistence fisher 

populations

Defining female subsistence consumer scenarios to 

include in the analysis (section 1.4.3)

Estimation of U.S. EGUattributable risk at each watershed 

for the 2016 scenario

(section 1.4.6)

CMAQ mercury 

deposition modeling for 

total and US EGU Hg 

deposition at each 

watershed for the 2005 

and 2016 air quality 

scenarios

(section 1.4.6.1)

Estimate risk (RfD-based 

hazard quotient) at each 

watershed for the 2016 scenario 

(section 1.4.5)

Mercury 

RfD for HQ 

estimation

Exposure modeling

Risk modeling

KEY: decision data input analysis step

Identify which watersheds 

will be modeled for each 

of the SES-differentiated 

female subsistence 

consumer scenarios* 

* A more generalized typical female subsistence consumer scenario is also included in the analysis , which is 

evaluated at all watersheds with fish tissue Hg data without consideration for the presence of a source population . 

Due to its broader geographic coverage, this scenario receives the greatest focus in presenting risk estimates. 

Project fish tissue Hg 

concentrations for the 2016 

scenario at each watershed

(section 1.4.6)
15 

underlying U.S. EGU emissions used in modeling this scenario are closer to actual 2010 

emissions than are emissions estimates used in modeling the 2005 scenario. However, as 

discussed below, Hg deposition estimates for the 2005 scenario are used in scaling fish tissue Hg 

levels to represent future levels associated with the 2016 scenario (which are in turn used in 

modeling risk for the 2016 scenario). We provide a brief description of each analytical step of 

the risk analysis reflected in Figure 1-2: 

11. Model mercury deposition: We modeled total and U.S. EGU-attributable mercury 

deposition for the continental U.S. for 2005 and 2016 using the CMAQ model at 12 km 

grid resolution. (See section 1.4.6.3).

12. Specify the spatial scale of the watersheds: We selected Hydrologic Unit Code (HUC) 12 

as the appropriate spatial scale for watersheds, which tend to measure a few kilometers 

(km) on a side and match up with the spatial resolution of the mercury deposition 

modeling. We interpolated the gridded mercury deposition estimates to deposition for 

each watershed in the continental U.S. (See section 1.4.1)

13. Characterize measured fish tissue Hg concentrations (75th percentile) at each watershed: 

We estimate the 75th percentile fish tissue Hg concentrations based on measurement data 

collected primarily by the states collected from 2000-2010 for inland freshwater fish 

species larger than 7 inches. We excluded watersheds without fish tissue data from 

remainder of the analysis. (See section 1.4.2). The fish tissue data did not include 

saltwater or estuarine fish. 

14. Project 75th percentile fish tissue Hg concentrations for the 2016 scenario: We used the 

assumption of a proportional relationship between Hg deposition and fish tissue Hg 

concentrations together with deposition estimates for the 2005 and 2016 scenarios to 

project fish tissue Hg concentrations for the 2016 scenario. Specifically, we used the ratio 

of 2016 deposition to 2005 deposition at a given watershed to adjust the 75th percentile 

fish tissue Hg concentration discussed in Step 3, to represent a 2016 concentration for 

that watershed (see section 1.4.2.1). 

15. Define female subsistence fish consumer scenarios: The analysis focused on females who 

consume subsistence-levels of fish that were caught at inland freshwater waterbodies. 

Our literature review identified seven female subsistence fish consumer scenarios that we 

selected to include in the analysis: (1) typical female subsistence fish consumer, (2) low 

income White fishers in the southeast, (3) low income Black fishers in the southeast, (4) 

low income Hispanic fishers, (5) Vietnamese fishers, (6) Laotian fishers, and (7) Tribal 

fishers (Chippewa active near the Great Lakes). (See section 1.4.3). 

16. Identify watersheds with subsistence fisher population activity: For the typical female 

subsistence fish consumer scenario, we assume subsistence fisher population activity at 

all watersheds where we have fish tissue Hg data. For the Tribal fishers, we assume 

activity within all watersheds with fish tissue Hg data located within territories ceded to 

these tribes. For the remaining scenarios, we use demographic data to determine if at least 

25 individuals with SES attributes matching those of the subsistence consumer 

population are present in the vicinity of a watershed, and if so, we assume that 

subsistence fishing activity might occur at that watershed. (See section 1.4.3) 
16 

17. Define self-caught fish consumption rates for the subsistence scenarios: We used survey 

data published in the literature to identify self-caught fish consumption rates for each 

female subsistence fish consumer scenario. In each case, at the high end (e.g., 90th to 99th

percentiles), consumption rates reached subsistence levels, ranging from an 8oz selfcaught fish meal every few days to an 8oz meal every day and higher in some cases. (See 

section 1.4.3). 

18. Estimate total fish consumption-related MeHg exposure (2016 scenario): We estimated 

exposure in the form of daily-average MeHg intake at the watershed-level for each 

female subsistence fish consumer scenario potentially active at a given watershed. These 

estimates of exposure use the projected 75th percentile fish tissue Hg concentrations for 

2016 described in Step 4 above. We were careful to match the fish sampling data with 

type of sampling (e.g., filet skin on, whole fish), cooking adjustment, and type of 

consumption rates (e.g., as purchased, as consumed). The exposure estimates represent 

potential exposure to MeHg from fish caught in the watershed, not population-weighted 

exposures. (See section 1.4.4) 

19. Estimate of total MeHg risk (RfD-based HQ) at each watershed (2016 scenario): We 

compare the watershed-level exposure estimates modeled for each female subsistence 

fish consumer scenario (for 2016) to the MeHg RfD to generate HQ estimates for total 

MeHg exposure. (See section 1.4.5) 

20. Estimate of U.S. EGU-attributable risk (2016 scenario): We used the same 

proportionality assumption discussed in Step 4, together with total and U.S EGUattributable Hg deposition estimates (generated at the watershed-level) for the 2016 

scenario to estimate the fraction of total HQ (for the 2016 scenario) attributable to U.S. 

EGUs. Specifically, we used the ratio of U.S. EGU-related deposition to total deposition 

(for the 2016 scenario) at a given watershed calculate the fraction of total risk (generated 

in Step 9) that is attributable to U.S. EGUs (see section 1.4.6).18 

1.4.1 Specifying the spatial scale of watersheds 

The first step in designing the analysis was to specify the spatial scale of the watersheds 

to use as the basis for risk characterization. As noted above, this risk assessment is based on 

estimating risk at watersheds for which we have measured Hg fish tissue data. Two studies 

(Knights et al., 2009, Harris et al., 2007) examining the response of aquatic freshwater 

ecosystems to changes in Hg deposition focused on watersheds with dimensions closest to HUC12. In each of the studies, researchers used watersheds reflecting a fairly refined spatial scale 

 

18 Given the assumption that fish tissue Hg concentrations and exposure and risk are linearly related, the 

proportionality assumption (together with the ratio of U.S. EGU-related to total Hg deposition for the 2016 scenario) 

could be applied either to the fish tissue Hg concentrations or to total risk in order to generate U.S. EGU-attributable 

risk. For the analysis, as reflected in Step 10, we applied the proportionality assumption and deposition ratio once 

total risk (for the 2016 scenario) had been generated. However, we could have used the proportionality assumption 

to estimate the fraction of the 75th percentile fish tissue Hg concentration (projected for the 2016 scenario) that was 

attributable to U.S. EGUs at each watershed and then used that fractional fish tissue value to estimate U.S. EGUattributable risk for the 2016 scenario. Both approaches would generate the same risk estimate, reflecting the 

underlying linearities in the exposure and risk models. 
17 

(approximately 5-10 km on a side). This suggests that, at least in the context of these studies, 

researchers believed that the relationship between changes in Hg deposition and changes in 

MeHg levels in aquatic biota could be effectively explored at the level of these more spatially 

refined watersheds. Each of the studies is briefly summarized below. 

The Knights et al., 2009 study focused on characterizing the temporal pattern of 

reductions in fish tissue Hg concentrations following reductions in Hg deposition over 

waterbodies and associated watersheds. This study relied on modeling and included simulation 

of five different types of waterbodies ranging from a seepage lake (with little watershed loading) 

in Florida to a stratified drainage lake in NH. The scale of the five watersheds included in the 

Knights et al., 2009 study range from 20 by 100 km (for the coastal plain river location in GA) to 

5 by 10km (for the Lake Waccamaw NC site). Three of the five locations had watersheds in the 

10 by 10km range (see Figure 2 in the article). Given that the majority of locations in the study 

had smaller watersheds (i.e., in the 10 by 10km range), we conclude that this would represent a 

reasonable watershed spatial scale to use in linking changes in aerial deposition to changes in 

fish tissue levels (i.e., as the basis for risk characterization in the analysis). 

An article by Harris et al., 2007, which is based on the METALLICUS study (specifically 

lake 658 catchment in northwestern Ontario, Canada), also examined the temporal profile 

associated with changes in media and biota Hg levels following a change in Hg deposition. In 

this study, a 3-yr loading of radio-labeled Hg to the waterbody and watershed (separate labeled 

Hg applied to each location) was followed by measurement of Hg in various media and biota to 

see how long it took for the loaded Hg to impact different media compartments. The single 

watershed involved in this study is relatively small (only a few km on a side). Therefore, the 

spatial scale of the watershed involved in this study also supports use of a more refined spatial 

scale for watersheds in the risk assessment. 

In addition to considering the scale of watershed reflected in these two studies of Hg 

loading response, use of a more refined spatial scale (i.e., use of HUC12s rather than a coarser 

scale of watershed) in linking changes in Hg deposition to changes in fish tissue Hg 

concentrations also reduces the potential for averaging out areas of high Hg deposition. The 

HUC12 represents the most refined scale of watershed currently available at the national level 

and therefore was chosen as the basis for linking changes in Hg deposition to changes in fish 

tissue Hg concentrations. Conversely, use of larger watersheds, while allowing us to model 

more of the country in the risk assessment, could result in the unwarranted dilution of areas of 

elevated Hg deposition from U.S. EGUs (and therefore, by association, the dilution of U.S. 

EGU-attributable Hg exposure and risk). The SAB expressed support for the use of HUC12-scale 

watersheds. 

1.4.2 Characterizing measured fish tissue Hg concentrations at the watershed level 

The next step in the analysis was to characterize fish tissue Hg concentrations at the 

watershed level using measured fish tissue Hg data. This process involved three tasks: (a) 

develop the database of fish tissue Hg concentrations based primarily on state-level data, using a 

number of filtering steps that are described below, (b) use the fish tissue database to generate 

percentile estimates (75th and 50th percentiles) for each watershed containing fish tissue Hg 
18 

measurement data and (c) filter the set of watersheds with fish tissue Hg percentile estimates to 

exclude locations potentially impacted by non-atmospheric Hg sources. 

The SAB recommended that EPA evaluate whether there were additional fish tissue data 

that could be used to update the fish tissue dataset (see Section 1.1). We implemented this 

recommendation, and as a result, the fish tissue Hg dataset used in the revised national-scale risk 

assessment is an augmented version of that used in the March assessment. Because the fish 

tissue Hg dataset is different from the March TSD, this section provides an overview of the 2010 

Mercury Fish Tissue (MFT) dataset as well as the augmentation steps of the 2010 MFT dataset 

based on additional fish tissue data identified for a handful of states. We have also included 

additional detail on the derivation of the fish tissue Hg dataset in response to an SAB 

recommendation. We describe each step to develop the dataset ultimately used in this risk 

assessment, which is illustrated in Figures 1-3 through 1-6. We also provide the number of fish 

tissue Hg measurements and watershed-level estimates associated with each step in the filtering 

process. 

Development of the 2010 Mercury Fish Tissue (MFT) database used in the March version of the 

national-scale mercury risk assessment

To develop the 2010 MRT dataset, we began with fish tissue samples from three main 

sources: 

 National Listing of Fish Advisory (NLFA) database. The NLFA, managed by EPA 

(http://water.epa.gov/scitech/swguidance/fishshellfish/fishadvisories/), collects and 

compiles fish tissue sample data from all 50 states and from tribes across the United 

States. In particular, the 2010 version of the NLFA used in this analysis contains data for 

over 45,000 Hg fish tissue samples collected from 1995 to 2007. 

 U.S. Geologic Survey (USGS) compilation of mercury datasets. As part of its 

Environmental Mercury Mapping and Analysis (EMMA) program, USGS compiled Hg 

fish tissue sample data from a wide variety of sources (including the NLFA) and has 

posted these data at http://emmma.usgs.gov/datasets.aspx. To avoid duplication in our 

analysis, we excluded all of the USGS data originating from the NLFA. As shown in 

Figure1-3, we included data from the USGS compilation that originated from two main 

categories of sources: 

(1) state-agency collected and reported data (including Delaware, Iowa, Indiana, 

Louisiana, Minnesota, Ohio, South Carolina, Virginia, Wisconsin, and West Virginia) 

from nearly 40,000 fish tissue samples, covering the period 1995 to 2007 (referred to 

as “USGS States” in Figure 1-3) 

(2) over 6,000 fish tissue samples from several other sources, including the National Fish 

Tissue Survey, the National Pesticide Monitoring Program (NPMP), the National 

Contaminant Biomonitoring Program (NCBP), the Biomonitoring of Environmental 

Status and Trends (BEST) datasets of the USFWS and USGS 

(http://www.cerc.cr.usgs.gov/data/data.htm), and the Environmental Monitoring and 
19 

Analysis Program (EMAP) (http://www.epa.gov/emap/). (referred to as “USGS 

Other” in Figure 1-3) 

 EPA’s National River and Stream Assessment (NRSA) study data. These data include 

nearly 600 fish tissue Hg samples collected at randomly selected freshwater sites across 

the United States during the period 2008 to 2009. 

All of the measurements in the NLFA database were obtained from state fish advisory 

programs managed by the health department, natural resource department or environmental 

protection agency in each state. In many cases, these data were not sampled randomly but instead 

reflect protocols intended to target (1) areas know to support recreational and or commercial 

fishing and (2) areas believed to have elevated levels of chemical contamination (i.e., MeHg) in 

fish.19 Therefore, the state-level data do not provide a representative characterization of the 

distribution of MeHg concentrations in fish tissue at waterbodies across the state and are instead, 

likely to be biased towards locations with higher Hg fish tissue concentrations as well as species 

that are typically consumed by the general public. However, because the goal of this analysis is 

to determine the potential for a public health hazard from Hg emitted by U.S. EGUs, it is 

beneficial to have fish tissue measurement data potentially biased towards waterbodies with 

greater Hg impacts as well as species typically consumed by recreational or commercial fishers 

because this reduces the likelihood that high risk watersheds would be omitted due to gaps in fish 

sampling. 

The majority of fish tissue Hg measurements in these datasets are for total Hg and not 

MeHg. However, research published in the literature suggests that 90 to 95 (or greater) percent 

of total Hg in fish tissue is MeHg (U.S. EPA, 2000). Based on this research, for purposes of 

assessing exposure and risk, we have assumed that 95% of each fish tissue Hg concentration is 

MeHg (see section 1.4.4). 

Data from the four datasets shown in Figure 1-3 were combined into a single “2010 

Master” Hg fish tissue (MFT) sample dataset covering the period 1995 to 2009. One problem 

encountered in combining these datasets is the potential duplication of samples in the NLFA and 

 

19 We reviewed approaches used by Pennsylvania and Wisconsin for determining where to collect fish tissue Hg 

measurements. These states were chosen because they either have relatively high fish tissue Hg concentrations 

(Wisconsin), or relatively elevated U.S. EGU-related Hg deposition (Pennsylvania). Pennsylvania identifies 

sampling locations based on (a) a need to verify or delist specific locations for fish advisories, (b) provide additional 

data points for their Water Quality Network and (c) respond to requests from selected State Environmental and 

Wildlife officials to sample locations of potential concern (Pennsylvania Department of Environmental Protection’s 

“Fish Tissue Sampling and Assessment Protocol” available at: 

http://www.portal.state.pa.us/portal/server.pt/community/fish_consumption/10560 ). Wisconsin selects sampling 

locations based on providing coverage for (a) northern parts of the state where high Hg fish tissue levels have been 

found, (b) areas were pollutions impacts are suspected, (c) areas with high fishing pressure and (d) locations that are 

considered “indicators” for specific watersheds (Wisconsin Department of Natural Resources, “A Summary of 

Mercury Concentrations in Fish (edible portions) from Wisconsin Waters 1990-2005”, available at: 

http://dnr.wi.gov/fish/consumption/1990-2005mercurysummary.pdf ). While the details of these two strategies do 

differ somewhat, generally both states are targeting areas either suspected of having a potential Hg problem, or areas 

with elevated fishing activity. In both cases, the set of fish tissue Hg concentrations are not going to be generally 

representative of trends across watersheds in the state and instead, are likely to favor coverage of more heavily 

impacted watersheds (at least for the fraction of samples collected to cover areas of potential concern with regard to 

mercury impacts). 
20 

USGS-States data. Unfortunately, these two datasets do not contain directly comparable and 

unique identifiers that allow duplicate samples to be easily identified and removed. In order to 

identify potentially duplicative samples, we subdivided the samples from these two datasets into 

data groups according to the year and state in which they were collected. If both datasets 

contained a data group for the same year and the same state, then the data group with the fewer 

number of observations was not included in the master data. This process excluded 18,860 

potentially duplicate samples from the master dataset. 

In finalizing the 2010 MFT sample dataset, the following filters were used to further 

screen the fish tissue samples: 

(a) excluded samples that were not geo-referenced (i.e., did not include latitude and 

longitude information) 

(b) excluded samples with missing date information 

(c) excluded samples that were not from freshwater fish species or freshwater locations 

(i.e., excluded estuarine locations), 

(d) excluded samples from fish smaller than 7 inches in length. 20 Additional discussion 

of uncertainty related to this filtering step is presented later in this section. 

These filters excluded a total of 21,674 samples from the dataset (in addition to the 18,860 

duplicates referenced earlier). For the majority of measurements in the database, we also have 

information on the type of waterbody (river/lake) and type of sampling method used (e.g., filet 

skin on). Additional detail on the process used to develop the master fish tissue dataset can be 

found in U.S. EPA, 2011a, Section 5.2.2. 

Even though we compiled fish tissue Hg sampling data for 1995 to 2009, we decided to 

restrict data to 2000 to 2009 in the risk assessment (the SAB supported this decision). We 

excluded fish tissue samples that likely reflected Hg deposition levels from the 1990’s when 

anthropogenic emissions in the U.S. were higher than after 2000. We recognize the complex 

spatial and temporal nature of the response of fish tissue Hg concentrations to changes in Hg 

deposition and loading and acknowledge that a portion of the sampling data from 2000 to 2009 

could still reflect higher Hg loading rates from earlier periods. Excluding the samples collected 

before 2000 further reduced the size of the initial dataset by 27,522 observations (the potential 

impact on the risk assessment related to our decision to focus on data from 2000 and later is 

discussed in section 2.7, Table 2-15, Entry I). 

After completing all of these filtering steps, the 2010 MFT sample dataset contained 

23,770 observations (samples) in the U.S. 

 

20 Seven inches represents a minimum size limit for a number of key edible freshwater fish species established at the 

State-level. For example, Pennsylvania establishes 7 inches as the minimum size limit for both trout and salmon 

(other edible fish species such as bass, walleye and northern pike have higher minimum size limits (Summary Book: 

2001 Pennsylvania Fishing Laws and Regulations available at: 

http://fishandboat.com/fishpub/summary/inland.html). 
21 

Figure 1-3 Diagram Illustrating Step-wise Procedure Used to Develop 2010 Mercury 

Fish Tissue (MFT) Dataset Used in the 2010 National-Scale Mercury Risk 

Assessment 

Augmentation of the 2010 MFT database with additional fish tissue mercury data obtained for a 

subset of states (i.e., MI, NJ, MN, PA, and WI).

Since the March TSD, we augmented the 2010 MFT sample database with additional 

sampling data from selected states and years. The composition and processing of these 

“augmentation” data are shown in Figure 1-4. All of these data were provided to EPA by the 

individual states, but they have not yet been incorporated into the most recent versions of the 

NLFA. The main criteria for selecting these particular 5 states are: (1) a majority of the sample 

data they provided to EPA were geo-referenced and included the year in which they were 

sampled (more recent data for a number of the other states did not have these critical descriptors) 

and (2) the states are located in areas of the country with relatively high levels of Hg deposition. 

To avoid potential duplication of samples with those included in the initial Hg fish tissue 

database, the augmentation data only included samples for the years 2003 to 2009 for MI, NJ, 

MN, and PA, and for 2000 to 2010 for WI (Figure 1-4 provides more detail).21 All of the data 

were reorganized and converted into Microsoft Access databases, using the NLFA fields and 

 

21 The version of the NLFA used in developing the 2010 MFT dataset did not include samples collected between 

2003 and 2009 for MI, NJ, MN or PA. Similarly, that earlier version of the NLFA did not include any WI samples 

collected between 2000 and 2010. Therefore, addition of samples from the latest version of the NLFA from these 

years for these 5 states would supplement the MFT without duplication. 

2010 Master

(72,966)

NLFA

(45,166)

NRSA

(590)

USGS States

(39,972)

USGS Others

(6,098)

Original Data 

Sources 

(>=1995) Reason

Dropped

N

Missing

Lat/Long

‐9,177

Missing Date ‐1,520

Not a

Freshwater

Fish

‐2,638

Not a

Freshwater

Location

‐3,192

Length < 7

inches

‐5,147

Year < 2000 ‐27,522

Total ‐49,196

Data Screening

Merged NLFA‐

USGS States

(66,278) 2010 MFT

Sample

Dataset

(23,770)

Data Merging
22 

formats. They were then combined into the “Augmentation Master” dataset shown in Figure 1-4, 

which includes a total of 12,864 samples. 

Using the same set of screening steps, we ended up with an Augmentation MFT sample 

dataset containing 11,797 observations in the U.S.22

For this analysis, we then combined the 2010 MFT sample dataset with the Augmentation 

MFT dataset (as shown in Figure 1-5) to create the 2011 MFT sample dataset. This combined 

dataset includes 35,567 fish tissue Hg samples from years 2000-2010. As with the original 2010 

MFT database, samples from the 2011 MFT database are located across the U.S.; however, they 

are more heavily focused in locations east of the Mississippi River. 

Figure 1-4 Diagram Illustrating Step-wise Procedure Used to Develop the Augmentation 

Mercury Fish Tissue (MFT) Dataset 

 

22 The data fish tissue sample data from PA did not include a measure of fish length; therefore, all of the samples 

were retained and assumed to be above the 7 inch threshold. In the March assessment, these samples were excluded, 

however, in order to provide a more complete spatial coverage in PA, which has high levels of U.S. EGU Hg 

deposition, we elected for this revised assessment to include these samples. 

Augmentation

Master

(12,864)

MI 2007‐2009

(81)

NJ 2007

(220)

MN 2003‐2007

(6,012)

PA 2003‐2008

(509)

Original Data 

Sources

Reason

Dropped

N

Missing Lat/Long ‐643

Missing Date ‐0

Not a Freshwater

Fish

‐13

Not a Freshwater

Location

‐0

Length < 7 inches ‐411

Year < 2000 ‐0

Total ‐1,067

WI 2000‐2010

(6,042)

Data Merging Data Screening

Augmentation

MFT Sample

Dataset

(11,797)
23 

 

Figure 1-5 Diagram Illustrating Step-wise Procedure Used to Combine the 2010 MFT and 

Augmentation MFT Datasets 

Use the fish tissue database to generate percentile estimates (75th and medians) for each HUC-12 

watershed containing fish tissue Hg measurement data

To conduct the risk and exposure analysis, we then spatially aggregated the 2011 MFT 

sample data to the watershed level. To begin this process, we used the latitude and longitude 

information from each sampling location to identify the HUC-12 watersheds in which each site 

was located. 

As a final data screening step, we then excluded HUC-12 watersheds (and the samples 

located within those watersheds) that either contained active gold mines or had other substantial 

non-U.S. EGU anthropogenic emissions of Hg.23 These watersheds were excluded because the 

assumption of linear proportionality between Hg deposition and fish tissue Hg concentrations, 

supported by the MMaps study is most supportable in those situations where aerial deposition is 

the dominant source of Hg loading to a watershed. We identified watersheds with gold mines 

 

23 The filtering out of locations with potentially significant non-air mercury loadings was also completed in 

generating the 2010 MFT database. At this stage, to restrict the analysis to the continental U.S., where we have Hg 

deposition modeling, we also excluded 11 samples that were located in 5 HUC-12 watersheds in Alaska. 

2010 MFT Sample

Dataset

(23,770)

2010 MFT HUC‐

Level Dataset*

(2,323  HUCs)

AugmentationMFT

SampleDataset

(11,797)

Augmentation

MFT HUC‐

Level Dataset

(940  HUCs)**

2011 MFT Sample

Dataset

(35,567)

2011 MFT HUC‐Level

Dataset*

(3,147)

Fish tissue 

samples

Spatial 

Aggregation 

(associating individual fish 

tissue sampling locations 

with HUC12 watersheds –

completed using GIS)

*An additional 11 observations were dropped due to 5 HUC locations outside of the continental U.S.

230 observations (associated with 15 HUC12

watersheds) dropped  because watersheds contain

gold mines  or have substantial non‐EGU emissions*

** 116 of these 940 HUCs were also included in the 2010 MFT dataset.

Fish tissue 

samples 

associated 

with specific 

HUC12 

watersheds
24 

using a USGS data set characterizing mineral and metal operations in the U.S. (USGS, 2005). 

The data represent commodities monitored by the National Minerals Information Center of the 

USGS, and the operations included are those considered active in 2003. We identified 

watersheds with substantial non-EGU anthropogenic emissions using a TRI-net query for 2008 

for non-EGU Hg sources with total annual on-site Hg emissions (all media) of 39.7 pounds or 

more.24 This threshold value corresponds to the 25th percentile annual U.S.-EGU Hg emission 

value as characterized in the 2005 NATA.25 The 25th percentile U.S.-EGU emission level was 

selected as a reasonable screen for additional substantial non-U.S. EGU emissions to a given 

watershed. The SAB endorsed this as a sound approach, while noting that the degree of 

conservatism implied by this approach is unknown. The SAB also commented that while other 

screening criteria could be applied, such as removing watersheds near urban areas with potential 

waste runoff, these would be unlikely to substantially change the results. In addition, applying 

additional screening criteria would have the negative impact of further reducing the geographic 

scope of the analysis. 

Application of the filtering described here (i.e., excluding locations with active gold 

mines or other substantial non-U.S. EGU anthropogenic emissions of Hg) resulted in 15 HUC12 

watersheds, containing a total of 230 observations, being excluded from the 2011 MFT dataset 

(see Figure 1-5). The final number of HUC12s with fish tissue Hg data included in the risk 

assessment is 3,141 (containing 35,567 samples), which represents a 33% increase in the number 

of watersheds assessed since the March TSD, as summarized in Figure 1-5. 

As shown in Figure 1-5, this approach yielded 3,141 HUC-level sets of fish tissue Hg 

estimates for the augmented (full) dataset used in the revised national-scale mercury risk 

assessment (this compares with 2,317 HUC-level fish tissue Hg estimates used in the March 

version of the risk assessment). 

Because most HUC-12 watersheds with measured fish tissue Hg data have multiple 

sampled values, often distributed over multiple sampling sites, we needed to identify and 

calculate summary statistics for each watershed, in order to represent fish tissue Hg levels in 

estimating exposure and risk. To do this, we generated summary statistics (means and 

percentiles) for MeHg concentrations in each HUC-12 and compiled these summary data into a 

separate HUC-level dataset. The following two-step procedure was used to generate HUC-level 

statistics: (a) calculate mean and percentile (25th, 50th, 75th and 90th) fish tissue Hg values for 

each sampling site within a HUC and (b) for the mean, or a given percentile, take the average of 

the applicable values across the sampling sites within a HUC to generate a single HUC-level 

estimates (for the mean, or specific percentile). For example, if we had two sampling sites (each 

with multiple samples) and we wanted a 75th percentile fish tissue Hg value for that HUC, we 

would first compute the 75th percentile values at each sampling site and then take the average of 

those two 75th percentile values. If there is only one sampling site in a given HUC, then we 

simply compute the mean or percentile of interest from the measurements at that site and that 

statistic is used to represent the HUC. This approach reflects a fisher who fairly consistently 

targets the same size (represented by percentile) fish at each location where they fish within a 

 

24 U.S. EPA Toxics Release Inventory (TRI) accessed at: http://www.epa.gov/tri/ . 25 U.S. EPA 2005 National Air Toxics Assessment (2005 NATA) accessed at: 

http://www.epa.gov/ttn/atw/nata2005/.. 
25 

watershed. We are then constructing a representative fish tissue Hg value for them, by taking the 

average of the fish they are simulated to catch across their fishing locations (within a given 

HUC). As noted earlier, our simulation of female subsistence fish consumer exposure and risk 

reflects the assumption that fishing activity is targeted within a given watershed.26 

Summary statistics for the 2011 MFT HUC-level datasets are reported in Table 1-1.27 The 

average number of fish tissue measurements for the period 2000 to 2009 for the 3,141 

watersheds is 11.23, although some watersheds contained up to 360 measurements. Most 

watersheds also have multiple species (mean of 2.69) and to a lesser extent, multiple sampling 

sites within the watershed (mean of 1.41). Multiple sampling sites in this context can be different 

streams and/or lakes located within the same watershed. Distributions of other HUC-level 

percentiles (and the mean) are also presented for purposes of comparison. 

Table 1-1 Summary Statistics for the 2011 MFT HUC-level Data 

Distribution of various attributes across the 3,141 HUCs Mean

Std. 

Dev. Min Max

Number of samples per HUC 11.23 21.05 1 360

Number of species per HUC 2.69 2.19 1 17

Number of sampling sites per HUC 1.41 1.25 1 33

Average of different HUC-level Hg concentration 

percentiles (including the mean) across the 3,141 HUCs 

Average of location-specific mean Hg concentrations (ppm) 

in HUC 0.27 0.24 0 3.56 

Average of location-specific 25th percentile Hg 

concentrations (ppm) in HUC 0.19 0.19 0 2.20 

Average of location-specific 50th percentile Hg 

concentrations (ppm) in HUC 0.25 0.23 0 3.56 

Average of location-specific 75th percentile Hg 

concentrations (ppm) in HUC * 0.32 0.31 0 6.61 

Average of location-specific 90th percentile Hg 

concentrations (ppm) in HUC 0.40 0.41 0 7.35 

* 75th percentile row bolded since this is the statistic used in the risk assessment 

 

26 The SAB recommended that a single fish tissue Hg value not be used to represent a watershed when there are 

multiple measurement sites (e.g., multiple lakes) within that watershed. This recommendation reflected concern that 

waterbodies, even when in close proximity within the same watershed, can display different methylation rates such 

that they are likely to respond differently to a unit change in Hg deposition. The approach outlined here for 

calculating the 75th percentile fish tissue Hg value does not use a single fish tissue Hg value in instances where there 

are multiple sites within a watershed. Instead, we calculate the 75th percentile fish tissue Hg for each site (i.e., with 

each value representing a specific sampling site, which often translates into a distinct waterbody) and then take the 

average of those values, reflecting fishing activity that is distributed across the sampling sites, or waterbodies within 

a specific watershed. 

27 In the 2010 MFT dataset used in the March version of the risk assessment, approximately 48% of the fish tissue 

Hg samples were obtained from rivers (with the rest coming from lakes). For the augmentation dataset, we did not 

specify river versus lake in developing the fish tissue Hg dataset, since this distinction was downplayed by the SAB 

as a factor significantly impacting application of the proportionality assumption in the context of estimating the U.S. 

EGU-attributable portion of exposure and risk. 
26 

As noted in Section 1.4, we selected the 75th percentile fish tissue value at each watershed 

as the main basis for exposure and risk characterization.28 Selection of the 75th percentile value 

was based on the assumption that a subset of subsistence fishers would favor larger fish which 

have the potential for higher bioaccumulation (i.e., use of a median or mean value could low-bias 

likely catch-related Hg levels). There is uncertainty associated with this assumption and should 

fishers at a particular watershed favor fish that are either larger or smaller than the type of fish 

reflected in the 75th percentile sample, risk estimates could be biased accordingly. Uncertainty 

related to use of the 75th percentile fish tissue Hg value in exposure modeling (specifically, 

potential bias in the estimation of the 75th percentile value) is discussed in section 2.9, Table 2-

15, Entry C. As recommended by the SAB, we also included of a sensitivity analysis using the 

median fish tissue Hg concentration at each HUC – see section 2.8). 

Figure 1-6 Diagram Illustrating Number of HUC12s with Fish Tissue Mercury Data (for 

2010 MFT, Augmentation MFT and the Combined 2011 MFT Datasets) 

Uncertainty related to low sample size for a substantial fraction of the HUC12s and exclusion of 

fish <7 inches in length in calculation the HUC-level percentile estimates

While the majority of the 3,141 watersheds included in the risk assessment do have 

multiple measurements, a substantial fraction (41%) have only 1-2 samples, which potentially 

biases low a 75th percentile estimate for those watersheds. To examine the potential magnitude of 

low bias in the 75th percentile estimates, we have summarized the distribution of 75th percentile 

 

28 We note that the 75th percentile value is a constructed statistic that reflects the fish tissue samples available at a 

given watershed. For some watersheds the number of samples is very low, 1 or 2 in some cases (see Table 1-2), and 

as a result, estimation of the true 75th percentile of fish tissue mercury concentrations in those watersheds is likely to 

be biased low. This is discussed later in this section. Depending on the degree to which the underlying fish tissue 

data are representative of actual fish targeted by subsistence fishers active at that waterbody, this statistic may or 

may not actually represent fish tissue Hg concentrations associated with fish consumption by those fishers. 

2,201 824

116

2010 MFT Data

(# of HUCs)

Augmentation MFT Data

(# of HUCs)

2011 MFT Data (Combining 2010 and Augmentation)

Total Number of HUC12 observations = 3,141

Number of HUC12 Locations
27 

fish tissue values (as used in the risk assessment) for various sample-size-based strata of the 

3,141 watersheds (see Table 1-2). We have also included two plots (as recommended in the 

SAB peer review) including (a) a histogram comparing the frequency of different sample sizes 

across the HUCs included in the risk assessment (Figure 1-7) and (b) a LOESS (locally-weighted 

scatter plot smoothing)-based fitted curve relating the 75th percentile HUC-level values and the 

frequency of samples sizes by HUC (Figure 1-8).29 The number of sampling sites also varies 

across watersheds included in the risk assessment, with some watersheds having up to 30 or 

more distinct sampling sites. 

While supporting the use of the 75th percentile fish tissue Hg value in the risk assessment, 

the SAB recommended a sensitivity analysis involving use of a median fish tissue Hg value in 

place of the 75th percentile value. This sensitivity analysis is discussed in section 2.6. However, 

we have provided fish tissue summary statistics here for median fish tissue statistics to support 

comparison with the 75th percentile values (Table 1-3). 

Table 1-2 Summary of 75th Percentile Hg Concentrations in Fish Tissue Samples by 

Number of Observations and Number of Sites per HUC 

Number of 

Observations per HUC 

Percentiles (of the 75th percentile HUC-level fish tissue 

Hg concentration - ppm) 

25 Mean N th 50th 75th 90th 95th

1 Observation 0.077 0.1564 0.2887 0.493 0.67834 0.226803 838 

2 Observations 0.09995 0.1775 0.315 0.5135 0.765 0.256943 458 

3-5 Observations 0.12 0.205625 0.339 0.577 0.8 0.283682 468 

6-10 Observations 0.185 0.282 0.464 0.743 0.985 0.374177 506 

> 10 Observations 0.25 0.37 0.553 0.85 1.083412 0.444645 871 

Total 0.1294 0.245 0.422684 0.673355 0.893 0.324052 3141 

Number of sampling 

sites per HUC 25th 50th 75th 90th 95th Mean N 

1 Site 0.12 0.233 0.4 0.672 0.87 0.31308 2371

2 Sites 0.160967 0.276706 0.45044 0.71 1.047125 0.360398 553

3-5 Sites 0.179823 0.299482 0.478662 0.603667 0.738476 0.350198 196

6-10 Sites 0.125972 0.224625 0.479822 0.693108 0.8652 0.325642 19

> 10 Sites 0.2698 0.410415 0.494276 0.847259 0.847259 0.419069 8

Total 0.1294 0.245 0.422684 0.673355 0.893 0.324052 3147 

Based on the observations-related data presented in Table 1-2 and Figure 1-8, we 

acknowledge the potential for low-bias in the 75th percentile values for HUCs with lower sample 

sizes. Specifically, in Table 1-2, the mean 75th percentile HUC-level value increases across 

strata as the sample size increases. This suggests that as the number of samples increases, 

estimates of the 75th percentile will be more likely to approach the true value, and thus are more 

likely to represent higher end fish tissue levels. Similarly, in Figure 1-8, the smoothed regression 

line (for the 75th percentile HUC-level trend) has a positive slope, denoting that higher 75th

 

29 The LOESS procedure involves fitting of a least-squares curve based on application of a smoothing (or 

bandwidth) parameter. Following the example provided by the SAB, we used a smoothing parameter of 0.2 in our 

application of the LOESS procedure. The LOESS-based curve presented in Figure 1-8 reflects exclusion of two 

outliers. 
28 

percentile values are associated with higher HUC-level sample sizes.30 Interestingly, the mean 

50th percentile HUC-level statistic across sample size strata (see Table 1-3) also tends to be 

higher with more samples, but the trend is not as substantial or consistent as for the 75th

percentile estimates. This is expected because the median is a central tendency statistic, and 

therefore any specific sample is likely to be closer to the central tendency than to other 

percentiles. As a result, the small sample size issue is not anticipated to introduce significant 

bias in the context of generating median estimates, but it may underestimate higher-end values 

such as the 75th percentile. Uncertainty related to sample size and potential bias in the 75th

percentile HUC-level fish tissue Hg concentrations is also discussed in section 2.7, Table 2-15. 

Interestingly, we do not see a consistent trend across the sampling site strata (i.e., the fish 

tissue Hg statistic does not increase in a consistent manner with increasing number of sites – see 

Table 1-2). This is not unexpected since the number of sampling sites is not directly correlated 

with the number of observations at a given watershed (e.g., there are watersheds that have a few 

sites each with a large number of observations and there are watersheds with a larger number of 

sites, but each with only 1-2 observations). 

Figure 1-7 Histogram Characterizing Frequency of Sample Sizes Across HUCs Included in 

the Risk Assessment (illustrates fraction of HUCs with small sample size of 1-2) 

 

30 This trend (higher sampled HUCs having higher 75th percentile fish tissue Hg values) could simply reflect the fact 

that some states may target sampling at locations believed to have higher mercury impacts based on previous 

sampling. 

0

100

200

300

400

500

600

700

800

900

Frequency

(# of HUCs)

Number of Hg Fish Tissue Samples in HUC*

HUC Frequency by Number of Hg Fish Tissue Samples

*When sample sizes are 20 or greater, a category is used i.e. 20 corresponds to 20 to 25, 25 corresponds to 26 to 30, etc.
29 

Figure 1-8 LOESS (locally-weighted scatter plot smoothing) Least-Square Regression of 

75th Percentile HUC-level Fish tissue Hg Levels Against HUC-level Sample Size 

Table 1-3 Summary of 50th Percentile Hg Concentrations in Fish Tissue Samples by 

Number of Observations and Number of Sites per HUC 

Number of 

Observations per HUC 

Percentiles (of the 50th percentile HUC-level fish tissue 

Hg concentration - ppm) 

25 Mean N th 50th 75th 90th 95th

1 Observation 0.077 0.1564 0.2887 0.493 0.67834 0.226803 838 

2 Observations 0.08055 0.1362 0.25 0.408 0.5235 0.195935 458 

3-5 Observations 0.08403 0.1443 0.235 0.425 0.639 0.205501 468 

6-10 Observations 0.1295 0.207913 0.36 0.524 0.72 0.274554 506 

> 10 Observations 0.178 0.252444 0.376818 0.59 0.728846 0.310015 877 

Total 0.104 0.19 0.3203 0.5033 0.674 0.25001 3147 

Number of sampling 

sites per HUC 25th 50th 75th 90th 95th Mean N 

1 Site 0.1 0.1815 0.31 0.505 0.6633 0.245554 2371

2 Sites 0.1165 0.2105 0.347335 0.531 0.7371 0.269281 553

3-5 Sites 0.134761 0.205034 0.32434 0.439024 0.496111 0.247729 196

6-10 Sites 0.100278 0.175125 0.338674 0.491046 0.638407 0.236554 19

> 10 Sites 0.199375 0.314303 0.41785 0.633312 0.633312 0.326412 8

Total 0.104 0.19 0.3203 0.5033 0.674 0.25001 3147 

There is also uncertainty due to excluding fish samples smaller than 7 inches. 

Specifically, if subsistence fishers also target fish smaller than 7 inches in (and if those smaller 

fish have lower MeHg concentrations), then the risk estimates could be high-biased for this 

Number of observations per watershed

Watershed‐level 75th percentile fish tissue Hg level (ppm)
30 

group of subsistence fishers, since we excluded those smaller fish in our modeling. However, it 

is important to reiterate that this analysis is not intended to generate a representative assessment 

of the distribution of exposure and risk across the entire set of higher consuming self-caught 

freshwater fishers. Given the goal of the analysis is to determine whether the potential exists for 

adverse health impacts linked to self-caught fish consumption, it is reasonable (and indeed a 

stated element of the scope of the analysis – see section 1.2) to focus on those behaviors by 

subsistence fishers that would place them at greater exposure and risk. Given this goal, it is 

reasonable to assume that a subset of subsistence fishers could focus on larger fish (i.e., > 7 

inches) as they attempt to supplement their diet. By basing exposure and risk modeling on the 

75th percentile fish and excluding fish smaller than 7 inches, we have targeted this subset of 

subsistence fishers in this risk assessment. 

While a reasonable assumption is that a subset of high-consuming subsistence fishers will 

choose to catch and eat fish larger than 7 inches, reflecting recommendations by SAB, we 

include a sensitivity analysis to examine the degree to which exclusion of fish smaller than 7 

inches affects risk estimates. Table 1-4 compares trends in different HUC-level percentile fish 

tissue Hg concentrations (and the mean) based on consideration for (a) only fish > 7 inches in 

length (used in the national-scale mercury risk assessment), and (b) fish of all lengths. Of 

particular interest are the bolded rows in the table that provide results based on the 75th percentile 

sample, which is the value used in the risk assessment. The results provided in Table 1-4 suggest 

that excluding fish smaller 7 inches did not have a large impact on the HUC-level percentiles of 

fish tissue Hg. This can be seen by comparing the 75th percentile HUC-level estimates using only 

the fish larger than 7 inches with the 75th percentile estimates based on all fish combined. For 

example, if we compare the average of the HUC-level 75th percentile values when only fish >7 

inches in length were included (0.32 ppm) with the average of the HUC-level 75th percentile 

values when all lengths are included (0.31 ppm) we see that the two values are similar. This 

observation holds even if we look at higher end percentiles of the HUC-level 75th percentile 

values (e.g., the 95th percentile of the HUC-level 75th percentile values are 0.89 ppm and 0.86 

ppm, for the >7 inch and all lengths included, datasets, respectively). One likely reason that the 

exclusion of fish smaller than 7 inches did not have a large impact on the 75th percentile HUClevel estimates is that this subset of smaller fish represents a relatively small fraction of the entire 

fish tissue sample dataset considered for this analysis (i.e., ~3,400 measurements for <7 inch fish 

versus 35,600 measurements for > 7inch fish, or roughly ten times more samples for >7 inch 

fish). Because the impact on fish tissue concentrations is small, we do not carry forward this 

sensitivity analysis through the calculation of risks, as the impact on risk is also likely to be 

small. 
31 

Table 1-4 Comparison of HUC-Level Fish Tissue Hg Statistics for (a) Fish Tissue Dataset 

with Fish >7 Inches and (b) Dataset with All fish Lengths Included 

HUC-level statistic N 

Fish tissue Hg concentration (ppm) - as relevant 

Mean 25th % 50th % 75th % 90th % 95th % 99th % 

 (a) Fish Length > 7 Inches (as used in the national-scale Hg risk assessment) 

mean 3,147 0.27 0.11 0.20 0.35 0.54 0.71 1.09 

25th % 3,147 0.19 0.08 0.14 0.25 0.40 0.52 0.89 

50th % 3,147 0.25 0.10 0.19 0.32 0.50 0.67 1.06 

75th % 3,147 0.32 0.13 0.25 0.42 0.67 0.89 1.36 

90th % 3,147 0.40 0.14 0.29 0.53 0.86 1.13 1.86 

Number of samples per HUC 3,147 11.23 1 4 12 28 44 106 

Number of species per HUC 3,147 2.69 1 2 4 6 7 10 

Number of sample sites per 

HUC 3,147 1.41 1 1 1 2 3 5 

 (b) All fish lengths

mean 3,268 0.25 0.10 0.19 0.33 0.52 0.68 1.05 

25th % 3,268 0.18 0.07 0.13 0.24 0.38 0.50 0.85 

50th % 3,268 0.24 0.10 0.18 0.30 0.48 0.65 1.02 

75th % 3,268 0.31 0.12 0.23 0.40 0.65 0.86 1.34 

90th % 3,268 0.39 0.13 0.27 0.51 0.83 1.10 1.82 

Number of samples per HUC 3,268 11.83 1 4 13 29 46 123 

Number of species per HUC 3,268 2.85 1 2 4 6 8 11 

Number of sampling sites per 

HUC 3,268 1.41 1 1 1 2 3 5 

1.4.2.1 Projecting 75th percentile fish tissue Hg concentrations for the 2016 scenario 

Once 75th percentile fish tissue Hg concentrations are generated for each of the 

watersheds, we then adjust these estimates to represent 75th percentile fish tissue Hg 

concentrations for the 2016 scenario which are used in modeling risk for this scenario. As 

mentioned in section 1.4 (Step 4 associated with Figure 1-2), projection of fish tissue Hg 

concentrations for 2016 is based on application of the proportionality assumption combined with 

Hg deposition estimates for the 2005 and 2016 scenarios. Specifically, for a given watershed, we 

multiply the 75th percentile measured fish tissue Hg concentration, by the ratio of total Hg 

deposition in 2016 to total Hg deposition in 2005. This results in a projection of 2016 fish tissue 

Hg concentrations. That 2016 projected total fish tissue Hg concentration is then used to model 

HQ for the 2016 scenario in that watershed. Detail on the proportionality assumption (supported 

by Mercury Maps simulations and more recent literature) is presented in section 1.4.6. 

1.4.3 Defining subsistence fisher scenarios to include in the analysis 

As described in section 1.4, defining subsistence fisher scenarios included in this analysis 

requires three tasks: (a) identify subsistence fisher populations, (b) assess where they might be 
32 

active and (c) define fish consumption rates for female subsistence fish consumers associated 

with those populations. These three tasks are described below. 

Identify subsistence fisher populations 

As discussed in sections 1.2 and 1.4, this analysis estimates risk for scenarios 

representing female subsistence fish consumers who have the potential to consume fish caught at 

inland freshwater locations, since these populations are expected to experience the greatest U.S. 

EGU-attributable risks. Therefore, in reviewing studies of fishing behavior, we prioritized 

surveys characterizing fishing activity for populations with these characteristics. In addition, we 

needed studies that provided statistically rigorous estimates of annual-average daily fish 

consumption rates. Subsistence levels of fish consumption are likely only experienced by 

relatively small fractions of the study populations surveyed (e.g., 90th to 99th percentile 

consumption rates). 

Although we reviewed many studies, we ultimately identified three studies that met our 

criteria for characterizing subsistence-level self-caught freshwater fish consumption. These three 

studies included several subsistence populations differentiated by ethnicity and SES status 

including: (a) White and Black populations (including female and low income strata) surveyed in 

South Carolina (Burger et al., 2002), (b) Hispanic, Vietnamese and Laotian populations surveyed 

in California (Shilling et al., 2010) and (c) Great Lakes Tribal populations (Chippewa and 

Ojibwe) active around the Great Lakes (Dellinger et al., 2004). 

These studies were used to characterize behavior for female subsistence fish consumers 

assumed to be associated with the subsistence fisher populations described in the studies.31

Specifically, based on these studies, we characterized behavior for seven distinct female 

subsistence fish consumer scenarios which were included in the risk assessment. These scenarios 

are listed (and briefly described) in Table 1-5. 

Table 1-5 Spatial Extent and Number of HUCs Reflected in Risk Modeling for the Female 

subsistence fish consumer Scenarios Included in the Risk Assessment 

Female 

subsistence 

fish consumer 

Scenario Description Spatial Extent 

Number of 

watersheds 

(HUC12s) 

Assessed For 

Risk 

Typical 

(non-SES 

differentiated) 

Generalized (non-SES differentiated) female subsistence 

fish consumers associated with fishing activity that 

could occur at any of the watersheds across the nation

with fish tissue Hg data (i.e., source population 

construct is not applied here). 

National (all HUCs 

with fish tissue Hg 

measurements) 

3,141 

Low income Low income White female subsistence fish consumers Southeast 860 

 

31 With the exception of the Burger et al., 2002 study, which did provide consumption rates for females, we assumed 

that rates provided for specific SES-differentiated populations could apply to pregnant females associated with those 

fishers (e.g., either as family members or acquaintances). We recognize that there is uncertainty associated with this 

assumption, but would also point out that the female consumption rates provided in Burger et al., 2002 (particularly 

for the higher percentiles) are in the range of values seen for males and for non-sex differentiated survey groups both 

in that study and in the other two studies of high-end self-caught fish consumption cited here. 
33 

Female 

subsistence 

fish consumer 

Scenario Description Spatial Extent 

Number of 

watersheds 

(HUC12s) 

Assessed For 

Risk 

White 

(southeast) 

active in the Southeast; in watersheds with fish tissue 

Hg data where there are at least 25 White individuals 

below the poverty line. 

Low income 

Black 

(southeast) 

Low income Black female subsistence fish consumers 

active in the Southeast, in watersheds with fish tissue 

Hg data where there are at least 25 Black individuals 

below the poverty line. 

Southeast 756 

Low income 

Hispanic 

Low income Hispanic female subsistence fish 

consumers active at any watersheds in the continental 

U.S. with fish tissue Hg data where there are at least 25 

Hispanic individuals below the poverty line. 

National (where 

there are low 

income Hispanics) 

1,325 

Great Lakes 

Tribal 

Tribal (Chippewa/Ojibwe) female subsistence fish 

consumers active at any watersheds with fish tissue Hg 

data within the ceded territories in the vicinity of the 

Great Lakes (i.e., source population construct not 

applied here). 

Vicinity of Great 

Lakes (ceded 

territories) 

361 

Laotian 

Laotian female subsistence fish consumers active at any 

watersheds in the continental U.S. with fish tissue Hg 

data where there are at least 25 Laotians. 

National (where 

there are Laotians) 131 

Vietnamese 

Vietnamese female subsistence fish consumers active at 

any watersheds in the continental U.S. with fish tissue 

Hg data where there are at least 25 Vietnamese. 

National (where 

there are 

Vietnamese) 

336 

Assess where the subsistence fisher populations might be active

The following approach was used to identify the spatial extent of activity for each of the 

seven female subsistence fish consumer scenarios. For all of the scenarios (with the exception of 

the Tribal scenario associated with Great Lakes), we assumed that high-end fishing behavior 

could be generalized beyond the specific geographic areas covered in a particular study. This 

type of generalization was necessary to provide sufficient coverage for the continental U.S. and 

in particular, the eastern part of the U.S. where U.S. EGU-attributable deposition is higher and 

where we have more measured Hg fish tissue data. In deciding how to extend coverage for each 

fisher population, we considered several factors including (a) the potential for high-end fishing 

activity to be culturally-related and therefore more likely to be followed by populations of a 

given ethnicity living across the U.S. and (b) the potential that subsistence fishing activity (and 

related consumption) might be driven by economic need. Because we assumed that fishing 

activity is culturally related, we generalized fishing activity by Hispanics, Laotians and 

Vietnamese beyond California, where the study providing consumption rates was conducted, to 

the nation. Similarly, we assumed that surveys of high-end fish consumption by low income 

Whites and low income Blacks in South Carolina might be generalized to the southeast, 

reflecting similar cultural practices by these groups within that region. 

Once we identified the regions of the country where a particular subsistence fisher 

population might be active, we then identified the specific subset of watersheds within that 

region where the high-end fishing activity (and consumption by female subsistence fish 

consumers associated with those subsistence fishers) might occur. This task was challenging 
34 

because we do not have data characterizing the number and location of subsistence fishers for 

any of the fishing populations under consideration. Therefore, we developed and applied a 

source population construct to guide identification of areas (watersheds) where a given 

subsistence scenario might be active.32 This approach requires that in order for a subsistence 

fisher scenario to have the potential to be active at a given watershed, that watershed must be in 

close proximity to a source population matching the ethnic/SES composition of the subsistence 

fishers being represented. In this case, we specified that a watershed would have to intersect a 

U.S. Census tract with at least 25 individuals with the same ethnic/SES composition as the 

subsistence fisher scenario (i.e., a source population) for there to be the potential for that type of 

fishing at that watershed. So, for example the Laotian subsistence fisher scenario was only 

assessed at those watersheds intersecting U.S. Census tracts with at least 25 Laotians. In the case 

of low income White and Black subsistence fishers in the southeast as well as Hispanics assessed 

nationally, we required that the watershed intersect U.S. Census tracts with at least 25 

individuals from that ethnic group who fall below the poverty line. The SAB peer review panel 

agreed that the criterion of using 25 persons within a source population to identify watersheds 

with the potential for subsistence fishing activity is a reasonable approach. The specific approach 

used to extrapolate coverage regionally and then determine the subset of watersheds to include in 

risk modeling for each subsistence fisher scenario is described both in Table 1-5 and in greater 

detail in Table 1-6 (in Table 1-6 see columns “C” and “E”). As described in Table 1-6 (columns 

“C” and “E”), the approach used for the Tribal population and typical female subsistence fish 

consumer scenarios differs from those used for the other scenarios. In the case of the Tribal 

subsistence fisher scenario, given the potential for fishing activity to be closely associated with 

heritage cultural practices, we have not extrapolated fishing activity for the Chippewa/Ojibwe 

outside of their ceded territories, although we do assume that all watersheds within those 

territories with fish tissue Hg data have the potential for fishing activity by this group. 

The typical female subsistence fish consumer scenario differs from the other scenarios in 

that it is not SES-differentiated and is assessed nationally at all watersheds with fish tissue Hg 

data (i.e., the source population construct is not used to constrain its spatial extent). The 

generalized nature of this scenario means that it provides broader coverage for subsistence fisher 

activity and for this reason we assume that this scenario (together with female subsistence 

consumption associated with this fishing activity) could potentially occur at any watershed with 

fish tissue Hg data.33 As noted in section 1.2 and 1.4, because the typical female subsistence fish 

consumer is the most generalized of the scenarios and has the greatest spatial extent, we place 

greater emphasis on this scenario in presenting risk estimates.34 Application of the typical female 

 

32 As described in section 1.4, a source population is a group of individuals with demographic attributes matching 

those of the subsistence fisher group being evaluated. While we cannot enumerate the subsistence fishers directly, 

we can use demographic data to determine if the underlying source population is present in the vicinity of a 

watershed with fish tissue Hg data. 

33 We are not suggesting that we expect the scenario to occur as a certainty at all watersheds, or that we can predict 

the level of activity (number of fishers or consumers) at each watershed. Rather, for this analysis, we are stating that 

we believe it reasonable to assume that the typical female subsistence fish consumer scenario (and associated fishing 

activity) could potentially occur at some subset of the watersheds with fish tissue Hg data. 

34 The typical female subsistence scenario included in this revised risk assessment is similar to the high consuming 

female scenario included in the March version of the Mercury Risk TSD. However, the typical female subsistence 

scenario is applied without consideration for a source population, while in the case of the high-consuming female 

angler; we did consider a source population based on poverty. Because the typical female subsistence scenario does 

not use a source population (and is applied uniformly to all watersheds with fish tissue Hg data except for those 
35 

subsistence fish consumers scenario to all watersheds with fish tissue Hg data also helps to 

address the concern raised by the SAB that the March version of the national-scale Hg risk 

assessment may have excluded more remote water bodies that could be fished by subsistence 

anglers (leading to an underestimation of the percent of watersheds where Hg exposure from 

U.S. EGU sources is a risk). As mentioned earlier, Table 1-5 lists the seven female subsistence 

fish consumer scenarios and presents the number of watersheds assessed for risk for each 

scenario. 

Define self-caught fish consumption rates for the subsistence scenarios

After identifying the set of female subsistence fish consumer scenarios to include in the 

analysis and determining where those scenarios should be applied, next we defined high-end 

(subsistence) self caught fish consumption rates for those scenarios. The three studies referenced 

in Table 1-6 provided either subsistence-level consumption rates at the 90th to 99th percentile, or 

the statistical parameters necessary to calculate those percentiles (e.g., median and standard 

deviations).35 In establishing consumption rates for the seven female subsistence fish consumer 

scenarios, we either used rates from the studies directly (as in the case of the typical female 

subsistence fish consumer which uses female consumptions rates from the Burger et al., 2002 

study) or we used non-sex differentiated consumption rates, assuming that they applied to 

women of childbearing age. As noted earlier in this section, there is uncertainty associated with 

the assumption that non-sex differentiated consumption rates could apply to women of childbearing age (see footnote 33). 

The subsistence-level consumption rates used in modeling risk for these scenarios are 

presented in Table 1-6, along with notes relevant to their interpretation in the context of this risk 

assessment. Of particular interest was whether the consumption rates represented annualaverages of daily “as consumed/prepared” values. As noted earlier in 1.4, this issue is critical to 

ensuring the appropriate technical linkages between the types of fish tissue Hg measurements 

made, application of preparation adjustment factors and ultimately, calculation of exposure and 

risk. This issue is discussed in greater length in the next section. 

The fish consumption rates presented in Table 1-6 clearly represent subsistence behavior 

since they reflect an individual making significant contributions to their diet through self-caught 

fish consumption. In most instances, the rates are equivalent to between one 8oz fish meal every 

few days to a larger fish meal (12oz or more) every day. While these consumption scenarios are 

identified as “subsistence” due to their magnitude, they could be experienced by individuals 

purposefully supplementing their diet due to economic need or due to high levels of recreational 

fishing activity. While these scenarios could represent either subsistence or high-end recreational 

fishing, the resulting exposure and risk characterization would be similar. For typical female 

subsistence fish consumers, as mentioned earlier, this scenario is applied uniformly across all 

watersheds with fish tissue Hg data, and whether we define consumption as subsistence or highend recreational fishing does not affect the exposure and risk characterization. In either case, the 

 excluded due to potential impacts from non-air deposition), this scenario does provide greater coverage 

geographically than did the high-consuming female which was only applied to watersheds with fish tissue Hg data 

and at least 25 members of the source population (individuals living below the poverty line). 

35 In those instances where a specific percentile was not provided, we estimated that value using the statistical 

parameters provided together with the assumption that the underlying fish consumption distribution was lognormal. 
36 

risk estimates represent risk experienced by an individual residing in the vicinity of a particular 

watershed who consumes self-caught fish at a subsistence level.36 In addition to the three studies 

we used to define subsistence fish consumption rates, we also reviewed additional studies 

characterizing higher-levels of self-caught fish consumption in the U.S. While these additional 

studies had limitations that prevented their use in this risk assessment, they generally support the 

rates of self-caught fish consumption modeled in the analysis. 

A subset of these “supporting” studies is briefly described below: 

 A study by Burger et al., 1999, examined recreational and subsistence fishing activity 

along the Savannah river in Georgia to determine the role played by socio-economic 

status (SES) factors (including race, education and income) and determined levels of selfcaught fish consumption in this study area. The study suggested that all three factors are 

associated with levels of fishing activity. Specifically, in the case of race, the study 

showed that Blacks tend to have much higher rates of fish consumption than Whites. For 

both groups, the study suggested that upper-end percentile consumption rates could be 

high enough to approach subsistence levels. For example, a ~200 g/day fish consumption 

rate represented the 98th percentile for Whites, but only the 92nd percentile for Blacks. 

This study supports the presence of high-end consumption rates for both Blacks and 

Whites that approach or meet subsistence levels in this area of the country. While this 

study did provide support for high self-caught fish consumption rates and SES 

differentiation of fish consuming populations, because of its geographically limited scope 

(i.e., focus on Savannah River), we elected not to use it directly in defining consumption 

rates for the risk assessment. 

 A study by Moya et al., 2008 examined factors associated with regional differences in 

patterns of fish consumption, including age, ethnicity (including Tribal affiliation), 

socioeconomic status (e.g., income, education), and type/source of fish consumed 

(freshwater, marine, and estuarine obtained from commercial sources versus self-caught). 

The study examined fishing activity in four states (i.e., CT, FL, MN, and ND) and 

provided estimates of high-end self-caught fish consumption for populations. Higher, 

subsistence-level consumption rates were identified for fishing populations in FL, MN 

(specifically for Tribes) and CT (for Asian populations, although it is not clear whether 

the rates for Asians are for self-caught fish consumption). Higher-end rates reported for 

ND and for general fishers in CT and MN did not approach the range of subsistence 

levels of consumption. However, the study designs used in these surveys may not 

effectively capture the small fraction of the overall population likely engaging in highend subsistence levels of self-caught fishing behavior. This study supports the existence 

of subsistence fishing populations in FL and for Tribes in MN. However, failure to 

capture similar behavior in ND and CT does not necessarily suggest that this type of 

 

36 To generate population-weighted risk estimates, it is important to differentiate between high-end recreational and 

necessity-based subsistence activity, since the associated populations at any given watershed could differ, leading to 

different population-weighted risk distributions. However, because we lacked other data needed to calculate 

population-weighted risks, this differentiation is not relevant. 
37 

behavior is non-existent, but it may suggest that subsistence behavior is less prevalent 

than in FL. We did not elect to use the data for FL to define consumption rates since we 

already had the Burger et al., 2002 study which covers a region of the Southeast (South 

Carolina) where we have a larger number of watersheds being modeled for risk. 
38 

Table 1-6 Fish consumption rates and additional behavior-related information for subsistence populations 

analysis 

(A) Fish consuming 

populations covered by 

study (and reference 

information) 

(B) Overview of 

study 

(C) Assumption regarding where 

the subsistence fisher scenario 

(and associated female subsistence 

fish consumers) might be active 

(D) Self-caught fish consumption rates 

(mean, 90th, 95th, 99th) g/day and notes on 

type of consumption rate 

(e.g., temporal averaging period and as 

purchased versus as consumed)

(E) 

infor

Higher self-caught fish consuming populations (White, Black and female) surveyed in South Carolina Citation: Daily consumption of wild fish and game: Exposures of high end recreationalists, Burger et al., International Journal of Environmental Health Research, 12:4, p. 343-354, July, 2002 

Random survey of participants in the Palmetto Sportsmen’s Classic in Columbia SC (1998). Population interested in fishing/hunting (not general population – represents outdoor enthusiasts in SC) 

- the Black and White fisher populations were extrapolated to cover watersheds modeled for risk in the Southeastern states. The rationale for this was that fishing activity by these two groups could be generalized in this region of the country. These scenarios were only assessed for watersheds in the Southeast located within U.S. Census tracts with at least 25 individuals from that ethnic group below the poverty line. - given the focus of the risk assessment on consumption by women (in considering exposures for pregnant women in particular), we extrapolated the typical female consumer scenario to all watersheds in the continental U.S. and, given the more generalized nature of this scenario (no ethnic or SES differentiation), we assessed the scenario for all watersheds included in the risk assessment (i.e., we did not apply the source population criterion used for the other scenarios). 

- Black: 171, 446, 557, NC * - White: 38.8, 93, 129, 286 - female: 39.1, 123, 173, 373 * the sample size for this population is only 39, reducing overall confidence in a 99th

consumption rate (therefore, this high-end percentile was not included in the risk assessment) Consumption rates are annual-average values expressed as meals or portions (as prepared) (median, 75th, 90th, 95th, and 99th). Survey asked respondents by month for number of meals of different type of fish and serving size (here models were used to demonstrate different meal or serving sizes). Authors could then use this to estimate monthly consumption rates and convert these into an annual average. 

Samplresponfemale Black other guncertvalues The auresults spread and m

percenthan thfor wiResultincomThese relativhigher rates aThis oour dethe subwaterstracts populafor the 
39 

(A) Fish consuming 

populations covered by 

study (and reference 

information) 

(B) Overview of 

study 

(C) Assumption regarding where 

the subsistence fisher scenario 

(and associated female subsistence 

fish consumers) might be active 

(D) Self-caught fish consumption rates 

(mean, 90th, 95th, 99th) g/day and notes on 

type of consumption rate 

(e.g., temporal averaging period and as 

purchased versus as consumed)

(E) 

infor

Higher self-caught fish consuming ethnic populations including Hispanics, Laotians and Vietnamese surveyed in California Citation: Contaminated fish consumption in California’s Central Valley Delta (Shilling et al., Environmental Research 110, p. 334-344 (2010) 

Study looks at subsistence fishing activity among ethnic groups associated with more urbanized areas near the Sacramento and San Joaquin rivers in the Central Valley in CA. 

- the Hispanic fishing scenario was extrapolated to cover watersheds located in U.S. Census tracts with at least 25 low income members of the ethnic populations (e.g., the Hispanic consumption rates would be applied to the subset of the 3,141 watersheds located in U.S. Census tracts with at least 25 low income Hispanic individuals). - the Laotian and Vietnamese fishing scenarios were extrapolated to cover watersheds located in U.S. Census tracts with at least 25 members of the underlying ethnic group. 

- Hispanic: 25.8, 98, 155.9, NC* - Lao: 47.2, 144.8, 265.8, NC* - Vietnamese: 27.1, 99.1, 152.4, NC* * 95th percentile values were provided in the 

study. 90th percentile values were calculated 

using Crystal Ball (based on the median and standard deviations provided) assuming a lognormality of the consumption rate distributions. 99th percentile consumption rates were not provided (or derived) for any of these populations due to small sample sizes of the study populations. Consumption rates are annual-average estimates (cooked weight - as filets) based on the fact that they compare their average values against other rates from the literature including rates used by the EPA in the regulatory context - all of which are annual averages. Survey used different sized models of cooked fish filets. 

The auethnic origin practicVietnaauthorconsumspecifSoutheline wstudie
40 

(A) Fish consuming 

populations covered by 

study (and reference 

information) 

(B) Overview of 

study 

(C) Assumption regarding where 

the subsistence fisher scenario 

(and associated female subsistence 

fish consumers) might be active 

(D) Self-caught fish consumption rates 

(mean, 90th, 95th, 99th) g/day and notes on 

type of consumption rate 

(e.g., temporal averaging period and as 

purchased versus as consumed)

(E) 

infor

High-end self-caught fish consuming Chippewa and Ojibwa Tribal populations active in the vicinity of the Great Lakes. Citation: Exposure assessment and initial intervention regarding fish consumption of tribal members in the Upper Great Lakes Region in the United States. Dellinger , Environmental Research 95 (2004) p. 325-340

This study contrasted selfreported fish consumption rates by Tribes in the Great Lakes area with “actual” fish consumption rates collected for a subset of the original study population (147 of 822 from 4 Tribal population/location combinations). The study found that actual fish consumption rates were lower than reported values. 

Activity only assumed to occur in areas ceded to the Tribes covered in the study (regions in the vicinity of the Great Lakes). Because fishing activity is highly variable across Tribes (and closely associated with heritage cultural practices) we have not extrapolated fishing behavior for these Tribes outside of the specific populations and regions covered. 

- reported value for all Tribal areas (in the study) combined: 62, 136.2, 213.1, 492.8 All higher percentiles (90th – 99th) were 

derived using Crystal Ball (based on median and standard deviations and an assumption of log-normally distributed variability in consumption rates) Consumption rates appear to be annual average values. Study includes reference to querying for the number of fish meals in a year, which suggests that estimates are annualaverages (but it is not explicitly stated). Ingestion rates are in-line with other high-end consumer rates cited in this table. It also appears that estimates are for amount consumed (i.e., meal weight), based on the fact that the "actual" estimates provided in the article focused on this type of consumption rate. 

While rates cfamilireportmagnifactors the repactual First, asampl“actuafrom 1(repreassocisurvey samplprobabwith hbroadethat thwalleyall fishvalues that, edecreapracticcould rates), associhistoribe impthe higrates dmean standasubsisfor othTherefconsumcompoconsumare coin the 
41 

1.4.4 Estimating total fish consumption-related MeHg exposure (2016 scenario) 

Next, we estimated total exposure to Hg for the set of seven female subsistence fish 

consumer scenarios being evaluated (for the 2016 scenario). To generate estimates of total Hg 

exposure, we combined the 75th percentile fish tissue Hg values (projected for 2016) with three 

exposure-related factors including: (a) the consumption rates for a subsistence fisher scenarios in 

grams of fish per day, (b) a conversion factor between total Hg measurements in fish and MeHg 

levels in fish and (c) a food preparation adjustment (cooking fish can increase MeHg 

concentrations). 

In modeling MeHg exposure through fish consumption, it is important to verify that the 

linkages between factors used in modeling exposure (i.e., the variables in the exposure equation 

listed above) are conceptually correct. For example, we need to convert the total Hg levels in fish 

to MeHg levels in order to calculate the HQ, which is based on MeHg intake. Similarly, if fish 

ingestion rates are specified in terms of “as cooked” or “as consumed” rather than “as 

purchased” or “as market basket,” then we must factor in cooking/preparation in specifying the 

fish tissue Hg concentrations. Failure to correctly link these exposure factors can result in biased 

estimates of MeHg intake and consequently biased risk estimates. Below we discuss each 

exposure factor, including the data used to specify that factor and the linkage of that factor to 

others in the exposure equation. Then, we present the exposure equation. 

Mercury fish tissue concentration (FTC): We calculated the 75th percentile fish tissue Hg 

estimate for each watershed and then project those to represent the 2016 scenario (as described in 

section 1.4.2.1). Generally, measured fish tissue Hg is in terms of total Hg. Because we need 

MeHg levels in fish, we convert total Hg levels measured in fish into MeHg levels using the 

mercury conversion factor (MCF). 

Mercury conversion factor (MCF): We applied a factor of 0.95 (unitless) to convert total Hg 

levels in fish tissue samples into MeHg levels. This conversion factor is based on conclusions 

reached in the Mercury Study Report to Congress (MRTC) (U.S. EPA, 1997) that more than 

90% of Hg in fish is MeHg. The conclusion presented in the MRTC is in turn based on two 

studies (Bloom, 1992 and Morgan et al., 1994). The 0.95 factor represents a median value 

between an assumption that all of the Hg in fish is MeHg and the lower bound value of 0.90 cited 

in the MRTC document. 

Food preparation/cooking adjustment factor (FPCAF): Because we use fish consumption 

rates for “as consumed” portions, we need to adjust the fish tissue Hg concentrations to reflect 

concentrations after food preparation. Cooking fish typically increases MeHg levels per unit fish 

because Hg concentrates in the muscle while preparation involves removal primarily of nonmuscle elements of the fish (e.g., water, fat etc) (Morgan et al., 1997). The FPCAF factor used in 

the analysis is 1.5 (i.e., an increase of 50% in the MeHg concentration per unit fish due to 

preparation/cooking). This factor is based on the Morgan et al., 1997 study, which estimated 

factors of between 1.1 and 1.5 for walleye and 1.5 and 2.0 for lake trout. 

The SAB recommended that additional studies be acknowledged as providing information 

regarding the FPCAF, citing two alternative studies (Farias et al., 2010 and Musaiger et al. 

2008). After assessing these studies, we conclude that they do not support a lower 
42 

preparation/cooking loss adjustment factor. The Farias study appears to suggest that preparing 

fish by Manaus residents in the Amazon could decrease Hg concentrations. Although this 

initially suggests that the 1.5 factor used in the risk assessment is biased high, a closer read of the 

Farias study suggests that the authors may have measured non-fish components added to dishes 

(e.g., onions, heavy breading etc) in post-cooking measurements, which could provide the 

appearance of a cooking loss in Hg while actual fish tissue Hg concentrations could have 

increased. The Musaiger et al. 2008 study compared Hg levels across different types of fish 

meals after preparation rather than pre and post-cooking Hg levels. In fact, the authors state that 

cooking is not a means of reducing Hg because it typically removes fat and water, while Hg is 

located in the meat and therefore will not be reduced.37 

Fish consumption rate (FCR): We reviewed the three studies providing fish consumption rates 

used in the risk assessment to verify that consumption rates (a) are annual-averages and not rates 

reflecting elevated consumption during shorter seasonal periods and (b) represent “as consumed” 

rather than “as purchased” values. Based on the review of the three studies we concluded that the 

consumption rates are annual average daily consumption rates for “as cooked/as prepared.” In 

some instances, a study did not explicitly state whether rates were annual-averages and/or “as 

cooked”, and we had to infer this from other information provided in the article. Details 

regarding our assessment of each of the three studies is provided in Table 1-6 (column D). 

The following equation shows how these factors were combined to generate estimates of 

annual-average daily MeHg exposure per kg body weight: 

IR = FTC(2016) * MCF * FPCAF * FCR 

 BW 

where, 

IR = daily MeHg intake rate (ug/kg-day) 

FTC(2016) = Hg fish tissue concentration (ug/g or ppm of total Hg) projected for the 

2016 scenario 

MCF = Hg conversion factor (unitless) 

FPCAF = food preparation/cooking adjustment factor (unitless) 

FCR = fish consumption rate (g/day) 

BW = body weight (kg) 

 

37 As part of revising the national-scale Hg risk assessment, we completed a literature review focusing on the issue 

of cooking/preparation of fish and the effect on Hg concentration. This literature review included the two studies 

recommended by the SAB. That review did not identify any studies that argued against use of an adjustment factor 

of 1.5 and consequently, we continue to use that factor in modeling exposure and risk. 
43 

1.4.5 Estimating risk (RfD-based hazard quotient) (2016 scenario) 

Once we estimate the MeHg intake rate for each female subsistence fish consumer 

scenarios at each watershed (for 2016), we compare these exposure estimates to the MeHg RfD 

to generate HQ estimates. The MeHg RfD is 0.0001 mg/kg-day (equivalent to 0.1 μg/kg-day) 

and was published by the EPA in the Integrated Risk Information System in 2001 (US EPA, 

2001a) (http://www.epa.gov/iris/subst/0073.htm). 

Reflecting precedent in interpreting HQ estimates, we consider exposures above the RfD 

(i.e., an HQ above one) to represent a potential public health hazard. Consequently, watersheds 

with female subsistence fish consumer exposures above the RfD are considered to have the 

potential for consumers of self-caught fish from that watershed to experience a public health 

hazard due to MeHg exposure. An important factor to consider in interpreting HQ’s is the 

precision underlying these risk estimates. While precision associated with the exposure estimates 

may be higher, the RfD is only reported to one significant digit, which limits the precision in the 

HQ estimate. Consequently, we interpret an HQ of 1.5 or greater as representing an exposure that 

exceeds the RfD (since this value will round to two expressed as a whole number). Conversely, 

exposures of 1.49999 or less are considered not to exceed the RfD given this rounding 

convention for HQs.38 

In response to SAB recommendations that the IQ loss endpoint may not fully capture the 

range of neurodevelopmental effects associated with Hg exposure, we have deemphasized this 

category of risk metrics and moved the IQ discussion to the appendices. We discuss the 

approach used to generate IQ loss estimates including the concentration-response function in 

Appendix A and we provide the results and approach used to interpret the public health 

significant of IQ loss in Appendix B. 

1.4.6 Estimation of U.S. EGU-attributable risk (2016 scenario) 

Next, we estimated the fraction of total risk (HQ) that is attributable to U.S. EGUs (for 

the 2016 scenario). The estimate of U.S. EGU-attributable risk is at the core of the 2-stage risk 

characterization framework (see Section 1.3). We estimate the U.S. EGU-attributable fraction of 

total risk using the linear proportionality assumption linking changes in Hg deposition over 

watersheds with changes in fish tissue Hg concentrations.39 As noted in section 1.4.2.1, this 

proportionality assumption states that, under steady state conditions, a change in Hg deposition 

over a given watershed will result in a proportional change in fish tissue Hg concentrations and 

associated exposure and risk. The proportionality assumption is supported by the Mercury Maps 

 

38 Note, that for the U.S. EGU incremental contribution analysis, an HQ of less than 1.5 does not necessarily 

indicate there is no public health hazard related to U.S. EGU emissions. Rather, it suggests that, for those specific 

watersheds, we need to also consider whether total risk (i.e., the HQ reflecting total MeHg exposure) exceeds 1.5 

and therefore represents a potential public health hazard. If that is the case, then we would consider the degree to 

which U.S. EGUs contribute to that total exposure because incremental exposures above the RfD increase the risk. 39 Because risk at the HUC-level is linearly related to the fish tissue Hg concentration (see sections 1.4.4 and 1.4.5), 

this proportionality assumption can be used to relate changes in mercury deposition to change in total risk at that 

HUC. 
44 

analysis (U.S. EPA, 2001b). In addition, a number of additional studies (some of which were 

highlighted by the SAB in their review of the March TSD) support a proportional relationship 

between changes in Hg deposition and changes in fish tissue Hg concentrations, although with 

important caveats related to the temporal response of aquatic systems to changes in Hg 

deposition/loading. There are also important criteria that must be met for the proportionality 

assumption to hold, which we discuss below. 

We used the CMAQ air quality model to estimate Hg deposition over U.S. watersheds for 

all sources of mercury, including U.S. and non-U.S. sources. We also modeled Hg deposition 

directly from U.S. EGUs by zeroing out Hg emissions from U.S. EGU sources and subtracting 

the results from total Hg deposition to isolate the U.S. EGU contribution. The CMAQ modeling 

for this analysis is described in section 1.4.6.3. 

Hg deposition estimates at each watershed coupled with the proportionality assumption 

described above can be used to estimate U.S. EGU-attributable HQ risk for the 2016 scenario. To 

estimate the U.S. EGU-attributable fraction of that 2016 HQ estimate, we use the ratio of U.S. 

EGU Hg deposition versus the estimate for total Hg deposition (both for the 2016 scenario in that 

watershed). The step-wise procedure for completing the estimate of U.S. EGU-attributable risk 

for 2016 is outlined below: 

Generate U.S. EGU-attributable HQ risk estimates for the 2016 scenario: We use 

the 2016 total and U.S. EGU-attributable CMAQ deposition results with the estimated 

total HQ risk estimates for 2016 to estimate the U.S. EGU-attributable HQ risk at each 

watershed as follows:

2016 EGU-HQ = 2016 THQ * (2016 EGU Hg dep / 2016 total Hg dep) 

Where: 

2016 EGU-HQ: U.S. EGU-attributable HQ risk for the 2016 scenario 

2016 THQ: HUC-level total HQ risk for 2016 generated as described in section 1.4.5. 

2016 EGU Hg dep: 2016 CMAQ-based projections of U.S. EGU-related Hg deposition 

over a given watershed) 

2016 total EGU dep: 2016 CMAQ-based projections of total Hg deposition over a given 

watershed 

 

1.4.6.1 Mercury Maps analysis 

Results of the EPA’s Office of Water’s Mercury Maps analysis support the 

proportionality assumption (U.S. EPA, 2001b). The Mercury Maps analysis used a simplified 

form of the IEM-2M model applied in EPA’s Mercury Study Report to Congress (U.S. EPA, 

1997). By simplifying the assumptions inherent in the freshwater ecosystem models that were 

described in the Report to Congress, the Mercury Maps model showed that these models 

converge at a steady-state solution for MeHg concentrations in fish that are proportional to 

changes in Hg inputs from atmospheric deposition. This steady-state solution only applies in 

situations where air deposition is the only significant source of Hg to a water body, and the 

physical, chemical, and biological characteristics of the ecosystem remain constant over time. 
45 

Consequently, the proportionality assumption used to estimate the U.S. EGU-attributable 

fraction of risk would ideally only be applied to watersheds where these criteria have been met. 

Application of the proportionality assumption in situations where these criteria have not been 

met introduces uncertainty in the apportionment of total risk. EPA recognizes that concentrations 

of MeHg in fish across all ecosystems may not reach steady state and that ecosystem conditions 

affecting Hg dynamics are unlikely to remain constant over time. EPA further recognizes that 

many waterbodies, particularly in areas of historic gold and Hg mining in western states, contain 

significant non-air sources of Hg.40 Finally, EPA recognizes that Mercury Maps does not 

estimate the time lag between a reduction in Hg deposition and a reduction in the MeHg 

concentrations in fish. 

In their peer review, the SAB noted that there are other modeling tools available to link 

deposition to fish tissue concentrations, but did not consider them to be superior for this analysis, 

nor did they recommend their use. The SAB specifically noted that the Regional Mercury 

Cycling Model (R-MCM) could also be used for a national assessment, but they also noted that 

the R-MCM is more data intensive and the results produced by the two model approaches should 

be equivalent. We did not have all of the data inputs (water chemistry, methylation potential, 

etc) that would be required to run the R-MCM, and given the SAB recommendation, and their 

comment that “it is unlikely that substantial additional insight would be gained with the 

alternative model framework,” we elected to use the proportionality assumption as supported by 

the Mercury Maps modeling and the peer-reviewed literature. Because the Mercury Maps 

approach only applies in those watersheds where aerial deposition is the dominant source of Hg 

loading, we excluded watersheds with substantial non-U.S. EGU anthropogenic emissions of Hg 

from the risk assessment as described in section 1.4.2. 

There are a number of limitations and uncertainties associated with the application of the 

Mercury Maps approach in the context of this risk assessment. These limitations are discussed 

here and addressed in Table 2-15. Applying Mercury Maps to apportion fish tissue Hg 

concentrations and consequently exposure and risk between U.S. EGUs and all other sources of 

Hg at the watershed-level assumes that the relationship between fish tissue levels and Hg 

deposition has remained fairly consistent such that near steady-state conditions have been 

reached. However, in reality, patterns of Hg deposition for the period during which the fish tissue 

samples were collected (2000 to 2009) have not remained constant. In addition, those fish tissue 

concentrations may actually reflect patterns of Hg deposition from earlier time periods (e.g., the 

1990s) when Hg emissions from U.S. sources were experiencing substantial decreases.41 In 

 

40 As described below, we have excluded those watersheds containing gold mines or with other non-EGU related 

anthropogenic Hg emissions exceeding specified thresholds. 41 The discussion of model uncertainty provided in the Technical Support Document describing the Mercury Maps 

analysis (MMAPs TSD, U.S. EPA, 2001) also addresses the fact that proportionality between a decrease in Hg 

deposition and changes in fish tissue Hg concentrations may not be fully realized for some time due to the lagging 

effect of Hg that has built up in sediment. Simulations discussed in the MMAPs TSD suggested that substantial 

concentrations of Hg could build up in sediment with these loadings effectively buffering the impact of reductions in 

Hg. This issue does not invalidate the application of the proportionality assumption in the national-scale mercury 

risk assessment, but it does suggest that the full effect of predicted reductions in risk may not be seen in the nearterm (this issue is discussed further in the context of other studies in section 1.4.6.2). The discussion of model 

uncertainty in the MMAPs TSD also addresses potential non-linearity in methylation. Citing the DiPasquale, et al, 

2000 study, the MMAPs TSD notes the potential for reduced methylation at very high Hg loadings typically 

experienced in mining areas. However, since our analysis excluded areas likely impacted by active gold mines, we 

believe that this issue has been largely ameliorated in the context of the national-scale mercury risk assessment. 
46 

addition, other factors that can affect rates of Hg methylation (e.g., sulfur deposition to 

waterbodies, pH of the waterbodies) also have not remained constant over the past 1-2 decades 

for most watersheds. The fact that many of these factors related to methylation in fish have not 

remained constant introduces uncertainty into the application of the Mercury Maps based 

proportionality assumption. However, we believe that the Mercury Maps approach for 

apportioning fish tissue Hg concentrations is still appropriate to use, particularly if we are not 

attempting to characterize the temporal response and instead, can assume that sufficient time has 

passed for near steady state conditions to be reached (see section 1.4.6.2 below). Furthermore, 

while we have excluded watersheds with substantial non-air loading of Hg from industrial 

activity and mines, we did not consider municipal sewage emissions of Hg to watersheds. This 

introduces additional uncertainty into the analysis, since watersheds could have a substantial 

fraction of Hg loading originating from municipal sewage treatment. In these instances, failure to 

consider this source of non-air loading could result in a high-bias in the estimates of the U.S. 

EGU-attributable fraction of deposition (and hence risk), since the contribution from this non-air 

source would not have been considered. 

1.4.6.2 Additional research supporting the proportionality assumption and 

examining the issue of temporal response 

The SAB commented that several recent publications have supported the finding of a 

linear relationship between Hg loading and accumulation in aquatic biota (Orihel et al., 2007; 

Orihel et al., 2008; Harris et al., 2007). The SAB noted that these studies suggest that that Hg 

deposited directly to aquatic ecosystems can become quickly available to biota and accumulated 

in fish, and reductions in atmospheric Hg deposition should lead to decreases in MeHg 

concentrations in biota. 

EPA has reviewed the studies identified by the SAB, together with a study by Knightes et 

al., (2009). All of these studies, to varying degrees, suggest that when we are considering 

reductions in fish tissue Hg concentrations following reductions in Hg deposition to a watershed, 

we are likely to see a 2-stage response including: (a) an initial more rapid phase of reduction 

(ranging from months to a few years) reflecting a decrease in direct loading to the waterbody and 

subsequent reductions in fish tissue Hg concentrations related to decreased water column Hg 

levels and (b) a second slower phase of reduction (years to decades or more) reflecting longer 

term changes in the rate of erosion/runoff loading to the waterbody and the potential buffering 

effect of historical reservoirs of Hg in sediment. These findings suggest that we might not see an 

equivalent fractional reduction in risk matching the reduction in aerial deposition for near-term 

reductions (less than one year) due to historical reservoirs of Hg in sediment that continue to load 

the benthic food web and buffer the Hg response. However, for reductions over a longer period 

of time (sufficient for steady state, or near steady state conditions to be met in the aquatic 

system), the proportionality assumption likely holds and we would expect to see a reduction in 

risk matching the fractional reduction in Hg deposition. Since we have stated that our risk 

estimates are based on an assumption that steady state, or near steady state conditions are met, 

regardless of how long that takes, then the temporal response is not a factor and we have 

increased confidence in applying the proportionality assumption. 
47 

1.4.6.3 CMAQ mercury deposition modeling 

We modeled total annual Hg deposition from U.S. and foreign anthropogenic and natural 

sources as well as the fraction of Hg deposition from U.S. EGUs using the CMAQ model. The 

Community Multi-scale Air Quality (CMAQ) model v4.7.1 (www.cmaq-model.org) is a state of 

the science three-dimensional Eulerian “one-atmosphere” photochemical transport model used 

to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). CMAQ 

simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM 

concentrations, and air toxics over regional and urban spatial scales for given input sets of 

meteorological conditions and emissions. Mercury oxidation pathways are represented for both 

the gas and aqueous phases in addition to aqueous phase reduction reactions (Bullock and 

Brehme, 2002). Mercury estimates from CMAQ have been compared to observations and other 

mercury modeling systems in several peer reviewed publications (Bullock et al., 2008, 2009; Lin 

et al., 2007). Additional information about the model, model inputs for this assessment, and 

model evaluation are available in the Air Quality Modeling TSD (U.S. Environmental Protection 

Agency, 2011). 

The 36km and both 12km modeling domains were modeled for the entire year of 2005. 

The emissions data used in the 2005 base year and 2016 total Hg emissions and U.S. EGU Hg 

emissions zero-out cases are based on the 2005 v4.1 platform. Emissions are processed to 

photochemical model inputs with the SMOKE emissions modeling system (Houyoux et al., 

2000). The 2016 total Hg emissions case is intended to represent the emissions associated with 

growth and controls in that year projected from the 2005 simulation year. Only anthropogenic 

emissions changed between the 2005 and 2016 simulations, all other model inputs are the same 

in both simulations. Other North American emissions of criteria and toxic pollutants (including 

mercury) are based on a 2006 Canadian inventory and 1999 Mexican inventory (U.S. EPA, 

2011b). 

Global emissions of criteria and toxic pollutants (including mercury) are included in the 

modeling system through boundary condition inflow. The lateral boundary and initial species 

concentrations are provided by a three-dimensional global atmospheric chemistry model, the 

GEOS-CHEM model (standard version 7-04-11). The GEOS-CHEM predictions were used to 

provide one-way dynamic boundary conditions at three-hour intervals and an initial 

concentration field for the 36 km CMAQ simulations. The 36 km photochemical model 

simulation is used to supply initial and hourly boundary concentrations to the 12 km domains. 

Initial and boundary conditions for the projected future year (2016) 36 km simulations are the 

same as the 2005 base year. The first 10 days of the 36 km modeling simulation are not used in 

the analysis, which is beyond the number of days necessary to remove the influence of initial 

conditions on mercury deposition estimates (Pongprueksa et al., 2008). 

The boundary inflow for the CMAQ mercury modeling used in the national-scale 

mercury risk assessment are based on a global model GEOS-CHEM simulation using a 2000 

based global inventory as described in (Selin et al., 2007). A comparison of global mercury 

emissions by continent for 2000 and 2006 was recently published in (Streets et al., 2009) and 

show there is no discernable change in mercury emissions from Asia between 2000 and 2006. 

Given these consistent emissions estimates from Asia, the 2005 boundary inflow to the 36 km 

CMAQ domain was not adjusted. Mercury boundary conditions are the same for both the 2005 
48 

and 2016 simulations based on the consistency in Asian mercury emissions between 2000 and 

2006, the declining ambient mercury concentrations in the northern hemisphere since 2000 

(Slemr et al, 2011), and the large uncertainties surrounding projected global inventories of 

mercury emissions. 

1.5 Differences between the 2005 Section 112(n) Revision Rule analysis and the 

current analysis in support of the Appropriate and Necessary Finding 

In 2005, EPA conducted a set of technical analyses to support revision of the 2000 

appropriate and necessary finding.42 This section identifies key differences between the 

watershed-level risk assessment completed in support of the 2005 revision rule and the current 

risk assessment. These differences include both technical factors related to the design of the 

assessments, as well as differences in the interpretation of potential public health significance of 

the risk estimates generated. Key differences between the two analyses include: 

Higher spatial resolution through use of CMAQ 12km grid cells: In this analysis, we modeled 

Hg deposition using CMAQ at a 12 km grid resolution, whereas in the 2005 analysis, we used 

CMAQ modeling with a 36 km grid cell resolution. The more refined spatial resolution at 12 km 

is more appropriate for representing areas of elevated U.S. EGU-attributable deposition (and 

total Hg deposition in general) compared with the 36 km resolution used in the 2005 analysis. 

The 12 km resolution also matches up with the more refined HUC12 watersheds now being used 

in the analysis, thereby allowing a more refined treatment of the intersection of aerial Hg 

deposition and measured fish tissue concentrations at the watershed level. 

Application of more refined HUC12 watersheds: The current analysis uses HUC12 watersheds as 

the basis for risk estimation (these watersheds typically are 5-10 km on a side). By contrast, the 

2005 analysis used HUC8s, which are much larger (averaging 40km on a side). The use of more 

spatially refined watersheds increases the potential for capturing areas of elevated aerial Hg 

deposition (combined with measured fish tissue levels). 

Inclusion of updated fish tissue data: For this analysis, we included measured fish tissue data 

collected between 2000 and 2010. By contrast, the 2005 analysis used data collected between 

1999 and 2003, which was the best available data at the time. 

Subsistence fisher activity better defined and considered more ubiquitous: Based on an extensive 

review of available literature, we identified studies characterizing high-end self-caught fish 

consumption for a wide variety of source populations (e.g., Hispanic, Vietnamese, Whites and 

Blacks in the southeast, Great Lakes Tribal populations). Although in many cases, it was 

necessary to extrapolate high-end fishing activity to regions beyond those covered in the 

underlying studies, we believe that the literature supports the plausibility of high-end 

subsistence-like fishing activity across the watersheds included in the analysis. Additionally, the 

variety of studies identifying self-caught fishing activity at subsistence levels (i.e., a meal every 

few days to a meal every day) for a variety of diverse SES-differentiated populations in different 

 

42 U.S.EPA. 2005. Technical Support Document: Methodology Used to Generate Deposition, Fish Tissue 

Methylmercury Concentrations, and Exposure for Determining Effectiveness of Utility Emission Controls.
49 

regions of the country, supports assessing this type subsistence fish consumption behavior across 

the modeled watersheds. 

By contrast, in the 2005 analysis, we concluded that the study data characterizing fishing activity 

available at that time was limited in its ability to support modeling of subsistence fisher activity 

for the following reasons: (a) it characterized regional or local activity that could not be readily 

extrapolated more broadly, (b) fishing activity queried included consumption of saltwater 

species, or (c) specific high-end percentiles were not identified (or if they were, they only 

applied during specific harvesting periods - e.g., spearfishing months for Great Lakes Tribes). 

Therefore, in the 2005 analysis, we applied a high-end self-caught percentile values (95th and 

99th percentiles) based on Tribal fishing practices in the Northwest to watersheds across the 

country.43 The updated literature review, for the current analysis, led us to revise several of our 

earlier conclusions regarding high-end fishing activity. Specifically, while many of the studies of 

subsistence-like activity are regional in nature, when considered together, we now conclude that 

they support modeling subsistence-like fishing activity more broadly across the entire study area. 

Additionally, while some of the studies may include saltwater fishing in addition to freshwater 

(e.g., Burger, 2002), when those studies clearly covered both saltwater and freshwater selfcaught fish consumption, we concluded that it was reasonable to assume that subsistence-like 

fishing activity could occur both at the coast and inland at freshwater bodies.44 

For the current analysis, we used the 75th percentile fish tissue Hg concentration reflecting the 

potential for high-end subsistence fishers to target larger fish, which would have greater 

bioaccumulation potential relative to the average fish. By contrast, in the 2005 risk assessment, 

we used the maximum of the average fish tissue Hg concentrations across species of fish in a 

given HUC8, which is potentially a more conservative approach (i.e., resulting in higher risk, 

other factors equal). 

Calculation of RfD-based HQ estimates including total and U.S. EGU-attributable risk and

calculation of IQ loss: For this analysis, we compared total exposure to the MeHg RfD to 

generate an HQ estimate based on total Hg exposure for fishers at a given watershed. 

Furthermore, to focus on the U.S. EGU component of that total risk, we have generated two 

related risk metrics: (a) U.S. EGU incremental contribution to total risk which essentially 

considers the magnitude of the HQ when deposition from U.S. EGUs is considered before taking 

into account deposition and exposures resulting from other sources of Hg and (b) the percent of 

 

43 These NW Tribal fishing estimates are subject to considerable uncertainty when extrapolated to other areas in the 

U.S. These specific high-end fish consumption rates were derived for Tribes active in the Northwest who engage in 

specific cultural practices focused around salmon fishing. There is also significant uncertainty in extrapolating this 

type of highly-specific cultural-based fishing activity to other Tribes, let alone to other fish consumers in the U.S. 

By contrast, extrapolation of more generalized high-consuming rates (for ethnic groups and Whites and Blacks) to 

cover portions of the U.S. as was done in the current analysis is subject to less uncertainty given that these SESdifferentiated populations are defined more generally and therefore are likely to demonstrate more consistency in 

culturally-related practices (such as subsistence fishing) across the country. 44 In the situation where a study specifically characterized low income high-end fishing populations, as is done in the 

Burger 2010 study of activity in SC, we considered it reasonable to assume that low income individuals would likely 

conduct their frequent fishing activity near home. In that case, some of these high-end fishers would likely be 

located near the coast and some inland. In the case of subsistence-like fishing activity in the southeast, other studies 

from rivers in that area also showed subsistence-like fish consumption rates when only freshwater rivers were 

considered (e.g., Burger et al., 1999 focusing on fishing activity on the Savannah river in GA). 
50 

total HQ risk attributable to U.S. EGUs. The calculation of U.S. EGU incremental contribution 

to total HQ is identical to the IDI (index of daily intake) metric used in the 2005 analysis. 

However an important distinction is that in the current analysis, we highlight the fact that this 

U.S. EGU-related risk is always associated with a total HQ which is generally substantially 

larger (i.e., the U.S.-EGU-attributable HQ should not be considered in isolation as was done in 

the 2005 analysis with the IDI). By contrast, for the 2005 analysis both of the risk metrics used 

(i.e., the IDI and the comparison of U.S. EGU-related fish tissue concentrations against EPA’s 

water quality criterion expressed as a Hg fish tissue value) essentially considered the U.S. EGU 

portion of risk in isolation. These risk metrics in the 2005 analysis were not contrasted with the 

much larger fraction of total Hg-related risk associated with the non-U.S. EGU portion of risk. 

1.6 Detailed Example Calculation (watershed-level risk HQ) 

This section provides a sample calculation of a watershed-level RfD-based HQ risk 

estimate generated for the 2016 scenario and walks the reader through each of the calculation 

steps associated with generating the estimate (see Figure 1-9). We cross-reference to sections of 

the Revised TSD that cover each of the intermediate calculations and data inputs involved in 

generating the risk estimate.45 Each of the calculation steps is summarized below: 

 Step 1: Based on the fish tissue Hg measurements available for a given watershed (after 

filtering), generate the 75th percentile fish tissue Hg concentration. We provide the 

median, 75th and 90th percentile values in Figure 1-9 to illustrate the spread in percentile 

values for this watershed). (see section 1.4.2). 

 Step 2: Obtain CMAQ Hg deposition estimates for the watershed for the 2005 and 2016 

scenarios. Compute the ratio of these two factors (i.e., 2016 deposition/2005 deposition). 

(see section 1.4.6.3). 

 Step 3: Multiply the ratio from Step 2 by the fish tissue Hg concentration to project the 

fish tissue Hg concentration value to represent the 2016 scenario for this watershed. This 

step involves application of the proportionality assumption (see section 1.4.2.1). 

 Step 4: Calculate intake rate for total Hg for the subsistence fisher scenario being 

assessed in that watershed using equation with fish tissue Hg concentrations calculated in 

Step 3(see section 1.4.4 for a discussion of this exposure calculation). 

 Step 5: Compare the exposure estimate generated in step 4 to the MeHg RfD to generate 

a hazard quotient reflecting total Hg exposure for the subsistence fisher at that watershed 

(see section 1.4.5). 

 

45 The SAB recommended including a cross reference between the calculation steps outlined in Figure 1-9 and the 

discussion of variability and uncertainty presented in section 2-7. However, the complex interplay of sources of 

uncertainty and variability with the analytical structure used in the risk assessment precludes presentation of a clean 

linkage between sources of uncertainty and variability and the analytical framework used in the risk assessment. 

Most sources of variability and uncertainty impact either directly or indirectly multiple (if not all) of the calculations 

steps involved in generating risk estimates. Careful consideration of the analytical approach reflected in Figure 2-1 

and the flow diagram outlining major analytical steps of the risk assessment presented in Figure 1-2 should allow the 

reader to determine which elements of the risk model are impacted by specific sources of uncertainty and/or 

variability. 
51 

 Step 6: Obtain CMAQ Hg deposition estimates for the watershed for both total and U.S. 

EGU-attributable Hg for the 2016 scenario. Compute the ratio of these two factors (i.e., 

U.S. EGU/total deposition). This ratio will be used to estimate the fraction of total risk 

attributable to U.S. EGUs (see section 1.4.6). 

 Step 7: Estimate the fraction of the total HQ risk that is attributable to U.S. EGUs (for the 

2016 scenario). This estimate is based on the proportionality assumption and uses the 

ratio developed in step 6 (see section 1.4.6). 
52 

Figure 1-9 Sample Calculation for watershed-level Risk HQ 

IR = FTC * MCF * FCR * CAF

BW

Example – Lower Kettle Creek in north central Pennsylvania (HUC12 ID: 20

IR: daily MeHg intake rate (ug/kg‐day)

FTC: MeHg fish tissue concentration (ug/g or ppm)MCR: mercury conversion factor – fraction fish tissue

mercury that is MeHg (unitless)

FCR: fish consumption rate (g/day)

FPCAF: fish preparation/cooking adjustment factor

RfD: methylmercury RfDFracEGU: fraction dep from US EGU

Fish tissue (ppm)Median: 0.2475th : 0.32

90th : 0.346 samples

PROJECTEDFish tissue

(ppm) 2016

Mean: 0.2075th : 0.26

90th : 0.28

Total Hg dep

over watershed(CMAQ):2005 = 30.42

2016 = 25.19

Or a ~17% reduction

95th%

consumption

rate:

173 g/day

Fish

preparation/

cooking

adjustment

Factor:

1.5

Body

weight:

64 g

IR = 1.01 ug/kg‐day

MeHg RfD:

0.1 ug/kg‐day

Total HQ = IR = 

RfD 

2016 Hg depover watershed(CMAQ):total = 25.19US EGU = 1.26

US EGU is 5%FracEGU = 0.050

US EGU risk = HQ 

= 10.1 *0.050 = 0.5

1

2

3

4

Mercury

conversion

factor:

0.95

#: calculation step

6
53 

2 Discussion of Analytical Results 

This chapter provides a discussion of the results of the 2016 simulation, including both 

risk estimates as well as intermediate calculations and inputs associated with generating those 

risk estimates. EPA’s projection of total Hg emissions from U.S. EGUs in 2016 (after other 

CAA-related regulations are fully implemented) is 29 tons.46 This national estimate is the same 

as the estimate of recent U.S. EGU Hg emissions based on information collected from industry 

through the Information Collection Request (ICR). This shows that significant reduction in U.S. 

EGU Hg emissions has occurred since 2005, when Hg emissions were estimated to be 52.9 tons, 

but that few additional reductions are expected to occur without additional regulations to reduce 

mercury emissions. The reductions between 2005 and 2016 are largely due to regulations and 

federal enforcement actions that achieve Hg reductions as a co-benefit of controls for NOx and 

SO2 emissions. Given these estimates of total Hg emissions, characterization of “current 

conditions” is better represented by the 2016 Scenario than the 2005 Base Case, since total Hg 

emissions for the former is equal to our projection of recent emissions. By contrast, the 2005 

analysis reflects total Hg emissions (52.9 tons) which are significantly higher than our estimate 

of recent emissions. For this reason, as mentioned earlier, we only present risk estimates for the 

2016 Scenario and have not generated risk estimates for the 2005 scenario (although the 2005 Hg 

deposition estimates are used in scaling fish tissue Hg concentrations to represent concentrations 

in 2016).

 In this section, we provide a brief overview of critical design elements of the risk 

analysis that the reader should keep in mind when reviewing the results (section 2.1). We then 

discuss the intermediate inputs and outputs for individual analytical steps associated with the risk 

assessment including: (a) Hg deposition from U.S. EGUs as modeled using CMAQ (section 2.2), 

(b) fish tissue Hg concentrations (section 2.3), (c) relationship between Hg deposition and Hg 

fish tissue concentrations (section 2.4) and (d) the MeHg RfD-based HQ risk assessment results 

for the 2016 scenario (section 2.5). In discussing each category of results, emphasis is placed on 

identifying key policy-relevant observations. In section 2.6, we discuss the results of several 

sensitivity analyses conducted to characterize the potential impact of specific sources of 

uncertainty on the risk estimates. In section 2.7, we discuss variability and uncertainty related to 

the risk assessment. In Section 3, we provide a summary of critical observations from the 

analysis, which draws on information provided in sections 2.2 through 2.7. 

2.1 Key design elements to consider when reviewing the risk assessment results 

The following design elements of the analysis should be considered when reviewing the 

results: 

 The analysis focuses on subsistence-like fishing activity at inland freshwater bodies. The 

analysis is not intended to capture more generalized recreational fishing activity or to 

reflect self-caught fisher exposure associated with saltwater fishing or fishing in the Great 

 

46 The modeling conducted for the March risk assessment included a representation of the proposed Cross State Air 

Pollution Rule, which was finalized in June 2011. We have evaluated differences in projected Hg emissions after 

taking into account differences between the proposed and final CSAPR and determined that the difference is less 

than 2 tons nationally, which would have little expected impact on our risk estimates. 
54 

Lakes. In comparing any risk profiles generated in this analysis to risks estimated in other 

contexts, the specific focus on these high-end populations shows risks substantially 

higher than risks for recreational fishers. 

 The risk estimates generated are based on a set of female subsistence fish consumer 

scenarios evaluated at watersheds where (a) we have fish tissue Hg data and (b) this type 

of subsistence fisher activity could potentially occur. Therefore, the risk assessment is 

watershed-focused, does not provide population-representative risk estimates for 

subsistence fishers. 

 Risks are estimated for 3,141 watersheds. This watershed coverage (only about 4% of 

U.S. watersheds) leaves much of the country not covered by the analysis, including a 

substantial number of watersheds with relatively elevated levels of U.S. EGU-related Hg 

deposition. Further, the eastern part of the U.S. is more heavily represented in the 

watershed-level estimates of risk. Given that U.S. EGU Hg deposition is generally higher 

in the eastern part of the U.S., the fact that the risk assessment is focused on this part of 

the country is considered to be a strength of the analysis. 

 The analysis uses a proportionality assumption to link changes in Hg deposition (over 

watersheds) to changes in Hg fish tissue levels. This approach assumes that near steadystate conditions are met, which may take years to decades at a given watershed following 

changes in Hg deposition. 

 The analysis estimates risk based on MeHg RfD-based HQ. The U.S. EGU-attributable 

HQ should always be considered in the context of total HQ which is typically 

substantially larger than the U.S. EGU-attributable HQ (reflecting the large contribution 

from non-U.S. EGU sources of Hg). We also assessed IQ loss in children, however, due 

to concerns that the IQ loss endpoint may not fully capture all of the neurodevelopmental 

effects associated with MeHg exposure, we have moved discussion of the IQ loss 

estimates to Appendices A and B. 

 Because it is not feasible to enumerate the female subsistence fish consumers modeled in 

this analysis, we could not generate distributions of population-weighted risk for specific 

scenarios assessed (e.g., low income Hispanic fishers, or Tribal fishers in the vicinity of 

the Great Lakes). However, we do believe, based on surveys of their behavior, that this 

type of subsistence-like activity could reasonably be expected to occur across some 

fraction of the 3,141 watersheds included in the analysis. Therefore, we have assessed 

female subsistence fish consumer risk for each watershed where we have fish tissue Hg 

data. We then consider the fraction of watersheds that meet the risk characterization 

criteria outlined in the risk characterization framework (see Section 1.3). 

2.2 Mercury Deposition from U.S. EGUs as Modeled Using CMAQ 

Below we provide the results of the CMAQ modeled Hg deposition for the 2005 and 

2016 scenarios for total deposition and U.S. EGU-attributable deposition.47 For this Revised 

 

47 During the Science Advisory Board (SAB) review of the March TSD held on June 15-17th, Panel members raised 

questions regarding patterns of mercury deposition reflected in Figures 2-1 though 2-4 of the March version of the 
55 

TSD we have also included maps of wet and dry deposition for the 2005 and 2016 scenarios.48 

Unlike the results presented in the subsequent sections that are limited to watersheds with fish 

tissue data, we modeled Hg deposition in all 88,000 watersheds in the continental U.S. We 

present a set of bulleted observations at the end of the section that draws on information 

conveyed in the figures and tables. The set of figures and tables presented include: 

 Figure 2-1 and 2-2: Maps presenting CMAQ modeling results for total Hg deposition 

(μg/m2) at the watershed-level, for the 2005 and 2016 scenarios respectively. 

 Figures 2-3 and 2-4: Maps presenting CMAQ modeling results for U.S. EGU-attributable 

Hg deposition (μg/m2) at the watershed-level, for the 2005 and 2016 scenarios, 

respectively. 

 Figures 2-5 through 2-8: Maps presenting CMAQ modeling results for wet and dry Hg 

deposition (μg/m2) for the 2005 and 2016 scenarios. These estimates are presented at the 

original CMAQ 12km grid resolution and have not been interpolated to the watershedlevel. Maps presenting wet and dry deposition modeling results have been included to 

allow readers to consider patterns of deposition over specific locations and potentially 

compare those with measured data, in those instances where relevant data are (or 

become) available. 

 Table 2-1: Summary of statistics (mean, 50th, 75th, 90th, 95th and 99th percentiles) for total 

Hg deposition and U.S. EGU-attributable deposition for the 2005 and 2016 scenarios. 

 Table 2-2: Summary of statistics (mean, 50th, 75th, 90th, 95th and 99th percentiles) for U.S. 

EGU-attributable deposition as a percent of total deposition for the 2005 and 2016 

scenarios. 

 Table 2-3: Summary of statistics (mean, 50th, 75th, 90th, 95th and 99th percentiles) for 

percent reduction of (a) total Hg deposition, and (b) U.S. EGU-attributable deposition, 

based on comparison of the 2016 scenario against the 2005 scenario. 

 Mercury Risk TSD. The figures in the March TSD were intended to show annual total mercury (Hg) deposition per 

unit area (in units of μg/m2) by watershed, however they actually displayed intermediate calculations that had not 

been adjusted by the waterbody-specific surface areas. We corrected the figures and provided them in a memo to the 

docket (EPA-HQ-OAR-2009-0234-15522). The figures presented here are the corrected figures and match those 

presented in that memo. 

48 Inclusion of maps presenting wet and dry deposition modeling results reflects suggestions made by SAB Panel 

members. 
56 

Figure 2-1 Total Mercury Deposition by HUC (μg/m2) for the 2005 Scenario 
57 

Figure 2-2 Total Mercury Deposition by HUC (μg/m2) for the 2016 Scenario 
58 

Figure 2-3 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2005 Scen
59 

Figure 2-4 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2016 Scen
60 

Figure 2-5 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario 
61 

Figure 2-6 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario 
62 

Figure 2-7 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario 
63 

Figure 2-8 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario 
64 

Table 2-1 Comparison of total and U.S. EGU-attributable mercury deposition (μg/m2

) for 

the 2005 and 2016 scenarios.* 

Statistic 

2005 scenario 2016 scenario 

Total Hg 

Deposition 

U.S. EGU-attributable 

Hg Deposition 

Total Hg 

Deposition 

U.S. EGU-attributable 

Hg Deposition 

Mean 19.41 0.89 18.66 0.34 

Median 17.25 0.24 16.59 0.15 

75th percentile 23.69 1.07 22.83 0.46 

90th percentile 30.78 2.38 29.90 0.85 

95th percentile 36.85 3.60 35.16 1.18 

99th percentile 58.32 7.77 56.23 2.41 

* Values are based on CMAQ results interpolated to the watershed –level and reflect trends across all ~88,000 

watersheds in the continental U.S. Percentiles for total Hg deposition and U.S. EGU attributable Hg 

deposition are not matched, e.g. the watershed with the 99th percentile for total Hg deposition will not be 

the same watershed as the watershed with the 99th percentile for U.S. EGU attributable Hg deposition.

Table 2-2 Comparison of percent of total mercury deposition attributable to U.S. EGUs for 

2005 and 2016.* 

Statistic 2005 scenario 2016 scenario 

Mean 5% 2% 

Median 1% 1% 

75th percentile 6% 3% 

90th percentile 13% 5% 

95th percentile 18% 6% 

99th percentile 30% 11% 

* Values are based on CMAQ results interpolated to the watershed –level and reflect trends across all ~88,000 

watersheds in the U.S. 

Table 2-3 Comparison of percent reduction of total mercury deposition, and U.S. EGUattributable deposition, based on comparing the 2016 scenario against the 2005 

scenario.* 

Statistics 

Percent Change in Total 

Hg Deposition 

Percent Change in U.S. 

EGU-attributable Hg 

Deposition 

Mean -4% NC** 

Median -1% -41% 

75th percentile -5% -70% 

90th percentile -12% -80% 

95th percentile -16% -85% 

99th percentile -27% -91% 

* Values are based on CMAQ results interpolated to the watershed –level and reflect trends across all ~88,000 

watersheds in the U.S. 

** A mean value was not calculated (NC) for this category due to presence of a number of watersheds with very 

small U.S. EGU-attributable deposition values which skewed this distribution. 
65 

We made the following observations based on the information presented in Figures 2-2 

through 2-4 and in Tables 2-1 through 2-3 regarding estimates of total and U.S. EGU-attributable 

Hg deposition for the 2005 and 2016 scenarios: 

 Patterns of total and U.S. EGU-related Hg deposition differ considerably: Areas of 

elevated total Hg deposition are distributed around the country (e.g., west coast, areas in 

Nevada, southern Mississippi, West Virginia, southeastern Georgia) (see Figures 2-1 and 

2-2). By contrast, U.S. EGU Hg deposition is concentrated in the eastern U.S., especially 

in the Ohio River Valley (see Figures 2-3 and 2-4). Figures 2-3 and 2-4 also illustrate that 

while some near-coastal areas and portions of the Great Lakes have elevated U.S. EGUattributable deposition, many of the highest areas (and largest expanses) of U.S. EGUattributable deposition occur inland (e.g., Ohio River Valley, areas in northeast Texas and 

along the Mississippi River). 

 U.S. Hg deposition is generally dominated by sources other than U.S. EGUs and the 

contribution from U.S. EGUs decreases between the 2005 and 2016 scenarios: On 

average., U.S. EGUs contribute 5% of total Hg deposition for the 2005 scenario, which 

decreases to 2% for the 2016 scenario (see Table 2-2). The remaining Hg deposition (i.e., 

~95% and ~98%, respectively for the two scenarios) originates from other U.S. sources 

of Hg emissions and from international sources (both anthropogenic and natural). U.S. 

EGU-attributable deposition decreases considerably between the 2005 and 2016 

scenarios, primarily from implementation of the Cross State Air Pollution Rule (CSAPR), 

state Hg regulations and Federal enforcement actions.49 The median reduction in U.S. 

EGU-attributable deposition was 41% with reductions ranging up to 85% for the 95th

percentile watershed (see Tables 2-2 and 2-3). 

 The contribution of U.S. EGU-attributable deposition to total deposition varies across 

watersheds and can represent a relatively large fraction in some instances: In the 2005 

scenario, while on average, U.S. EGUs only contributed 5% of total Hg deposition in the 

U.S., this contribution ranged up to 30% for the 99th percentile watershed (see Table 2-2). 

While overall U.S. EGU –attributable deposition decreased substantially between the 

2005 scenario and the 2016 scenario, U.S. EGUs contributed 11% of total Hg deposition 

for the 99th percentile watershed in 2016 ( (see Table 2-2). 

2.3 Fish Tissue Mercury Concentrations 

This section characterizes the subset of U.S. watersheds with fish tissue Hg data included 

in the current the risk assessment. This dataset includes (a) the original HUC-level fish tissue 

dataset used in the March version of the risk assessment (post augmentation data set with 2,317 

watersheds) and (b) the additional 940 watersheds with fish tissue data identified as part of our 

refinement to the March risk assessment (for a total of 3,141 watersheds). In this section, we 

provide the 75th percentile fish tissue sample for each watershed. As discussed in Sections 

1.4.2.1 and 1.4.6, we used the proportionality assumption together with Hg deposition coverages 

 

49 Controls on PM precursors, including directly emitted PM and SO2, can significantly reduce divalent and particlebound mercury, both of which primarily deposit locally and regionally. For more information on the emission 

reductions from CSAPR, see the final Regulatory Impact Analysis, which is available at 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf. 
66 

for 2005 (total and U.S. EGU-related) and 2016 (total and U.S. EGU-related) to project fish 

tissue Hg concentrations for the 2016 scenario and to estimate the U.S. EGU-attributable 

fraction of total fish Hg. 

We provide figures and tables to summarize the fish tissue data. As discussed in Section 

2.2, most of the areas with elevated U.S. EGU-attributable Hg deposition are located in the 

eastern U.S., where we also have more Hg fish tissue data and most of the U.S. EGU emission 

reductions between 2005 and 2016. We also present a set of bulleted observations based on the 

figures and tables. The set of figures and tables presented include: 

 Figure 2-9: Map of 3,141 watersheds with fish tissue sampling data used in the current 

version of the risk assessment, including (a) watersheds included in the previous version 

of the risk assessment (2010 MFT) and (b) additional fish tissue data incorporated to 

enhance the current version of the risk assessment (augmentation). This map illustrates 

the uneven coverage of the fish tissue dataset across the continental U.S and highlights 

which states were included in the additional data incorporated for this version of the risk 

assessment (i.e., the augmentation HUCs shown in red). 

 Figure 2-10: Map of 3,141 watersheds with fish tissue data showing distribution of 

sampling frequency (number of fish tissue measurements) across watersheds. This map 

can be used to show the relationship between trends in sampling frequency and fish tissue 

Hg concentrations. 

 Figure 2-11 and 2-12: Maps presenting Total Fish Tissue Mercury Concentrations for the 

2005 and 2016 scenarios respectively. 

 Figures 2-13 and 2-14: Maps presenting EGU-Attributable Fish Tissue Mercury 

Concentrations for 2005 and 2016 scenarios, respectively.50

 Table 2-4: Comparison of watershed-level fish tissue Hg concentrations (including means 

and various percentiles) for the March and revised version of the national-scale Hg risk 

assessment. Table 2-4 includes percentile data for (a) 2010 MFT dataset (the fish tissue 

data used in the March risk assessment), (b) the augmentation dataset (additional fish 

tissue data collected to supplement data used in the last risk assessment) and (c) the 2011 

MFT dataset (the dataset used in the current revised version of the risk assessment). 

 Table 2-5: Summary of statistics (mean and various percentiles) for both total and U.S. 

EGU-attributable Hg fish tissue levels (for the 2005 and 2016 scenarios). These statistics 

are based on watershed-level data. In addition, this table also presents the percent 

reduction (between the 2005 and 2016 scenarios) for both total and U.S. EGUattributable Hg fish tissue levels. 

 50 Note, in the March version of the Mercury Risk TSD, we included a series of maps showing fish tissue 

Hg concentrations at the watershed-level for the upper 10 percent of watersheds with regard to total fish 

tissue Hg concentrations and EGU-attributable fish tissue Hg concentrations. However, we decided to 

remove these maps from the revised version of the Mercury Risk TSD given the limited coverage that the 

fish tissue Hg sampling data provides for watersheds across the U.S.
67 

Figure 2-9 Set of 3,141 HUC12s with Fish Tissue Mercury Data Used in the Risk Assessment* 

* Includes 2010 Mercury Fish Tissue (MFT) dataset used in the March version of the risk assessment as w

augmentation dataset incorporated for the current version of the risk assessment. 
68 

Figure 2-10 Fish Tissue Measurement Sampling Frequency for HUCs with Fish Tissue Data Included in 
69 

Figure 2-11 Total Fish Tissue Mercury Concentrations for 2005 Scenario (HUC12-level 75th percentile va
70 

Figure 2-12 Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12-level 75th per

ppm)* 

 * Values estimated by adjusting fish tissue Hg data presented in Figure 2-12 to reflect different patterns o

2016 (relative to 2005). 
71 

Figure 2-13 EGU-Attributable Fish Tissue Mercury Concentrations for 2005 Scenario 

(HUC12-level 75th percentile values, ppm)* 

* Scales are different between the EGU-attributable and total Hg fish tissue concentrations maps. 
72 

Figure 2-14 EGU-Attributable Fish Tissue Mercury Concentrations Projected for 2016 Scenario 

(HUC12-level 75th percentile values, ppm)* * Scales differ between the EGU-attributable and total Hg fish tissue concentrations maps. 
73 

Table 2-4 Comparison of HUC-Level Fish Tissue Mercury Concentrations Across Datasets 

Used in the March Version and Current Version of the Risk Assessment 

statistic 

Hg fish tissue concentration (ppm) 

(HUC12-level 75th percentile) 

2010 MFT dataset 

(used in the March 

version of the risk 

assessment) 

(2,317) 

Augmentation 

dataset 

(collected as part of 

refining analysis) 

(940) 

2011 MFT dataset 

(used in the current 

version of the risk 

assessment) 

(3,141) 

Mean 0.31 0.37 0.32 

Median 0.23 0.30 0.25 

75th % 0.39 0.47 0.42 

90th % 0.67 0.72 0.67 

95th % 0.91 0.86 0.89 

99th % 1.33 1.44 1.35 

Table 2-5 Comparison of total and U.S. EGU-attributable Hg fish tissue concentrations 

(including % change) for the original fish tissue dataset and 2016 scenarios* 

 

Statistic 

Hg fish tissue concentration (ppm) % change (2016 versus 

original fish tissue 

dataset) in Hg fish 

Used for 2005 scenario Projected for 2016 scenario tissue concentration 

Total 

U.S. EGUattributable 

U.S. EGU 

as percent 

of total Total 

U.S. EGUattributable 

U.S. EGU 

as percent 

of total Total 

U.S. EGUattributable 

Mean 0.32 0.024 9.1% 0.30 0.008 3.4% -8.0% -43.9% 

Median 0.25 0.014 6.0% 0.22 0.005 2.6% -4.9% -61.4% 

75th % 0.42 0.032 13.5% 0.40 0.011 4.4% -12.7% -76.0% 

90th % 0.67 0.057 20.4% 0.65 0.018 6.6% -18.6% -82.6% 

95th % 0.89 0.081 26.2% 0.86 0.026 8.7% -23.0% -85.4% 

99th % 1.35 0.152 42.9% 1.30 0.045 16.4% -38.3% -91.7% 

* These percentiles are not for matched HUCs (i.e., the 99th percentile for total Hg fish concentration for the 2005 

scenario could occur in a different HUC than the 99th percentile U.S. EGU attributable percent reduction). 

We made the following observations based on Figures 2-10 through 2-14 and in Tables 2-

4 and 2-5: 

 U.S. EGU-attributable fish tissue Hg levels are higher in the eastern half of the U.S.: 

This reflects primarily the fact that levels of U.S. EGU Hg deposition (that largely drives 

U.S. EGU-attributable Hg fish tissue levels) are much higher in the east (see Figures 2-3 

and 2-4). 

 Augmenting the fish tissue Hg dataset significantly improved coverage for Wisconsin, 

Minnesota, Pennsylvania and New Jersey: Although the additional fish tissue Hg data 
74 

significantly expanded coverage for watersheds in areas with elevated U.S. EGUattributable Hg deposition (Pennsylvania) and for areas with relatively elevated fish 

tissue Hg concentrations (Minnesota and Wisconsin), the additional data did not 

substantially change the overall distribution of fish tissue Hg concentrations. This 

conclusion is based on a comparison of percentiles across the different datasets (i.e., 2010 

MFT, augmentation, and 2011 MFT datasets) as presented in Table 2-4. 

 U.S. EGUs contribute a larger fraction to total Hg fish tissue levels in the U.S. than they 

do to total Hg deposition (in terms of percent), reflecting the fact that Hg fish tissue 

samples are focused in the east where U.S. EGU-attributable deposition is greater. While 

U.S. EGUs contribute ~5% of total Hg deposition in the U.S. (for the 2005 scenario – see 

Table 2-2), their contribution to Hg fish tissue levels (summarized at the watershed-level) 

for the 2005 scenario is larger at ~9% (see Table 2-5). This reflects the fact that Hg fish 

tissue samples are heavily weighted in the eastern portion of the U.S. where U.S. EGU 

Hg deposition is typical higher than in the west. By providing greater coverage for the 

eastern half of the country, the Hg fish tissue sampling data generally provides greater 

coverage for regions with potentially greater U.S. EGU-attributable risk.

 Relative to the combined impact of other sources, U.S. EGUs represent a smaller, but 

still potentially important contributor to total fish tissue Hg concentrations: U.S. EGUs 

contribute ~9% of Hg fish tissue levels on average under the 2005 scenario (see Table 2-

5). Under the 2016 scenario, the U.S. EGU contribution decreases to ~ 3% on average 

(see Table 2-5). While U.S. EGU-attributable Hg fish tissue decreases notably between 

the 2005 and 2016 scenarios, the impact on total Hg fish tissue levels is not that 

noticeable given that U.S. EGUs contribute a relatively small fraction on total Hg fish 

tissue levels in general (contrast the pattern of reduction seen in Figures 2-13 and 2-14 

for U.S. EGU-attributable Hg fish tissue levels with the relatively smaller changes seen in 

Figures 2-11 and 2-12 for total Hg fish tissue levels). 

 Despite the relatively small fraction of U.S. EGU-attributable fish tissue Hg on average, 

for a subset of watersheds, U.S. EGU-attributable deposition has a significant 

contribution on fish tissue: Under the 2005 scenario, U.S. EGUs contribute up to 43% of 

total Hg fish tissue levels (for the 99th percentile watershed). Under the 2016 Scenario, 

this pattern is reduced, but U.S. EGUs can still contribute up to 16% of total fish tissue 

Hg concentrations (again, for the 99th percentile watershed) (see Table 2-5). 

2.4 Comparing Patterns of Hg Deposition with Hg Fish Tissue Data for the 3,141 

Watersheds Included in the Risk Assessment 

We also compared spatial patterns of CMAQ modeled Hg deposition and fish tissue Hg 

concentrations, which can inform interpretation of the risk estimates. Specifically we can 

consider: (a) whether the watershed-level Hg fish tissue levels are correlated with total Hg 

deposition, (b) how patterns of Hg deposition for the 3,141 watersheds with Hg fish tissue data 

compare with patterns for the 88,000 watersheds in the continental U.S. and (c) to what extent 

the watersheds for which we have fish tissue Hg data correspond to areas with elevated U.S. 
75 

EGU-attributable deposition. To address these questions, we developed a series of figures 

including: 

 Figures 2-15 and 2-16: Maps comparing (a) watersheds with fish tissue Hg data with (b) 

the general pattern of U.S. EGU-attributable Hg deposition across the continental U.S. 

(for 2005 and 2016). These maps include both the fish tissue dataset used in the March 

TSD (2010 MFT dataset) and the augmented dataset. These maps show the degree of 

overlap between fish tissue Hg data and areas of elevated U.S. EGU-attributable Hg 

deposition (for both 2005 and 2016 scenarios).51 

 Figure 2-17: Plot comparing the fish tissue Hg concentrations versus total Hg deposition 

(2005 scenario) by watershed. This plot allows consideration for whether there appears to 

be a correlation between these two factors at the watershed level. 

 Figure 2-18: Cumulative distribution plots comparing U.S. EGU-attributable deposition 

for the 3,141 watersheds used in the risk assessment with U.S. EGU-attributable 

deposition of the 88,000 watersheds in the continental U.S. Given emphasis in the risk 

assessment on the 2016 scenario, we only provided plots for this scenario. These plots 

allow us to consider whether the watersheds with fish tissue Hg data tended to fall in 

regions with higher U.S. EGU-attributable Hg deposition and the degree to which this 

subset of watersheds provided coverage for areas with relatively elevated U.S. EGU Hg 

deposition across the country. 

 

51 These two maps replace Figures 2-15 and 2-16 from the March TSD. Those earlier maps also compared 

watersheds with fish tissue Hg data to areas of the country that had elevated U.S. EGU-attributable Hg deposition. 

However, in identifying areas of elevated U.S. EGU-attributable Hg deposition for those earlier maps, we attempted 

to be much more precise. Specifically, we used a deposition threshold for identifying areas of high U.S. EGUattributable Hg deposition based on the average rate of deposition over watersheds modeled for risk that had U.S. 

EGU-attributable exposure exceeding the MeHg RfD. In other words, we identified a deposition level associated 

generally with watersheds having high U.S. EGU-attributable risks and used this bright line as the basis for 

identifying areas of high U.S. EGU deposition across the U.S.; the implication being, that these could be areas of 

high U.S. EGU-attributable risk. However, elevated U.S. EGU-attributable risk typically reflects both elevated U.S. 

EGU-attributable Hg deposition and elevated baseline fish tissue Hg concentrations. Therefore, attempting to 

consider only U.S. EGU-attributable deposition as a means for identifying areas of potential elevated risk is subject 

to uncertainty, since it does not also consider the baseline fish tissue Hg concentrations. The current set of figures 

seeks to support a more general comparison of the watersheds we have modeled for risk against the national pattern 

of U.S. EGU-attributable Hg deposition, by not attempting to identify a bright line for classifying areas of the 

country with regard to the magnitude of elevated U.S. EGU-attributable Hg deposition. 
76 

Figure 2-15 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of U.S. EGU

Deposition (2005 scenario) * Fish tissue Hg data includes (a) 2010 MFT data used in March version of the risk assessment and (b) auadded for this revised version. 
77 

Figure 2-16 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of U.S. EGU

Deposition (2016 scenario) * Fish tissue Hg data includes (a) 2010 MFT data used in March version of the risk assessment and (b) auadded for this revised version. 
78 

Figure 2-17 For the 2005 Scenario, Scatter Plot of Hg Fish Tissue Concentrations Versus 

Total Hg Deposition for the 3,141 Watersheds Included in the Risk Assessment 

 

Figure 2-18 Cumulative distribution plots of U.S. EGU-attributable Hg deposition over the 

3,141 watersheds used in modeling the high-end female consumer population as 

contrasted with all 88,000 watersheds (2016 Scenario). 

HUC‐level fish tissue mercury levels(ppm)

for 3,141 HUCs used in risk assessment

CMAQ‐based total Hg deposition by HUC

(2005 Scenario) (ug/m3)

0

20

40

60

80

100

120

140

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
79 

We made the following observations based on the information presented in Figures 2-15 

through 2-18 regarding how estimates of Hg deposition estimates relate to measured fish tissue 

Hg concentrations at the watershed-level: 

 The fish tissue Hg sampling data (watersheds modeled for risk) provide varying coverage 

for areas with elevated U.S. EGU-attributable Hg deposition. The augmented dataset 

significantly expanded coverage in key areas. However, the number of “at-risk” 

watersheds may still be substantially higher than we estimated: As depicted in Figures 2-

15 and 2-16, the 3,141 watersheds used in the risk assessment provide reasonable 

coverage for some of the regions having the highest U.S. EGU-attributable Hg deposition 

(i.e., Ohio River Valley), especially with the augmented dataset for Pennsylvania. 

However, even with the expanded coverage, the majority of areas with elevated U.S. 

EGU-attributable deposition are not covered in the risk assessment. Therefore, we 

believe that the number of watersheds with elevated U.S. EGU-attributable exposure 

and/or risk could be substantially larger (depending on the underlying fish tissue Hg 

concentrations).52 

 Hg fish tissue levels are not correlated with total Hg deposition because the relationship 

is highly dependent on methylation potential of individual waterbodies: As shown in 

Figure 2-17, total Hg fish tissue levels at the watershed-level are not correlated with 

levels of total Hg deposition across watersheds (i.e., the highest total Hg deposition 

watersheds do not always have the highest fish tissue Hg concentrations). This is not 

unexpected because the relationship between total Hg deposition and total Hg fish tissue 

levels is highly dependent on the methylation potential at the waterbody, which is driven 

in part by the presence of wetlands, levels of aqueous organic carbon, Ph and sulfate 

deposition (see section 1.4.6). 

 Hg fish tissue samples were generally collected in regions with elevated total Hg 

deposition: As demonstrated in Figure 2-18, Hg fish tissue sampling appears to have 

favored areas with relatively higher total Hg deposition.53 This can be seen by comparing 

cumulative plots of modeled watersheds (where we have fish tissue Hg data) against plots 

for the 88,000 watersheds. This comparison suggests that watersheds where fish tissue 

Hg data were collected tended to have higher total Hg deposition than the full set of 

watersheds. This likely reflects to some extent, the fact that fish tissue sample are focused 

in the eastern U.S., which has elevated total Hg deposition compared to the broad central 

region (see Figures 2-2 and 2-3). 

 

52 As noted earlier, in order for a watershed to have elevated U.S. EGU-attributable risk, in the context of this risk 

assessment, that watershed must have high U.S. EGU-attributable Hg deposition (as a fraction of total Hg 

deposition) and elevated baseline fish tissue Hg concentrations. 

53 Because the risk estimates for the 2005 scenario are being de-emphasized, we decided to only provide these 

cumulative plots for the 2016 scenario. However, we note that the same relationship noted here (between U.S. EGUrelated Hg deposition for all ~88,000 watersheds and for the subset included in the risk assessment), holds for the 

2005 scenario. 
80 

2.5 Overview of Risk Estimates 

This section provides an overview of risk estimates generated for the 3,141 watersheds 

included in the revised risk assessment. As noted earlier in Section 1.4.3, presentation of risk 

estimates focuses on the typical female subsistence fish consumer scenario assessed at the 

national-level, since this scenario provides the most comprehensive coverage for watersheds with 

Hg fish tissue data across the U.S. and because the consumption rates used to model this scenario 

represent subsistence levels supported by a number of studies. While this scenario is emphasized 

in summarizing risk estimates, we also provide risk estimates for the other scenarios (i.e., low 

income Blacks and Whites in the southeast, Tribal populations near the Great Lakes, Hispanics). 

In summarizing risk estimates, we focus on the 2016 air quality scenario, since as discussed in 

section 2.3, U.S. EGU-related Hg emission levels reflected in this scenario are closest to recent 

conditions. The remainder of this section is organized as follows: 

 Overview of percentile risk estimates generated for the fisher scenarios (Section 2.5.1): 

We provide percentile risk estimates for the typical female subsistence fish consumer 

scenario at the national level and summarize percentile risk estimates for the other SESdifferentiated female subsistence fish consumer scenarios assessed in the analysis. These 

percentile risk estimates allow us to compare risk (total and U.S. EGU-attributable) 

across the female subsistence fish consumers scenarios assessed in the risk assessment. 

 Overview of the number (and frequency) of watersheds with populations potentially atrisk due to U.S. EGU-attributable Hg deposition (section 2.5.2): This set of risk estimates 

provides the main input to the risk characterization framework (see section 1.3). 

Specifically, watersheds with populations potentially at-risk comprise: 

o Watersheds where total risk is considered to represent a potential public health 

concern and where U.S. EGUs contribute to that total risk. We considered various 

increments of U.S. EGU contribution including ≥ 5%, ≥10%, ≥15% and ≥20%, 

although, we focus on cases where U.S. EGUs contribute ≥5 % as noted in section 

1.3. 

o Watersheds where risk from U.S EGUs alone (focusing on U.S. EGU deposition 

and excluding other non-U.S. EGU sources) represents a potential public health 

concern. 

To support the discussion of risk estimates, we present a series of tables summarizing 

those estimates and a list of observations based on this information. 

2.5.1 Overview of percentile risk estimates (2016 scenario) 

This section compares risk percentiles (for total and U.S. EGU-attributable risk) across 

the set of female subsistence fish consumer scenarios included in the risk assessment, with an 

emphasis on the typical female subsistence fish consumer scenario. We also compare and 

contrast risk for the other female consumer scenarios with the more generalized typical female 

subsistence fish consumer scenario. 
81 

These percentile estimates are not population-weighted, but instead represent the specific 

watershed-level risk estimates that fall at a specific point within the larger distribution of 

watershed-level risk estimates for each female subsistence fish consumer scenario. In the 

following tables, we sorted the total and U.S. EGU-attributable risk estimates separately for each 

female subsistence fish consumer scenario. This means that the total and U.S. EGU-attributable 

risk for a particular percentile are not matched by watershed (i.e., the 90th percentile total and 

U.S. EGU-attributable risk for the Vietnamese subsistence fisher likely occur at different 

watersheds).54 While this risk assessment focuses on subsistence levels of fish consumption, the 

risk tables also include risk estimates based on mean fish consumption rates for these female 

subsistence fish consumer scenarios, which are relatively high compared to general recreational 

angler rates but do not represent true subsistence rates. 

Table 2-6 provides risk percentiles for RfD-based HQs for all of the female subsistence 

fish consumer scenarios assessed in the analysis for the 2016 scenario. 

Table 2-6 Percentile risk estimates for the full set of female subsistence fish consumer 

scenarios included in the analysis (2016 scenario) (for both total and U.S. EGU 

incremental RfD-based HQ)* 

Fisher 

consumption rate 

(g/day) and 

percentile** 

Watershed percentile risk (RfD-based HQ)* 

Total U.S. EGU 

50th 75th 90th 95th 99th 50th 75th 90th 95th 99th 

typical female subsistence fish consumer assessed nationally 

39 (mean) 1.9 3.5 5.6 7.4 11.2 - 0.1 0.2 0.2 0.4 

123 (90th) 6.1 10.9 17.7 23.4 35.4 0.2 0.3 0.5 0.7 1.2 

173 (95th) 8.5 15.3 24.9 32.9 49.7 0.2 0.4 0.7 1 1.7 

373 (99th) 18.4 33 53.7 70.9 107.2 0.5 0.9 1.5 2.1 3.7 

Low income White female subsistence fish consumer in the Southeast 

39 (mean) 2.1 4.1 6.8 9.3 12.5 0.1 0.1 0.2 0.3 0.6 

93 (90th) 4.9 9.8 16.3 22.4 29.9 0.2 0.3 0.5 0.7 1.4 

129 (95th) 6.8 13.6 22.7 31 41.5 0.2 0.4 0.8 1 2 

286 (99th) 15.2 30.2 50.3 68.8 92 0.5 1 1.7 2.3 4.3 

Low income Black female subsistence fish consumer in the Southeast 

171 (mean) 9.4 19.4 32.8 42.2 56.4 0.3 0.6 1 1.4 2.6 

446 (90th) 24.6 50.6 85.6 110.1 147.2 0.8 1.6 2.7 3.6 6.8 

557 (95th) 30.8 63.2 106.9 137.5 183.8 0.9 1.9 3.4 4.4 8.4 

(99th) NC NC 

Low income Hispanic female subsistence fish consumer evaluated nationally 

 

54 Presentation of non-matched percentile risk estimates represents a change from the March TSD. In that version, 

we used risk bands to match trends in total risk to a specific U.S. EGU-attributable risk percentile. However, given 

that total and U.S. EGU-attributable risk are not closely correlated, we determined that attempting to present 

matched percentiles was of little utility. Instead, we have decided for this version of the Mercury Risk TSD to 

simply provide percentile total and U.S. EGU-attributable risk estimates based on direct queries of the underlying 

risk distributions. 
82 

Fisher 

consumption rate 

(g/day) and 

percentile** 

Watershed percentile risk (RfD-based HQ)* 

Total U.S. EGU 

50th 75th 90th 95th 99th 50th 75th 90th 95th 99th 

26 (mean) 1.2 2.2 3.5 4.9 8.3 0 0.1 0.1 0.2 0.3 

98 (90th) 4.5 8.3 13.3 18.6 31.5 0.1 0.2 0.4 0.6 1.1 

156 (95th) 7.2 13.2 21.2 29.6 50.1 0.2 0.4 0.7 0.9 1.7 

(99th) NC NC 

Vietnamese female subsistence fish consumer 

27 (mean) 1.1 2 3 4.1 6.8 0 0 0.1 0.1 0.3 

99 (90th) 4.1 7.2 11.1 15 24.9 0.1 0.2 0.3 0.5 1 

152 (95th) 6.3 11 17.1 23 38.3 0.2 0.3 0.5 0.8 1.5 

(99th) NC NC 

Laotians female subsistence fish consumer 

47 (mean) 1.9 3.7 5.3 5.9 7.7 0.1 0.1 0.2 0.3 0.9 

145 (90th) 5.9 11.3 16.1 18 23.7 0.2 0.3 0.6 0.9 2.6 

226 (95th) 10.9 20.8 29.6 33 43.4 0.3 0.5 1.2 1.7 4.9 

(99th) NC NC 

Tribal (near Great Lakes) female subsistence fish consumer 

62 (mean) 5.4 7.7 11 13.2 17.5 0.1 0.1 0.2 0.3 0.4 

136 (90th) 11.8 17 24.1 29.1 38.4 0.2 0.3 0.4 0.6 0.9 

213 (95th) 18.5 26.6 37.7 45.5 60.2 0.3 0.4 0.7 0.9 1.3 

493 (99th) 42.8 61.4 87.3 105.2 139.1 0.6 1 1.5 2.1 3.1 

* Percentile risk estimates presented for “total” and “U.S. EGU” are not matched by watershed (e.g., the 90th 

percentile total and U.S. EGU-attributable risk estimates for a particular female subsistence fish consumer 

scenario may are unlikely to be from the same watershed). 

** Means are provided along with upper-end percentile values. 

“-“: RfD-based HQ is ≤ 0.1. 

NC: It was not possible to derive a 99th percentile consumption rate for this population due to insufficient sample 

size in the underlying study. Consequently, risk estimates for the 99th percentile consumption rates were not 

generated. 

We made several observations based on the information in Table 2-6 regarding the 

potential health hazard associated with MeHg RfD-based HQ estimates (see Section 1.4.5).: 

 Risk estimates for the typical female subsistence fish consumer (assessed at the nationallevel) generally provide coverage for the Hispanic, Vietnamese and Tribal scenarios: 

Risk estimates (for both total and U.S. EGU-attributable) generated for the typical female 

subsistence fish consumer scenario are generally higher than estimates generated for the 

Hispanic and Vietnamese scenarios. Risk estimates for the typical female subsistence fish 

consumer scenario are also higher than risks for the Tribal population for U.S. EGUattributable risk although the Tribal populations tend to have higher total risks. Given 

emphasis on U.S. EGU-attributable risk in the policy context, these trends would suggest 
83 

that the typical female subsistence fish consumer scenario provides general coverage for 

the other three scenarios (Hispanic, Vietnamese and Tribal), since the typical female 

subsistence population tends to have higher U.S. EGU-attributable risk estimates. 

 Risk estimates for the typical female subsistence fish consumer (assessed at the nationallevel) may not provide full coverage for risks experienced by low income Blacks and low 

income Whites in the Southeast, or for Laotians assessed at the national-level: U.S. 

EGU-attributable risk estimates for the Southeastern low income White and low income 

Black scenarios and for the Laotian scenario are higher than those for the typical female 

subsistence fish consumer. In the case of low income Whites, this likely reflects the fact 

that some regions in the Southeast (specifically South Carolina) have relatively elevated 

total and U.S. EGU-attributable fish tissue Hg concentrations compared with the national 

distribution (see Figures 2-13 and 2-15). In the case of low income Blacks, higher fish 

tissue Hg concentrations in South Carolina are combined with fish consumption rates that 

are substantially higher than those used for the typical female subsistence fish consumer 

scenario (see Table 1-6). While both the low income White and low income Black 

subsistence fisher scenarios in the Southeast have higher risk estimates than the typical 

female subsistence fish consumer scenario (assessed nationally), because the typical 

female subsistence scenario is assessed at all 3,141 watersheds with fish tissue Hg data, 

we focus on the typical female subsistence fish consumer scenario to provided policyrelevant risk information to support this rulemaking (i.e., we have greater overall 

confidence in risk estimates generated for the typical female subsistence fish consumer 

scenario because it is assessed at a larger number of watersheds with broader spatial 

coverage). However, the higher risks estimates for these two Southeastern subsistence 

fisher scenarios should also be considered in the context of interpreting the results of this 

risk assessment in a science-policy context. In the case of the Laotian scenario, while 

U.S. EGU-related risks are higher than for the typical female subsistence fish consumer 

scenario, this scenario was assessed for a small number of watersheds (131 of the 3,141 

watersheds with fish tissue Hg data). Specifically, risk estimates generated for the 

Laotian scenario may reflect relatively rare localized interactions between fish tissue Hg 

concentrations and U.S. EGU-related Hg deposition, resulting in areas of elevated U.S. 

EGU-attributable exposure and risk. 

2.5.2 Overview of number and percentage of watersheds with populations 

potentially at-risk due to U.S. EGU mercury emissions (2016 scenario) 

This section discusses risk estimates based on identifying the number and percent of 

watersheds with fish tissue Hg samples with populations potentially at-risk due to Hg emitted 

from U.S. EGUs (for the 2016 scenario). As noted in Section 1.3, the “at-risk population” 

classification is based on identifying watersheds where: (a) U.S. EGUs contribute to total risk at 

watersheds where that total risk is considered to represent a potential public health hazard and/or 

(b) risk at the watershed-level represents a potential public health hazard from U.S. EGUattributable deposition when considered alone, without taking into account deposition from other 

sources. The estimates of watersheds with at-risk populations discussed in this section are used 

in the 2-Stage risk characterization framework described in Section 1.3 for interpreting risk 

estimates. Specifically, the first category of at-risk populations described above comprise Stage 

1a of the 2-Stage approach, while the second category comprises Stage 1b (see Figure 1-1). The 
84 

combination (i.e., mathematical union) of these two groups of watersheds with at-risk 

populations comprises the set of watersheds represented in Stage 2 of the framework. 

In summarizing risk estimates in this section, we focus on estimates generated using the 

current risk assessment that reflects refinements to the risk model (e.g., inclusion of additional 

fish tissue data and evaluating the typical female subsistence fish consumer for all watersheds 

with fish tissue Hg data). However, when we present the Stage 2 risk estimates, we also include a 

summary of Stage 2 estimates from the March version of the Mercury Risk TSD (then referred to 

as “stage 3” results) for comparison. 

With regard to the HQ estimates, any contribution of Hg from EGUs to watersheds with 

potential exposures exceeding the MeHg RfD represents a hazard to public health, but for 

purposes of this analysis, we have focused on those waterbodies where we determined EGUs 

contributed 5% or more to the hazard. This is a reasonable, conservative approach because risks 

are associated with all exposures above the MeHg RfD. 

The watersheds with potentially at-risk populations are all based on the underlying risk 

estimates generated for the typical female subsistence fish consumer scenario. The estimates of 

watersheds with potentially at-risk populations are summarized in tables described below, 

followed by a set of observations regarding these risk estimates. 

 Tables 2-7: Identifies watersheds with potentially at-risk populations based on 

consideration for different degrees of U.S. EGU contribution (i.e., ≥5, ≥10, ≥15 and 

≥20%) at watersheds where total risk is considered to represent a potential public health 

hazard (i.e. HQ>1). For reference purposes, the table also identifies the total number of 

watersheds (out of the 3,141 assessed for the typical female subsistence fish consumer) 

with total risk exceeding the HQ threshold, regardless of the U.S. EGU percent 

contribution (see the “≥ 0%” row of results in the table). In presenting results, the tables 

include both the number and percent of watersheds with fish tissue Hg samples meeting 

specific criteria that this represents. (Stage 1a of the 2-Stage framework) 

 Table 2-8: Identifies watersheds with potentially at-risk populations based on U.S. EGUattributable deposition considered alone, without taking into account other sources of Hg 

(Stage 1b of the 2-Stage framework) 

 Table 2-9: Presents the union of the two categories of watersheds with potentially at-risk 

populations (i.e., mathematical union of the Stage 1 and 2 estimates presented in Tables 

2-7 and 2-8, see Figure 1-1 for explanation). Table 2-9 considers the number and percent 

of watersheds that have (a) U.S. EGUs contributing to total risk of an HQ>1, OR (b) an 

HQ>1 based on considering U.S. EGU Hg deposition alone, without taking into account 

deposition from other sources (i.e., U.S. EGU-attributable HQ>1). This represents Stage 

2 of the risk characterization framework. 

 Table 2-10: Presents summary of the same Stage 2 risk metric as Table 2-9 with the 

March Mercury Risk TSD results. 
85 

Table 2-7 Watersheds with potentially at-risk populations based on consideration for 

various degrees of U.S. EGU contribution to total risk (2016 scenario) 

EGU risk 

threshold 

Number and percentage of HUCs meeting risk threshold 

criteria 

(RfD-based HQ > 1)* 

2016 analysis 

90th percentile fish 

consumption 

95th percentile 

fish 

consumption 

99th percentile fish 

consumption 

≥ 0% 2853 (91%) 2983 (95%) 3112 (99%) 

≥ 5% 679 (22%) 726 (23%) 754 (24%) 

≥ 10% 104 (3%) 114 (4%) 119 (4%) 

≥ 15% 37 - 40 - 40 - 

≥ 20% 15 - 17 - 17 - 

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a given row).

Table 2-8 Watersheds with potentially at-risk populations based on consideration for risk 

based on U.S. EGU mercury deposition and resulting exposure considered alone, 

without taking into account other sources of mercury deposition (2016 scenario) 

EGU risk 

threshold 

Number and percentage of 3,141 HUCs meeting risk 

threshold criteria 

(RfD-based HQ risk)* 

2016 analysis 

90th fish 

consumption 

95th percentile

fish 

consumption 

99th percentile fish 

consumption 

≥ 1.5* 17 - 52 (2%) 327 (10%) 

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a the “>1.5” row).

Table 2-9 Combination of watersheds with potentially at-risk populations based on either 

consideration for (a) U.S. EGU percent contribution to total risk OR (b) risk when 

U.S. EGU mercury deposition is considered alone, without taking into account 

deposition from other sources (2016 scenario) 

EGU risk 

threshold 

Number and percentage of HUCs meeting risk threshold criteria 

2016 analysis 

90th percentile fish 

consumption 

95th percentile fish 

consumption 

99th percentile fish 

consumption 

U.S. EGU-attributable risk ≥1.5* HQ OR total risk ≥1.5* HQ and U.S. EGU contribution of: 

≥ 5% 679 (22%) 744 (24%) 917 (29%) 

≥ 10% 107 (3%) 141 (4%) 385 (12%) 

≥ 15% 47 - 80 (3%) 339 (11%) 

≥ 20% 29 - 64 (2%) 331 (11%) 

*** Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 

(see section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a given row).
86 

Table 2-10 Reflecting the March version of the risk assessment - combination of 

watersheds with potentially at-risk populations based on either consideration for (a) 

U.S. EGU percent contribution to total risk OR (b) risk when U.S. EGU mercury 

deposition is considered alone, without taking into account deposition from other 

sources (2016 scenario) 

EGU risk 

threshold 

Number and percentage of HUCs meeting risk threshold criteria 

90th percentile fish 

consumption 

95th percentile fish 

consumption 

99th percentile fish 

consumption 

U.S. EGU-attributable risk ≥1.5* HQ OR total risk ≥1.5* HQ and U.S. EGU contribution of: 

≥ 5% 484 (20%) 524 (22%) 672 (28%) 

≥ 10% 96 (4%) 121 (5%) 325 (14%) 

≥ 15% 49 (2%) 76 (3%) 292 (12%) 

≥ 20% 34 - 61 (3%) 286 (12%) 

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a given row).

We made several observations regarding watersheds with potentially at-risk populations 

due to U.S. EGU–attributable Hg deposition based on the information in Tables 2-7 through 2-

10, oriented around the 2-Stage risk characterization framework. 

 Depending on the percentile fish consumption rate, between 3 and 24% of those 

watersheds with total risk HQs >1 have U.S. EGUs contributing at least 5% of total Hg 

deposition (2016 scenario): With this risk metric, we consider the degree to which U.S. 

EGUs contribute to total risk at watersheds where total risk represents a potential public 

health hazard (i.e., total exposure leads to an HQ > 1). Considering a 5% U.S. EGU 

contribution at watersheds where total risk is considered a potential public health hazard, 

we have up to 24% of the watersheds falling into this category (with the 24% value 

reflecting risk modeled using the 99th percentile fish consumption rate for the high-end 

female consumer – see Table 2-7). It is important when considering this risk metric to 

reiterate that any exposure above the MeHg RfD represents a potential public health 

hazard. 

 Depending on the percentile fish consumption rate, between 2 and 12% of the watersheds 

have HQs > 1, based on U.S. EGU Hg deposition before factoring in any other sources 

of Hg (i.e., an U.S. EGU-attributable HQ>1) (2016 scenario): Our analysis suggests that 

between 2 and 12% of the 3,141 watersheds modeled in the risk assessment for high-end 

female consumers could have an HQ >1 from U.S. EGU-attributable Hg deposition when 

considered alone, without taking into account other sources of deposition. The low end of 

the range reflects the 95th percentile consumption rate, and the high end reflects the 99th

percentile consumption rate (again for the typical female subsistence fish consumer). 

 Depending on the percentile fish consumption rate, between 22 and 29% of the 

watersheds are at-risk based on at least one of the risk characterization criteria (2016 

scenario): Combining the two categories of watersheds with populations at-risk due to 

U.S. EGU Hg emissions summarized in the last two bullets, we get a total estimate 

ranging from 22 to 29% of watersheds at-risk. These estimates are sensitive to the 
87 

specification of the percent U.S. EGU contribution. If a 10 percent contribution threshold 

is applied, the range of watersheds at-risk ranges from 3% to 12%. The ranges for each 

percent contribution threshold also reflect the different fish consumption rates considered 

for the high-end female consumer (i.e., 90th, 95th and 99th percentile fish consumption 

rates). The results summarized here for total “at-risk” watersheds map to Stage 2 of the 

2-stage risk characterization framework. 

 The revised risk assessment and the March Mercury Risk TSD generate very similar 

Stage 2 risk estimates (2016 scenario): Comparison of risk estimates presented in Tables 

2-9 and 2-10 suggest that refinements to the risk assessment implemented since the 

March TSD have not substantially affected overall Stage 2 results (formerly known as 

“Stage 3” results in the March version of the Mercury Risk TSD). 

2.6 Sensitivity Analyses 

This section discusses several sensitivity analyses conducted to assess the potential 

impact of sources of uncertainty related to: (a) the application of the proportionality assumption 

in linking Hg deposition and fish tissue Hg concentrations (see sections 1.4.2.1 and 1.4.6) and (b) 

use of the watershed-level 75th percentile fish tissue Hg concentration as the basis for generating 

watershed-level risk estimates (see section 1.4.2). Given the emphasis placed in the nationalscale mercury risk assessment on risk estimates generated for the 2016 scenario, we have also 

based the sensitivity analyses on simulating risks for the 2016 scenario. 

To address uncertainty in applying the proportionality assumption, we conducted two 

sensitivity analyses focused on concerns that the risk assessment may have included some 

watersheds that are disproportionately impacted by non-air Hg sources. We included the 

proportionality assumption in the sensitivity analysis because it represents a critical element of 

the analysis and is acknowledged as representing a potentially important source of uncertainty 

(see Table 2-15 in section 2.7).55 As noted in section 1.4.6, the proportionality assumption 

linking Hg deposition over watersheds with fish tissue Hg concentrations, only holds for 

watersheds where aerial Hg deposition is the primary source of loading. The two sensitivity 

analyses include: (a) excluding four states where we have concerns over the potential for non-air 

Hg playing a greater role (ME, MN, SC and LA)56 and (b) constraining the risk analysis to only 

include those watersheds in the upper 25th percentile of total Hg deposition (i.e., watersheds with 

 

55 In the March TSD, we included a sensitivity analysis on the proportionality assumption for flowing versus 

stationary waterbodies. However, comments by the SAB suggested that this issue was unlikely to represent a 

significant source of uncertainty in the analysis and consequently we have not repeated it here. 56 We excluded ME, MN and LA in this sensitivity analysis for reasons specific to each state. ME was excluded 

because Hg fish tissue levels there are fairly high, while Hg deposition is not relatively elevated (compared to other 

eastern states) – this raises the concern that other factor may affected fish tissue Hg (e.g., other non-air sources, or 

increased methylation potential). MN was excluded because taconite mining could contribute non-air Hg loading. 

Finally LA was excluded due to substantial industrial activity that could contribute non-air Hg loading. We note 

that, in the March TSD, we excluded SC from this sensitivity analysis because of higher fish Hg levels and Hg air 

deposition that (while elevated in some locations) is not uniformly higher than other states. As part of our 

explanation for the higher fish tissue Hg concentrations in SC we pointed to a history of gold mining in SC (i.e., the 

potential that significant non-air Hg loading may be a factor). SAB panel members with expertise in this area did 

point out that the methylation potential of many of the waterbodies in SC (particular central and eastern SC) is 

relatively high and that this is likely the reason for the higher fish tissue Hg concentrations. 
88 

relatively elevated levels of total Hg deposition so we have increased confidence that aerial 

deposition plays a significant role in loading). 

To address uncertainty in use of the watershed-level 75th percentile fish tissue Hg 

concentration in risk modeling, we conducted a sensitivity analysis using the watershed-level 

median fish tissue Hg. This sensitivity analysis, which was recommended by SAB, considers the 

situation in which subsistence fishers do not necessarily target the larger (higher trophic level) 

fish and consequently could catch fish with lower fish tissue Hg concentrations. 

The results of both sensitivity analyses are presented in terms of their impact on the 2-

Stage Risk Characterization Framework results (i.e., their impact on estimates of the number and 

percent of watersheds with potentially at-risk populations – section 1.3). Therefore, we provide 

three tables (Tables 2-11, 2-12 and 2-13) that correspond to each stages of the 2-Stage Risk 

Characterization Framework. Each table first presents core results, followed by results for each 

of the three sensitivity analyses (this allows the results of each sensitivity analysis to be readily 

compared against the core analysis). We present observations from the sensitivity analyses at the 

end of this section. All results presented in the three tables are based on simulating risk for the 

typical female subsistence fish consumer scenario. 

Table 2-11 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on U.S. EGUs making a specified contribution to total risk (2016 

scenario)

EGU risk threshold 

(percent U.S. EGU 

contribution to risk) 

Number and percentage of watersheds meeting risk threshold 

criteria 

(Total HQ ≥ 1.5 and U.S. EGU percent contribution as dimensioned 

below)* 

90th percentile fish 

consumption 

95th percentile fish 

consumption 

99th percentile fish 

consumption 

All watersheds (core analysis)

≥ 0% 2853 (91%) 2983 (95%) 3112 (99%) 

≥ 5% 679 (22%) 726 (23%) 754 (24%) 

≥ 10% 104 (3%) 114 (4%) 119 (4%) 

≥ 15% 37 - 40 - 40 - 

≥ 20% 15 - 17 - 17 - 

Sensitivity analysis A (exclude watersheds in MN, LA, SC and ME)

≥ 0% 2104 (89%) 2224 (94%) 2347 (99%) 

≥ 5% 638 (27%) 685 (29%) 713 (30%) 

≥ 10% 90 (4%) 100 (4%) 105 (4%) 

≥ 15% 32 - 35 - 35 - 

≥ 20% 14 - 16 - 16 - 

Sensitivity analysis B (include watersheds in top 25th percentile with regard to total Hg 

deposition)

≥ 0% 712 (91%) 740 (94%) 776 (99%) 

≥ 5% 154 (20%) 166 (21%) 178 (23%) 

≥ 10% 39 (5%) 42 (5%) 45 (6%) 
89 

EGU risk threshold 

(percent U.S. EGU 

contribution to risk) 

Number and percentage of watersheds meeting risk threshold 

criteria 

(Total HQ ≥ 1.5 and U.S. EGU percent contribution as dimensioned 

below)* 

90th percentile fish 

consumption 

95th percentile fish 

consumption 

99th percentile fish 

consumption 

≥ 15% 20 (3%) 20 (3%) 20 (3%) 

≥ 20% 9 - 9 - 9 - 

Sensitivity analysis C (generate risks using median watershed-level fish tissue Hg 

concentration)

≥ 0% 2774 (88%) 2932 (93%) 3095 (99%) 

≥ 5% 659 (21%) 717 (23%) 751 (24%) 

≥ 10% 100 (3%) 111 (4%) 118 (4%) 

≥ 15% 37 - 39 - 40 - 

≥ 20% 15 - 17 - 17 - 

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). 

Table 2-12 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on consideration for U.S. EGU-attributable HQ risk (2016 scenario) 

(risk considering U.S. EGU Hg deposition before considering other sources of Hg 

deposition) 

U.S. EGU-attributable 

risk threshold 

Number and percentage of 3,141 watersheds meeting risk 

threshold criteria 

(U.S. EGU-attributable HQ)* 

90th percentile fish 

consumption 

95th percentile 

fish 

consumption 

99th percentile fish 

consumption 

All watersheds (core analysis)

≥ 1.5* 17 - 52 (2%) 327 (10%) 

Sensitivity analysis A (exclude watersheds in MN, LA, SC and ME)

≥ 1.5* 7 - 22 - 209 (9%) 

Sensitivity analysis B (include watersheds in top 25th percentile with regard to total Hg 

deposition)

≥ 1.5* 9 - 25 (3%) 104 (13%) 

Sensitivity analysis C (generate risks using median watershed-level fish tissue Hg 

concentration)

≥ 1.5* 11 - 24 - 173 (6%) 

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). 
90 

Table 2-13 Sensitivity analysis results presented as: Combination of watersheds with 

potentially at-risk populations based on either consideration for (a) U.S. EGU 

percent contribution to total risk OR (b) risk when U.S. EGU mercury deposition is 

considered alone, without taking into account deposition from other sources (2016 

scenario) 

EGU risk threshold 

(percent U.S. EGU 

contribution to risk) 

Number and percentage of watersheds meeting risk threshold 

criteria 

(U.S. EGU-attributable risk ≥1.5* HQ OR total risk ≥1.5* HQ and 

U.S. EGU: percent contribution as dimensioned below)* 

90th percentile fish 

consumption 

95th percentile fish 

consumption 

99th percentile fish 

consumption 

All watersheds (core analysis)

≥ 5% 679 (22%) 744 (24%) 917 (29%) 

≥ 10% 107 (3%) 141 (4%) 385 (12%) 

≥ 15% 47 - 80 (3%) 339 (11%) 

≥ 20% 29 - 64 (2%) 331 (11%) 

Sensitivity analysis A (exclude watersheds in MN, LA, SC and ME)

≥ 5% 638 (27%) 689 (29%) 782 (33%) 

≥ 10% 90 (4%) 108 (5%) 267 (11%) 

≥ 15% 34 - 49 (2%) 221 (9%) 

≥ 20% 19 - 34 - 213 (9%) 

Sensitivity analysis B (include watersheds in top 25th percentile with regard to total Hg 

deposition)

≥ 5% 154 (20%) 174 (22%) 232 (30%) 

≥ 10% 41 (5%) 54 (7%) 122 (16%) 

≥ 15% 26 (3%) 38 (5%) 108 (14%) 

≥ 20% 16 (2%) 30 (4%) 105 (13%) 

Sensitivity analysis C (generate risks using median watershed-level fish tissue Hg 

concentration)

≥ 5% 659 (21%) 721 (23%) 822 (26%) 

≥ 10% 101 (3%) 118 (4%) 241 (8%) 

≥ 15% 42 - 53 (2%) 188 (6%) 

≥ 20% 23 - 36 - 179 (6%) 

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). 

Observations regarding the sensitivity analyses include: 

 Generating risk estimates excluding watersheds located in four states (AL, SC, ME and 

MN): This sensitivity analysis resulted in different effects on the Stage 1a and Stage 1b 

risk estimates, although neither was substantially different than the core estimates. 

Estimates of the number of watersheds with at-risk populations due to U.S. EGUs 

contributing at least 5% to total risk (Stage 1a) demonstrated a range of moderate 

increases compared with the core analysis, depending on the “percent contribution” 

category considered (see Table 2-11). Similarly, estimates of the percentage of 
91 

watersheds with at-risk populations when considering U.S. EGU Hg deposition (without 

other sources – Stage 1b) based on dropping out the four states was 9% as compared with 

10% when considering all states (for the 99th percentile fish consumption rate - see Table 

2-12). This sensitivity analysis suggests that the risk estimates are relatively robust to 

exclusion of the four states considered in this sensitivity analysis. 

 Generating risk estimates including only those watersheds falling in the top 25th

percentile with regard to total Hg deposition: These sensitivity analysis results suggest 

that focusing on those watersheds with higher total deposition results in a mixture of 

impacts on both Stage 1a and Stage 1b risk metrics. Estimates of the number of 

watersheds with at-risk populations due to U.S. EGUs contributing between 5% and 10% 

is lower for the sensitivity analysis than for the core analysis. However, estimates for 

contributions of ≥ 10% and ≥20% are notably higher than for the core analysis (see Table 

2-11). Similarly, estimates of the percentage of watersheds with at-risk populations when 

considering U.S. EGU Hg deposition alone (without taking into account deposition from 

other sources – Stage 1b) is 13% as compared with 10% when considering all watersheds 

(for the 99th percentile fish consumption rate - see Table 2-11). This sensitivity analysis 

suggests that the risk estimates would be somewhat higher (particularly for the tail of the 

risk distribution) if we focus on those watersheds receiving the greatest amount of 

atmospherically deposited Hg. However, the magnitude of risk is not substantially 

different from the core analysis. 

 Generating risk estimates using the median watershed-level fish tissue Hg concentration 

(instead of the 75th percentile as used in the core analysis): These sensitivity analysis 

results are notably different for the two categories of risk metric considered. Estimates of 

the number of watersheds with at-risk populations due to U.S. EGUs contributing ≥ 5% 

are essentially the same as the core risk estimates. However, estimates of the percentage 

of watersheds with at-risk populations when considering U.S. EGU Hg deposition 

(without other sources – Stage 1b) is substantially lower than the core analysis (i.e., 5% 

when the median fish tissue Hg concentration is used as compared with 10% with the 

core analysis). These results suggest that for some risk metrics, the results of the analysis 

would be significantly affected were we to use the median fish tissue Hg concentration 

rather than the 75th percentile. The SAB peer-review panel concluded that “using the 75th

percentile of fish tissue values as a reflection of consumption of larger, but not the 

largest, fish among sport and subsistence fishers is a reasonable approach and is 

consistent with published and unpublished data on predominant types of fish consumed.” 

2.7 Discussion of key sources of variability and uncertainty 

This section provides a qualitative discussion of variability and uncertainty associated 

with the risk assessment. Regarding variability, we focus on identifying the key sources of 

variability associated with modeling risk for the scenarios included in the analysis and then 

discuss the degree to which those sources are reflected in the design of the risk assessment and 

implications for the risk estimates generated. The risk assessment has been designed to reflect 

critical sources of variability to the extent allowed by available methods and data and given the 

resources and time available. Key sources of variability associated with the analysis include: 
92 

 The pattern of total and U.S. EGU-attributable Hg deposition across watersheds in the 

U.S. (including variation in the global inflow of Hg into the U.S.) 

 The patterns of fish tissue Hg concentrations across watersheds within the U.S. 

 The sampling protocols for measuring fish tissue Hg concentrations used by States which 

results in varying degrees of spatial coverage of fish tissue Hg concentrations across the 

states. 

 The response of watersheds to Hg loading including variation in the temporal response 

and degree to which fish tissue Hg concentrations change in response to altered Hg 

loadings. 

 The degree of subsistence fishing activity by different groups across watersheds. 

 Subsistence fishing behavior including: consumption rates, species harvested, degree to 

which activity is focused on one or more waterbodies and differences in 

cooking/preparation practices. Variation in bodyweight can also impact exposure and risk 

estimates. 

These sources of variability and the degree to which each is reflected in the design of the 

analysis are identified and described in Table 2-14. 

Regarding uncertainty, we focus first on identifying potentially significant sources of 

uncertainty impacting the analysis. Then we characterize (a) the nature of the impact of each 

source on risk estimates and (b) the degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis (including whether sensitivity analyses 

completed for the risk assessment address a particular source of uncertainty). The results of this 

qualitative assessment are presented in Table 2-15. 

The SAB commented extensively on the sources of variability and uncertainty that should 

be addressed in the revised version of this section and those recommendations are reflected in 

Tables 2-14 and 2-15. 
93 

Table 2-14 Key sources of variability associated with the analysis and degree to which they are reflected in 

analysis 

Source of variability Description 

Degree to which source is reflected in design of risk ana

for the risk estimates generated 

(A) 2005 Hg emissions Emissions approaches have been applied to create a 2005 

Hg inventory for separate categories of emissions (e.g., 

Coal utilities, Portland Cement, Electric Arc Furnaces). Within each category, types of variability for total Hg include (1) annual variability related to product demand, (2) sub-annual (e.g., hourly) temporal variability from 

production related fluctuations at each facility, (3) variability of emissions control systems over time depending on the control device age and process stages such as startup/shutdown and operating conditions even during standard operations, (4) fuel characteristics including the Hg content of the fuel, and (5) differences in similar pieces of equipment that are relevant when 

emission factors are applied to a class of process (e.g., not all fluidized bed boilers will have the same emissions 

potential). Further, the speciation characteristics of the total Hg has variability based on many of the same factors 

as for total Hg (e.g., production parameters, control systems, fuels, physical properties of individual units) 

The analysis accounts for annual variability in that estimates developed for the particular 2005 year as the midpoint of the projected to 2016 to estimate risks in the future baseline. The estimate sub-annual variability through use of (a) hourly EGU developed from several years of hourly Continuous Emissionheat input data and (b) monthly, day-of-week, and diurnal tem

for non-EGU sources. The approach for estimating 2005 EG

1999 control assumptions and fuels, which creates some unceTable 2-15. The variability associated with different pieces oof process was not accounted for explicitly, but the Hg emissfor EGU boilers and other unit types were developed to repreemissions from a given unit type by averaging emissions test at facilities within a given class. The emission factors used to EGUs were assigned based on boiler type, fuel (coal or oil), aconfiguration. The same characteristics were used to assign s

(B) 2016 Hg emissions The sources of variability identified for the 2005 

emissions also apply to 2016; however, they are addressed 

differently because of the differences in approach associated with estimating emissions for a future year. 

Annual variability is accounted for by the Integrated Planning of Hg emissions. This model accounts for expected electricitplace before 2016, and forecasts of fuel prices resulting in preFurther, the model predicts the Hg content of the fuel used in based on forecasts of which coal mine will supply coal to eaca more complete job than is done in 2005 for including controAs in 2005, sub-annual variability uses a) hourly EGU demanfrom several years of hourly Continuous Emissions Monitorindata and (b) monthly, day-of-week, and diurnal temporal alloEGU sources. IPM further accounts for differences in equipmModification Factors (EMFs), which adjust the Hg emissions type and control device configuration. The EMFs are not sperather were developed to represent, on average, emissions chacontrol device types. Finally, the speciation fractions used in in 2016, but the fractions were assigned based on the 2016 uncontrol devices, rather than the base year configuration. 

(C) Pattern of total and U.S. EGU-attributable Hg deposition across 

Patterns of annual deposition of Hg including total (all source) and estimates of the U.S. EGU fraction (by watershed) displays considerable spatial variability across 

By extrapolating CMAQ grid cell results at the more spatially watershed level, we retain the greater degree of spatial resoluHg deposition obtained through the use of the 12k CMAQ gr
94 

Source of variability Description 

Degree to which source is reflected in design of risk ana

for the risk estimates generated 

watersheds in the US (including variation in the global inflow of Hg into the U.S.) 

the U.S. based on the results of 12 km grid cell CMAQ modeling (see Section 2.4). This variability includes changes over time in global-scale ambient mercury concentrations due to changes in global emissions and chemistry. 

Regarding variation in the global inflow of Hg, the global GE

does provide ambient Hg inflow estimates used in the CMAQ vary spatially in the horizontal direction and vertically from ttroposphere. The Hg inflow concentrations also vary for each within each day. However, the ambient speciated Hg inflow i2005 and 2016 scenarios. 

(D) Patterns of fish tissue Hg concentrations across watersheds within the U.S. 

Mercury fish tissue measurements can display considerable spatial variability across watersheds (see section 2.3). 

We have fish tissue measurements for roughly 4% of the wat

3,141 watersheds with measured values out of 88,000 based o2010). These measurements are concentrated in the Eastern pthere are some measurements in almost all states. While our levels do generally provide some degree of coverage for areadeposition (in terms of both total and U.S. EGU-attributable)and there are a large number of watersheds with high total anHg deposition for which we do not have fish tissue measurem

(E) Sampling protocols for measuring fish tissue Hg concentrations used by States differ, which results in varying degrees of spatial coverage of fish tissue Hg concentrations across the states. 

States utilize varied approaches for targeting waterbodies 

for fish sampling and Hg measurement. In virtually all cases, sampling strategies are not intended to provide a representative characterization of fish tissue Hg concentrations across the state and instead are intended to identify and then target areas suspected of having higher Hg levels, or higher fishing activity. 

The sampling protocol used by states in collecting fish tissue directly reflected in the fish tissue dataset used in the risk asspotential bias in the collection of fish tissue data (i.e., tendenc

highly impacted waterbodies) is reflected in that dataset. HowHg data that are potentially biased towards more heavily Hg-not problematic given the focus of the risk assessment. Speciattempting to capture reasonable high-end subsistence fisher data biased towards more highly impacted waterbodies is actuincrease the potential that we will identify areas of higher expto the subsistence fisher scenario (it would only be problemat

to generate a representative picture of more generalized recre

(F) Response of watersheds to Hg loading including variation in the temporal response and degree to which fish tissue Hg concentrations change in response to altered Hg loadings. 

The response of fish tissue Hg concentrations to mercury deposition can vary greatly depending on a number of factors (e.g., role of watershed in loading to waterbody, methylation potential of the waterbody, rates of sulfate deposition, nature of aquatic biotic foodweb including mix of upper-level trophic fish etc). These factors can also affect the temporal profile of that response. 

Variation in methylation potential is reflected directly in the vmeasured Hg levels in fish across the different sampling loca

very important, and in fact is a primary determinant of variaticoncentrations across watersheds. This will have a large imptotal Hg exposure, and as shown in Figure 2-17, results in a lototal Hg deposition and fish tissue Hg across watersheds. Howill not have an impact on the attribution of exposure at any because the scientific literature supports a linear relationship deposition of Hg at a watershed and changes in fish tissue Hg Furthermore, because we are not predicting temporal trends iinstead consider a future point in time (once near-steady state reached), variation in the temporal profile of changes in fish tdifferences in methylation potential of different watersheds is factor in the analysis. 

(G) Differences in the spatial distribution and 

Studies reviewed in developing the approach for this analysis suggests that there can be considerable variation 

Surveys of high- self-caught fish consuming populations allo

this type of activity for specific areas covered by those survey
95 

Source of variability Description 

Degree to which source is reflected in design of risk ana

for the risk estimates generated 

extent of subsistence fishing activity by different groups across watersheds in the U.S. 

in the nature of high-end self-caught fisher populations across regions of the country. This variation reflects ethnic 

and cultural practices and can also be driven be socioeconomic status (SES) related factors (e.g., poverty, levels of income). In addition, we would expect that access to freshwater vs saltwater fishing locations would also play a role. 

Vietnamese and Laotian fishing activity in specific regions of fishing activity by Blacks and Whites in South Carolina and TGreat Lakes). However, available studies on this type of highinland freshwater bodies do not provide comprehensive coverthe U.S. Therefore, we extrapolated coverage from areas refleother portions of the U.S. There is uncertainty associated with discussed in Table 2-15 (entry E) a lack of comprehensive susubsistence fishing activity for all regions in the U.S. is a limThis also prevents us from generating population-weighted es

(H) Subsistence fishing behavior including: consumption rates, species harvested, degree to which activity is focused on one or more waterbodies and differences in cooking/preparation practices. Variation in bodyweight can also impact exposure and risk estimates. 

Subsistence fisher populations can display significant 

variability in their fishing behavior, which can impact overall exposure and risk. Variation in consumption rates has a direct impact on exposure, as do differences in the types of fish (size and species) potentially targeted. Tendency to focus fishing activity in one spot, versus 

distributing it across different waterbodies can also impact risk. Specifically, activity distributed across multiple waterbodies will tend to dilute out locations with higher 

fish tissue Hg concentrations, while focused activity means that one or more fishers may get all of their fish from the more impacted lake. Differences in cooking/preparation can also impact Hg levels on a per unit of fish basis, which, when combined with “as cooked” consumption rates can effected modeled exposure and risk. Body weight will also affect the calculation of exposure, when standardized by body weight (e.g., a heavier individual ingesting the same amount of fish will have a lower body-weighted adjusted exposure than a lighter individual ingesting the same amount of fish). 

While we have fairly comprehensive information on consump

female subsistence fish consumer scenarios we included in thhave information (for those fishers) characterizing several of related factors including types of species and size of fish targwhich fishers target a single waterbody, versus distribute theimultiple waterbodies. However, given the focus of this assessreasonable high-end fish consumption by subsistence fishers, scenarios assuming that (a) they tend to favor somewhat large

percentile watershed-level fish tissue Hg concentration) and (fishing activity on a single watershed. While we do not know these types of higher-exposure related behaviors are within thpopulations covered in the risk assessment, we do believe it ithat some subset of each population could engage in this type regard to fish cooking/preparation, we have not considered vabehavior, although the factors used (1.5 – see section 1.4.4), ithe range of adjustment factors seen with different types of fifor different types of fish. Similarly for body weight, we havtendency factor and not explicitly modeled variability in this 
96 

Table 2-15 Key sources of uncertainty associated with the analysis, the nature of their potential impact on 

degree to which they are characterized as part of the analysis 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

Characterizing the pattern of fish tissue Hg concentrations across watersheds in the U.S. (reflects small fraction of total watersheds with fish tissudifferences in state-level protocols for collection of fish tissue data, substantial number of watersheds with relatively low sample sizes (e.g., 1-2 samplefiltering watersheds to exclude locations with potentially significant non-air Hg impacts) (A) Relatively small fraction of U.S. watersheds with measured fish tissue Hg data 

Fish tissue Hg measurements are available for 3,141 of the ~88,000 watersheds in the U.S. (i.e., ~4% of the watersheds). This relatively low coverage raises concerns that the risk assessment may miss areas of elevated U.S. EGU Hg deposition and/or areas with elevated fish tissue Hg concentrations, both of which could result in a low-biased assessment of U.S. EGU-related risk. 

Supplemental data for Pennsylvania that has been incorporated into the fish tissue dataset to enhance the current version of the risk assessment reduces somewhat concerns over missing areas of high U.S. 

EGU deposition (Pennsylvania has high U.S. EGU deposition and in the March version of the risk assessment had very little coverage by fish tissue Hg 

data). Similarly, additional data for Wisconsin and Minnesota reduces concerns over missing areas with elevated fish tissue Hg concentrations. However, there is still the potential that areas of high U.S. EGU Hg deposition and/or areas with high fish tissue Hg concentrations have been excluded from the analysis. In addition, there is still an overall low bias in our estimates of the number of watersheds with specified levels of risk since only a fraction of watersheds have fish tissue Hg measurements. 

We have semi-quantitatively potentially missing Hg depoFigures 2-15 and 2-16, which Hg data for areas of the counHg deposition. Examination example, that while Pennsylvcovered by available fish tissand Ohio are less well coverout that even while general cHg deposition areas by measimproved for this version of potential still exists that subsimpact watersheds have not bassessment due to a lack of flocations. 

(B) Differences in state-level protocols for collection of fish tissue data which results in differing degrees of spatial coverage across states and differing degrees of potential bias in the samples collected 

States use different strategies for determining where fish tissue samples will be taken geographically as well as the types of fish that will be measured (and the methods of collection). Generally, however, there is likely to be a bias towards targeting waterbodies suspected of having elevated fish tissue Hg concentrations for most states. Differences in the strategies used to collect fish tissue data together with the potential for bias favoring more highly impacted waterbodies, introduces uncertainty into the risk assessment. Specifically, there is 

Because the goal of the risk assessment is to capture risk for subsistence fishers that are likely to experience elevated U.S. EGU-attributable risk, having fish tissue Hg concentrations that likely reflect targeting of more highly impacted waterbodies (i.e., biased towards 

higher risk locations) is not problematic and in fact, is preferable. If it is reasonable to assume that subsistence fisher activity could occur at these sampled watersheds that have relatively higher fish tissue Hg concentrations, then having risk estimates based on these measurements strengthens the analysis from a science policy standpoint. By contrast, if the analysis had been designed to generate a fully representative populationweighted assessment of subsistence fisher risk, then bias in the fish tissue Hg concentrations would be problematic (since it would high-bias an assessment targeted at capturing “typical” risk for these highconsumers). 

Given that potential high-biafish tissue Hg concentrations included in this risk assessmmay in fact be preferable, we the impact of this factor on r
97 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

likely to be varying degrees of high-bias in the characterization of fish tissue Hg concentrations across states. 

(C) Substantial number of watersheds with relatively low sample sizes (e.g., 1-2 samples) 

Only 28% of the watersheds with fish tissue Hg data have at least 10 samples (~41% of the watersheds have only 1 or 2 samples). Generating 75th

percentile risk estimates for watersheds with a small number of samples (e.g., <10 to 20) is subject to uncertainty and will on average understate the true 75th

percentiles across watersheds, although the bias in the estimate of the 75th percentile for any specific watershed is unknown. 

The potential that a substantial fraction of the 3,141 watersheds included in the risk assessment could have low bias in the 75th percentile fish tissue Hg 

concentrations used in modeling risk means that in turn, risk estimates for a substantial number of watersheds could be low-biased. For the very low samples sizes (e.g., 1-2 samples per watershed), it is likely that, as a trend, the estimates of the 75th percentile values are actually more likely representative of the central tendency, e.g. the median. 

We examined trends in the w

estimates across different strstrata were based on sample discussed in section 1.4.2, th

percentile estimates increasestrata (i.e., as the number of calculated a 75th percentile in

percentiles also increases). Tequal (see below), there is a in the 75th percentile fish tiss

characterization for those wasizes. The important caveat tassumes that there is no corrof fish tissue Hg concentratiowatersheds. This may not be waterbodies with higher absoconcentrations are also targeis likely the case). Thereforethe 75th percentile estimates 

samples probably needs to bis the potential for these watemeasured fish tissue Hg leve

(D) Filtering watersheds to exclude locations with potentially significant non-air Hg impacts 

As described in section 1.4.2, we filtered watersheds (with fish tissue Hg data) to exclude those potentially impacted by non-air Hg sources including gold mines and any industrial sources meeting a specified Hg release threshold. However, we did not filter out watersheds located near large urban areas which have the potential to release Hg through waste water treatments effluent. Recall that the proportionality assumption used as the basis for generating U.S. EGU-relevant exposure and risk does not hold in 

In those instances where risk is modeled for a watershed assuming that air Hg deposition is dominant, when in reality, municipal wastewater discharge may be contributing significantly to Hg loading, then estimates of U.S. EGU-attributable risk for that watershed could be biased high. In this case, we would not have assigned a portion of risk to the municipal discharges and instead would have over-estimated the U.S. EGU-attributable fraction of risk. 

Although we did not explicitwastewater Hg loading in outhe sensitivity analyses did euncertainty in filtering our wwith substantial non-air Hg ltwo sensitivity analyses sugggenerated in the core analysiwhen we (a) exclude a set of substantial non-air Hg loadinhave higher methylation pote(b) focus risk modeling on thquartile with regard total Hg more likely to be the dominawatershed). 
98 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

instances where non-air Hg loading is substantial. 

Characterizing subsistence fishing activity across areas of high U.S. EGU mercury deposition (includes assessing potential locations for subsistendetails regarding actual fishing and fish consumption behavior) (E) Predicting which watersheds are likely to experience subsistence fishing activity 

There is uncertainty associated with estimating which of the watersheds for which we have fish tissue Hg data are likely to experience subsistence fishing activity associated with the female subsistence fish consumer scenarios considered in the analysis. For the typical female subsistence fish consumer scenario, we assumed that activity could occur at any of the watersheds with fish tissue data. However, for the remainder of the female subsistence fish consumer scenarios, we used the concept of “source population” (see section 1.4.3) to guide identification of watersheds with the potential for subsistence fisher activity by these specific SES-differentiated groups of fishers. 

Because the goal of the analysis is to determine whether there is the potential for significant risk for female subsistence fish consumers and not to generate representative population-weighted risk distributions for those populations, the importance of rigorously assessing where these populations are active (and the numbers of fishers at each location) is reduced somewhat. However, bias could be introduced into the analysis if we have modeled risk for high-impact watersheds when in reality there is no likelihood of subsistence fishing activity at those locations. The potential impact of this source of uncertainty is difficult to assess. However, as discussed above, because we are not attempting to generate population-weighted risk distributions, it is not expected to substantially impact the risk assessment. 

We did not explicitly examinrelated to where specific submay be active. 

(F) Focus on risks to subsistence fishing populations and associated female subsistence fish consumers (concern that analysis does not cover high-end recreational angler risk and risk by other potentially highconsuming groups) 

The SAB raised concerns that by focusing on subsistence fishers, we may have not provided coverage in the risk assessment for other high-consuming populations including the subset of recreational anglers who frequently fish and consume the fish they catch, and thus have near subsistence level consumption rates. 

The typical female subsistence fish consumer scenario 

is actually characterized (in terms of consumption rates) using a survey of recreational anglers conducted in South Carolina (Burger et al., 2002). This population is characterized as “subsistence” for purposes of this analysis due to the magnitude of the upper percentile consumption rates used (i.e., the 90th to 99th percentile 

values) which are considered subsistence since they represent a substantial contribution to dietary intake of protein. Furthermore, given that in this revised analysis, the typical female subsistence fish consumer scenario was assessed at all watersheds with fish tissue Hg data (excluding those filtered due to high-non air Hg impacts), this scenario would provide complete coverage for high-end recreational anglers, both in terms of consumption rate and potential location of 

We did not examine this issuhigh-end recreational anglers cell to the left, the typical femconsumer scenario provides recreational angler in the congiven the goal of assessing sconsumption at each watershweighting). 
99 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

activity. There is no systematic bias introduced into the analysis due to its focus on female subsistence fish consumer scenarios that is consistent with the overall goal of the analysis (i.e., to characterize risk for the most highly impacted fisher populations). 

(G) Characterizing subsistence fisher behavior (including consumption rates, types/size of fish targeted and the degree to which activity is focused at specific waterbodies) 

There is uncertainty associated with characterizing subsistence fisher behavior including consumption rates, types and size of fish targeted and the degree to which fishers target specific watersheds rather than distribute their activity across multiple watersheds. 

There are relatively comprehensive survey data available for characterizing fish consumption rates for 

the subsistence scenarios included in the analysis. In fact, the subsistence fisher scenarios included in the analysis were selected, in large part, based on the availability of survey data to characterize consumption rates. In addition, the consumption rates display a reasonable degree of consistency in terms of magnitude across the studies (see Table 1-6). Therefore, we believe that there is relatively low uncertainty associated with characterizing this aspect of subsistence fisher behavior 

and little concern for the introduction of bias into the analysis.57

However, the other critical aspects (degree to which fishers target specific sizes and species of fish and the degree to which their activity is focused at one or more watersheds) are not well characterized in the literature and therefore are subject to considerable uncertainty. However, because the analysis is focused on modeling reasonable high-end risk, the importance of uncertainty associated with these additional behavior-related factors is reduced substantially. Specifically, if it is reasonable 

to assume that a subset of subsistence fishers would 

Given that the focus of this apopulations likely to experieU.S. EGU-attributable risk, wreasonable to assume that sofishing populations could foc

watershed (and could favor lbehavior in our analysis withuncertainty. If these assumptrelaxed (i.e., fishers were assdistribute their activity acrosrisk will be reduced.* Howevmodel these alternative behaon capturing reasonable estim* The observation that distribwatersheds will reduce risk nWhile distributing fishing acmay or may not impact centracross a group of fishers, higindividuals at the upper tail owill be lower, since instanceat a single high Hg watershefishing activity at that high Haveraged with activity at oth

57 Concerns have been raised by the SAB that some of the studies providing the consumption rates are older and that therefore, these studies may not 

practices. In addition, concerns were raised that sampling frequencies characterizing higher consumption levels for some of the fishing populations wefishers in the Burger et al., 2002 study). The sources of uncertainty described here would have a potentially significant impact on the risk assessment i

generate a representative picture of the actual distribution of risk across the full body of fishers reflected in each scenario. In that case, errors related to having insufficient survey samples to full characterize specific high-end percentiles of consumption would be a significant concern. However, when th

identify a set of high-end subsistence-like fish consumption rates that we believe could exist for a subset of each fishing population (and not to generatpopulation-weighted picture of risk), then the potential impact of these sources of uncertainty is reduced. Concerns were also raised by the SAB that thcould reflect seasonally-differentiated consumption rates (i.e., higher rates may only occurred for part of the year during increased fishing activity). Re

seasonality in the consumption rates, our evaluation of all three studies suggests that they provide annualized consumption rates (see Table 1-6) and thlikely impact of this source of uncertainty is limited. 
100 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

target larger fish and focus their fishing activity at a specific watershed, then uncertainty in these two factors is largely ameliorated. 

(H) Estimating unit concentration of MeHg in cooked fish (application of adjustment factors for the fraction of total Hg that is MeHg in fish and cooking/ preparation). 

Two adjustment factors are applied to the 75th percentile fish tissue Hg concentration at a given watershed to generate an estimate of MeHg in the cooked fish serving (in ppm). These include a Hg conversion factor for estimating the fraction of Hg in fish that is MeHg and a fish preparation/cooking adjustmentfactor for adjusting the MeHg concentration in the fish to reflect preparation of the fish. There is uncertainty associated with both of these factors. 

Mercury conversion factor: the factor used in this 

analysis (0.95) reflects consideration for the range of 

values provided in the Mercury Study Report to Congress (U.S. EPA, 1997) (i.e., ≥0 .90) (see section 1.4.4). It would be possible to include a sensitivity analysis considering 0.90 and 0.99, but given that this factor is linearly related to both exposure and risk, the outcome would be similarly modest: approximately 5% lower and higher risk, respectively for a given watershed-specific estimate. Given available information, we do not believe that any systematic bias is introduced into the analysis due to the use of this parameter value. Fish preparation/cooking adjustment factor: The factor used in this analysis (1.5) reflects consideration for the range of preparation factors provided in Morgan et al., 1997) (i.e., 1.1 to 1.5 for walleye and 1.5 to 2.0 for lake trout - see section 1.4.4). Again, as with the Hg conversion factor, we could consider a range of factors as a sensitivity analysis, but again, given that exposure and risk are linearly related to this factor, the outcome would is predictable. In this case, the impact on risk could be more pronounced, since the potential factors range from 1.0 to 2.0. The SAB identified several alternative studies for characterizing this factor, suggesting that those studies would support a smaller (or no) positive adjustment of the MeHg levels in fish reflecting preparation. However, as discussed in section 1.4.4, close analysis of these studies resulted in a conclusion that they in fact, did not support application of alternative (lower) factors. Furthermore, given available information, we do not believe that any systematic bias is introduced into the analysis due to the use of this parameter value. 

Uncertainty in these factors wassessed as part of the sensitnoted in the cell to the left, thboth factors and exposure an

determine the magnitude of ithese factors could have on r

Application of proportionality assumption in generating estimates of the U.S. EGU-attributable fraction of risk (I) Fish tissue measurement data 

Concerns that the proportionality assumption linking Hg deposition 

If air deposition patterns from the 1990s are reflected in 

some of the Hg fish tissue measurements we are using 

We did not quantitatively assuncertainty and its impact on 
101 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

used in the analysis may still reflect earlier historical patterns of Hg deposition (from the 1990’s) more so than deposition from the simulation period (i.e., ~2005) 

and fish tissue Hg concentrations may not hold if fish tissue Hg concentrations actually reflect an earlier (higher Hg deposition) period. In this case, bias could be introduced into estimates of reduction in fish tissue Hg concentrations (based on the proportionality assumption) since the effect of changes in current Hg deposition on fish tissue Hg concentrations could be overstated (i.e., there could be an effective buffering of the fish tissue Hg concentrations by the residual effects of earlier higher Hg deposition). 

the implications can vary depending on the nature of that difference. If fish tissue levels for a watershed still reflect higher 1990 deposition values, then there may be an upward bias to the estimates of U.S. EGUattributable risk, since in reality we would expect the underlying fish tissue levels to decrease as the impact of those earlier higher deposition values dissipates. Conversely, if total deposition remains the same, but only the source distribution has changed since the 1990’s, then the effect on our risk assessment may not be that significant, since we are making projections based on the current source-mix (or future source-mix) assuming near steady-state assumptions are reached given those specific source mixes. Given currently 

available information, we cannot evaluate the interplay 

of these two factors and the way in which they come together to introduce potential bias into estimates of U.S. EGU-attributable risk (we can only discuss each as a source of uncertainty as has been done here). 

to use only fish tissue samplto reduce this source of unce

(J) Watersheds can display substantially different methylation potentials, resulting in differences in the impact of a unit change in Hg deposition on fish tissue Hg concentrations 

Differences in methylation potential cause variation in fish tissue Hg concentrations across waterbodies (even when those waterbodies have similar rates of Hg loading). In addition, differences in methylation potential can also translate into different temporal profiles for the response of fish tissue Hg concentrations to changes in Hg loading. Failure to reflect these sources of variability in methylation potential can introduce uncertainty into the analysis. 

Differences in methylation potential across watersheds, when present, are likely to be reflected in the underlying fish tissue Hg concentrations themselves. Therefore, these methylation differences will not to have a substantial impact on our analysis and are not expected to introduce systematic bias (the time sequence of changes in fish tissue levels will depend on differences in methylation potentials, but we are not attempting to predict these temporal profiles). 

We did not quantitatively assissue. Our determination is tsource of uncertainty for our 

(K) Factors related to methylation in watersheds (e.g., sulfate deposition, pH) have not remained constant 

This source of uncertainty is distinct from the issue of methylation potential discussed above (here we talk about factors which can interact with the underlying methylation potential 

As with methylation potential discussed above, any changes in factors affecting the underlying methylation potential of a waterbody would eventually be reflected in fish tissue Hg concentrations and therefore not introduce systematic bias into the analysis. For this reason, our concern over this issue is reduced. However, 

We did not quantitatively assissue. 
102 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

over time, resulting in variation in the methylation potential of watersheds over time. 

to increase or decrease it). If these factors (pH and sulfate deposition for example), have changed for a given watershed, then the methylation rate of that waterbody may also change, thereby impacting the degree to which fish bioconcentrate and ultimately (for higher trophic levels) bioaccumulate Hg. Changes in methylation factors can also impact the temporal profile for changes in fish tissue Hg concentrations. 

if changes in methylation factors are continuous and extend into the current simulation period, then there is more concern, since there could be more of a gradual change in methylation across our simulation period (a change that would not be fully reflected in the fish tissue Hg values we have in the dataset). However, it is difficult to characterize the magnitude of the potential impact of this source of uncertainty on risk estimates, including the direction and magnitude of any bias, given the complex interplay of the factors involved (i.e., potential for ongoing changes in pH and sulfate to produce methylation rates that vary over time). 

(L) Effort to exclude watersheds with substantial non-air Hg deposition58

Despite efforts to exclude watersheds with substantial nonair sources of Hg loading (see section 1.4.2), some watersheds with substantial non-air impacts may have been retained in the analysis. 

The potential that we may have failed to exclude watersheds with significant non-air Hg loadings could introduce high-bias into our estimates of U.S. EGUattributable risk, since we would overstate the role of U.S. EGUs in contributing to risk, by overlooking the other non-air sources. 

We completed two sensitivitand its potential impact on riof risks when watersheds falare excluded (there is concerhave significant non-air Hg rmethylation potential) and (bonly on those watersheds fal

percentile with regard to totaestimating risk when we focuwe are more confident that adominant role). The sensitiviexclusion of the four states wnon-air Hg loading does not However, when we estimatewatersheds with the highest 25th percentile), we did see a 

estimates. 

(M) Potential that the modeling framework is not sufficiently refined from a spatial standpoint to capture elevated levels of Hg 

If the overall approach for linking Hg deposition (specifically the fraction attributable to U.S. EGUs) is not sufficiently spatially refined, then areas of high U.S. EGU Hg impact and resulting risk 

We believe the level of uncertainty associated with precision in modeling exposure and specifically the ability to identify U.S. EGU-related risk “hot spots” (again, using SAB terminology to refer to areas of 

elevated Hg-related risk) is moderate. CMAQ modeling is provided at the 12km grid resolution which matches 

The issue of spatial scale in cfisher risk has not been quanthe risk assessment. 

58 For the March version of the Mercury Risk TSD, we also explored the issue of whether the proportionality assumption was more app

waterbodies (lakes/ponds) then flowing waterbodies (rivers/streams). However, for this version of the Mercury Risk TSD, reflecting recSAB that the proportionality assumption can be readily applied for either category of waterbody, we have not repeated that sensitivity asource of uncertainty. 
103 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

deposition (“hot spots” as termed in the SAB comments) (Note, this source of uncertainty also involves CMAQ modeling) 

may not be reflected in the risk assessment. This issue speaks to spatial precision in (a) the CMAQ air quality modeling, (b) scale of the watersheds (and degree to which subsistence activity is focused within specific watersheds) and (c) applicability of the proportionality assumption in relating changes in Hg deposition to changes in fish tissue Hg concentrations (or relating fraction of total Hg deposition coming from U.S. EGUs to the fraction of fish tissue Hg attributable to U.S. EGUs). 

well the watershed scale used in the analysis (i.e., the HUC12). The HUC12 watersheds, in turn, represent a small watershed which is appropriate if we assume that the subsistence fisher being modeled focuses their activity primarily at the same waterbody (i.e., within a given HUC12 watershed). Therefore, given the focus of this analysis on a reasonable highly-exposed subsistence fisher (i.e., an individual who focuses their activity at a given waterbody), then the modeling framework, including the spatial scale of the various modeling elements (CMAQ modeling, watershed, fish tissue data), would seem appropriate. For this reason, we believe there is little concern for the introduction of bias into the risk assessment due to the precision of the modeling and our ability to capture potential hot spots. 

Factors relating to the estimation of mercury deposition over watersheds using the CMAQ model (e.g., estimating Hg emissions from U.S. EGUs associated with Hg fate and transport, prediction of wet and dry deposition, and global inflow of Hg into the U.S.) (N) Hg 2005 U.S. EGU emissions, uncertainties and sources of bias 

2005 Hg emissions from EGUs used for this analysis are from the 2005 inventory developed for the National Air Toxics Assessment (NATA). These data were calculated by EPA by scaling Hg data from the 1999 National Emissions Inventory (NEI) using the ratio of 2005 heat input to 1999 heat input. Thus, uncertainties and bias in the 2005 data fall into two classes – those that are due to the 1999 estimates and those resulting from the scaling approach used to estimate 2005 emissions. The 1999 NEI data uncertainties include uncertainties traditionally associated with emissions estimation, such as uncertainties in the accuracy of test data, the applicability of the test data, the representativeness of emission 

The approach taken for the 1999 NEI used estimates developed from the data collected for the ICR done for the Clean Air Mercury Rule. These data were preferentially used over other estimates available from states, local agencies, or tribes. While these approaches include some uncertainties, they used best available practices and data available from the very rich data source of the 1999 ICR. In general, emissions estimates relying on test data will result in the best available emission estimates for sources with such data, and the uncertainties associated with measurement error are considered negligible in comparison to the other sources of uncertainty. Estimates based on emission factors will have greater uncertainty but will not significantly contribute to bias unless the emission factors are not representative. Care was taken to apply the most representative emission factors data to the sources in the cases where this was needed. A great deal was known about throughput at these sources from the ICR data, and so that is not considered a large source of uncertainty. These uncertainties can cause both overestimates and underestimates of emissions, but 

since there is no way to assess that, it is not possible to quantify the potential impact (including the direction 

The impact of this source of quantitatively evaluated. 
104 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

factors for specific units, the oversimplification occurring when emission factors are used rather than continuous monitoring, and the accuracy of throughput estimates. The 2005 approach adds uncertainty as well as bias to emissions estimates because it does not include the impacts of controls added to sources between 1999 and 2005 or changes in fuels, including the Hg content of the fuel. In addition, uncertainties in the throughput information used to scale the emissions can affect the emissions estimates. Lastly, the temporal and speciation representation of emissions in the 2005 model inputs is another source of uncertainty. However, as noted in column to right, the net effect of high-bias in 2005 emissions would be to low-bias risk estimates for the 2016 scenario. 

and magnitude of bias) on risk estimates. The 2005 scaling approach will tend to overestimate emissions in 2005 because of additions of control technologies and coal used between 1999 and 2005 that would have reduced emissions. These reductions were not included because only the changes to throughput were considered in the approach. This most likely causes uncertainties that result in a high bias (overestimate) for the emissions in 2005. Since the 2005 deposition results are used in the denominator of the risk scaling approach to predict risk in 2016, overestimates of 2005 emissions are expected to result in an underestimate of risk in 2016. The uncertainties associated with the heat input data in 2005 are very low, since most units included in the mercury estimates had CEM data to record and report heat input for 2005. The speciation approach used test data from the 1999 data collection, which assigned speciation factors based on boiler type, fuel type, and control configuration. While imperfect, these data will tend to minimize inaccuracies in speciation assigned since the dataset used to calculate the fractions was fairly large, and was certainly the most comprehensive Hg speciation data available. Finally, the temporal allocation approach was kept the same between 2005 and 2016 so that any small impacts of these assumptions on deposition results will cancel out using the scaling approach for risk. 

(O) Hg 2016 U.S. EGU emissions, uncertainties and sources of bias 

There are three key variables that figure in the modeling of 2016 Hg emissions from U.S. electric generating units (U.S. EGUs): the mercury content of the fuel combusted (essentially various ranks and grades of coal), the extent of coal consumed, and type and performance characteristics of the controls to reduce mercury emissions at a given U.S. EGU. Uncertainty may figure in each of these key variables. 

As a result of uncertainty, the 2016 mercury emissions from EGUs may be over or under-estimated. However, the extent of the impact of uncertainty is considerably reduced (as is concern over bias that may impact risk estimates) due to the number of directly measured and quality assured data points that are used to characterize the variables described in the previous column, by the mature nature of the technologies for mercury emission 

control, and by the commensurate experience and expertise in characterizing their performance as a result of more than a decade of in-field operation of the controls. 

The impact of this source of quantitatively evaluated. 
105 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

(P) Hg Non-EGU emissions, general uncertainties and consideration of potential bias 

The 2005 NATA inventory for non-EGU sources is a compilation of state data and data associated with sector-specific rulemaking activities at EPA. Uncertainties include uncertainties traditionally associated with emissions estimation, such as uncertainties in the accuracy of test data, the applicability of the test data, the representativeness of emission factors for specific units, the oversimplification occurring when emission factors are used rather than continuous monitoring, and the accuracy of throughput estimates. In general, the data based on source tests and individual source reporting are of higher quality with less uncertainty. In addition, speciation and temporal allocation considerations add sources of uncertainty. These uncertainties are also applicable to the 2016 emissions, since these emissions are derived by scaling the 2005 emissions based on expected reductions between 2005 and 2016. 

Mercury emissions based on facility test data have been used whenever possible for the 2005 NATA inventory, focused on the categories with the highest emissions. For example, emissions sources with 2 or more tons in 

2005 include Portland cement, electric arc furnaces, boilers, chemical manufacturing, hazardous waste incineration, chlor-alkali plants, gold mining, and municipal waste combustors. For these sources, which represent 36 tons out of 47 tons non-EGU Hg emissions, 65% of these sources used data developed for rules associated with these sectors. The rule data arein large part based on test data and have otherwise 

had extensive review by industry experts and the facilities themselves. In combination with the U.S. EGU data, emissions from these higher quality data 

sources comprises 76% of the total anthropogenic U.S. Hg inventory. Thus, EPA’s approach reflects an inventory with much lower uncertainty than if it had been based solely on more generic emission factors. Speciation data used for this analysis are highly 

uncertain, but reflect best available information. Since the analysis was not quantifying the impact of non-EGU sources, the speciation of non-EGU sources tends to contribute less to the impact on the analysis. Additionally, while temporal allocation uncertainties are also high, the temporal allocation approaches for each sector are used consistently in 2005 and 2016, so their impacts are lessened through the use of the scaling approach. There are no known sources of bias related to estimation of emissions from non-EGU sources in this 

analysis. Every attempt was made to use the best available emissions data in 2005 and future year projections to 2016. However, a possible (but not likely) source of non-EGU emissions underestimates is from natural sources. Public comments on EPA’s approach suggest that some natural sources were not considered. However, EPA does include natural elemental Hg emissions from volcanoes, oceans, and land. Further estimates of recycled Hg emissions from oceans and land are 

The impact of this source of quantitatively evaluated. 
106 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

included. One source of uncertainty is the treatment of Hg from fires. While EPA did not explicitly include these emissions in the analysis as fires, the model calibration approach used to estimate the recycled land emissions captures such emissions (Seigneur, et. al. 2004). To the extent that there is any underestimation bias, it would reduce the fractional contribution of U.S. EGU sources, however, given the low likelihood of these sources of bias, the impacts on risk are expected to be small. 

(Q)Hg Emissions, changes in uncertainties and bias between 2005 and 2016 

The sources of change between 2005 and 2016 emissions are the U.S. EGU emissions and the stationary non-EGU emissions. All other emissions sources (mobile and non-anthropogenic) stayed constant between the two years. For U.S. EGUs, different sources of uncertainties exist in the two different approaches for 2005 and 2016 emissions, as described above. The possible changes in uncertainties from 2005 to 2016 are caused by the changes in methods. For non-EGU sources, uncertainties in 2016 emissions can result from assumptions about emissions reductions associated with other EPA regulations as well as predicting future activity levels associated with economic or other changes. 

For EGUs, some types of uncertainties could be greater in 2016 than in 2005 since the emissions are forecast to the future, for example, the future expected electricity demand and coal usage. 2005 emissions estimates have different uncertainties and it is not possible to determine without extensive uncertainty analysis which sources are greater than others. As described above, a bias most likely exists in the 2005 data that does not exist in the 2016 data, resulting in a possible underestimate of risk from the deposition-based scaling approach used for 2016 risk estimates. For non-EGUs, several source categories reduced emissions from 2005 to 2016 as a result of other EPA regulations. While the reductions are estimated, the uncertainties are relatively low given the extensive analyses done to develop the regulations and since compliance with EPA emissions reductions requirements tends to be very high. Uncertainties are introduced because EPA does not include co-benefits of reductions from controls for other pollutants such as PM2.5, which might lead to overestimation of the 2016 

non-EGU emissions, lessening the impacts of U.S. EGUs, and therefore lowering the risk estimates from EGUs in our analysis. Uncertainty is also introduced in this analysis for non-EGU sources because the possible growth or reduction in emissions associated with economic changes is not included in the 2016 estimates. However, it is not possible to determine the impact of excluding such considerations since non-EGU industryspecific forecasts are highly uncertain themselves, there is a complex relationship between economic changes, regulations on emissions, and new source requirements 

The impact of this source of quantitatively evaluated. 
107 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

that can reduce emissions. 

(R) Uncertainty in location of 2016 emissions reductions 

Uncertainties in the 2016 scenario regarding the specific geographic locations of reductions in EGUderived mercury deposition as a fraction of total mercury deposition 

The upstream U.S. EGU sources of Hg emissions are provided by EPA’s power sector model, the Integrated Planning Model (IPM). The uncertainty in the extent of projected Hg emissions from these sources is treated 

above (see entry “O”). Here the focus is on the question of uncertainty in the location of the 2016 emissions reductions from the standpoint of which EGUs are projected to operate and so serve as sources of Hg emissions. IPM is an extremely detailed bottom-up deterministic linear programming representation of the U.S. power sector. There is no stochastic component in 

the model that would allow one to characterize the likelihood that the location of EGU sources and the 

extent of emissions from these sources are shifting. Therefore, we are not able at this time, to evaluate the potential for systematic bias being introduced into risk estimates through this step in our modeling. However, the model’s rigorous adherence to economic and engineering fundamentals and detailed representation of 

all factors affecting power system operation are designed to limit uncertainty and ensure that the model’s locational projections are reasonable. 

The impact of this source of quantitatively evaluated. 

(S) Global inflow of mercury into the continental United States 

There is considerable uncertainty in the global emissions inventory for mercury and given the long residence time of elemental mercury it is possible that inflow into the modeling domain may reflect deficiencies in the global emissions inventory. Some studies suggest global mercury emissions would increase without control implementation (Pacyna et al., 2009; Streets et al., 2009). Recent measurements of ambient mercury at remote locations from 1975 to 2008 suggest global mercury burden is steadily decreasing (Slemr et al., 2011). Given this conflicting information and the uncertainty in projecting 

Mercury deposition may be over or under-estimated  (i.e., potential bias introduced is not clear in terms of direction or magnitude). 

This analysis did not address Mercury inflow was adjusted shown to have a larger impaccompared to wet deposition. perturbations within realistic changed distributions of tota(Pongprueksa et al., 2008). Tmodel spin-up of one week iinfluence of initial conditionThe CMAQ modeling in this than one week making initianegligible. 
108 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

the current global emission inventory, boundary inflow is kept constant between 2005 and 2016. 

(T) Photochemical model prediction of mercury wet deposition estimates 

The uncertainty in the wide variety of model inputs such as emissions, meteorology, global inflow to the modeling domain, and chemistry manifest in model estimates of mercury wet deposition. 

Mercury wet deposition may be under-estimated during the summer season and over-estimated during the winter (i.e., potential bias introduced is not clear in 

terms of direction or magnitude). Model performance is 

described in “Air Quality Modeling Technical Support Document: Point Source Sector Rules” (USEPA, 2011b). 

Model estimated weekly mercompared to observation datsimulating this component owet deposition measurementsites that are part of the Merc(http://nadp.sws.uiuc.edu/MD

National Atmospheric Deposgenerally consistent with othcoarser grid resolution, older emission inventories (Bulloc2007). 

(U) Photochemical model prediction of mercury dry deposition estimates 

The uncertainty in the wide variety of model inputs such as emissions, meteorology, global inflow to the modeling domain, and chemistry manifest in model estimates of mercury dry deposition. 

Mercury dry deposition may be over or under-estimated (i.e., potential bias introduced is not clear in terms of direction or magnitude). 

This analysis did not address There is a lack of dry deposimakes a direct quantitative oto modeled estimates impossshown differences in estimatchanges in ambient mercurychanges to the dry deposition Lin et al., 2007; Pongprueksal., 2007a; Ryaboshapko et athat these studies are based oor other models that are not rpurposes and that mercury chhas changed through model vdeposition estimates in the vanalysis would be comparab

(V) Mercury Chemistry 

The complete set of mercury oxidation and reduction reactions has not been identified by the scientific community. 

Mercury deposition may be over or under-estimated (i.e., potential bias introduced is not clear in terms of direction or magnitude). 

This analysis did not address Other studies have shown dimercury deposition based on (Bullock et al., 2009; Lin et 2008; Ryaboshapko et al., 202007b). However, given that earlier versions of CMAQ or routinely used for regulatory chemistry and deposition has versions it is not clear that toestimates in the version of Cwould be comparable to earl
109 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates 

Degree to which the poten

uncertainty is character

Application of the MeHg RfD in generating hazard quotient (HQ) risk estimates (W) Degree to which the RfD as calculated provides coverage for low SES status groups which may be at greater risk for adverse health effects following MeHg exposure 

Given a number of factors (e.g., nutritional deficiencies, reduced access to health care and healthrelated information), there is concern that the MeHg RfD may not provide sufficient coverage for these low SES populations. 

The RfD is defined as the amount of the substance of concern that can be consumed without expectation of 

harm for a lifetime by populations including sensitive subpopulations. The calculated RfD for MeHg includes an uncertainty factor to account for human pharmacokinetic variability (3 fold) and uncertainty 

and variability in pharmacodynamics. This may be sufficient to account for increased sensitivity to IQ decrements or adverse effects on neurobehavioral 

functions in low socio-economic status populations. There are no published analyses by EPA or other parties that would permit estimation of uncertainty for this factor and the potential for resulting bias being introduced into the analysis. Two of the human populations on which the RfD was based (the Faroese and the Seychellois) are relatively homogenous for some aspects of SES. 

We did not explore this potequantitatively. However, as nto the method used in calculathe RfD may not provide covpopulations is reduced. 

(X) The MeHg RfD was derived based on saltwater fish consumption (which can involve higher levels of nutrients that ameliorate the adverse effects of MeHg). However, the RfD is now being applied in the context of freshwater fish consumption (which can involve lower levels of these nutrients). 

Recent studies (e.g. Oken et al 2008; Choi et al 2008; ) point to the potential for nutrients in fish (particularly marine fish) to ameliorate some of the observed adverse effects of MeHg when coexposure occurs. However, there was no correction for potential confounding by nutrients in marine fish and mammals in calculation of the benchmark doses used in the RfD derivation. Therefore, there is the potential that these benchmark doses may be underestimates, in which case the HQ estimates based on the RfD could be biased low, particularly in the case of freshwater fish which may have lower levels of these nutrients. 

Failure to consider the effects of fish nutrients as a covariate or confounder for nerodevelopmental effects 

associated with MeHg exposure could result in HQ estimates being biased low. However, currently, available information does not support a rigorous adjustment of the RfD to address potential confounding by fish nutrients. 

We did not explore this potequantitatively. 
110 

3 Summary of Key Observations 

This section provides key policy-relevant observations drawn from discussions presented 

in Sections 2.1 through 2.7. It is important to emphasize, that the risk estimates and additional 

supporting information summarized here are intended to inform a determination by the 

Administrator as to whether Hg emitted from U.S. EGUs represents a public health hazard. 

These observations are not intended to be conclusionary in nature and instead, focus on 

characterizing the nature and magnitude of risk associated with U.S. EGU Hg emissions. 

 Estimates of U.S. EGU Hg emissions suggest that the 2016 Scenario is likely closer to 

recent (2010) emissions compared with the 2005 scenario (which has substantially higher 

total Hg emissions for this sector). Therefore, risk estimates have been generated only for 

the 2016 scenario for the revised risk assessment (CMAQ-based Hg deposition estimates 

for the 2005 scenario are used in scaling fish tissue concentrations for use in modeling 

2016 scenario risk).

 Risk characterization is based on estimates of RfD-based HQ. Due to concerns raised by 

the SAB peer-review panel that the IQ loss endpoint may not fully capture the range of 

neurodevelopmental effects associated with MeHg exposure, IQ loss estimates are 

presented in brief summary form in Appendix B and not used in the risk characterization.

 Based on the 2016 scenario, U.S. EGUs can contribute up to 11% of total Hg emissions 

for a subset of watersheds. However, in general, other sources besides U.S. EGUs 

dominate Hg deposition. In 2016 U.S. EGUs contributed on average about 2% of total 

Hg deposition across the country. U.S. EGU-related Hg deposition is higher in the eastern 

part of the country with elevated contributions in a number of specific areas, including 

most notably, the Ohio River valley. U.S. EGU-related Hg deposition estimates show a 

significant reduction between 2005 and 2016 scenarios, reflecting mainly co-benefits 

from implementation of criteria pollutant controls with the average U.S. EGU-attributable 

deposition decreasing from ~5% of total to ~2% for the 2005 and 2016 scenarios, 

respectively. 

 Based on the 2016 scenario, U.S. EGUs can contribute up to 16% of MeHg in fish tissue. 

However, generally, U.S. EGUs contribute a much smaller fraction averaging 3% for the 

2016 Scenario. 

 Comparing the magnitude of Hg fish tissue levels with total Hg deposition (as 

characterized at the watershed-level) suggests that there is not a strong correlation. This is 

not surprising given the variety of factors which effect methylation potential; factors 

which can demonstrate substantial spatial variation. However, available evidence 

supports a steady state linear relationship between changes in Hg deposition and changes

in fish tissue Hg concentrations. 

 The additional fish tissue Hg data added for the revised risk assessment have significantly 

improved coverage for watersheds located in areas with elevated U.S. EGU-related Hg 

deposition such as the Ohio River Valley. However, we still conclude that, generally, our 
111 

coverage for high U.S. EGU impact areas remains limited. For this reason, we continue to 

believe that the actual number of “at-risk” watersheds (i.e., watersheds where U.S. EGUs 

could contribute to a public health concern) could be substantially larger than estimated. 

 Based on application of the 2-stage risk characterization framework described in Section 

1.3, we estimate that from 22 to 29% of the watersheds included in this risk assessment 

could be classified as potentially having at-risk populations under the 2016 Scenario. 

This estimate is based on risks modeled for the typical female subsistence fish consumer

and reflects aggregation of results from Stages 1a and 1b of the 2-Stage Risk 

Characterization Framework (i.e., watersheds where mercury released from U.S. EGUs 

when considered alone, without taking into account mercury deposition from other 

sources, would produce an HQ >1 or watersheds where total HQ (reflecting Hg from all 

sources) is > 1 and U.S. EGUs make at least at 5% contribution to that-risk). 

 Comparison of risk estimates generated for the typical female subsistence fish consumer

scenario with estimates generated for the other six SES-differentiated female subsistence 

fish consumer scenarios included in this risk assessment results in the following 

observations: (a) total and U.S. EGU-attributable risks for the Hispanic and Vietnamese 

scenarios are generally lower than for the typical female subsistence fish consumer

scenario, (b) U.S. EGU-attributable risks for the Tribal scenario are similar to those for 

the typical female subsistence fish consumer scenario, although total risks are generally 

higher for the Tribal scenario and (c) U.S. EGU-attributable risks for the Laotian and low 

income southeastern White and Black scenarios are generally higher than for the typical 

female subsistence fish consumer scenario, although total risks can be higher or lower 

depending on the scenario. These trends suggest that generally, the typical female 

subsistence fish consumer scenario will provide coverage (in terms of representing risk) 

for Hispanic, Vietnamese and Tribal scenarios. However, the typical female subsistence 

fish consumer scenario may not provide full coverage for the Laotian and low income 

southeastern White and Black scenarios, particularly in terms of U.S. EGU-attributable 

risk. 

 If U.S. EGU impacts to watersheds included in the risk assessment were zeroed-out, for a 

significant majority of those watersheds, total exposure would still exceed (and in most 

cases, significantly exceed) the RfD. Reductions in U.S. EGU attributable Hg will 

reduce the magnitude of the risk, although substantial total exposure and risk from Hg 

deposition will remain. 

 Sensitivity analyses conducted primarily to examine uncertainty in applying the 

proportionality assumption linking Hg deposition to Hg fish tissue levels, suggest that 

uncertainty related to the proportionality assumption is unlikely to substantially effect an 

assessment of whether Hg emissions from U.S. EGUs constitute a public health concern. 

Use of watershed-level 50th percentile fish tissue Hg concentrations (instead of the 75th

percentile values used in the core analysis) can result in notable reductions in risk 

estimates in some instances, but the SAB peer review panel supports use of the 75th

percentile estimates. 
112 

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Appendices: Additional Technical Detail on Modeling Elements and Presentation of 

Supplemental Risk Estimates 

(Citations for the appendices are provided in the citation list for the main document – 

see above) 

As noted in section 1.4.5, the IQ loss risk metric has been de-emphasized in presenting 

risk estimates due to concerns that it may not fully capture the range of adverse 

neurodevelopmental effects associated with MeHg exposure. For this reason, both the 

description of the approach used in modeling IQ loss as well as a summary of IQ loss estimates 

and a discussion of uncertainty related to those risk estimates is presented in appendices, with the 

technical approach being described in Appendix A and the summary of IQ loss risk estimates 

and discussion of uncertainty being presented in Appendix B. Appendix C presents the SAB 

Mercury Panel peer review letter (the attachment to that letter provides the original EPA charge 

questions). 

Appendix A. Technical Approach Used in Modeling IQ Loss 

Estimation of IQ loss in children begin with the same exposure estimates used in 

generating HQ estimates (i.e., estimates of MeHg exposure generated for a given subsitence 

fisher scenario - see section 1.4.4 for derivation of these exposure estimates). However, these 

estimates of body weighted-adjusted MeHg exposure need to be converted into an equivalent 

maternal hair concentration since the IQ loss function uses hair Hg as the dose measure. To do 

that, we use a dose-to-hair conversion factor (DHCV) of 12.5 (units ppm per unit μg/kg-day) that 

converts ingested dose (IR) to hair Hg concentration in ppm. The DHCV factor is based on a one 

compartment toxicokinetic model used for deriving the MeHg RfD by Swartout and Rice (2000).

After generating an estimate of maternal hair Hg level for the subsistence fisher (at the 

particular watershed being modeled), we then apply a concentration-response (CR) function 

relating material hair Hg levels to IQ points lost in the child born to that mother. This CR 

function was published in Axelrad et al., 2007and is based on application of a Bayesian 

hierarchical model which integrates data from the three key epidemiological studies (Seychelles, 

New Zealand and Faroe Islands).59 

Since the CR function was published in the Axelrad et al., 2007 study, a number of 

authors have raised the possibility that neurological deficits related to Hg exposure through fish 

consumption could be masked to some degree by the neurologically-beneficial effects of fish oil 

consumption. Some authors have suggested that the IQ loss factor should be adjusted upward to 

compensate for this masking effect (see Rice et al., 2010 and Oken, 2008). However, no rigorous 

basis for a specific adjusted estimate has been provided to-date and therefore, we address this 

potential for low-bias as part of our qualitative uncertainty discussion (see Table B-2, Appendix 

B). 

 

59 The IQ loss model uses a linear slope of 0.18 IQ points per ppm hair Hg concentration (Axelrad et al., 2007). 
118 

Appendix B. Supplemental Risk Estimates (IQ loss estimates) 

This section provides summaries of IQ loss risk estimates generated for the 2016 scenario 

and discusses key sources of uncertainty associated with the IQ loss estimates. In assessing the 

potential public health significance of the IQ loss risk estimates, based on recommendations 

provided by the Clean Air Science Advisory Committee (CASAC) in the context of the last 

National Ambient Air Quality Standard (NAAQS) review for lead completed in 2008 (US EPA, 

2007a), we interpreted IQ loss estimates of 1-2 points as being clearly of public health 

significance. All of the risk estimates summarized here are based on application of the revised 

version of the risk assessment model. Specific tables include: 

 Table B-1: provides risk percentiles for the IQ loss risk metric for children born to 

members of the typical female subsistence fish consumer scenario for the 2016 scenario. 

 Table B-2: identifies and discusses key sources of uncertainty associated with modeling 

IQ loss (uncertainty related to modeling exposure, which also impacts HQ estimates, is 

covered in Table 2-15). 

 

Table B-1. Percentile IQ loss risk estimates for children born to members of the typical 

female subsistence fish consumer scenario assessed nationally (2016 scenario) (for 

both total and U.S. EGU incremental risk, including IQ loss and MeHg RfD-based HQ 

estimates) 

Typical female 

subsistence fish 

consumer rate 

(g/day) and 

percentile/mean 

Watershed percentile 

Total IQ points lost U.S. EGU-attributable IQ points lost 

50th 75th 90th 95th 99th 50th 75th 90th 95th 99th 

39 (mean) 0.4 0.8 1.3 1.7 2.5 - - - 0.1 0.1 

123 (90th) 1.4 2.4 4 5.3 8* - 0.1 0.1 0.2 0.3 

173 (95th) 1.9 3.4 5.6 7.4* 11.2* - 0.1 0.2 0.2 0.4 

373 (99th) 4.1 7.4* 12.1* 16* 24.1* 0.1 0.2 0.3 0.5 0.8 

- IQ loss is <0.1 point 

* IQ loss estimate subject to greater uncertainty due to application of the underlying concentration-response function 

for IQ loss at levels of exposure above those in the underlying epidemiological studies (see Appendix A) 

Table B-2. Key sources of uncertainty related to modeling IQ loss, the nature of their 

potential impact on risk estimates, and the degree to which they are characterized as 

part of the analysis 

Source of 

uncertainty Description 

Nature of potential impact on 

the exposure and risk estimates 

Degree to which the potential 

impact of the source of 

uncertainty is characterized as 

part of the analysis 

(A) IQ may not 

fully capture 

the most 

IQ may not represent the most 

sensitive cognitive endpoint for 

Hg exposure (Axelrad et al., 

Given this concern, we have 

focused the risk assessment on the 

RfD-based HQ risk estimates and 

Because we do not have readily 

available data to support 

quantitative analyses of the first 
119 

Source of 

uncertainty Description 

Nature of potential impact on 

the exposure and risk estimates 

Degree to which the potential 

impact of the source of 

uncertainty is characterized as 

part of the analysis 

sensitive 

cognitive 

endpoints 

associated with 

Hg exposure 

2007 - see Section 1.2). In 

addition deficits in some 

categories of cognitive 

functioning are not captured by 

IQ. Together, these sources of 

uncertainty suggest that we 

could be under-estimating the 

extent of cognitive impacts 

associated with Hg exposure 

(when we focus on modeling the 

IQ loss endpoint alone). 

have deemphasized IQ loss 

estimates. 

two sources of uncertainty (IQ 

loss not capturing all of the 

cognitive effects and potential 

confounding by LCPFAs), we 

could only address these factors 

qualitatively. In both cases, the 

potential effect on the risk 

assessment would be to 

potentially down-bias our 

estimate of cognitive endpointrelated risk for children. 

(B) Potential 

confounding 

from nutrients, 

including longchained 

polyunsaturated 

fatty acids 

(LCPFAs), 

found in fish 

This issue is similar to the 

nutrient confounding issue 

discussed above under the RfDbased HQ entry. Specifically, 

there are concerns that fish 

nutrients may mask to some 

extent, the adverse 

neruodevelopmental effects of 

MeHg, resulting in a 

concentration-response function 

for IQ loss which could be 

biased low (particularly in the 

case of freshwater fish 

consumption which may have 

lower levels of relevant fish 

nutrients) 

Potential confounding (masking) 

of the neurodevelopmental effects 

of MeHg could result in 

concentration-response functions 

for IQ loss that are low-biased, 

which in turn would mean that 

estimates of IQ loss could be low 

biased. However, at this point we 

do not have the information 

necessary to derive a 

concentration-response function 

for IQ loss that would account for 

this masking effect and therefore, 

cannot quantitatively evaluate this 

source of uncertainty. 

(C) How to 

treat potential 

outliers from 

the 

epidemiological 

datasets used in 

deriving the IQ 

loss functions. 

Regarding outliers, when an 

outlier datapoint from the 

Seychelles study was included in 

the integrated derivation of the 

IQ loss slope factor, the factor 

was reduced by 25 percent (from 

-0.18 IQ points per unit ppm hair 

Hg, to -0.125). If in reality, this 

outlier actually reflects the true 

response for a subset of the 

populations, then risks (as 

modeled) could be biased high 

specifically for this 

subpopulation. 

In the case of excluding the 

outlier from the Seychelles study, 

we note that the effect (given the 

linear nature of the IQ loss slope) 

would be to simply result in a 

25% reduction in risk, if we were 

to include the outlier in derivation 

of the slope function (i.e., a 

formal rerun of the model with 

this alternative slope is not 

required – we can just consider 

this magnitude of impact on the 

primary risk estimates we 

generate for the analysis). 

Although it is possible given the 

linear relationship of exposure 

and IQ loss to readily determine 

the impact of a sensitivity 

analysis involving application of 

an IQ loss slope factor that 

reflects inclusion of the outliers, 

we did not explicitly complete 

such an analysis (primarily 

because the IQ loss risk metric 

has been de-emphasized with 

primary focus being placed on 

the RfD-based HQ metric). 
120 

Appendix C. SAB Mercury Panel Peer Review Letter: Review of EPA's Draft 

National-Scale Mercury Risk Assessment 

This appendix presents a copy of the SAB’s Mercury Panel Peer Review Letter. The 

original charge questions provided by EPA to the SAB are included at the end of the letter as 

Appendix A. 
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON D.C. 20460

OFFICE OF THE ADMINISTRATOR

SCIENCE ADVISORY BOARD

September 29, 2011

EPA-SAB-11-017

The Honorable Lisa P. Jackson

Administrator

U.S. Environmental Protection Agency

1200 Pennsylvania Avenue, N.W.

Washington, D.C. 20460

Subject: Review of EPA’s Draft National-Scale Mercury Risk Assessment

Dear Administrator Jackson:

EPA’s Office of Air and Radiation requested that the Science Advisory Board (SAB) review a draft 

Technical Support Document: National-Scale Mercury Risk Assessment Supporting the Appropriate and 

Necessary Finding for Coal and Oil-Fired Electric Generating Units - March 2011. The goal of this 

draft document is to characterize human health exposure and risk associated with U.S. electrical 

generating unit (EGU) mercury emissions with a focus on a highly exposed subpopulation, subsistence 

fishers. The SAB was asked to comment on the risk assessment, including the overall design and 

approach, as well as the use of specific models and key assumptions. The SAB was also asked to 

comment on the extent to which specific facets of the assessment are well characterized in the Technical 

Support Document.

The SAB could not evaluate the risk assessment based solely upon information provided in the 

Technical Support Document. Important elements of the methods and findings are missing or poorly 

explained. Additional information provided by EPA representatives during an SAB public meeting on 

June 15-17, 2011 and a public teleconference on July 12, 2011 allowed the SAB to gain a sufficient 

understanding of the risk assessment to conduct this review.

The risk assessment is designed to assess how a reduction in mercury emissions will translate to 

reductions in fish tissue methylmercury concentrations, and in turn, to a reduction in potential risk to 

subsistence fishers that would result from the consumption of self-caught fish from inland watersheds. 

EPA sought advice from the SAB on key components of its analysis. In response, the SAB reviewed 

available information and has made the following findings. The SAB considers the spatial resolution of 

the modeling of mercury deposition to watersheds to be appropriate for the analysis. There is agreement 

that the approach used to identify watersheds to include in the assessment is reasonable. This approach 

is based upon the availability of fish tissue methylmercury data and census data on target populations 

with potential subsistence fishers. The SAB agrees that EPA’s calculation of a hazard quotient for each 

watershed is appropriate as the primary means of expressing risk because it is based on an established 

reference dose for methylmercury that reflects a range of potential neurobehavioral effects. Intelligence 
Quotient (IQ) loss is also used in the assessment to evaluate risk. The SAB considers IQ loss to be an 

insensitive indicator of methylmercury neurobehavioral effects and is concerned that its use as an 

endpoint could underestimate risk. The SAB recommends that IQ loss be de-emphasized in the risk 

assessment and explored as one of several possible secondary public health endpoints as a supplement to 

the main analysis. Although the SAB considers the number of watersheds included in the assessment 

adequate, some watersheds in areas with relatively high mercury deposition from U.S. EGUs were 

under-sampled due to lack of fish tissue methymercury data. The SAB encourages the Agency to contact 

states with these watersheds to determine if additional fish tissue methylmercury data are available to 

improve coverage of the assessment.

The SAB identifies additional sources of variability and uncertainty in the risk assessment, as well as 

limitations imposed by the availability of data. The uncertainties are appropriate for a screening-level 

public health assessment. The SAB regards the design of the risk assessment as suitable for its intended 

purpose, to inform decision-making regarding an “appropriate and necessary finding” for regulation of 

hazardous air pollutants from coal and oil-fired EGUs, provided that our recommendations are fully 

considered in the revision of the assessment.

In summary, based on its review of the draft Technical Support Document and additional information 

provided by EPA representatives during the public meetings, the SAB supports the overall design of and 

approach to the risk assessment and finds that it should provide an objective, reasonable, and credible 

determination of the potential for a public health hazard from mercury emitted from U.S. EGUs. The 

SAB finds the current draft document deficient, however, because of its lack of transparency in 

describing key analytical methods and findings. We urge the Agency to revise the document based on 

our recommendations.

We appreciate the opportunity to review the draft mercury risk assessment. We look forward to your 

response.

Sincerely,

/Signed/ /Signed/

Dr. Deborah L. Swackhamer Dr. Stephen M. Roberts

Chair Chair

Science Advisory Board SAB Mercury Review Panel
i

NOTICE

This report has been written as part of the activities of the EPA Science Advisory Board (SAB), a public 

advisory group providing extramural scientific information and advice to the Administrator and other 

officials of the Environmental Protection Agency. The SAB is structured to provide balanced, expert 

assessment of scientific matters related to problems facing the Agency. This report has not been 

reviewed for approval by the Agency, and, hence, the contents of this report do not necessarily represent 

the views and policies of the Environmental Protection Agency, nor of other agencies in the Executive 

Branch of the Federal government. Mention of trade names of commercial products does not constitute a 

recommendation for use. Reports of the SAB are posted on the EPA website at http://www.epa.gov/sab.
ii

U.S. Environmental Protection Agency

Science Advisory Board

Mercury Review Panel

CHAIR

Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for 

Environmental and Human Toxicology, University of Florida, Gainesville, FL

MEMBERS

Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas, Austin, TX

Dr. Thomas Burbacher, Professor of Environmental and Occupational Health Sciences, Director of the 

Infant Primate Research Laboratory, Department of Environmental and Occupational Health Sciences, 

School of Public Health, University of Washington , Seattle, WA

Dr. James Burch, Associate Professor, Department of Epidemiology and Statistics, Arnold School of 

Public Health, University of South Carolina, Columbia, SC

Dr. Hillary Carpenter, Toxicologist, Health Risk Assessment, Environmental Health, Minnesota 

Department of Health, St. Paul, MN

Dr. Celia Chen, Research Professor, Department of Biological Sciences, Dartmouth College, Hanover, 

NH

Dr. Miriam L. Diamond, Professor, Department of Geography, University of Toronto, Toronto, 

Ontario, CANADA

Dr. Charles T. Driscoll, Jr., Professor, Department of Civil and Environmental Engineering, College of 

Engineering and Computer Science, Syracuse University, Syracuse, NY

Dr. Thomas M. Holsen, Professor, Department of Civil and Environmental Engineering, Clarkson 

University, Potsdam, NY

Dr. James Hurley, Director, Environmental Health Division, Wisconsin State Laboratory of Hygiene, 

and Associate Professor, Department of Civil and Environmental Engineering, University of WisconsinMadison, Madison, WI

Dr. David Krabbenhoft, Research Scientist, Wisconsin Water Science Center, U.S. Geological Survey, 

Middleton, WI

Dr. Leonard Levin, Technical Executive, Air Toxics Health & Risk Assessment, Environment Sector, 

Electric Power Research Institute, Palo Alto, CA

Dr. C. Jerry Lin, Department of Civil Engineering, Lamar University, Beaumont, TX
iii

Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado, Boulder, 

CO

Dr. M. Christopher Newland, Alumni Professor, Department of Psychology, Auburn University, 

Auburn, AL

Dr. Nicholas Ralston, Human Health Effects Research Group Leader, Energy & Environmental 

Research Center (EERC), University of North Dakota , Grand Forks, ND

Dr. Stephen L. Rathbun, Professor of Biostatistics, Department of Epidemiology and Biostatistics, 

University of Georgia, Athens, GA

Dr. Eric P. Smith, Professor, Department of Statistics, 406A Hutcheson Hall , Virginia Polytechnic 

Institute and State University, Blacksburgh, VA

Dr. Alan Stern, Section Chief-Risk Assessment/ Adjunct Associate Professor, Division of Science, 

Research & Technology/Dept. of Environmental & Occupational Health, New Jersey Department of 

Environmental Protection/University of Medicine and Dentistry of New Jersey-Robert Wood Johnson 

Medical School., Trenton, NJ (Affiliation for identification purposes only)

Dr. Edward Swain, Research Scientist, Minnesota Pollution Control Agency, Saint Paul, MN

Dr. Edwin van Wijngaarden, Associate Professor, Community and Preventive Medicine, 

Environmental Medicine, and Dentistry, School of Medicine and Dentistry, University of Rochester, 

Rochester, NY

Dr. Robert Wright, Associate Professor, Pediatrics, Division of Environmental Health, Harvard School 

of Public Health, Boston, MA

SCIENCE ADVISORY BOARD STAFF

Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency, Science 

Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-564-2218,

Fax: 202-565-2098, (nugent.angela@epa.gov)
iv

U.S. Environmental Protection Agency

Science Advisory Board

CHAIR

Dr. Deborah L. Swackhamer, Professor and Charles M. Denny, Jr., Chair in Science, Technology and 

Public Policy, Hubert H. Humphrey School of Public Affairs and Co-Director of the Water Resources 

Center, University of Minnesota, St. Paul, MN

SAB MEMBERS

Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas, Austin, TX

Dr. Claudia Benitez-Nelson, Full Professor and Director of the Marine Science Program, Department 

of Earth and Ocean Sciences , University of South Carolina, Columbia, SC

Dr. Timothy Buckley, Associate Professor and Chair, Division of Environmental Health Sciences, 

College of Public Health, The Ohio State University, Columbus, OH

Dr. Patricia Buffler, Professor of Epidemiology and Dean Emerita, Department of Epidemiology, 

School of Public Health, University of California, Berkeley, CA

Dr. Ingrid Burke, Director, Haub School and Ruckelshaus Institute of Environment and Natural 

Resources, University of Wyoming, Laramie, WY

Dr. Thomas Burke, Professor, Department of Health Policy and Management, Johns Hopkins 

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

Dr. Terry Daniel, Professor of Psychology and Natural Resources, Department of Psychology, School 

of Natural Resources, University of Arizona, Tucson, AZ

Dr. George Daston, Victor Mills Society Research Fellow, Product Safety and Regulatory Affairs, 

Procter & Gamble, Cincinnati, OH

Dr. Costel Denson, Managing Member, Costech Technologies, LLC, Newark, DE

Dr. Otto C. Doering III, Professor, Department of Agricultural Economics, Purdue University, W. 

Lafayette, IN

Dr. David A. Dzombak, Walter J. Blenko Sr. Professor of Environmental Engineering , Department of 

Civil and Environmental Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, 

PA

Dr. T. Taylor Eighmy, Vice President for Research, Office of the Vice President for Research, Texas 

Tech University, Lubbock, TX

Dr. Elaine Faustman, Professor, Department of Environmental and Occupational Health Sciences, 

School of Public Health and Community Medicine, University of Washington, Seattle, WA
v

Dr. John P. Giesy, Professor and Canada Research Chair, Veterinary Biomedical Sciences and 

Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Dr. Jeffrey Griffiths, Associate Professor, Department of Public Health and Community Medicine, 

School of Medicine, Tufts University, Boston, MA

Dr. James K. Hammitt, Professor, Center for Risk Analysis, Harvard University, Boston, MA

Dr. Bernd Kahn, Professor Emeritus and Associate Director, Environmental Radiation Center, Georgia 

Institute of Technology, Atlanta, GA

Dr. Agnes Kane, Professor and Chair, Department of Pathology and Laboratory Medicine, Brown 

University, Providence, RI

Dr. Madhu Khanna, Professor, Department of Agricultural and Consumer Economics, University of 

Illinois at Urbana-Champaign, Urbana, IL

Dr. Nancy K. Kim, Senior Executive, Health Research, Inc., Troy, NY

Dr. Kai Lee, Program Officer, Conservation and Science Program, David & Lucile Packard Foundation, 

Los Altos, CA (affiliation listed for identification purposes only)

Dr. Cecil Lue-Hing, President, Cecil Lue-Hing & Assoc. Inc., Burr Ridge, IL

Dr. Floyd Malveaux, Executive Director, Merck Childhood Asthma Network, Inc., Washington, DC

Dr. Lee D. McMullen, Water Resources Practice Leader, Snyder & Associates, Inc., Ankeny, IA

Dr. Judith L. Meyer, Professor Emeritus, Odum School of Ecology, University of Georgia, Lopez 

Island, WA

Dr. James R. Mihelcic, Professor, Civil and Environmental Engineering, State of Florida 21st Century 

World Class Scholar, University of South Florida, Tampa, FL

Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado, Boulder, 

CO

Dr. Christine Moe, Eugene J. Gangarosa Professor, Hubert Department of Global Health, Rollins 

School of Public Health, Emory University, Atlanta, GA

Dr. Horace Moo-Young, Dean and Professor, College of Engineering, Computer Science, and 

Technology, California State University, Los Angeles, CA

Dr. Eileen Murphy, Grants Facilitator, Ernest Mario School of Pharmacy, Rutgers University, 

Piscataway, NJ

Dr. Duncan Patten, Research Professor, Hydroecology Research Program , Department of Land 

Resources and Environmental Sciences, Montana State University, Bozeman, MT
vi

Dr. Stephen Polasky, Fesler-Lampert Professor of Ecological/Environmental Economics, Department 

of Applied Economics, University of Minnesota, St. Paul, MN

Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT

Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for 

Environmental and Human Toxicology, University of Florida, Gainesville, FL

Dr. Amanda Rodewald, Professor of Wildlife Ecology, School of Environment and Natural Resources, 

The Ohio State University, Columbus, OH

Dr. Jonathan M. Samet, Professor and Flora L. Thornton Chair, Department of Preventive Medicine, 

University of Southern California, Los Angeles, CA

Dr. James Sanders, Director and Professor, Skidaway Institute of Oceanography, Savannah, GA

Dr. Jerald Schnoor, Allen S. Henry Chair Professor, Department of Civil and Environmental 

Engineering, Co-Director, Center for Global and Regional Environmental Research, University of Iowa, 

Iowa City, IA

Dr. Kathleen Segerson, Philip E. Austin Professor of Economics , Department of Economics, 

University of Connecticut, Storrs, CT

Dr. Herman Taylor, Director, Principal Investigator, Jackson Heart Study, University of Mississippi 

Medical Center, Jackson, MS

Dr. Barton H. (Buzz) Thompson, Jr., Robert E. Paradise Professor of Natural Resources Law at the 

Stanford Law School and Perry L. McCarty Director, Woods Institute for the Environment, Stanford 

University, Stanford, CA

Dr. Paige Tolbert, Professor and Chair, Department of Environmental Health, Rollins School of Public 

Health, Emory University, Atlanta, GA

Dr. John Vena, Professor and Department Head, Department of Epidemiology and Biostatistics, 

College of Public Health, University of Georgia, Athens, GA

Dr. Thomas S. Wallsten, Professor and Chair, Department of Psychology, University of Maryland, 

College Park, MD

Dr. Robert Watts, Professor of Mechanical Engineering Emeritus, Tulane University, Annapolis, MD

Dr. R. Thomas Zoeller, Professor, Department of Biology, University of Massachusetts, Amherst, MA

SCIENCE ADVISORY BOARD STAFF

Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency, Science 

Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-564-2218,

Fax: 202-565-2098, (nugent.angela@epa.gov)
vii

Table of Contents

Acronyms, Abbreviations and Definitions of Terms................................................................... viii

1. Executive Summary................................................................................................................ 1

2. Introduction............................................................................................................................. 6

3. Response to charge questions................................................................................................. 7

3.1. Overall design ................................................................................................................. 7

3.2. Critical health endpoints besides IQ loss........................................................................ 7

3.3. Use of an IQ loss metric benchmark............................................................................... 9

3.4. Spatial scale of watersheds ............................................................................................. 9

3.5. Measured fish tissue mercury concentrations............................................................... 10

3.6. Use of the 75th percentile fish tissue methylmercury value .......................................... 12

3.7. Consumption rates and location for high-end consumers............................................. 15

3.8. Use of Census data to identify high-end fish consuming populations.......................... 16

3.9. Use of the Mercury Maps approach.............................................................................. 16

3.10. Exclusion of watersheds with significant non-air loadings .......................................... 19

3.11. Concentration-response function used in modeling IQ loss......................................... 19

3.12. Uncertainty and variability ........................................................................................... 21

3.13. Discussion of analytical results..................................................................................... 25

3.14. Responsiveness to the goals of the study...................................................................... 31

3.15. Confidence in the analysis............................................................................................ 31

4. Summary list of recommendations ....................................................................................... 32

References..................................................................................................................................... 40

Appendix A: Agency Charge Questions..................................................................................... A-1
viii

ACRONYMS, ABBREVIATIONS AND DEFINITIONS OF TERMS

CASAC Clean Air Scientific Advisory Committee

CMAQ Community Multiscale Air Quality Modeling 

System

EGU Electrical Generating Unit

EPA Environmental Protection Agency

GEOS-Chem A global 3-D chemical transport model (CTM) for 

atmospheric composition driven by meteorological 

input from the Goddard Earth Observing System 

(GEOS) of the NASA Global Modeling and 

Assimilation Office.

HAP Hazardous Air Pollutant

Hg Mercury

HQ Hazard Quotient

HUC Hydrologic Unit Codes

IQ Intelligence Quotient

MDN Mercury Deposition Network

MeHg Methylmercury

MMAP Mercury Maps

PUFA Polyunsaturated Fatty Acid 

RfD Reference Dose

R-MCM Regional Mercury Cycling Model

SAB Science Advisory Board

TRI Toxic Release Inventory

TSD Technical Support Document
1

1. Executive Summary

EPA has proposed National Emission Standards for Hazardous Air Pollutants for coal- and oil-fired 

Electric Utility Steam Generating Units (EGUs). These proposed standards would require EGUs to

decrease emissions of mercury and other hazardous air pollutants (HAP). In order to regulate HAP 

emissions under the Clean Air Act, Section 112(b), the Agency must make a determination that such 

regulation is appropriate and necessary based upon a study of the hazards to public health reasonably 

anticipated from HAP emissions. As part of this determination, hazards to public health from U.S. EGU 

mercury emissions are evaluated in a draft national-scale risk assessment entitled Technical Support 

Document: National-Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding 

for Coal and Oil-Fired Electric Generating Units (March 2011). This SAB report uses the terms “risk 

assessment” and “Technical Support Document” interchangeably to refer to EPA’s draft document.

The draft risk assessment considers hazards from mercury released from U.S. EGUs and deposited in 

watersheds within the continental United States. Mercury deposition is estimated using the Community 

Multi-scale Air Quality (CMAQ) model for watersheds classified using 12-digit Hydrologic Unit Codes 

(HUC12). The risk assessment focuses on hazard from consumption of methylmercury in self-caught 

fish, specifically hazard to children born to women who consume local fresh water fish in a subsistence 

manner. Exposure from fish consumption is estimated for watersheds with data on methylmercury 

concentrations in fish tissue, and a hazard quotient (HQ) is calculated based upon the current reference 

dose (RfD) for methylmercury. The contribution of U.S. EGUs to the HQ for each watershed is 

calculated by comparing U.S. EGU deposition rates with total deposition to the watershed, including 

other sources, assuming that the contribution of U.S. EGUs to fish tissue concentrations and risk is 

proportional to their contribution to total deposition. Intelligence Quotient (IQ) loss is also modeled as a 

health endpoint, with a loss of one or more points from methylmercury exposure considered as a public 

health concern. Estimated hazards associated with U.S. EGU emissions in 2005 are compared with 

estimated hazards expected to remain in 2016 “after imposition of the requirements of the Act.”

The SAB Mercury Review Panel was asked to comment on the draft risk assessment, including the 

overall design and approach as well as various technical aspects. The Panel was also asked to comment 

on the extent to which specific “observations” or conclusions in the risk assessment are supported by the 

analytical results. During the course of deliberations, the Panel reviewed background materials provided 

by the Office of Air Quality Planning and Standards, as well as public comments on the topic. The SAB 

reviewed and approved the report of the Panel. EPA asked the SAB to address fourteen charge 

questions, many with multiple parts. This Executive Summary highlights the main findings. Detailed 

responses to the individual charge questions are provided in the body of the report.

The SAB finds the Technical Support Document to lack critical details regarding both the methods used 

and the results presented. This made the document difficult to review and, in the view of the SAB, 

unsuitable in its present form to fully support Agency decision-making. Presentations and information 

provided by Agency representatives helped the SAB understand how the risk assessment was conducted, 

the rationale for some of the decisions made in approach and the use of data and the translation of the 

results. With this additional information, the SAB views the risk assessment favorably, concluding that 

it is able to provide an objective, reasonable, and credible determination of the potential for a public 

health hazard from mercury emitted from U.S. EGUs. However, the SAB considers the integrity of the 

risk assessment to be dependent in part on a transparent description of the analysis, and the Technical 

Support Document needs to be strengthened to provide this description.
2

This review is based on the text of the report and the additional information provided by the above cited 

presentations and discussion provided by EPA representatives. Responses to charge questions indicate 

where improvements need to be made, and a summary of the most critical recommendations is provided 

in section 4. The SAB’s support for the risk assessment is contingent on its recommendations being fully 

considered in the revision of the assessment.

Overall design

The Panel finds the overall design and general approach used in the risk assessment to be scientifically 

credible. The Technical Support document, however, needs a more detailed description of the modeling 

methods and data sources. The report’s introduction should make clear from the start that the analysis is 

a determination of potential exposure at the scale of watersheds.

Critical health endpoints besides IQ loss; use of an IQ loss metric benchmark; and concentrationresponse function used in modeling IQ loss

The SAB supports the use of the HQ approach in the risk assessment. SAB members agree that because 

the RfD from which the HQ is calculated is an integrative metric of neurodevelopmental effects of 

methylmercury, it constitutes a reasonable basis for assessing risk. Other potential health endpoints were 

also considered by the SAB. The SAB notes that a number of measures of potential neurodevelopmental 

effects of methylmercury exist, some of which have greater sensitivity to differential mercury exposure

than does IQ loss. However, none are viewed by the SAB as suitable for quantitative risk estimation 

with a reasonable degree of scientific certainty at the present time, and consequently none are 

recommended for incorporation into the analysis. The SAB does not consider it appropriate for EPA to

use IQ loss in the risk assessment and recommends that this aspect of the analysis be de-emphasized, 

moving it to an appendix where IQ loss is discussed along with other possible endpoints not included in 

the primary assessment.

While the SAB agrees that the concentration-response function for IQ loss used in the risk assessment 

has validity, IQ loss is not a sensitive response endpoint for methylmercury and its use likely 

underestimates the impact of reducing methylmercury in water bodies. The SAB agrees that if IQ loss 

were retained in the risk assessment despite these reservations, a loss of one or two points on average in 

a population would be an appropriate benchmark. The SAB agrees that fish nutrients (e.g., omega-3 

fatty acids) can potentially ameliorate neurologic effects associated with methylmercury, but there is not 

sufficient information to recommend a quantitative adjustment in health endpoint measures. However, 

the SAB agrees that because the RfD from which the HQ is calculated is an integrative metric of risk, it 

constitutes a reasonable basis for assessing risk. The RfD is an integrative measure because it considers 

the weight of the evidence and determines a quantitative value that is based on the most sensitive 

endpoints across multiple studies and endpoints.

Spatial scale of watersheds

The SAB agrees that HUC12 watersheds provide the appropriate level of spatial resolution and offer 

advantages over previous assessments at lower resolution (e.g., HUC8). The comparability of this scale 

to CMAQ output makes the transferability and applicability of deposition modeling to the watershed 

scientifically robust. Further, the finer resolution of HUC12 watersheds is better suited to follow 

deposition patterns of a single source such as an EGU and increases the likelihood that measured 

deposition within a watershed is homogeneous. The SAB notes that one disadvantage of smaller 
3

watershed size is that, within a given watershed, the number of fish samples with methylmercury data is 

diminished. During the public meeting, the SAB questioned some of the figures with maps showing 

modeled deposition across the United States. Some areas showed intense deposition with no obvious 

source, leading SAB members to question the accuracy of the modeling or data presentation in the 

March draft report. EPA provided clarification and updated maps in July 2011 (Pekar 2011). The SAB

supports EPA’s plans to include these updated mercury deposition maps in the revised report so they 

correctly reflect total annual mercury deposition per square meter by watershed.

Measured fish tissue mercury concentrations

The SAB agrees that fish tissue methylmercury data are an appropriate basis to estimate the number and 

percentage of fish-sampled watersheds where populations may be at risk. Although fish data were only 

available for 2,461 HUC12 watersheds out of 88,000 HUC12 watersheds in the continental United 

States, this is viewed as sufficient to estimate the number and percentage of fish-sampled watersheds 

where populations may be at risk. The SAB notes advantages and disadvantages of the Agency decision 

to limit fish tissue concentration data to the period after 1999 but agrees with this approach, given that 

older data might not be representative of conditions during the 2005 reference deposition year. The SAB

is concerned about the absence of fish tissue data from some watersheds with higher levels of mercury 

deposition. The EPA is encouraged to contact states with these watersheds to determine whether 

additional fish tissue data are available to improve coverage of the analysis. The SAB discussed the use 

of modeling to estimate fish methylmercury concentrations as a means to include more watersheds. With 

further development, this approach could be used for a national scale assessment such as this in the 

future but the SAB does not recommend it for the current assessment.

Use of the 75th percentile fish tissue methylmercury value

As a means of selecting methymercury fish concentrations representative of larger, but not the largest, 

edible fish, the risk assessment uses the 75th percentile fish concentration for watersheds with one or 

more fish concentration value. The SAB considers this percentile reasonable but is concerned that over 

half of the watersheds in the assessment have only four or fewer fish samples with methylmercury 

concentration, and a significant number of these have a single fish sample. The SAB notes that in 

watersheds where only a few fish samples are available, the 75th percentile concentration and exposure 

most likely will be underestimated. This should be explained in the report, and a sensitivity analysis 

should be conducted using the median fish tissue concentration to better represent the distribution of 

concentrations when the sample size is only one fish. The SAB also recommends that the report describe 

the sources of fish methylmercury concentration data more fully, including the state sampling programs 

that provide most of the data. Discussion of sampling programs should include the types and sizes of 

fish obtained, as well as uncertainties associated with this data set, to improve the transparency of the 

analysis.

Consumption rates and location for high-end consumers

The SAB finds that the consumption rates and locations for fishing activity for likely highly exposed 

consumers, i.e., self-caught fish consuming populations modeled in the analysis, are supported by the 

data presented in the document and are generally reasonable and appropriate given the available data. A 

diverse range of susceptible populations is represented in the assessment. There are caveats, however, 

associated with the sources of fish consumption data, the data sets selected for inclusion, and the 
4

suitability of data for inclusion in the risk assessment (e.g., in terms of providing annual average intakes 

of the edible portion of the fish) that should be acknowledged more fully in the document. 

Use of Census data to identify high-end fish consuming populations

The SAB agrees that the criterion of using at least 25 persons per census tract from a given target 

subsistence fisher population is a reasonable approach to identify watersheds with potentially highly 

exposed fish consuming populations. While other approaches are possible, none is viewed as being more 

effective or feasible. The SAB recommends that the document clarify how many watersheds were 

eliminated due to this inclusion criterion.

Mercury Maps approach

The SAB agrees with the Mercury Maps approach used in the analysis and has cited additional work that 

supports a linear relationship between mercury loading and accumulation in aquatic biota. These studies 

suggest that mercury deposited directly to aquatic ecosystems can become quickly available to biota and 

accumulated in fish, and reductions in atmospheric mercury deposition should lead to decreases in 

methylmercury concentrations in biota. The SAB notes other modeling tools are available to link 

deposition to fish concentrations, but does not consider them to be superior for this analysis or 

recommend their use. The integration of Community Multiscale Air Quality Modeling System (CMAQ)

deposition modeling to produce estimates of changes in fish tissue concentrations is considered to be 

sound. Although the SAB is generally satisfied with the presentation of uncertainties and limitations 

associated with the application of the Mercury Maps approach in qualitative terms, it recommends that 

the document include quantitative estimates of uncertainty available in the existing literature. 

Exclusion of watersheds with significant non-air loadings

In order to reduce uncertainty associated with the Mercury Maps approach, watersheds with significant 

non-air loadings of mercury are excluded from the analysis. The SAB agrees with the exclusion criteria 

used by the Agency. Additional exclusion criteria were discussed, but their application would be 

unlikely to substantially change the results of the assessment. The SAB also recommends that the EPA 

provide additional discussion of uncertainties in the mercury emissions from U.S. EGUs and non-EGU 

sources and the implications of these uncertainties.

Uncertainty and variability and discussion of analytical results

The SAB discussed the characterization of variability and uncertainty in the Technical Support 

Document in detail. Sources of variability and uncertainty in the assessment are summarized in 

Appendix F of the draft document. The qualitative nature of this presentation is considered appropriate, 

but the identification of important sources of variability and uncertainty is considered incomplete.

Inclusion of several additional sources of variability and uncertainty is recommended. The SAB notes

that the degree of uncertainty associated with the analysis is consistent with a screening level analysis, 

and despite the various sources of uncertainty inherent in the approach, the analysis is sound and 

reasonable.

The SAB finds that observations in five areas (mercury deposition from U.S. EGUs, fish tissue 

methylmercury concentrations, patterns of mercury deposition with mercury fish tissue data, percentile 

risk estimates, and number and frequency of watersheds with populations potentially at risk due to U.S. 
5

EGU mercury emissions) are generally supported by the analytical results presented in the document.

However, there are many examples where results are poorly presented, and in most areas the 

uncertainties, variability and data limitations are not well characterized. The SAB has numerous specific 

recommendations to improve presentation of findings and observations.

Responsiveness to the goals of the study

The section of the document on Summary of Key Observations does not encapsulate well the critical 

issues and significant results of the analysis. The SAB recommends revising this section to link back 

directly with the goals of the studies as articulated on Page 13 of the document, i.e.: (a) what is the 

nature and magnitude of the potential risk to public health posed by current U.S. EGU mercury 

emissions? (b) what is the nature and magnitude of the potential risk posed by U.S. EGU mercury 

emissions in 2016 considering potential reductions in EGU Hg emissions attributable to CAA (Clean Air 

Act) requirements? and (c) how is risk estimated for both the current and future scenario apportioned 

between the incremental contribution from U.S. EGUs and other sources of mercury?
6

2. Introduction

EPA’s Office of Air and Radiation requested peer review of a Technical Support Document: NationalScale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for Coal and OilFired Electric Generating Units - March 2011, developed to support a proposed rule published in the 

Federal Register on March 16, 2011 to regulate emissions of hazardous air pollutants from for coal- and 

oil-fired Electric Utility Steam Generating Units (EGUs). Section 112(n)(1) of the Clean Air Act

requires EPA to determine whether it is “appropriate and necessary” to regulate hazardous air pollutants

emissions from EGUs under section 112. The “appropriate and necessary” finding requires EPA to 

perform a study of the hazards to public health reasonably anticipated to occur as a result of hazardous 

air pollutant emissions, including mercury.

The Science Advisory Board formed an expert ad hoc Panel to peer review the draft Technical 

Document. The Panel addressed fourteen Agency charge questions (see Appendix A) and developed the 

responses below. The Panel held a public meeting on June 15-17, 2011 to peer review this document and 

held a public teleconference on July 20, 2011 to discuss the Panel’s draft report. The chartered SAB held 

a quality review to approve the draft report on September 7, 2011.

The SAB had difficulty evaluating the Technical Support Document because it lacked critical details.

During the public meeting, presentations and information provided by Agency representatives helped 

the SAB understand technical aspects of the analysis. With this additional information and clarification, 

the SAB views the risk assessment positively. However, the SAB considers the integrity of the risk 

assessment as dependent in part on a transparent description of the methods and findings. The Technical 

Support Document needs to better explain what was done and why, translate the results into findings 

that relate to the key goals of the analysis and describe where the uncertainties lie. The SAB’s support 

for the risk assessment is contingent upon a development of a revised document that addresses these 

issues.

The body of this report is organized to respond to each of EPA’s charge questions. Section 3 provides

responses and specific suggestions and recommendations for revising the Technical Support Document.

Because this SAB report provides many recommendations for strengthening the Technical Support 

Document, Section 4 provides a list of the specific recommendations made by the SAB. 
7

3. Response to Charge Questions

3.1. Overall design

Question 1: Please comment on the scientific credibility of the overall design of the mercury risk 

assessment as an approach to characterize human health exposure and risk associated with U.S. 

EGU mercury emissions (with a focus on those more highly exposed).

Response: 

3.2. Critical health endpoints besides IQ loss

The SAB finds that the overall design and general approach used in the assessment are 

scientifically credible.

The overall approach used in the study is to estimate potential risk at a national scale, attributable to 

mercury released from U.S. EGUs and deposited to inland waterbodies, for recent (2005) and future 

(2016) emissions levels. To accomplish this, the analysis links a series of models and data to estimate 

methylmercury exposure via fish consumption and then compares the exposure with a toxicological 

benchmark. The series of models allows for the estimation of deposition of mercury emitted by U.S. 

EGUs into watersheds. The assessment uses measured concentrations of methylmercury in fish tissue 

samples, as well as estimates of future fish methylmercury concentrations, to estimate the number and 

percentage of watersheds where populations may be at risk. Human exposure and potential health effects 

in these at-risk watersheds are then assessed through the pathway of ingestion of self-caught fish from 

inland water bodies for vulnerable subsistence fisher populations.

Although the overall design and general approach are scientifically credible, the SAB has a number of 

suggestions and recommendations for enhancing the assessment, based on review of the draft Technical 

Support Document and supplementary presentations and information provided by EPA. The responses to 

the charge questions below provide those recommendations and suggestions in detail. It will be 

important for EPA to address these issues. The Technical Support Document would benefit from a more 

detailed description of the modeling methods and data sources, and results need to be presented more 

clearly. The Introductory section should make clear, at the earliest possible point, that the analysis is a 

determination of watershed impact with exposure addressed as a potential outcome. Despite weaknesses 

in the Technical Support Document and uncertainties inherent in an analysis such as this, the SAB

agrees that the risk assessment makes an objective and reasonable determination of the potential for a 

public health hazard from mercury emitted from U.S. EGUs.

Question 2: Are there any additional critical health endpoint(s) besides IQ loss which could be 

quantitatively estimated with a reasonable degree of confidence to supplement the mercury risk 

assessment (see section 1.2 of the Mercury Risk TSD for an overview of the risk metrics used in the risk 

assessment)?

Response: This charge question raises issues about the use of IQ, as well as use of alternative 

quantitative measures. While several alternative approaches were discussed that might supplement IQ 

scores, no substitute can be quantitatively estimated with a “reasonable degree of confidence.”

Moreover, there are doubts that IQ met this standard. The response to this charge question addresses 

both of these issues below.
8

Use of IQ. There are significant concerns about the use of IQ for identifying the impact of consuming 

fish from water bodies with unacceptable levels of methylmercury because IQ will likely result in an 

underestimation of potential neurobehavioral impacts, compared to analyses using the hazard quotient 

(HQ). Thus, the SAB considers HQ to be a stronger basis for evaluation of methylmercury hazard. The 

HQ is based upon the methylmercury reference dose (RfD), which is an integrative measure reflecting a 

range of neurobehavioral effects, and it incorporates pharmacokinetic variability. The RfD considers the 

weight of the evidence and determines a quantitative value that is based on the most sensitive endpoints 

across multiple studies and endpoints. Sensitive endpoints, in this context, are adverse effects that occur 

at the lowest exposures. 

In contrast, the loss of IQ points is likely to underestimate the impact of reducing methyl mercury in 

water bodies. The reason is that IQ score has not been the most sensitive indicator of methylmercury’s 

neurotoxicity in the populations studied. As noted in the Technical Support Document, in the Faroe 

Island study the most sensitive indicators were in the domains of language (Boston Naming Test), 

attention (continuous performance) and memory (California Verbal Learning Test). These two tests are

neuropsychological tests that are not subtests of IQ tests and whose relationship with global IQ is not 

well characterized. In the Seychelles study, the Psychomotor Development Index was the most sensitive 

measure and, while this index is a component of the Bailey Scales of Infant Development, it is not 

highly correlated with cognitive measures (Davidson et al. 2008). 

Additionally, the use of IQ, or any neuropsychological measure, distracts from the main goal of the 

document. The analysis in the document emphasizes the number of fish-sampled water bodies from 

which subsistence fishers would be at risk based on an elevated HQ. As is clear in Tables 2-9 to 2-11 in 

the Technical Support Document, an analysis based on IQ identifies far fewer water bodies than one 

based on the HQ. This is because IQ underestimates hazard, as noted above. 

The SAB recommends that EPA reframe the document’s discussion of IQ. EPA should incorporate IQ 

and other neuropsychological measures as supplemental information and focus on HQ as the primary 

critical health endpoint. It is not suggested that the analyses of IQ be removed altogether but rather that 

the analyses be framed in an appendix to the report as a secondary analyses of impact of reduced 

exposure on potential health-related outcome. The appendix should discuss the basis for selecting a HQ 

at or above 1.5 as the criteria for selecting potentially impacted watersheds should be explained. The 

appendix should also include discussion of potential effects on other measures like developmental 

delays (Grandjean et al. 1997) or neuropsychological tests (as discussed by van Wijngaarden et al.

2006), presented in the overall context of the weight of evidence. 

Alternative quantitative measures. One alternative is developmental delay as described by Grandjean et 

al. (1997). Here, an estimate of the number of months of delay in verbal skills as tapped by the Boston 

Naming Test or in learning and short-term memory as tapped by the California Verbal Learning Test

was made based on regression coefficients describing the relationship among age, methylmercury

exposure, and scores on these tests. The delays were on the order of five to seven months associated 

with a 10-fold increase in cord blood methylmercury. 

A recent analysis by van Wijngaarden et al. (2006) derived Benchmark Dose Level-Lower 95% 

confidence interval values for 26 endpoints, including IQ and other neuropsychological measures from 

the nine-year follow up of the Seychelles child development study main cohort. This paper could be 

cited in a discussion of markers of health impacts of lowering mercury deposition and reducing intake 

by subsistence fishers. 
9

One SAB member suggests the use of blood markers of selenium-dependent enzyme function, noting 

that methylmercury irreversibly inhibits selenium-dependent enzymes that are required to support vitalbut-vulnerable metabolic pathways in the brain and endocrine system. Impaired selenoenzyme activities 

would be observed in the blood before they would be observed in brain, but the effect is also expected to 

be transitory. The use of these measures is a minority view among the SAB members. 

The SAB recommends that the Technical Support Document acknowledge and discuss alternative

quantitative measures but does not recommend a re-analysis based on these measures. 

3.3. Use of an IQ loss metric benchmark

Question 3: Please comment on the benchmark used for identifying a potentially significant public 

health impact in the context of interpreting the IQ loss risk metric (i.e., an IQ loss of 1 to 2 points or 

more representing a potential public health hazard). Is there any scientifically credible alternate 

decrement in IQ that should be considered as a benchmark to guide interpretation of the IQ risk 

estimates (see section 1.2 of the Mercury Risk TSD for additional detail on the benchmark used for 

interpreting the IQ loss estimates).

Response:

3.4. Spatial scale of watersheds

The consensus is that if IQ were to be used, then a loss of 1 or 2 points as a population 

average is a credible decrement to use for this risk assessment. This metric seems to be derived from the 

lead literature and was peer-reviewed by the Clean Air Scientific Advisory Committee (U.S. EPA 

CASAC 2007). While its applicability to methylmercury is questionable, the size of the decrement is 

justified based on the extensive analyses available from the literature reviewed by CASAC. The support 

for the model of the relationship between IQ and methylmercury exposure comes from Axelrad and 

Bellinger (2007) and from a whitepaper produced by Bellinger (2005).

The analysis in Table 2-10 showing the effect of using a one- or two -point loss is helpful in evaluating 

the sensitivity of this measure to the magnitude of the decrement.

Question 4: Please comment on the spatial scale used in defining watersheds that formed the basis for 

risk estimates generated for the analysis (i.e., use of 12-digit hydrologic unit code classification). To 

what extent do HUC12 watersheds capture the appropriate level of spatial resolution in the relationship 

between changes in mercury deposition and changes in MeHg fish tissue levels? (see section 1.3 and 

Appendix A of the Mercury Risk TSD for additional detail on specifying the spatial scale of watersheds 

used in the analysis).

Response: The choice of using the HUC12 (Hydrologic Unit Code) watershed delineation of the 

contiguous 48 United States for this risk assessment is more appropriate and offers at least two distinct 

advantages over the 2001 Mercury Maps study report that employed the larger-scale HUC8 delineation.

First, HUC8s are “cataloguing units” delineation and do not actually represent true watersheds (areas of 

land where surface water drainage accumulates to an outflow location). Instead, many HUC8 areas have 

flow lines that cross the unit boundaries, thus making this larger scale delineation not technically correct 

for any mass accounting procedure like Mercury Maps. The use of HUC12s, which are true watershed 

delineations, does not violate this mass accounting assumption. A second strength of the use of 

HUC12’s is that they have a similar physical scale to the spatial resolution of the CMAQ output (12 km 

CMAQ square grid compared to the HUC12 watersheds that are typically about 5-10 km on a side).

Comparable scales make the transferability and applicability of deposition modeling to the watershed 
10

more scientifically robust. The use of finer scale watersheds enables modeling and deposition runs that 

have the detail to follow deposition patterns from a single source, including EGUs. The fine-scale 

watershed resolution decreases the likelihood that there is a significant deposition gradient within the 

HUC. Further, the relative biogeochemical and ecological homogeneity of an individual HUC12 

watershed allows better validity for ascribing fish concentrations to a specific watershed and that those 

fish will respond in proportion to changes in atmospheric mercury deposition. The SAB notes, however,

that one potential disadvantage of HUC12 is that a number of HUC12 watersheds contain a very limited 

number of fish samples because of their inherent small size, but other factors described in this response 

override this concern.

The Technical Support document acknowledges and this SAB agrees that the fish distribution data are 

highly skewed toward the Eastern United States. That said, the legend of Figure 2-6 in the Technical 

Support Document indicates that 2,170 out of 2,461 watersheds were from the Eastern United States, 

leaving approximately 300 samples from Western sites. Given the apparent distribution of high 

deposition zones in CMAQ modeling runs displayed in Figures 2-1 and 2-2 in the Technical Support 

Document that are not ground-truthed in Mercury Deposition Network deposition measurement, the 

SAB is concerned not only about the reality of the identified intense deposition zones (i.e., whether they 

are truly intense deposition zones, for example, in the state of Nevada), but also whether these 

watersheds were included in this report’s analysis. Fish distribution data appear to overlap with some of 

these zones of modeled high mercury deposition, and, with 300 fish samples from the Western United 

States, there is a high probability for overlap. 

The SAB is concerned about the possibility that in some watersheds, multiple small lakes may be 

included within a single HUC12. In some cases, lakes within a small geographic zone have been shown 

to have quite different chemistry and biological productivity. For instance, within Voyageurs National 

Park in northern Minnesota, the mercury content of similarly-sized fish of a given species in about 20 

lakes varies by a factor of 10 (Wiener et al. 2006), indicating that even lakes near each other can 

bioaccumulate mercury to greatly differing degrees. In HUCs with multiple lakes, the SAB recommends

against using a single fish methylmercury value to describe the HUC. In response to this concern and 

other charge questions, the SAB recommends that the authors provide a summary table describing the 

characteristics of the watersheds where fish were collected, including the fraction of fish samples 

collected from rivers versus lakes, and whether from single or multiple sites.

3.5. Measured fish tissue mercury concentrations

Question 5: Please comment on the extent to which the fish tissue data used as the basis for the risk 

assessment are appropriate and sufficient given the goals of the analysis. Please comment on the extent 

to which focusing on data from the period after 1999 increases confidence that the fish tissue data used 

are more likely to reflect more contemporaneous patterns of mercury deposition and less likely to reflect 

earlier patterns of mercury deposition. Are there any additional sources of fish tissue MeHg data that 

would be appropriate for inclusion in the risk assessment?

Response: The measured fish tissue data serve as an appropriate basis for the mercury risk assessment 

because they are widely available and reflect the actual environmental conditions that influence fish 

methylmercury concentrations and human exposure to methylmercury by the target populations. The 

SAB notes that the relevant form of mercury in fish tissue for this risk assessment is methylmercury, but 

there is sometimes ambiguity as to the mercury form actually measured in surveys from which the fish 

tissue data were taken. Many surveys measure total mercury and assume all mercury present in fish is in 
11

the methyl form. Although empirical data available are largely supportive of this assumption, the 

Technical Support Document needs to clearly acknowledge this aspect of the fish tissue data.

While it is always desirable to have a larger sample size, the sample size of 2,461 HUC12 watersheds is 

adequate for the goals of the risk assessment. However, as detailed below, the SAB is concerned about 

the sources of bias and uncertainty resulting from the state sampling designs used to select watersheds 

where fish tissue samples were obtained. For purposes of hazard assessment, it is reasonable to have an 

over-representation of HUC12s in the eastern part of the country given the prevalence of EGUs in the 

East. However, the description of the character of the data, as well as the selection of analyzable data 

(e.g., sizes, distribution of fish sizes across watersheds), should be better detailed in the report. 

There are advantages and disadvantages to using fish methylmercury data prior to 1999 for the risk 

assessment. The advantage is that considerable fish data were obtained prior to 1999 and the use of these 

data could increase the information available for the national risk assessment. The disadvantage is that 

fish methylmercury concentrations may have changed since 1999 and these older data may not be 

representative of conditions during the 2005 reference deposition year. Unfortunately, there are few high 

quality time series data of fish methylmercury concentrations, so it is difficult to quantify the extent to 

which fish methylmercury concentrations have changed since the 1990s. As a result, the SAB

recommends that the EPA utilize fish methylmercury data collected since 1999 for the risk assessment.

Given the spatial distribution of mercury deposition from EGUs and the density of fish methylmercury 

measurements (Figure 2-15), there are some states that receive what the Technical Support Document

terms “relatively elevated” mercury deposition from U.S. EGU emissions and have limited fish 

methylmercury measurements. These states include Pennsylvania, New Jersey, Kentucky and Illinois.

The SAB suggests that the EPA contact these states to investigate if additional recent (since 1999) fish 

methylmercury data are available to improve the coverage for the mercury risk assessment. For example,

the Pennsylvania Department of Environmental Protection, Pennsylvania Fish Monitoring Program has 

700 sites for the measurement of the methylmercury content of recreational sport fish, with samples 

collected from 1979-2007.

EPA’s reliance on the National Listing of Fish Advisory and U.S. Geological Survey (USGS) 

compilation of methylmercury data sets contributes to uncertainty because these data were collected by 

state agencies with various sampling designs and state protocols. Most of the data are not from 

probability-based sampling designs, so it is not entirely clear what population the fish tissue samples 

represent. The direction of impact on the risk assessment of this variation in sampling designs cannot be 

ascertained. Moreover, some states have greater sampling efforts than others. Particularly strong 

sampling efforts were observed in South Carolina, Louisiana, Indiana, Iowa, West Virginia and Virginia.

As a consequence of this variability in fish-tissue sampling effort, the risk assessment will be strongly 

influenced by states with high sampling efforts. Moreover, Figure 2-18 suggests that the sample is 

biased in favor of watersheds with higher mercury deposition and higher EGU-attributable deposition as 

predicted by the CMAQ model. This bias could in part be due to the over-representation of HUC12s in 

the East but could also occur if states with high deposition also have high fish-tissue sampling effort.

Nevertheless, as per the limitations of the available data, the risk assessment focuses on that portion of 

the fish-sampled watersheds at risk, rather than attempting to make inferences to the larger population of 

all 88,000 HUC12 watersheds.

Researchers have developed empirical models for fish methylmercury concentrations using water 

chemistry and land cover data (Chen et al. 2005; Driscoll et al. 2007; and Watras et al. 1998). These 
12

empirical relationships have been used to estimate methylmercury concentrations for different fish 

species at state and regional spatial scales. Such an empirical modeling approach could be used to 

provide more comprehensive estimates of fish methylmercury concentrations across water resources and 

potentially improve the extent of future mercury risk assessments. However, if this empirical modeling 

approach was to be used in a risk assessment such as this, it would need to be developed and evaluated 

at a national scale. Moreover, empirical models would contribute additional uncertainty in the estimation 

of fish methylmercury concentration. The SAB is not recommending that this approach be used for the 

current risk assessment. Rather, the EPA might consider use of empirical modeling to improve the 

information available related to fish methylmercury concentrations in future assessments. 

To strengthen the Technical Support Document the SAB recommends that it be revised to provide a 

better description of the character of the data, as well as the selection of analyzable data (e.g., sizes, 

distribution of fish sizes across watersheds). The SAB also recommends that EPA contact some states 

that receive what the Technical Support Document terms “relatively elevated” mercury deposition from 

U.S. EGU emissions and have limited fish methylmercury measurements to investigate if additional 

recent (since 1999) fish methylmercury data are available to improve the coverage for the mercury risk 

assessment.

3.6. Use of the 75th percentile fish tissue methylmercury value

Question 6: Given the stated goal of estimating potential risks to highly exposed populations, please 

comment on the use of the 75th percentile fish tissue MeHg value (reflecting targeting of larger but not 

the largest fish for subsistence consumption) as the basis for estimating risk at each watershed. Are 

there scientifically credible alternatives to use of the 75th percentile in representing potential 

population exposures at the watershed level?

Response: Using the 75th percentile of fish tissue values as a reflection of consumption of larger, but not 

the largest, fish among sport and subsistence fishers is a reasonable approach and is consistent with 

published and unpublished data on predominant types of fish consumed. While the choice of the 75th 

percentile is reasonable for the estimation of the methymercury levels of consumed fish, the 

appropriateness of this approach depends on the data from which the value is derived. The SAB is 

concerned that around 29% of watersheds sampled have only one fish sample with a fish tissue 

methylmercury concentration available. Figure 1 below shows a plot of the number of fish tissue 

samples available for rivers (N= 1551 samples from rivers, 41.5% have one fish measurement) using data 

provided to the SAB by EPA. There is clear evidence of a very high proportion of samples with only one 

fish.
13

Figure 1. Frequency of fish samples of different sizes for rivers using Excel data provided to the SAB. The x axis 

corresponds to the number of fish tissue observations per HUC. When sample sizes are 20 or greater, a category is 

used i.e. 20 corresponds to 20 to 25, 25 corresponds to 26 to 30, etc. 

Thus, the estimate of the 75th percentile has considerable uncertainty. The use of only one tissue value 

for a given watershed is likely to underestimate fish tissue levels if the single fish collected was, on 

average, smaller than the true 75th percentile, as would occur if the collection were random. Support for 

this notion is provided by Figure 2 below, which relates the 75th percentile fish tissue methylmercury

concentration (on y axis) to the number of fish samples available for any given watershed. The estimate 

of the 75th percentile appears to increase with increasing sample size, thus suggesting that the 75th

percentile fish tissue concentration for watersheds with few fish samples is underestimated.
14

Figure 2. Comparison of 75th percentiles of fish methylmercury concentrations for fish samples across watersheds 

with different sample sizes, using Excel data on rivers provided to the SAB. The fitted curve is based on a Loess 

smoother with smoothing parameter set to 0.2.

The SAB recommends inclusion of a graph depicting the number of tissue samples available for analysis 

by tissue concentration. The SAB also recommends that the document discuss this source of uncertainty, 

including adding a table with the distribution of the number of available fish samples and the fish size 

from which they were obtained across watersheds to indicate the extent of the problem. The Technical 

Support Document should describe in more detail why including fish tissue concentrations from one fish 

sample is likely to result in an underestimate of the number of watersheds at risk. Furthermore, the SAB

recommends that EPA should also conduct a sensitivity analysis using the median fish tissue 

concentration to better represent the distribution of fish tissue methylmercury levels where the sample 

size is one and provide a bound on the risk assessment. The use of other percentiles in the sensitivity 

analysis is not recommended given the limitations of the fish tissue data available.

The SAB acknowledges that fish sampling programs can result in the collection of fish sizes that can be 

either larger or smaller than the actual ecosystem distribution depending on sample collection methods 

and objectives (e.g., states may focus on collection of larger predator fish or areas where higher mercury

levels tend to be found). The SAB recommends that the document describe more clearly the source of 

the fish methylmercury data and provide at least a general discussion of how fish sampling programs 

differ in ways that can contribute variability and uncertainty to the data set, such as fish capture methods 

and criteria for selecting fish to measure methylmercury concentrations. Given that fish sizes are likely a 

variable in most datasets, the report should also include information on the sizes of fish that were 

analyzed. In doing so, the Technical Support Document may be able to quantify the impact, if any, of 
15

the size of fish sampled in watersheds with few fish tissue samples available on estimated mercury

concentrations. The SAB also recommends that the Technical Support Document clarify that the 75th

percentile represents available fish tissue data that may or may not represent the fish in the watershed or 

the fish consumed.

3.7. Consumption rates and location for high-end consumers

Question 7: Please comment on the extent to which characterization of consumption rates and the 

potential location for fishing activity for high-end self-caught fish consuming populations modeled in the 

analysis are supported by the available study data cited in the Mercury Risk TSD. In addition, please 

comment on the extent to which consumption rates documented in Section 1.3 and in Appendix C of the 

Mercury Risk TSD provide appropriate representation of high-end fish consumption by the subsistence 

population scenarios used in modeling exposures and risk. Are there additional data on consumption 

behavior in subsistence populations active at inland freshwater water bodies within the continental 

U.S.?

Response: The SAB finds that the consumption rates and locations for fishing activity are supported by 

data presented in Section 1.3 and in Appendix C of the Technical Support Document. In addition, the 

targeted locations and fish consumption data used in the analysis are generally appropriate and 

reasonable given the available data. The risk assessment uses sources that reported daily consumption 

for populations of low socioeconomic status African- and European-Americans females as the target 

population for the risk assessment. In addition, consumption rates from a study that targeted Laotianand Vietnamese-Americans, previously identified in the central valley of California, are included in the 

assessment, as well as those from a study of Great Lakes tribes. Thus, a diverse range of susceptible 

populations is represented in the assessment. 

The SAB recommends that a few caveats should be acknowledged more fully in the document. The 

main consumption estimates comes from a relatively small survey of individuals attending a fishing 

convention in South Carolina, so the consumption estimates reported in the Burger 2002 study may be 

imprecise, in particular for women. The SAB recommends that the Technical Support Document

acknowledge that, while several estimates of fish consumption rates are used in the risk assessment, 

other estimates reported by Burger could be used. For example, median fish consumption estimates may 

better represent the distribution of fish consumption data than mean estimates. It should also be 

acknowledged that the Burger survey was conducted in 1998, and that fish consumption rates even in 

subsistence populations may have changed.

Another issue raised by the SAB focuses on the seasonality of fish consumption. Data on consumption 

generated from Southern states (e.g., Burger 2002 data from South Carolina) may reflect year-round 

consumption, whereas fishers in Northern states may only fish for nine months a year or less. While 

failure to take seasonality of fishing into consideration could result in overestimation of fish-derived 

methylmercury exposure for some regions, the SAB notes that some communities preserve fish for 

consumption outside the fishing season. It is important to be certain that fish consumption rates used in 

the risk assessment are in the form of annual averages, e.g., consumption rate expressed in terms of 

grams of fish consumed per week per year. Also, it is unclear whether the risk assessment uses

annualized fish consumption rates and whether fish consumption is based on concentrations that are “as 

caught” or “as prepared.” The SAB recommends that this information concerning seasonality be 

clarified in the Technical Support Document. There is a general agreement that the Technical Support 

Document adequately utilizes existing data to identify consumption rates and target populations that are

representative of the most highly exposed susceptible populations.
16

Regarding alternative approaches, the SAB notes that population-based fish consumption rates could be 

applied, although these data tend to show lower fish consumption rates than surveys focusing on 

subsistence and sport-caught fish (Knobeloch et al. 2005). This would tend to underestimate risks and 

would not be consistent with the Technical Support Document objective to target sensitive, highly 

exposed individuals. Therefore, this alternative is not recommended. 

In regard to fish consumption generally, the SAB recommends that EPA better explain its rationale for 

assuming that subsistence consumers eat fish larger than seven inches in length and asks EPA to provide 

references supporting its assumptions and to discuss uncertainties associated with this assumption. 

3.8. Use of Census data to identify high-end fish consuming populations

Question 8: Please comment on the approach used in the risk assessment of assuming that a high-end 

fish consuming population could be active at a watershed if the “source population” for that fishing 

population is associated with that watershed (e.g. at least 25 individuals of that population are present 

in a U.S .Census tract intersecting that watershed). Please identify any additional alternative 

approaches for identifying the potential for population exposures in watersheds and the strengths and 

limitations associated with these alternative approaches (additional detail on how EPA assessed where 

specific high-consuming fisher populations might be active is provided in section 1.3 and Appendix C of 

the Mercury Risk TSD).

Response:

3.9. Use of the Mercury Maps approach

Overall, the SAB agrees that the criterion of using at least 25 persons per census tract from a 

given target population (Laotian, poor Hispanic, American Indian populations, amongst others) is a 

reasonable approach. The approach is driven by the necessity of using existing data to identify 

watersheds with susceptible proximal populations. While the source population selected is somewhat 

arbitrary, the SAB agrees that it is a reasonable approach, and that other approaches may not be as 

effective or feasible. Regardless of what number is chosen, the prevalence of subsistence fishing in the 

target communities remains unknown. EPA indicated that a sample of 25 individuals or greater was 

selected to be reasonably certain that at least one subsistence fisher is potentially active at the watershed.

No major concerns are raised by the SAB concerning this issue. However, the SAB recommends that the 

Technical Support Document clarify how many census tracts were eliminated due to the use of this cut 

point. The Technical Support Document should also include information on the relative distribution of 

the sample size of the susceptible populations in the census tracts that were targeted. That is, an absolute 

sample of 25 may represent different proportions of the total target population in a given census tract, 

which may reflect differences in subsistence fishing behavior. The Technical Support Document should 

also discuss the possibility that more remote waterways are fished by subsistence anglers as well and the 

potential of this uncertainty for underestimating exposures

Question 9: Please comment on the draft risk assessment’s characterization of the limitations and 

uncertainty associated with application of the Mercury Maps approach (including the assumption of 

proportionality between changes in mercury deposition over watersheds and associated changes in fish 

tissue MeHg levels) in the risk assessment. Please comment on how the output of CMAQ modeling has 

been integrated into the analysis to estimate changes in fish tissue MeHg levels and in the exposures and 

risks associated with the EGU-related fish tissue MeHg fraction (e.g., matching of spatial and temporal 

resolution between CMAQ modeling and HUC12 watersheds). Given the national scale of the analysis, 

are there recommended alternatives to the Mercury Maps approach that could have been used to link 
17

modeled estimates of mercury deposition to monitored MeHg fish tissue levels for all the watersheds 

evaluated? (additional detail on the Mercury Maps approach and its application in the risk assessment 

is presented in section 1.3 and Appendix E of the Mercury Risk TSD).

Response:

Regarding the limitations and uncertainty associated with the application of Mercury Maps, it is 

acknowledged that the Mercury Maps approach (i.e., the assumption of proportionality between input 

changes and fish response) represents both a critical element of the analysis and a potentially important 

source of uncertainty. The sensitivity analyses conducted in the risk assessment addresses two specific 

Limitations/uncertainty associated with Mercury Maps (MMaps) approach and 

proportionality assumption. The risk assessment’s qualitative characterization of the limitations and 

uncertainty in the application of Mercury Maps approach is appropriate. The SAB recommends that 

EPA also summarize quantitative estimates of the uncertainty published in the existing literature in 

Appendix F of the Technical Support Document. CMAQ is considered to be the appropriate tool for 

providing the link between EGU emissions and mercury deposition to HUC12 watersheds with 

methylmercury fish data. There are quite a few comparisons, for example, between mercury wet 

deposition as modeled by CMAQ and as observed by the Mercury Deposition Network (e.g., Lin et al. 

2007, Prongprueksa et al. 2008, and Bullock et al. 2009). A similar search of the literature for other 

components of this risk assessment would allow at least partial quantification of the variability or 

uncertainty in this risk assessment, including any literature relating to the time lag in the response of 

waterbodies to changes in mercury deposition (e.g., Munthe et al. 2007).

The Mercury Maps model states that for steady-state conditions, reductions in fish tissue concentrations 

are expected to track linearly with reductions in air deposition to a watershed with an intercept of zero 

for watersheds receiving mercury input exclusively via atmospheric deposition. This proportionality 

assumption is extended for the Technical Support Document study so that methylmercury levels in fish 

could be apportioned among mercury sources based on the associated apportionment of mercury 

deposition within a given watershed. The model is a reduced form of the IEM-2M watershed model used 

in the Mercury Study Report to Congress (U.S. EPA, 1997), whereby the equations of these models are 

reduced to steady state and consolidated into a single equation relating the ratio of current/future air 

deposition rates to current/future fish tissue concentrations.

Given these assumptions, Mercury Maps will work only with watersheds in which air deposition is the 

sole significant source of mercury and steady-state conditions are assumed. This indicates that the 

extension of the proportionality is valid only when other factors influencing methylation potential and 

catch profiles (species and trophic levels) remain relatively constant in a given watershed. Watersheds in 

which mercury input sources other than air deposition, such as mineral recovery operations using 

mercury, mercury cell chloralkali facilities and geologically high mercury inputs, are present and 

contribute loads that are significant relative to the air deposition load to that watershed are set aside from 

analysis in this risk assessment.

Since the Mercury Maps approach was developed, several recent publications have supported the 

finding of a linear relationship between mercury loading and accumulation in aquatic biota (Orihel et al.

2007; Orihel et al. 2008; Harris et al. 2007). These studies suggested that mercury deposited directly to 

aquatic ecosystems can become quickly available to biota and accumulated in fish, and that reductions in 

atmospheric mercury deposition should lead to decreases in methylmercury concentrations in biota. 

These results substantiate EPA’s assumption that proportionality between air deposition changes and 

fish tissue methylmercury level changes is sufficiently robust for its application in this risk assessment. 
18

uncertainties related to application of Mercury Maps: (1) concerns over including watersheds that may 

be disproportionately impacted by non-air mercury sources, and (2) application of the Mercury Maps to 

both flowing and stationary freshwater bodies to verify if the two scenarios would produce different 

results. The results of these sensitivity analyses suggest that uncertainty related to the Mercury Maps 

approach is unlikely to substantially alter the assessment result that mercury emissions from U.S. EGUs 

potentially constitute a public health concern. 

Integration of CMAQ data to HUC12 watersheds for estimating changes in fish MeHg, exposures and 

risks). The use of 12-km spatial resolution in CMAQ modeling is a significant refinement of the 

previous analysis, which was conducted using 36-km resolution. The integration of CMAQ data at this 

finer resolution into the analysis for estimating changes in fish tissue methylmercury levels is sound,

provided that the proportionality assumption holds true (discussed in the previous response to this 

charge question).

CMAQ modeling at a 12-km spatial resolution is used to estimate total annual mercury deposition 

caused by U.S. and non-U.S. anthropogenic and natural sources over each watershed. For the purposes 

of the risk analysis, watersheds are classified using HUC12 codes (USGS, 2009), representing a fairly 

refined level of spatial resolution with watersheds generally 5 to 10 km on a side, which is consistent 

with research on the relationship between changes in mercury deposition and changes in methylmercury 

levels in aquatic biota. Although interpolating the deposition data from a coarser model grid (CMAQ) to 

a finer watershed grid (HUC12) will somewhat diffuse the peak deposition near large point sources, the 

data integration approach is sound.

The CMAQ modeling at 12-km resolution is a considerable (nine-fold) spatial refinement of the 

modeling conducted to support the Clean Air Mercury Rule (36-km resolution). Modeling results at 

finer resolution can be used to better resolve deposition patterns near point sources. The confidence in 

applying the 12-km resolution CMAQ results for estimating fish tissue methylmercury changes and its 

associated exposure/risk is heavily dependent on the robustness of the proportionality assumption in the 

Mercury Maps approach. The limitation and uncertainty of this assumption has been elaborated on in the 

response to the first part of this charge question.

Alternatives to the Mercury Maps approach linking modeled deposition to monitored MeHg fish tissue 

levels. The SAB agrees with the application of Mercury Maps in this assessment. There are other 

modeling tools capable of making a national scale assessment, such as the Regional Mercury Cycling 

Model (R-MCM). However, the R-MCM is more data intensive and the results produced by the two 

model approaches should be equivalent.

The R-MCM, a steady-state version of the time-dependent Dynamic Mercury Cycling Model, has been 

publicly available to and used by the EPA (Region 4, Athens, Environmental Research Laboratory) for a 

number of years. R-MCM requires more detail on water chemistry, methylation potential, etc., and

yields more information as well. Substantial data support the Mercury Maps and the R-MCM steadystate results, so that the results of the sensitivity analysis and the outcomes from using the alternative 

models would be equivalent between the two modeling approaches. Though running an alternative 

model framework may provide additional reassurance that the Mercury Maps “base case” approach is a 

valid one, it is unlikely that substantial additional insight would be gained with the alternative model 

framework.
19

3.10. Exclusion of watersheds with significant non-air loadings

Question 10: Please comment on the EPA’s approach of excluding watersheds with significant non-air 

loadings of mercury as a method to reduce uncertainty associated with application of the Mercury Maps 

approach. Are there additional criteria that should be considered in including or excluding watersheds?

Response:

• Watersheds that are near urban areas, since those may have significant mercury inputs from 

runoff which are not included in the TRI reporting database, and

The technique used to exclude watersheds that may have substantial non-air inputs is sound.

Although additional criteria could be applied, they are unlikely to substantially change the results.

EPA excludes those watersheds that either contained active gold mines or had other substantial non-U.S. 

EGU anthropogenic releases of mercury. Identification of watersheds with gold mines is based on a 

2005 USGS data set characterizing mineral and metal operations in the United States. The data represent 

commodities monitored by the National Minerals Information Center of the USGS, and the operations 

included are those considered active in 2003. The identification of watersheds with substantial non-EGU 

anthropogenic emissions is based on a Toxic Release Inventory (TRI) net query for 2008 non-EGU 

mercury sources with total annual on-site mercury emissions (all media) of 39.7 pounds or more. This 

threshold value corresponds to the 25th percentile annual U.S. EGU mercury emission value as 

characterized in the 2005 National Air Toxics Assessment. The EPA team considers the 25th percentile 

U.S. EGU emission level to be a reasonable screen for additional substantial non-U.S. EGU releases to a 

given watershed.

This appears to be a sound approach. The caveat is that TRI reporting may be biased high or low by the 

reporting entities, so it is not possible to judge whether the exclusion is reasonably conservative or not. 

There is no particular step EPA can take to rectify this uncertainty, although sensitivity tests could be 

run on different reporting thresholds and the number (and area) of excluded watersheds that result. As a 

minimum, the uncertainty in the TRI should be acknowledged, and the number of watersheds excluded 

in the base case and the uncertainty analysis should be explicitly stated.

Other criteria that EPA could consider for exclusion of particular watersheds are:

• Watersheds that are excessively polluted, for example by sanitary sewer discharges or highly 

anoxic conditions that might deter overall consumer fishing by many users.

3.11. Concentration-response function used in modeling IQ loss

Question 11: Please comment on the specification of the concentration-response function used in 

modeling IQ loss. Please comment on whether EPA, as part of uncertainty characterization, should 

consider alternative concentration-response functions in addition to the model used in the risk 

assessment. Please comment on the extent to which available data and methods support a quantitative 

treatment of the potential masking effect of fish nutrients (e.g. omega-3 fatty acids and selenium) on the 

adverse neurological effects associated with mercury exposure, including IQ loss. Detail on the 

concentration-response function used in modeling IQ loss can be found in section 1.3 of the Mercury 

Risk TSD.

Response: As noted in the response to questions 2 and 3, the analyses of IQ should assume a less 

important role in the final document than in the present one. Question 11 contains three questions 

pertaining to the concentration-response function describing methylmercury’s effect on IQ. The 

response to the first question is that the rationale for the concentration-response function is appropriate, 
20

but with qualifications noted below. The response to the second question is that there is no alternative 

concentration response function that should be considered, but the analysis should be tempered, 

qualitatively, by factors that could influence the shape of the concentration function. The response to the 

third question is that masking by fish nutrients could influence the shape of the concentration response 

function, but there is not sufficient information to recommend a quantitative adjustment. These three 

responses are expanded upon in order below. 

The specification of the concentration-response function. The function used comes from a paper by 

Axelrad and Bellinger (2007) that seeks to define a relationship between methylmercury exposure and 

IQ. A whitepaper by Bellinger (Bellinger 2005) describes the sequence of steps in relating 

methylmercury exposure to maternal hair mercury and then hair mercury to IQ. The Technical Support 

Document furthers notes that IQ has shown utility in describing the health effects of other 

neurotoxicants. These are appropriate bases for examining a potential impact of reducing methylmercury

on IQ, but the SAB does not consider these compelling reasons for using IQ as a primary driver of the 

risk assessment. Instead, IQ should serve as a secondary measure along with other measures discussed in 

the responses to questions 2 and 3. The modeling of the impact of IQ should be placed in the appendix 

and accompanied by the qualifications noted below.

Alternative Concentration Response functions. The concentration-response function derived by Axelrad 

and Bellinger (2007) is acceptable for use in supplementary analyses in the Technical Support 

Document. It should be noted, however, that this function is likely to underestimate the effect on IQ of 

reducing mercury deposition for the reasons itemized here and in the response to charge question 2.

There is another reason that a model based on a linear relationship between exposure and 

neurobehavioral effect may underestimate the true effect of reducing exposure. It is evident from animal 

studies conducted under highly controlled conditions that the relationship between daily intake and brain 

mercury (the most suitable biomarker of exposure) is not linear, but rather is a power function with a 

power coefficient that is greater than 1.0; the power coefficient was 1.3 in a review of animal studies

(Newland et al. 2008). This means that a decrease in intake will produce a greater-than-linear decrease 

in brain concentration. Thus, the impact of any reductions produced by reducing mercury emissions 

could be underestimated by the linear model used in the document. This observation is not intended to 

suggest that a new model be used, only that a qualitative argument should be made that the potential 

health impact may be underestimated. 

A quantitative treatment of the mitigating impact of nutrients. The factors listed in this section could 

mitigate the concentration-effect relationship and should be mentioned in the Technical Support 

Document, but there is not enough known about their quantitative impact to support a recommendation 

of a re-analysis. 

There is evidence from the Seychelles study that nutrients can mask effects of prenatal methylmercury

exposures. Davidson et al. (2008), Strain et al. (2008) and Stokes-Riner et al. (2011), demonstrated that 

maternal hair mercury was associated with protein disulfide isomerase only after controlling for the 

effects of maternal omega-3 polyunsaturated fatty acid (PUFA) status. Controlling for omega-3 PUFAs 

steepened the slope of the concentration effect relationship (Strain et al. 2008). These nutrients are found 

in many marine fish species, but less is known about their concentration in freshwater fish and the 

concentrations may be lower. This issue is important because the concentration-effect relationship used 

in the Technical Support Document analysis derives from the consumption of marine fish but it is 

applied in the Technical Support Document to the consumption of freshwater fish. Since the slope might 
21

be steeper with freshwater fish, it is possible that the analysis in the Technical Support Document

underestimates the impact of reducing mercury deposition on consumers of freshwater fish.

Not only do omega-3 PUFAs mask methylmercury’s neurotoxicity, but they confer benefits of their own 

that are of direct interest in considering the health impact of fish consumption. The studies by Oken et 

al. (2005, 2008) directly compared the benefits of fish consumption with the hazards associated with 

methylmercury exposure. These provide further evidence that the benefits of consuming marine fish 

may mask methylmercury’s effects, a conclusion that is directly relevant to freshwater fish. 

One SAB member points out that methylmercury is a potent inhibitor of multiple families of seleniumdependent enzymes that are required by the brain and endocrine system (Carvalho et al. 2008; 2011; 

Seppanen et al. 2004; Ralston and Raymond 2010). Therefore, the adverse effects of high 

methylmercury exposures on these enzymes could be accentuated among populations with poor 

selenium nutritional intakes and diminished among those with rich selenium status. Since the 

subsistence fish consumers that form the focus of this study are at notable risk of having poor nutrition, 

mercury exposures may be non-linearly related to toxicity risks. Other SAB members note that effects of 

selenium on methylmercury toxicity are based primarily on observations in animals, and there is 

disagreement in the scientific community regarding the significance of these observations to humans. 

The same SAB member also suggests that since selenium abundance is largely observed to be inversely 

related to mercury bioaccumulation (Chen et al. 2001; Paulsson and Lindberg 1989; Belzile et al. 2004), 

diminishments in fish methylmercury concentrations following reductions in mercury deposition will 

not be uniform across watersheds. Selenium's inverse relationships to methylmercury bioaccumulation 

and toxicity may interact to exacerbate mercury exposure risks in watersheds with low selenium 

availability. This SAB member thinks that special consideration should be given to evaluating potential 

health risks from consumption of fish with high mercury contents that originate from watersheds in low 

selenium regions. Other SAB members note that the Mercury Maps (proportional response) approach is 

not affected by spatial differences in fish methylmercury content, and in fact this is one of the strengths 

of this approach. Changes in fish methylmercury concentrations may differ among aquatic ecosystems in 

absolute terms when mercury loading declines, depending upon whether initial fish concentrations are 

high or low. However, the reductions in fish methylmercury concentrations within these watersheds are 

nonetheless expected to be proportional to the decreases in loading.

Additional Point. Finally a statement on Page 84, Table F-2 references the Seychelles study instead of 

the New Zealand study. This should be corrected. The statement is: “Regarding outliers, when an outlier 

data point from the New Zealand study was included in the integrated derivation of the IQ loss slope 

factor, the factor was reduced by 25 percent (from -0.18 IQ points per unit ppm hair mercury, to -

0.125).” This uncertainty should be acknowledged more explicitly in the body of the document rather 

than being merely mentioned in detail in a table in the Appendix. No additional analyses in the 

Technical Support Document are necessary; it could just be mentioned in the section on limitations and 

uncertainties that risk assessment estimates would be reduced by 25%.

3.12. Uncertainty and variability

Question 12: Please comment on the degree to which key sources of uncertainty and variability 

associated with the risk assessment have been identified and the degree to which they are sufficiently 

characterized.
22

Response:

Sample Calc – High US EGU Impact (2016)

3

IR = FTC * FIR * CAF

BW 

Example – lake watershed on OH-WV border

(HUC12 ID: 50302011009)

IR: daily MeHg intake rate (ug/kg-day)

FTC: MeHg fish tissue concentration (ug/g or ppm)

FIR: fish ingestion rate (g/day)

CAF: cooking adjustment factor

RfD: methylmercury RfD

FracEGU: fraction dep from US EGU

Fish tissue (ppm) 2005

Mean: 0.15

75th : 0.19

90th : 0.32

Samples: 44

PROJECTED 

Fish tissue 

(ppm) 2016

Mean: 0.13

75th : 0.16

90th : 0.27

Total Hg dep

over watershed

(CMAQ): 

2005 = 39.55

2016 = 33.07

Or a 14.8% reduction

95th% ingestion rate:

172 g/day

Cooking adjustment

Factor:

1.5

Body

weight:

64 g

IR = 0.65 ug/kg-day

MeHg RfD:

0.1 ug/kg-day

Total HQ = IR = 0.65 = 6.5

RfD 0.1

2016 Hg dep

over watershed

(CMAQ): 

total = 33.07

US EGU = 7.97

US EGU is 23.6%

FracEGU = 0.236

US EGU risk = HQ * FracEGU

= 6.5 *0.236 = 1.5

1

2

3

4

5

6

To answer this question, the SAB defines variability and uncertainty according to EPA's 

standard usage, which is consistent with the definitions given by Cullen & Frey, 1999. These definitions 

are as follows:

"Variability refers to temporal, spatial, or interindividual differences (heterogeneity) in 

the value of an input. In general, variability cannot be reduced by additional study or 

measurement."

"Uncertainty may be thought of as a measure of the incompleteness of one's knowledge 

or information about an unknown quantity whose true value could be established if a 

perfect measuring device were available."

The Technical Support Document presents a qualitative overview of variability and uncertainty in 

Appendix F. The qualitative nature of the discussion is appropriate since this is a conditional analysis. 

However, the SAB recommends an expanded discussion in Appendix F of variability and uncertainty to 

make explicit the uncertainties associated with the Agency’s key analytical choices, which the SAB

supports. This discussion could be organized according to the figures depicting sample calculations of 

high and low EGU impact that were provided at the SAB’s public meeting on June 15, 2011 and 

reproduced below (see Figures 3 and 4, next page). The SAB recommends that these figures be added to 

the report along with an explanation of how the calculations were conducted.

Figure 3: U.S. EPA-provided (June 16, 2011) schematic showing sample calculation – high U.S. EGU impact
23

Sample Calc – Low US EGU Impact (2016)

4

IR = FTC * FIR * CAF

BW 

Example – river watershed on NY

(HUC12 ID: 20200040205)

IR: daily MeHg intake rate (ug/kg-day)

FTC: MeHg fish tissue concentration (ug/g or ppm)

FIR: fish ingestion rate (g/day)

CAF: cooking adjustment factor

RfD: methylmercury RfD

FracEGU: fraction dep from US EGU

Fish tissue (ppm) 2005

Mean: 0.27

75th : 0.42

90th : 0.47

5 samples

PROJECTED 

Fish tissue 

(ppm) 2016

Mean: 0.25

75th : 0.40

90th : 0.44

Total Hg dep

over watershed

(CMAQ): 

2005 = 15.46

2016 = 14.63

Or a 5.4% reduction

95th% ingestion rate:

172 g/day

Cooking adjustment

Factor:

1.5

Body

weight:

64 g

IR = 1.61 ug/kg-day

MeHg RfD:

0.1 ug/kg-day

Total HQ = IR = 1.61 = 16

RfD 0.1

2016 Hg dep

over watershed

(CMAQ): 

total = 14.63

US EGU = 0.20

US EGU is 1.4%

FracEGU = 0.014

US EGU risk = HQ * FracEGU

= 16 *0.014 = 0.2 (i.e., <1)

1

2

3

4

5

6

Figure 4: U.S. EPA-provided (June 16, 2011) schematic showing sample calculation – low U.S. EGU impact

In addition to the explicit discussion of variability and uncertainty, the SAB suggests that language be 

used throughout the Technical Support Document to clarify the scope of the results vis-à-vis variability 

and uncertainty in data and methods. For example, the Technical Support Document should cite the 

evaluation of uncertainty in the CMAQ and MMAPs source documents. Notwithstanding the 

uncertainties in the approach, the SAB considers the approach presented in the Technical Support 

Document sound and reasonable.

Variability. The SAB notes the topics covered in Appendix F regarding variability. The clarity of the 

documentation of the impact of individual sources of variability could be improved. Carefully selected 

maps and additional figures could be particularly helpful in providing this clarity. The SAB recommends 

that the following sources of variability be included in Appendix F to avoid misinterpretation of study 

results and outcomes.

• The effect of temporal variability in the following on estimates of mercury deposition

o Appendix F should describe CMAQ boundary conditions that are necessary to 

establish in order to run the model for the 2 temporal scenarios

• Variation in geographic patterns of populations of subsistence fishers.

o Appendix F addresses geographic variability in total and U.S. EGU-attributable 

mercury deposition and fish tissue concentrations. Appendix F should be expanded to 

discuss spatial variability in populations of subsistence fishers, noting the limited 

geographic coverage of watersheds with fish tissue concentrations.

• Variability in nature and protocols of state collection of fish data (see the response to 

Question 5, also mentioned below).

• Variation in fisher populations; for example, variation in body weights (potentially across 

race/ethnicities) and fishing and consumption habits.
24

• Variability in the factor used to translate mercury concentration measured at time of 

collection (i.e., expressed per unit wet weight) in comparison to mercury concentration at 

point of consumption following cooking. 

Uncertainty. Appendix F defines sources of uncertainty for several components of the overall approach 

and selected parameter characterizations. The level of uncertainty is consistent with a screening level 

analysis. The SAB advises EPA to strengthen the discussion of each uncertainty presented by 

identifying at least qualitatively the direction of its effect on the overall risk assessment. For example, 

the small fish sample sizes results in underestimates of the 75th percentiles, which propagates to 

conservative underestimates of risk. 

The SAB has discussed some sources of uncertainty in responses to other Charge Questions (e.g., 

Question 9). To summarize, the SAB recommends that Appendix F be expanded to provide a more 

complete listing and discussion of key uncertainties associated with the assessment. Additional sources 

of uncertainty that should be considered for expanded discussion include:

• Overall emission inventories, especially the non-EGU inventory derived as a modified 

version of the National Emissions Inventory (NEI). Appendix F should discuss the 

uncertainties in inventory components; whether and how the uncertainty changes between the 

2005 to 2016 scenarios, including uncertainties in the TRI database; whether there is bias in 

the EGU and non-EGU components of the inventory; and whether the EGU emission 

estimates are derived from the best performing facilities or from the complete set of facilities.

• Alternative future scenario forecasts. Appendix F should more clearly describe the variables 

that are held constant versus factors that are varied between the two scenarios.

• Uncertainty in location of 2016 emissions reductions. Due to EPA's projection methods, 

there is uncertainty about where emissions reductions will occur between 2005 and 2016, 

which in turn influences the spatial patterns of deposition from EGUs in the 2016 scenario. 

Appendix F should address the uncertainties in the 2016 scenario regarding the specific 

geographic locations of reductions in EGU-derived mercury deposition as a fraction of total 

mercury deposition.

• Use of CMAQ and performance evaluation of CMAQ. Appendix F should provide more 

detailed description of uncertainty in CMAQ, including references to existing evaluations of 

the model.

• “Hot spots” 

o Appendix F should address whether the Mercury Maps approach, as implemented, is 

adequate to characterize the existence and extent of mercury “hot spots."

• Impacts of excluding watersheds from the analysis. 

o Appendix F should detail the criteria used for excluding watersheds, characterize the 

watersheds excluded by different criteria, and describe the estimated deposition in 

these watersheds. 

• Representativeness of approximately 2,500 watersheds compared to 88,000 HUC12 

nationwide. 

o Appendix F should characterize any bias introduced by looking at this subset of 

watersheds (e.g., some states are over-represented, such as Indiana and Minnesota, 

while others are under-represented such as Pennsylvania).

• Fish populations and fish tissue database (see SAB responses to questions 5, 6 and 13 for 

more detail). Appendix F should include discussion of:

o Sample size for characterization of Implications of a data set with a low number of 

fish per watershed. Appendix F should identify the distribution of fish samples per 
25

watershed and the possible implications of this distribution, including the 

implications of sample size for characterization of 75th percentile fish tissue 

concentration.

o Uncertainty in methylmercury fish tissue concentrations from differences in sampling 

and analytical protocols used by States that contribute data and errors introduced by 

potential misidentification of locations, etc. 

• Adjustment between wet and cooked weight of fish. EPA relies upon a single older study to 

derive an adjustment factor of 1.5. Alternative and newer peer reviewed studies of cooking 

effects on mercury in fish should be acknowledged (e.g., Musaiger et al. 2008; Farias et al.

2010) and used to discuss uncertainty associated with this assumed value. 

o Appendix F should note that this is a constant value applied in the calculation and 

thus does not bias but could skew the results.

• Uncertainty of the assumption of proportionality and the MMAPs approach (see SAB

response to Question 9).

• Characterization of susceptible human populations (see SAB responses to Questions 7 and 8)

o Characterizing subsistence fishing activity within high EGU deposition sites.

o Implications of choosing subsistence fishers and excluding high-end sport fishers. 

o Census information that may exclude groups such as students, immigrants).

• Fish consumption rates (see SAB Response to Question 7).

o Limitations of the single study used to support the Technical Support Document’s 

fish consumption rate for female subsistence fishers.

o Size of fish consumed.

• Derivation of the concentration-response relationship and RfD based on data from marine 

fish and mammal species, not inland freshwaters. 

o Appendix F should discuss the uncertainty introduced by not using RfDs derived 

based on studies of consumption of fish from inland freshwaters. (See SAB response 

to Question 11). 

• Applicability of the concentration-response relationship and RfD for low socio-economic 

status populations. This relationship has not been examined. 

o Appendix F should discuss how this relationship may bias the report toward

underestimating risk.

• Effect of the nutritional benefits of fish consumption in comparison to risks from mercury. 

Appendix F should address how the lack of consideration of this factor that may bias the 

analysis toward underestimating risk (see SAB response to Question 11).

3.13. Discussion of analytical results

Question 13: Please comment on the draft Mercury Risk TSD’s discussion of analytical results for each 

component of the analysis. For each of the components below, please comment on the extent to which 

EPA’s observations are supported by the analytical results presented and whether there is a sufficient 

characterization of uncertainty, variability, and data limitations, taking into account the models and 

data used.

Mercury deposition from U.S. EGUs

Response: EPA’s observations in section 2.3 of the Technical Support Document (p. 35)1

 1 Section 2.3 observations:

are generally 

supported by EPA's observations about mercury deposition as depicted in analytical results provided to 
26

the SAB by EPA following the SAB meeting in the form of a Memorandum from Zachary Pekar, July 1, 

2011, entitled “Clarification and Updating of Mercury Deposition Maps Provided in the Technical 

Support Document: National-Scale Mercury Risk Assessment.” The SAB supports EPA’s plan to 

include updated figures from the memorandum in EPA’s Technical Support Document as replacements 

for Figures 2-1 to 2-4 in the March 2011 draft so that they correctly reflect total annual mercury

deposition per square-meter by watershed. The SAB recommends that the spatial patterns of simulated 

deposition shown in Figure 2-1 to 2-4 be better explained and that EPA should characterize data 

limitations more effectively.

EPA’s observations about mercury deposition as depicted in Technical Support Document Figures 2-1 

to 2-4 are supported by analytical results. However the 12-km deposition maps are very different than 

previously produced maps on the 36-km scale (for example in Texas and Nevada). The SAB

recommends that EPA explain these differences and that EPA consider including separate maps of wet 

and dry deposition and/or aggregating the results into an approximately 36 km grid scale for comparison 

to earlier maps and to data plots, such as national deposition maps from the Mercury Deposition 

Network.

In general, the uncertainties associated with these results are not well characterized or adequately 

quantified. For example, there have been several intercomparison studies among numerical models for 

long-range transport of mercury and studies on model uncertainty evaluation that are not discussed or 

referenced. The SAB recommends that EPA summarize these references (Bullock et al. 2009; 

Pongprueksa et al. 2008; Lin et al. 2007; Ryaboshapko 2007) to help frame the overall uncertainty of the 

deposition estimates.

In addition, EPA should discuss more completely the inputs that are kept constant for the 2016 scenario 

and the inputs that are varied (and by how much). This information may merit discussion earlier in the 

report. In addition, the CMAQ results are very dependent on global boundary conditions that are 

supplied by the GEOS-Chem model. Uncertainty in those inputs will be carried through to the results.

This should be noted.

Fish tissue methyl mercury concentrations

Response: The observations listed in section 2.4 of the Technical Support Document (pp. 43-44)2

 

• Patterns of total and U.S. EGU-related Hg deposition differ considerably.

are 

generally supported by the analytical results. The SAB recommends that EPA clarify the text to improve 

the description of the analytical results for each bulleted observation as described below.

• US Hg deposition is generally dominated by sources other than U.S. EGUs (with the contribution from U.S. EGUs 

decreasing between the 2005 and 2016 scenarios).

• The contribution of U.S. EGU deposition to total deposition does vary across watersheds and can represent a 

relatively large fraction in some (more limited) instances. 2 Section 2.4 observations:

• Focus on U.S. EGU-attributable Hg fish tissue levels is in the eastern half of the U.S.

• U.S. EGUs contribute a larger fraction to total Hg fish tissue levels in the U.S. than they do to total Hg deposition 

(in terms of percent), this reflects the fact that Hg fish tissue samples are focused in the east where U.S. EGU 

deposition is greater.

• Relative to the combined impact of other sources, U.S. EGUs represent a smaller, but still potentially important 

contributor to total fish tissue MeHg levels.

• Despite the relatively small fraction of total fish tissue MeHg associated with U.S. EGUs on average, for a subset of 

watersheds, they can make a substantially larger contribution.
27

Although there is sufficient characterization of variability, EPA should characterize uncertainty and data 

limitations more fully. Specifically, the small sample sizes of mercury concentrations in fish for the 

individual watersheds (~29% of watersheds have n=1) will result in lower estimates of mercury

concentrations in the 75th percentile as shown earlier in Figures 1 and 2 in this SAB report. This data 

limitation bias will be propagated to underestimate the hazard in the risk assessment.

The text in the observations should be modified to refer to tissue and mercury “concentrations” rather 

than “levels” to be more precise. “Level” is a generic term and can refer to any number of different 

metrics. Finally, where the percentages of EGU-contribution to fish methylmercury are mentioned, EPA 

should clarify that those values pertain to only fish-sampled watersheds. Given the under-sampling in 

watersheds where there are high levels of deposition, the percentages indicated could be higher. 

Some figures and tables would also benefit from modification or elimination. Figures 2-7 to 2-10 are 

difficult to interpret because the symbols do not reflect the number of observations for that site. 

Improved plots should display symbols proportional to sample size and provide color or shading of 

symbols to represent observed fish concentrations. In addition, the maps shown in Figures 2-7 to 2-14 

need to include the western continental United States. These figures unnecessarily cut off the western

continental United States. While the SAB understands the reason for this omission (there is minimal 

expected change in EGU emissions in the western United States), it is important to show the results for 

the entire United States in the figures of this national assessment. In the absence of national maps, the 

reader (especially someone with interest in the western United States) many be left wondering about 

current fish methylmercury concentrations in this region (see Figure 2-6), as well as the model predicted 

changes in fish methylmercury for the 2016 scenario. 

The legend for Figure 2-8 should make it clear that the 2016 mercury tissue concentrations are computed 

by adjusting the 2005 concentrations to account for lower expected deposition as per the Mercury Maps

approach. The third bullet item on page 36 of the Technical Support Document should be corrected to 

indicate that Figures 2-7 and 2-8 give concentrations of mercury in fish, not total mercury deposition. In 

addition, the figures showing the top 10th percentile (2-11 to 2-14) should be removed since the pattern 

of mercury is greatly affected by high sampling effort in South Carolina, Indiana, West Virginia and 

Louisiana. The current maps could also result in undue public concern in those states. Finally, the text 

describing Table 2-5 needs to be clarified to state that the relationships are not causal.

Patterns of mercury deposition with mercury fish tissue data

Response: Overall, the SAB agrees that the observations in section 2.5 of the Technical Support 

Document (pp. 48-49)3

 3 Section 2.5 observations:

are supported by the analytical results presented and there is a sufficient 

characterization of uncertainty, variability, and data limitations. However, a number of changes are 

needed to better clarify these points. The Technical Support Document should clearly describe the 

degree to which the non-uniform, state-specific data availability influences this analysis. For example, 

South Carolina, Louisiana and Indiana all have abundant data availability compared to most states. EPA 

should discuss how this data availability bias affects the analytical results. The SAB recommends that 

• The fish tissue MeHg sampling data (summarized at the watershed-level) provides limited coverage for areas with 

elevated U.S. EGU Hg deposition. Therefore, the number of “at risk” watersheds as characterized in this risk 

assessment may be substantially higher than estimated.

• Hg fish tissue levels are not correlated with total Hg deposition (the relationship is highly dependent on methylation 

potential of individual waterbodies).

• Hg fish tissue samples were generally collected in regions with elevated total Hg deposition.
28

this section be substantively rewritten to improve clarity and to highlight the major relevant points. As 

discussed below, EPA should revise the text in footnote 36, which is critical to the understanding of 

Figures 2-15 and 2-16, and yet is not clearly enough written for the reader to understand the key 

information. Also, the figure legends within each of Figures 2-15 and 2-16 need to be changed because 

the “blue areas” are not “water bodies,” but rather “watersheds,” which include water bodies that 

sometimes are more obvious than their watersheds (e.g., the Minnesota portion of Lake Superior, Long 

Island Sound and perhaps erroneously, the Canadian portion of Lake Champlain). The SAB

recommends that these two maps be replotted with a third color that clearly identifies the areas of 

overlap.

Figure 2-17 is critically important not only to this section, but also to the overall document. The SAB

suggests that this figure could be brought into this document much earlier because it adds value to 

understanding the lack of direct relationships between deposition and mercury in fish. In a sense, it 

frames the justification for the approach taken in the overall analysis. The SAB recommends that EPA 

provide a more complete introduction to Figure 2-17 that would state the important premises of the 

analysis applied in this risk assessment - that spatial variability of deposition rates is only one major 

driver of spatial variability of fish methylmercury and that variability of ecosystem factors that control 

methylation potential (especially wetlands, aqueous organic carbon, pH, and sulfate) also play a key 

role. A question was also raised as to whether Figure 2-17 has been truncated, and if so, did it need to 

be? That is, are there data above 1.0 ppm fish concentration and 40 ug/m2-yr deposition? The SAB

suspects that there are. 

Figure 2-18 could similarly be moved to an earlier section of the document because it indicates that the 

analysis identified watersheds with higher rates of deposition than the national (~88,000 HUC 12 

watersheds) trend and that the watersheds with available fish data are in fact, those with higher EGUderived mercury deposition rates.

The red areas of Figures 2-15 and 2-16 are labeled in each map’s legend as “Watersheds with relatively 

elevated US EGU Hg dep.” Footnote 36 explains how the red areas are identified, an explanation that is 

densely written, as follows:

Footnote 36

It is troublesome that footnote 36 implies that the threshold for what constitutes “relatively elevated U.S. 

EGU Hg deposition” is different in the two maps. The red area in Figure 2-15 is characterized as an 

average deposition rate of 3.79 and for Figure 2-16, 1.29 ug/m2

. The next, and last, sentence is 

confusing, and implies that 3.79 and 1.29 are used as thresholds for identifying the red areas: “These 

values were used as the basis for identifying watersheds...” This characterization may confuse readers, in 

that readers probably expect similarly colored geographic areas in adjacent similar maps to be presented 

as portraying quantitatively similar environmental information, an expectation that these maps 

. Areas of “elevated U.S. EGU-related Hg deposition” refer to areas that are at or above 

the average deposition level seen in watersheds with U.S. EGU-attributable exposures above the 

MeHg RfD. Specifically, we used exposure estimates based on the 95th percentile fish consumption 

rate (for the female high consumer scenario assessed nation-wide) to identify watersheds with U.S. 

EGU-attributable exposures above the MeHg RfD and then queried for the average U.S. EGUrelated Hg deposition across that subset of watersheds. This average deposition rate differed for the 

2005 and 2016 Scenarios (i.e., 3.79 and 1.28 ug/m2, respectively). These values were used as the 

basis for identifying watersheds with levels of U.S. EGU-related Hg deposition for the 2005 and 

2016 Scenarios presented in Figures 2-13 and 2-14.
29

apparently do not meet. The SAB suspects that the deposition rate threshold for inclusion in the map is 

probably relatively constant, and communicating the threshold would be a more useful characterization 

than describing the average deposition rates, which are different for understandable, but unexplained, 

reasons. For any given watershed, the threshold is the EGU-attributable deposition rate that produces 

EGU-attributable exposure “above the MeHg RfD.” In practical terms for this risk assessment of 

subsistence fishers, this threshold is a modeled EGU-attributable increment in fish concentration that is 

greater than 0.038 ppm methylmercury, a concentration that does not correspond to a constant mercury

deposition rate because the concentration varies among watersheds in accordance with the 

proportionality principle described in the risk assessment. However, the SAB notes that the average 

mercury deposition rate that produces this incremental methylmercury concentration will be similar 

between the 2005 and 2016 scenarios. If so, the red areas could then be characterized, for example, as 

“elevated U.S. EGU-related mercury deposition that refers to areas where deposition from EGU 

emissions has the potential, even in the absence of mercury from other sources, to cause exposures 

above the methylmercury RfD.” The average threshold EGU-attributable mercury deposition rate for 

exceeding the threshold could be presented, along with the average deposition in the red area. The 

revised document should explain why the average deposition rate is lower in the 2016 scenario red area, 

rather than assume that the reader will immediately know why. 

However the red area is dealt with, a more complete and understandable explanation needs to be 

presented than the current explanation of footnote 36.

Percentile risk estimates

Response. Generally, the percentile risk estimates in 2.6.1 are calculated in a reasonable manner and the 

observations on pages 53-54 of the Technical Support Document 4

 4 Section 2.6.1 observations:

are appropriate. The Technical 

Support Document especially provides a useful discussion of the uncertainties of high values in Tables 

2-5 and 2-7. The SAB has several suggestions to improve the presentation of the material and results for 

other parts of section 2.6.1.

The Technical Support Document should include an explanation of why the values in Tables 2-6 and 2-7 

decrease when going from the 50th to 75th percentile. This is likely because the ranked risk values are 

not the same as the ranked EGU contributions. This difference should be mentioned. Perhaps the tabled 

values should be referred to in some way as averaged.

The values in Tables 2-6 and 2-7 are based on averaging the values that are 2.5% below and 2.5% 

above. EPA should consider whether it is better to use a 2.5% range or use the 10 nearest values. EPA 

should also describe how the range is selected for the 99th percentile.

• For the high-end female consumer assessed at the national-level, total IQ loss and total HQ estimates do not change 

in a systematic way between the 2005 and 2016 Scenarios with these levels often being of potential health concern 

across a wide variety of consumption rates and watershed percentiles.

• By contrast (again focusing on the high-end female consumer assessed nationally), both U.S. EGU-incremental IQ 

loss and the U.S. EGU increment-based HQ display notable reductions between the 2005 and 2016 Scenarios, but 

U.S. EGU-attributable risk still exceeds potential levels of concern for a over a quarter of watersheds.

• Estimates of risks generated for the high-end female consumer population (assessed at the national-level) are 

generally higher than risks estimated for the other high-end fisher populations, with the exception of white and black 

fisher populations assessed in the southeast.
30

In section 2 page 54, the paragraph comparing "risks" for high-end females with other populations is 

oversimplified. Depending on the percentiles considered, "risks" for Laotians, Vietnamese and Tribal 

fish consumers can also be higher than for high-end females. The highest consumption rates should be 

summarized in an appendix.

In section 2 page 55, EPA should provide more information on the gold-mining impacted watersheds in 

the Southeast. For example, it seems that gold mining occurred historically in a relatively small region 

of South Carolina, and only a few mines have recently been re-activated. Is it really appropriate to 

discount or question concerns about EGU affected exposure across the whole Southeast on this basis?

In Tables 2-6 and 2-7, EPA should consider reporting consumption rates and putting the percentiles in 

parentheses rather than reporting the percentiles and having the rates in parentheses.

In Table 2-15 and other places, the mean is included. Since the mean is not a percentile, the table header 

should be changed or the median used.

Number and frequency of watersheds with populations potentially at risk due to U.S. EGU mercury 

emissions

Response: The SAB has no significant concerns regarding the observations in section 2.6.2 of the 

Technical Support Document (pp. 57-58).

5

With regard to the target population in a broader context, the size of the potentially impacted population 

is a key factor to consider in this risk assessment. This issue is outside the scope of the data available for 

the risk assessment, even though it is very relevant to the objectives of the Technical Support Document

and its application to public health policy. The document focuses on subsistence fishing populations as a 

target population likely to be the most severely impacted by methylmercury consumption in fish. There 

is scant evidence documenting the prevalence or extent of subsistence fishing in the United States. Some 

SAB members note similarities in consumption rates among sport fishers and subsistence fishing 

populations. The inclusion of sport fishers with relatively higher fish consumption rates could expand 

the size and extent of the targeted susceptible population. Similarly, only limited information on the 

locations or characteristics of watersheds that are excluded from the analysis is provided (p. 63, bullet 4, 

Figs 2-15, 2-16). The SAB suggests that more detailed information be included regarding these 

watersheds and the uncertainties associated with their exclusion. In addition, the document should 

The SAB recommends that language be added regarding the 

change in the percentage of watersheds that continue to be above the RfD (or above a change in one to 

two IQ points, if this aspect of the risk assessment is retained) after EGU emissions are removed.

Furthermore, on the SAB recommends that the first bullet point on page 57 to change the language 

“before taking into account deposition...” to something that does not imply temporality (e.g., “when you 

factor out other sources of mercury deposition”). The SAB also recommends that if the document 

discusses loss of IQ points, the revised document should refer to this change in relation to “populations 

living close to watersheds” rather than “watersheds”.

 5 Section 2.6.2 observations:

• Less than 1% of the watersheds have an IQ loss of 1 point when deposition from U.S. EGUs is considered before 

taking into account deposition and exposures resulting from other sources of Hg.

• Between 2 and 12% of the watersheds have HQs ≥ 1.5, based on U.S. EGU mercury deposition before factoring in 

any other sources of mercury.

• Combining the two categories of watersheds with populations at-risk due to U.S. EGU mercury emissions 

summarized in the last two bullets, we get a total estimate ranging from 2 to 28% of watersheds, with this range 

reflecting in part the U.S. EGU percent contribution that is considered (e.g., 5, 10, 15 or 20%).
31

address the excluded watersheds within the context of predicted mercury deposition patterns. Some 

enumeration of the extent to which the target population would be expanded if these factors had been 

incorporated into the analysis would help provide important additional information on the potential 

scope and magnitude of the hazards estimated in the assessment. The SAB recognizes that some 

additional data may be available on the consumption patterns of recreational anglers, but that EPA did 

not have time or resources to integrate this information into the current analysis.

3.14. Responsiveness to the goals of the study

Question 14: Does section 2.8 respond to the goals of the study and does it encapsulate the critical 

issues and the significant results of the analysis?

Response:

3.15. Confidence in the analysis

Section 2.8 responds to the goals of the study, but the manner in which it highlights the key 

findings could be improved. The section should be revised to explicitly respond to each of the goals of 

the study as set out on page 13 of the Technical Support Document:

(a) What is the nature and magnitude of the potential risk to public health posed by current 

U.S. EGU mercury emissions?

(b) What is the nature and magnitude of the potential risk posed by U.S. EGU mercury 

emissions in 2016 considering potential reductions in EGU mercury emissions 

attributable to Clean Air Act requirements? and

(c) How is risk estimated for both the current and future scenario apportioned between the 

incremental contribution from U.S. EGU’s and other sources of mercury?

In response to these goals, the SAB sees that the major finding of the study is that a reduction in 

mercury emissions will translate to reductions in fish tissue methylmercury concentrations, and in turn, 

to a reduction in potential risk to subsistence fishers that would result from the consumption of selfcaught fish from inland watersheds. While there are numerous unquantified sources of variability and 

uncertainty that are contained in the numerical estimates of potential risk, the variability and uncertainty 

do not contradict this basic finding.

Question 15: Despite the uncertainties identified, is there sufficient confidence in the analysis for it to 

determine whether mercury emissions from U.S. EGUs represent a potential public health hazard for the 

group of fish consumers likely to experience the highest risk attributable to U.S. EGU?

[Note: This question was not among the original charge questions. It was formulated by the SAB as an 

alternative to the second subquestion originally posed by EPA for Charge Question 14, which read as 

follows: “In addition, please comment on the degree to which the level of confidence and precision in 

the overall analysis is sufficient to support use of the risk characterization framework described on page 

18.”]

Response: Notwithstanding the uncertainties inherent in this analysis, the Technical Support Document, 

after incorporation of the recommendations of the SAB, should provide an objective, reasonable and 

credible determination of the potential for a public health hazard from mercury emitted from U.S. EGUs. 
32

4. Summary List of Recommendations

This SAB report contains many recommendations for improving the Technical Support Document 

presented in the responses to the charge questions. These recommendations can be organized into three 

general themes:

1. Improve clarity of the Technical Support Document in terms of the methods used in the risk 

assessment and presentation of results. The reader should be able to understand how risk 

calculations were performed, the rationale for key decisions regarding the use of models and 

sources of input data, and results obtained from the analysis and the conclusions. SAB support 

for the risk assessment is contingent upon this improvement in clarity being accomplished. 

2. Expand the discussion of sources of variability and uncertainty in the risk assessment. Several 

additional sources of uncertainty should be acknowledged and discussed briefly in the Technical 

Support document. 

3. De-emphasize IQ loss as an endpoint in the risk assessment.

For convenience, specific recommendations have been extracted from the body of the report and are 

listed below.

• The Introductory section should make clear, at the earliest possible point, that the analysis is a 

determination of watershed impact with exposure addressed as a potential outcome.

Question1: Overall design

• The SAB recommends that EPA reframe the document’s discussion of IQ. EPA should

incorporate IQ and other neuropsychological measures as supplemental information and focus on 

HQ as the primary critical health endpoint.It is not suggested that the analyses of IQ be removed 

altogether but rather that the analyses be framed in an appendix to the report as a secondary 

analysis of impact of reduced exposure on potential health-related outcome. The appendix should 

discuss the basis for selecting a HQ at or above 1.5 as the criteria for selecting potentially 

impacted watersheds should be explained. The appendix should also include discussion of 

potential effects on other measures like developmental delays (Grandjean et al. 1997) or 

neuropsychological tests (as discussed by van Wijngaarden et al. 2006), presented in the overall 

context of the weight of evidence. 

Question 2: Critical health endpoints besides IQ

• The SAB recommends that the Technical Support Document acknowledge and discuss 

alternative (to HQ) quantitative measures but does not recommend a re-analysis based on these 

measures.

• In HUCs with multiple lakes, the SAB recommends against using a single fish methylmercury 

value to describe the HUC.

Question 4: Spatial scale of watersheds

• The SAB recommends that the authors provide a summary table describing the characteristics of 

the watersheds where fish were collected, including the fraction of fish samples collected from 

rivers versus lakes, and whether from single or multiple sites.
33

• The SAB recommends that the EPA utilize fish methylmercury data collected since 1999 for the 

risk assessment.

Question 5: Measured fish tissue mercury concentration

• The SAB recommends that it be revised to provide a better description of the character of the 

data, as well as the selection of analyzable data (e.g., sizes, distribution of fish sizes across 

watersheds), should be better detailed in the report. 

• The SAB recommends that EPA contact some states that receive what the Technical Support 

Document terms “relatively elevated” mercury deposition from U.S. EGU emissions and have 

limited fish methylmercury measurements to investigate if additional recent (since 1999) fish 

methylmercury data are available to improve the coverage for the mercury risk assessment.

• The SAB recommends inclusion of a graph depicting the number of tissue samples available for 

analysis by tissue concentration.

Question 6: Use of the 75th percentile fish tissue methylmercury value

• The SAB also recommends that the document discuss this source of uncertainty, including 

adding a table with the distribution of number of available fish samples and the fish size from 

which they were obtained across watersheds to indicate the extent of the problem. The Technical 

Support Document should describe in more detail why including fish tissue concentrations from 

one fish sample is likely to result in an underestimate of the number of watersheds at risk. 

• The SAB recommends that EPA should also conduct a sensitivity analysis using the median fish 

tissue concentration to better represent the distribution of fish tissue methylmercury levels where 

the sample size is one and provide a bound on the risk assessment. 

• The use of other percentiles in the sensitivity analysis is not recommended given the limitations 

of the fish tissue data available.

• The SAB recommends that the document describe more clearly the source of the fish 

methylmercury data and provide at least a general discussion of how fish sampling programs 

differ in ways that can contribute variability and uncertainty to the data set, such as fish capture 

methods and criteria for selecting fish to measure methylmercury concentrations.

• The report should include information on the sizes of fish that were analyzed. In doing so, the 

Technical Support Document may be able to quantify the impact, if any, of the size of fish 

sampled in watersheds with few fish tissue samples available on estimated mercury 

concentrations. 

• The SAB also recommends that the Technical Support Document clarify that the 75th percentile 

represents available fish tissue data that may or may not represent the fish in the watershed or the 

fish consumed.
34

• The SAB recommends that a few caveats should be acknowledged more fully in the document. 

The main consumption estimates came from a relatively small survey of individuals attending a 

fishing convention in South Carolina, so the consumption estimates reported in the Burger 2002 

study may be imprecise, in particular for women. The SAB recommends that the Technical 

Support Document acknowledge that, while several estimates of fish consumption rates were 

used in the risk assessment, other estimates reported by Burger could have been used. For 

example, median fish consumption estimates may better represent the distribution of fish 

consumption data than mean estimates. It should also be acknowledged that the Burger survey 

was conducted in 1998, and that fish consumption rates even in subsistence populations may 

have changed.

Question 7: Consumption rates and location for high-end consumers

• The SAB recommends that this information concerning seasonality be clarified in the Technical 

Support Document

• The SAB recommends that EPA better explain its rationale for assuming that subsistence 

consumers eat fish larger than seven inches in length and asks EPA to provide references 

supporting its assumptions and to discuss uncertainties associated with this assumption.

• The SAB recommends that the Technical Support Document clarify how many census tracts 

were eliminated due to the use of the 25 individual cut point. 

Question 8: Use of census data to identify high-end fish consuming populations

• The Technical Support Document should include information on the relative distribution of the 

sample size of the susceptible populations in the census tracts that were targeted.

• The Technical Support Document should discuss the possibility that more remote waterways are 

fished by subsistence anglers as well and the potential of this uncertainty for underestimating 

exposures.

• The SAB recommends that the quantitative estimates of the uncertainty associated with use of 

the Mercury Maps approach published in the existing literature be summarized in Appendix F of 

the Technical Support Document.

Question 9: Use of the Mercury Maps approach

• The uncertainty in the TRI (screen) should be acknowledged, and the number of watersheds 

excluded in the base case and the uncertainty analysis should be explicitly stated.

Question 10: Exclusion of watersheds with significant non-air loadings

• IQ should serve as a secondary measure along with other measures discussed in the responses to 

questions 2 and 3. The modeling of the impact of IQ should be placed in the appendix and 

accompanied by the qualifications discussed in section 3.11 of this SAB report.

Question 11: Concentration-response function used in modeling IQ loss

• A statement on Page 84, Table F-2 references the Seychelles study instead of the New Zealand 

study. This should be corrected.
35

• The SAB recommends an expanded discussion in Appendix F of variability and uncertainty to 

make explicit the uncertainties associated with the Agency’s key analytical choices, which the 

SAB supports. This discussion could be organized according to the figures depicting sample 

calculations of high and low EGU impact that were provided at the SAB’s public meeting on 

June 15, 2011 and reproduced as Figures 3 and 4. The SAB recommends that these figures be 

added to the report along with an explanation of how the calculations were conducted.

Question 12: Uncertainty and variability

• The SAB suggests that language be used throughout the Technical Support Document that 

clarifies the scope of the results vis-à-vis variability and uncertainty in data and methods. For 

example, the Technical Support Document should cite the evaluation of uncertainty in the 

CMAQ and MMAPs source documents.

• The SAB recommends that the following sources of variability to be included in Appendix F to 

avoid misinterpretation of study results and outcomes.

o The effect of temporal variability in the following on estimates of mercury deposition.

o Appendix F should describe CMAQ boundary conditions that are necessary to establish 

in order to run the model for the 2 temporal scenarios.

o Variation in geographic patterns of populations of subsistence fishers.

o Appendix F addresses geographic variability in total and U.S. EGU-attributable mercury 

deposition and fish tissue concentrations. Appendix F should be expanded to discuss 

spatial variability in populations of subsistence fishers, noting the limited geographic 

coverage of watersheds with fish tissue concentrations.

o Variability in nature and protocols of state collection of fish data (see the response to 

Question 5, also mentioned below). 

o Variation in fisher populations; for example, variation in body weights (potentially across 

race/ethnicities) and fishing and consumption habits.

o Variability in the factor used to translate mercury concentration measured at time of 

collection (i.e., expressed per unit wet weight) in comparison to mercury concentration at 

point of consumption following cooking. 

• The SAB advises EPA to strengthen the discussion of each uncertainty presented by identifying 

at least qualitatively the direction of its effect on the overall risk assessment. For example, the 

small fish sample sizes results in underestimates of the 75th percentiles, which propagates to 

conservative underestimates of risk.

• The SAB recommends that Appendix F be expanded to provide a more complete listing and 

discussion of key uncertainties associated with the assessment. Additional sources of uncertainty 

that should be considered for expanded discussion include:

o Overall emission inventories, especially the non-EGU inventory derived as a modified 

version of the National Emissions Inventory (NEI). Appendix F should discuss the 

uncertainties in inventory components; whether and how the uncertainty changes between 

the 2005 to 2016 scenarios, including uncertainties in the TRI database; whether there is 

bias in the EGU and non-EGU components of the inventory; and whether the EGU 

emission estimates were derived from the best performing facilities or from the complete 

set of facilities.

o Alternative future scenario forecasts. Appendix F should more clearly describe the 

variables that were held constant versus factors that were varied between the two 

scenarios.

o Regarding uncertainty in location of 2016 emissions reductions. Due to EPA's projection 

methods, there is uncertainty about where emissions reductions will occur between 2005 

and 2016, which in turn influences the spatial patterns of deposition from EGUs in the 
36

2016 scenario. Appendix F should address the uncertainties in the 2016 scenario 

regarding the specific geographic locations of reductions in EGU-derived mercury 

deposition as a fraction of total mercury deposition.

o Use of CMAQ and performance evaluation of CMAQ. Appendix F should provide more 

detailed description of uncertainty in CMAQ, including references to existing evaluations 

of the model.

o Appendix F should address whether the Mercury Maps approach, as implemented, is 

adequate to characterize the existence and extent of mercury “hot spots."

o Appendix F should detail the criteria used for excluding watersheds, characterize the 

watersheds excluded by different criteria, and describe the estimated deposition in these 

watersheds. 

o Regarding representativeness of approximately 2,500 watersheds compared to 88,000 

HUC12 nationwide, Appendix F should characterize any bias introduced by looking at 

this subset of watersheds (e.g., some states are over-represented, such as Indiana and 

Minnesota, while others are under-represented such as Pennsylvania).

o Fish populations and fish tissue database (see SAB responses to questions 5, 6 and 13 for 

more detail). Appendix F should include discussion of:

 Sample size for characterization of Implications of a data set with a low number 

of fish per watershed. Appendix F should identify the distribution of fish samples 

per watershed and the possible implications of this distribution, including the 

implications of sample size for characterization of 75th percentile fish tissue 

concentration.

 Uncertainty in methylmercury fish tissue concentrations from differences in 

sampling and analytical protocols used by States that contribute data and errors 

introduced by potential misidentification of locations, etc. 

o Regarding adjustment between wet and cooked weight of fish: EPA relied upon a single 

older study to derive an adjustment factor of 1.5. Alternative and newer peer reviewed 

studies of cooking effects on mercury in fish should be acknowledged (e.g., Musaiger et 

al. 2008; Farias et al. 2010) and used to discuss uncertainty associated with this assumed 

value. 

 Appendix F should note that this is a constant value applied in the calculation and 

thus does not bias but could skew the results.

o Regarding uncertainty of the assumption of proportionality and the MMAPs approach 

(see SAB response to Question 9 for specifics to be discussed in Appendix F).

o Characterization of susceptible human populations (see SAB responses to Questions 7 

and 8)

 Characterizing subsistence fishing activity within high EGU deposition sites.

 Implications of choosing subsistence fishers and excluding high-end sport fishers. 

 Census information that may exclude groups such as students, immigrants).

o Fish consumption rates (see SAB Response to Question 7).

 Limitations of the single study used to support the Technical Support Document’s 

fish consumption rate for female subsistence fishers. 

 Size of fish consumed.

o Derivation of the concentration-response relationship and RfD based on data from marine 

fish and mammal species, not inland freshwaters. 

 Appendix F should discuss the uncertainty introduced by not using RfDs derived 

based on studies of consumption of fish from inland freshwaters. (See SAB

response to Question 11). 
37

o Applicability of the concentration-response relationship and RfD for low socio-economic 

status populations. This relationship has not been examined. 

 Appendix F should discuss how this relationship may bias the report toward 

underestimating risk.

o Effect of the nutritional benefits of fish consumption in comparison to risks from 

mercury. Appendix F should address how the lack of consideration of this factor that may 

bias the analysis toward underestimating risk (see SAB response to Question 11).

• The SAB recommends that the spatial patterns of simulated deposition shown in Figure 2-1 to 2-

4 be better explained and that EPA should characterize data limitations more effectively.

Question 13: Discussion of analytical results

Mercury deposition from U.S. EGUs

• The 12-km deposition maps are very different than previously produced maps on the 36-km scale 

(for example in Texas and Nevada). The SAB recommends that EPA explain these differences 

and that EPA consider including separate maps of wet and dry deposition and/or aggregating the 

results into an approximately 36 km grid scale for comparison to earlier maps and to data plots, 

such as national deposition maps from the Mercury Deposition Network.

• There have been several intercomparison studies among numerical models for long-range 

transport of mercury and studies on model uncertainty evaluation that are not discussed or 

referenced. The SAB recommends that EPA summarize these references (Bullock, 2009; 

Pongprueksa et al., 2008; Lin et al, 2007; and Ryaboshapko, 2007) to help frame the overall 

uncertainty of the deposition estimates.

Fish tissue methyl mercury concentrations

• EPA should characterize uncertainty and data limitations more fully. Specifically, the small 

sample sizes of mercury concentrations in fish for the individual watersheds (~29% of 

watersheds have n=1) will result in lower estimates of mercury concentrations in the 75th 

percentile as shown earlier in Figures 1 and 2 in this document.

• The text in the observations should be modified to refer to tissue and mercury “concentrations” 

rather than “levels” to be more precise.

• Where the percentages of EGU-contribution to fish methylmercury are mentioned, EPA should 

clarify that those values pertain to only fish-sampled watersheds. Given the under-sampling in 

watersheds where there are high levels of deposition, the percentages indicated could be higher.

• EPA should modify or eliminate some figures and tables. 

o For figures 2-7 to 2-10, improved plots should display symbols proportional to sample 

size and provide color or shading of symbols to represent observed fish concentrations.

o The maps shown in Figures 2-7 to 2-14 need to include the western continental United 

States.

o The legend for Figure 2-8 should make it clear that the 2016 mercury tissue 

concentrations were computed by adjusting the 2005 concentrations to account for lower 

expected deposition as per the Mercury Maps approach.

o The third bullet item on page 36 of the Technical Support Document should be corrected 

to indicate that Figures 2-7 and 2-8 give concentrations of mercury in fish, not total 

mercury deposition. 

o Figures showing the top 10th percentile (2-11 to 2-14) should be removed since the 

pattern of mercury is greatly affected by high sampling effort in South Carolina, Indiana, 

West Virginia, and Louisiana. 

o The text describing Table 2-5 needs to be clarified to state that the relationships are not 

causal.
38

• Patterns of mercury deposition with mercury fish tissue data. The Technical Support Document 

should clearly describe the degree to which the non-uniform, state-specific data availability 

influences this analysis

• The SAB recommends that this section be substantively rewritten to improve clarity and to 

highlight the major relevant points. 

• EPA should revise the text in footnote 36, which is critical to the understanding of Figures 2-15 

and 2-16.

• Also, the figure legends within each of Figures 2-15 and 2-16 need to be changed because the 

“blue areas” are not “water bodies,” but rather “watersheds,” which include water bodies that 

sometimes are more obvious than their watersheds (e.g., the Minnesota portion of Lake Superior, 

Long Island Sound, and perhaps erroneously, the Canadian portion of Lake Champlain). The 

SAB recommends that these two maps be replotted with a third color that clearly identifies the 

areas of overlap.

• The SAB recommends that EPA provide a more complete introduction to Figure 2-17 that would 

state the important premises of the analysis applied in this risk assessment - that spatial 

variability of deposition rates is only one major driver of spatial variability of fish 

methylmercury and that variability of ecosystem factors that control methylation potential 

(especially wetlands, aqueous organic carbon, pH, and sulfate) also play a key role.

• The revised document should explain why the average deposition rate is lower in the 2016 

scenario red area.

Percentile risk estimates

• The Technical Support Document should include an explanation of why the values in Tables 2-6 

and 2-7 decrease when going from the 50th to 75th percentile. This is likely because the ranked 

risk values are not the same as the ranked EGU contributions. This difference should be 

mentioned. Perhaps the tabled values should be referred to in some way as averaged.

• The values in Tables 2-6 and 2-7 are based on averaging the values that are 2.5% below and 

2.5% above. EPA should consider whether it is better to use a 2.5% range or use the 10 nearest 

values. EPA should also describe how the range is selected for the 99th percentile.

• Section 2 page 54: the paragraph comparing "risks" for high-end females with other populations 

is oversimplified. Depending on the percentiles considered, "risks" for Laotians, Vietnamese and 

Tribal fish consumers can also be higher than for high-end females. The highest consumption 

rates should be summarized in an appendix.

• Section 2 page 55: EPA should provide more information on the gold-mining impacted 

watersheds in the Southeast. For example, it seems that gold mining occurred historically in a 

relatively small region of South Carolina, and only a few mines have recently been re-activated. 

Is it really appropriate to discount or question concerns about EGU affected exposure across the 

whole Southeast on this basis?

• In Tables 2-6 and 2-7, EPA should consider reporting consumption rates and putting the 

percentiles in parentheses rather than reporting the percentiles and having the rates in 

parentheses.

• In Table 2-15 and other places, the mean is included. Since the mean is not a percentile, the table 

header should be changed or the median used.

Number and frequency of watersheds with populations potentially at risk due to U.S. EGU mercury 

emissions

• The SAB recommends that language be added regarding the change in the percentage of 

watersheds that continue to be above the RfD (or above a change in one to two IQ points, if this 

aspect of the risk assessment is retained) after EGU emissions are removed.
39

• The SAB recommends that the first bullet point on page 57 to change the language “before 

taking into account deposition...” to something that does not imply temporality (e.g., “when you 

factor out other sources of mercury deposition”). 

• The SAB also recommends that if the document discusses loss of IQ points, that it should refer to 

this change in relation to “populations living close to watersheds” rather than “watersheds."

• EPA should revise section 2.8 to explicitly respond to each of the goals of the study as set out on 

page 13 of the Technical Support Document.

Question 14: Responsiveness to the goals of the study
40

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A-1

Appendix A: Agency Charge Questions

Background and Charge for the SAB Review of EPA’s Technical Support Document: NationalScale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for Coal- and 

Oil-Fired Electric Generating Units (March 2011) 

May 23, 2011

Background

On March 16, 2011, EPA proposed National Emission Standards for Hazardous Air Pollutants 

(NESHAP) for coal- and oil-fired Electric Utility Steam Generating Units (EGUs). The proposed 

NESHAP would protect air quality and promote public health by reducing emissions from EGUs of the 

hazardous air pollutants (HAP) listed in Clean Air Act (CAA) section 112(b), including both mercury 

and non-mercury HAP. Specifically, the proposed rule would require EGUs to decrease emissions of 

mercury, other metal HAP, organic HAP, and acid gas HAP. Section 112(n)(1) of the CAA requires 

EPA to determine whether it is “appropriate and necessary” to regulate HAP emissions from EGUs 

under section 112. Before the Agency is authorized to make the appropriate and necessary 

determination, section 112(n)(1) requires EPA to perform a study of the hazards to public health 

reasonably anticipated to occur as a result of HAP emissions, including mercury, from EGUs after 

imposition of the requirements of the CAA. EPA completed the required study in 1998. (Utility Air 

Toxics Study, 1998). Based in part on the results of that study , EPA made a finding in December 2000 

that it was appropriate and necessary to regulate HAP emissions from coal- and oil-fired EGUs. In the 

recently proposed NESHAP, EPA confirmed that finding and concluded that it remains appropriate and 

necessary to regulate HAP emissions from coal- and oil- fired EGUs. EPA confirmed the finding in part 

by conducting a new analysis of the human health risks posed by consuming freshwater fish containing 

mercury that is attributable to U.S. EGU emissions of mercury. EPA is seeking peer review of the data 

and methods used in the national scale mercury risk assessment as documented in the Technical Support 

Document: National-Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding 

for Coal and Oil-Fired Electric Generating Units (hereafter referred to as the “Mercury Risk TSD”). 

In determining whether U.S. EGUs pose a hazard to public health, we developed an approach for 

assessing the nature and magnitude of the risk to public health posed by U.S. EGU mercury emissions 

(the 2005 scenario). We also estimated the health risks associated with US EGU mercury emissions 

estimated to remain “after imposition of the requirements of the Act” (the 2016 scenario). Specifically, 

for the 2016 scenario, we looked at certain regulations, including, for example, the proposed Transport 

Rule, which have a co-benefit impact on mercury.

Our approach focused on identifying the number of watersheds where the U.S. EGU contribution to 

total methylmercury (MeHg) risk is considered to represent a potential public health hazard. To do this, 

we focused on estimating risk associated with human exposures at those watersheds in the U.S. where 

we have measured data on fish tissue MeHg concentrations (about 4% of the watersheds, or 2,461 out of 

~88,000 U.S. watersheds – see section 2.4 and Appendix B of the Mercury Risk TSD). For each of the 

2,461 watersheds, we modeled potential risk from high-end (i.e., subsistence-level) self-caught fish 

consumption. Specifically, we used the fish tissue MeHg data combined with self-caught fish ingestion 

rates to model exposure, and then we translated that into estimates of total MeHg-related risk (see 

sections 1.3, 2.1 and Appendices C and D of the Mercury Risk TSD).
A-2

In our analyses, we estimated both total risk associated with emissions from all emissions sources, 

including global emissions, and the incremental contribution to the total risk that was attributable to 

mercury emissions from U.S. EGUs. We used an assumption of proportionality between mercury 

deposition over a watershed and the levels of MeHg in fish (and, by association, the levels of exposure 

and risk). This proportionality assumption is based on the U.S. EPA Office of Water's Mercury Maps 

assessment (see section 1.3 and Appendix E of the Mercury Risk TSD). Mercury Maps demonstrated 

that, under certain conditions, a fractional change in mercury deposition will ultimately translate into a 

similar fractional change in MeHg levels in fish. We note that the time delay between changes in 

deposition and changes in MeHg levels in fish is not well characterized (there are a range of 

assumptions and limitations associated with the Mercury Maps approach which we have considered -

see below). Application of the Mercury Maps approach allowed us to translate any changes in mercury 

deposition to changes in MeHg fish tissue levels. It also allowed us to apportion MeHg levels in fish 

(and, by association, exposure and risk estimates) based on the proportionality assumption. In other 

words, if the estimated U.S. EGU-related emissions comprise10% of total deposition over a watershed, 

assuming near steady-state conditions are met, we would assume that eventually 10% of the MeHg in 

fish (and, therefore, 10% of the total human exposure and risk) would be attributable to U.S. EGUs. 

Mercury deposition modeling was completed for two scenarios: 2005 and 2016. The analysis included 

consideration of mercury emitted from (a) US EGUs, (b) other non-EGU sources in the U.S. (including 

natural and anthropogenic), and (c) sources outside of the U.S. (both anthropogenic and natural) whose 

mercury is deposited in the U.S. following long range atmospheric transport. Estimates of mercury 

deposition within the U.S., both of total deposition and of EGU-related deposition, were completed 

using the Community Multiscale Air Quality model (CMAQ) version 4.7.1, which generates estimates at 

the 12 km grid cell-level of resolution.6,7 CMAQ modeling reflects mercury oxidation pathways for both 

the gas and aqueous phases in addition to aqueous phase reduction reactions. Mercury "re-emission" is 

not explicitly modeled in this version of CMAQ; however, approximations of these emissions are 

included in the CMAQ model and called "recycled" emissions. Speciation of U.S. EGU mercury 

emissions is based on a factor approach reflecting coal rank, firing type, boiler/burner type, and postcombustion emissions controls. Emissions of mercury from sources in Canada and Mexico are based on 

the 2006 Canadian inventory and 1999 Mexican inventory, respectively. Estimates of mercury 

transported into the U.S. from outside North America (i.e., specification of lateral boundary 

concentrations, pollutant inflow into the photochemical modeling domain, and initial species 

concentrations) are provided by a three-dimensional global atmospheric chemistry model, the GEOSCHEM model (standard version 7-04-11). The GEOS-CHEM predictions were used to provide one-way 

dynamic boundary conditions at three-hour intervals and an initial concentration field for the 36 km 

CMAQ simulations. The 36 km photochemical model simulation is used to supply initial and hourly 

boundary concentrations to the 12 km domains.8 Mercury initial and boundary conditions were based on 

a GEOS-CHEM simulation using a 2000 based global anthropogenic emissions inventory that includes 

1,278 Mg/yr of Hg(0), 720 Mg/yr of Hg(II), and 192 Mg/yr of particle bound mercury.9

 6 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., Gilliam, 

R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., Bash, J.O., 2010. Incremental testing of the Community Multiscale Air Quality 

(CMAQ) modeling system version 4.7. Geoscientific Model Development 3, 205-226. 7 Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the 

models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51-77. 8 USEPA, 2010. Air Quality Modeling Technical Support Document: Point Source Sector Rules (EPA-454/R-11-003), 

Research Triangle Park, North Carolina.

9 Selin, N.E., Jacob, D.J., Park, R.J., Yantosca, R.M., Strode, S., Jaegle, L., Jaffe, D., 2007. Chemical cycling and deposition 

of atmospheric mercury: Global constraints from observations. Journal of Geophysical Research-Atmospheres 112.

The description 
A-3

of emissions and modeling presented above pertains to the 2005 scenario evaluated in the risk 

assessment. For the 2016 scenario, EPA projected US EGU emissions based on an Integrated Planning 

Model (IPM) run.10

For the risk assessment, we focused on high-end (subsistence) fish consumption by women of childbearing age at inland fresh water bodies; the consumption rates used ranged from the 90th to 99th

percentiles and were obtained from peer-reviewed studies of fish consumption by specific populations 

active within the continental U.S. (see section 1.3 and Appendix C of the Mercury Risk TSD). This 

overall approach reflects our assumption that U.S. EGUs will have the greatest public health impact on 

the subset of watersheds in the U.S. that (a) have relatively elevated fish tissue MeHg levels (increasing 

overall risk levels associated with MeHg exposure through fish consumption at those watersheds), (b) 

have relatively larger mercury deposition from U.S. EGUs (translating into a greater fractional risk

associated with U.S. EGUs), and (c) have subsistence-level fishing activity (resulting in higher selfcaught fish intake and higher risk). We have not focused on recreational fishing activity. Recreational 

fishing may be important from a population risk standpoint; however, these fishers consume less fish 

overall and will not have the levels of individual-risk likely to be experienced by subsistence fishers.

Furthermore, we have not considered U.S. EGU impacts on commercial fish from international or near 

coastal locations. Although MeHg levels can be relatively high in fish from these locations, the U.S. 

EGU contribution (as a fraction of overall mercury impacts) is both highly uncertain and likely to be 

low. The high degree of uncertainty associated with linking U.S. EGU deposition to MeHg levels in fish 

that are either self-caught or commercially harvested near the U.S. shore led us to exclude consideration 

of risks linked to consumption of these fish. Specifically, given the greater mobility of these fish and the 

greater dilution of deposited mercury in the ocean and near coastal waters, application of the Mercury 

Mercury emissions from other U.S. anthropogenic sources are projected to 2016 

based on growth factors and known controls (e.g., boilers, cement kilns). The estimates for non-U.S. 

global emission sources (i.e., both natural and anthropogenic) were not adjusted for the 2016 scenario.

The risk assessment for mercury focuses on two risk metrics: (a) comparison of estimated exposures to 

the MeHg Reference Dose (MeHg RfD) to determine the hazard quotient (HQ) for each watershed 

evaluated, and (b) an estimate of the number of IQ points lost to children born to mothers exposed to 

MeHg during pregnancy (see 1.2 of the Mercury Risk TSD). The current EPA MeHg RfD reflects the 

full range of potential neurodevelopmental impacts including effects on IQ, educational development, 

motor skills and attention. For the risk assessment, we did not estimate the incidence of adverse health 

effects for health endpoints other than IQ loss, as the literature and available data supporting the 

modeling of IQ loss is considered to be the strongest and has received the most review by the scientific 

community. 

For each of the risk metrics modeled (RfD-based HQ and IQ loss), we identified a benchmark for a 

potentially significant public health impact to guide interpretation of the risk estimates. For the RfDbased HQ, we considered any exposure above the RfD (equal to an HQ of 1) to represent a potential 

public health hazard with recognition, as noted above, that the RfD provides coverage for the full range 

of neruodevelopmental impacts. In the case of IQ loss, we considered a loss of 1 or more points to 

represent a clear public health concern. This benchmark was based on advice received from the Clean 

Air Science Advisory Committee (CASAC) in relation to the Pb NAAQS review. It is important to note 

that CASAC identified this level of IQ loss in the context of a population-level impact (see 1.2 of the 

Mercury Risk TSD for additional detail on the benchmarks used to help interpret risk metrics). 

 10 USEPA, 2010. Air Quality Modeling Technical Support Document: Point Source Sector Rules (EPA-454/R-11-003), 

Research Triangle Park, North Carolina.
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Maps approach is subject to significantly greater uncertainty relative to its application to inland fresh 

water bodies.

The RfD-based risk characterization was done by developing HQs for each watershed. The HQ is 

defined as the estimate of MeHg exposure divided by the MeHg RfD. Generally (both for 

methylmercury and for all pollutants) a HQ of 1 or less is considered to represent a level of daily 

exposure for the human population (including sensitive subgroups) that is likely to be without an 

appreciable risk of deleterious effects during a lifetime. We developed a 3-stage risk characterization 

framework to estimate the number of watersheds where the U.S. EGU contribution to total MeHg risk is 

considered to represent a potential public health hazard based on consideration of the HQ metric:

• Stage 1: estimate the number of watersheds where (a) potential exposure for subsistence level fish 

consumers exceeds the RfD (e.g., HQ > 1.0), and (b) U.S. EGUs contribute a specific fraction of 

mercury deposition to those watersheds (and by association, a specific fraction of total exposure and 

risk). Several fractions of mercury deposition were considered ranging from >5 to >20%. 

• Stage 2: estimate the number of watersheds where the deposition from U.S. EGUs would result in 

exposures to MeHg that exceed the RfD before considering exposures to MeHg attributable to other 

sources. While we may consider the U.S. EGU increment of exposure, particularly in the context of 

comparing exposure to the MeHg RfD, it is critical to place the U.S. EGU-incremental exposure in 

the context of the larger total exposure at a given watershed. This reflects the fact that the MeHg 

RfD is for total exposure and not increments of exposure considered in isolation. 

• Stage 3: estimate the total number of watersheds where populations are at risk from exposures 

attributable to U.S. EGU mercury emissions by merging the two sets of watersheds identified in 

stages 1 and 2. 

(see section 1.2 of the Mercury Risk TSD for additional detail on the 3-stage framework)

The second risk characterization was done by modeling potential IQ loss attributable to U.S. EGU 

emissions resulting in increased MeHg exposure (see section 1.2 of the Mercury Risk TSD). In 

modeling IQ loss, we first converted annual-average ingested dose estimates for MeHg into equivalent 

maternal hair mercury levels, since the CR function for IQ loss is based on estimated exposure 

characterized as maternal hair mercury levels. This was accomplished using a factor based on a one 

compartment toxicokinetic model used in deriving the methylmercury RfD. Then a CR function relating 

hair mercury levels to IQ points lost in children born to mothers whose exposure is modeled in this 

analysis was used to predict IQ points lost for those children. This CR function is based on application 

of a Bayesian hierarchical model which integrates data from the three key epidemiological studies 

(Seychelles, New Zealand and Faroe Islands). 

As part of the risk assessment, EPA also addressed both variability and uncertainty. Regarding 

variability, we assessed the degree to which key sources of variability associated with the scenarios 

being modeled were reflected in the design of the risk model (see sections 1.4, 2.7 and Appendix F, 

Table F-1 of the Mercury Risk TSD). Regarding uncertainty we included a number of sensitivity 

analyses intended to consider the potential impact of key sources of uncertainty (with emphasis on 

application of the Mercury Maps assumption). We also qualitatively discussed additional sources of 

uncertainty and the nature and magnitude of their potential impact on risk estimates that were generated 

(see section 2.7 and Appendix F, Table F-2 of the Mercury Risk TSD). 

Figure 1 provides a conceptual diagram for the key steps in the risk assessment.
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This peer review is intended to focus on the linkages of the key data inputs, and the critical inputs 

related to fish consumption rates, dose-response information, and fish MeHg levels. Two key inputs to 

the risk assessment are the MeHg RfD and the estimates of mercury deposition from CMAQ. We 

believe the MeHg RfD is the appropriate indicator to use because it reflects the full range of potential 

neurodevelopmental impacts, including effects on IQ, educational development, motor skills, and 

attention. We are not requesting that this panel review the scientific basis for the MeHg RfD, rather, this 

review is focused on the estimation of potential exposures to MeHg for comparison against the existing 

RfD. The current RfD has been subject to extensive peer review and is the EPA reference value for 

assessing MeHg ingestion risk.11 In addition, the CMAQ model has been extensively peer reviewed and 

the mercury fate and transport algorithms are documented in several peer reviewed publications.12,13,14

 11 U.S. Environmental Protection Agency (U.S. EPA). 2002. Integrated Risk Information System File for Methylmercury. 

Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available 

electronically at: http://www.epa.gov/iris/subst/0073.htm. 12 Bullock, O. R., Jr., et al. (2008), The North American Mercury Model Intercomparison Study (NAMMIS): Study 

description and model-to-model comparisons, J. Geophys. Res., 113, D17310, doi:10.1029/2008JD009803. 13 Bullock, O. R., Jr., et al. (2009), An analysis of simulated wet deposition of mercury from the North American Mercury

Model Intercomparison Study, J. Geophys. Res., 114, D08301, doi:10.1029/2008JD011224 14 Pongprueksa, P., et al (2008), Scientific uncertainties in atmospheric mercury models III: Boundary and initial conditions, 

model grid resolution, and Hg(II) reduction mechanism, Atmospheric Environment 42: 1828–1845

Thus, we are not seeking peer review of the mercury components of the CMAQ model. However, as 

reflected in the charge questions, we are looking for comment on how CMAQ outputs (i.e., mercury 

deposition estimates) are integrated into the risk assessment to estimate changes in fish tissue MeHg 

levels and in exposures and risks associated with the EGU-related fish tissue MeHg fraction.
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Figure 1. Flow Diagram of Risk Analysis Including Major Analytical Steps and Associated 

Modeling Elements (Note, GEOS-CHEM results are input into CMAQ modeling box)

Specifying 

spatial scale of 

watersheds

Characterizing 

measured fish tissue 

Hg concentrations at 

the watershed-level

Defining nearsubsistence and 

subsistence fisher 

populations by 

watershed

Estimating total fish 

consumption-related 

Hg exposure for fishers 

active at each 

watershed

US Demographic data 

characterizing source 

populations for highend fishers at the US 

Census tract-level

Studies characterizing fish 

consumption rates for 

high-consumption fisher 

populations

Defining near-subsistence and 

subsistence fisher scenarios

Apportioning total 

exposure between 

total and US EGUattributable at each 

watershed

CMAQ 

modeling 

results for total 

and US EGU Hg 

deposition at 

each watershed 

Estimation of HQ and 

IQ loss at each 

watershed including total 

risk and US EGUattributable fraction

Mercury RfD for 

HQ estimation

IQ loss concentrationresponse function

Factor translating Hg 

ingestion dose into Hg hair 

concentrations (in ppm) 

Modeling IQ loss

Exposure modeling

Risk modeling

KEY: decision data input analysis step
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Charge Questions

The charge questions presented below are organized by topic and track specific sections within the 

Mercury Risk TSD beginning with Purpose and Scope of the Analysis (section 1.1). We have included 

brief overviews of the technical focus of each section to help reviewers place each section in context 

with regard to the overall risk assessment (Note, we did not include any charge questions addressing 

elements of the Executive Summary since all technical content provided in that introductory section is 

covered in greater detail in the other sections of the TSD for which we have included charge questions).

Purpose and Scope of the Analysis (section 1.1)

This section presents the policy-related questions that were developed to guide the design of the risk 

assessment. It also highlights some important technical factors related to air-sourced mercury, in 

particular, mercury released from U.S. EGUs that were considered in designing the risk assessment. And 

finally, the section provides an overview of key elements of the scope of the risk assessment.

Question 1. Please comment on the scientific credibility of the overall design of the mercury risk 

assessment as an approach to characterize human health exposure and risk associated with U.S. 

EGU mercury emissions (with a focus on those more highly exposed).

Overview of Risk Metrics and the Risk Characterization Framework (section 1.2)

This section describes the risk metrics used in the risk assessment (i.e., IQ loss and MeHg RfD-based 

HQs, including both total risk and U.S. EGU-attributable risk). The section also presents the 3-stage risk 

characterization framework which uses these risk metrics to estimate the number of watersheds where 

populations may be at risk due to MeHg exposure with consideration for the U.S. EGU attributable 

fraction of that exposure. Questions for this section focus on the IQ calculations. As explained above, 

we are not asking for peer review of the current mercury RfD or its suitability as a benchmark for 

comparison with mercury exposures.

Question 2. Are there any additional critical health endpoint(s) besides IQ loss which could be 

quantitatively estimated with a reasonable degree of confidence to supplement the mercury risk 

assessment (see section 1.2 of the Mercury Risk TSD for an overview of the risk metrics used in 

the risk assessment)?

Question 3. Please comment on the benchmark used for identifying a potentially significant 

public health impact in the context of interpreting the IQ loss risk metric (i.e., an IQ loss of 1 to 

2 points or more representing a potential public health hazard). Is there any scientifically 

credible alternate decrement in IQ that should be considered as a benchmark to guide 

interpretation of the IQ risk estimates (see section 1.2 of the Mercury Risk TSD for additional 

detail on the benchmark used for interpreting the IQ loss estimates).

Overview of Analytical Approach (section 1.3)

This section of the Mercury Risk TSD (together with the referenced appendices) provides a detailed 

overview of the technical design and inputs to the risk assessment, with the section being further divided 

into subsections (unnumbered) that address each of the design elements. Charge questions presented 

below which address the design of the risk assessment are grouped by each of these design elements.
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Specifying the spatial scale of watersheds (presented within section 1.3)

This section describes the spatial unit used as the basis for the risk assessment (the HUC-12 watershed, 

representing a fairly refined level of watersheds approximately 5-10 km on a side) and provides the 

rationale for the decision to use that specific spatial scale and spatial unit in the analysis.

Question 4: Please comment on the spatial scale used in defining watersheds that formed the 

basis for risk estimates generated for the analysis (i.e., use of 12-digit hydrologic unit code 

classification). To what extent do HUC12 watersheds capture the appropriate level of spatial 

resolution in the relationship between changes in mercury deposition and changes in MeHg fish 

tissue levels? (see section 1.3 and Appendix A of the Mercury Risk TSD for additional detail on 

specifying the spatial scale of watersheds used in the analysis).

Characterizing measured fish tissue Hg concentrations (presented within section 1.3)

This section describes the fish tissue MeHg sampling data used in the risk assessment, including the 

underlying sources of data used in developing the dataset and factors considered in developing the 

dataset (e.g., inclusion of data sampled between 2000 and 2009). This section also provides the rationale 

for using the 75th percentile fish tissue MeHg value (within a given watershed) as the basis for exposure 

and risk characterization. 

Question 5: Please comment on the extent to which the fish tissue data used as the basis for the 

risk assessment are appropriate and sufficient given the goals of the analysis. Please comment 

on the extent to which focusing on data from the period after 1999 increases confidence that the 

fish tissue data used are more likely to reflect more contemporaneous patterns of mercury 

deposition and less likely to reflect earlier patterns of mercury deposition. Are there any 

additional sources of fish tissue MeHg data that would be appropriate for inclusion in the risk 

assessment?

Question 6: Given the stated goal of estimating potential risks to highly exposed populations, 

please comment on the use of the 75th percentile fish tissue MeHg value (reflecting targeting of 

larger but not the largest fish for subsistence consumption) as the basis for estimating risk at 

each watershed. Are there scientifically credible alternatives to use of the 75th percentile in 

representing potential population exposures at the watershed level?

Question 7: Please comment on the extent to which characterization of consumption rates and 

the potential location for fishing activity for high-end self-caught fish consuming populations 

modeled in the analysis are supported by the available study data cited in the Mercury Risk TSD. 

In addition, please comment on the extent to which consumption rates documented in Section 1.3 

and in Appendix C of the Mercury Risk TSD provide appropriate representation of high-end fish 

Defining subsistence fisher scenarios (presented within section 1.3)

This section describes the high-end self-caught freshwater fish consuming populations evaluated for 

exposure and risk in the risk assessment. The section includes detailed discussion of the self-caught fish 

consumption rates used in modeling exposure for these study populations.
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consumption by the subsistence population scenarios used in modeling exposures and risk. Are 

there additional data on consumption behavior in subsistence populations active at inland 

freshwater water bodies within the continental U.S.?

Question 8: Please comment on the approach used in the risk assessment of assuming that a 

high-end fish consuming population could be active at a watershed if the “source population” 

for that fishing population is associated with that watershed (e.g. at least 25 individuals of that 

population are present in a U.S .Census tract intersecting that watershed). Please identify any 

additional alternative approaches for identifying the potential for population exposures in 

watersheds and the strengths and limitations associated with these alternative approaches 

(additional detail on how EPA assessed where specific high-consuming fisher populations might 

be active is provided in section 1.3 and Appendix C of the Mercury Risk TSD).

Apportioning total MeHg exposure between total and U.S. EGU-attributable exposure (presented 

within section 1.3)

This section describes the application of the Mercury Maps based proportionality assumption to link 

changes in mercury deposition (over watersheds) to changes in fish tissue MeHg levels. The section also 

discusses the use of CMAQ modeling output (i.e., gridded mercury deposition estimates for both total 

mercury and U.S. EGU-attributable mercury) as part of this process of linking changes in U.S. EGU 

mercury emissions ultimately, to changes in fish tissue MeHg levels in watersheds assessed for risk in 

the risk assessment. 

Question 9: Please comment on the draft risk assessment’s characterization of the limitations 

and uncertainty associated with application of the Mercury Maps approach (including the 

assumption of proportionality between changes in mercury deposition over watersheds and 

associated changes in fish tissue MeHg levels) in the risk assessment. Please comment on how 

the output of CMAQ modeling has been integrated into the analysis to estimate changes in fish 

tissue MeHg levels and in the exposures and risks associated with the EGU-related fish tissue 

MeHg fraction (e.g., matching of spatial and temporal resolution between CMAQ modeling and 

HUC12 watersheds). Given the national scale of the analysis, are there recommended 

alternatives to the Mercury Maps approach that could have been used to link modeled estimates 

of mercury deposition to monitored MeHg fish tissue levels for all the watersheds evaluated? 

(additional detail on the Mercury Maps approach and its application in the risk assessment is 

presented in section 1.3 and Appendix E of the Mercury Risk TSD).

Question 10: Please comment on the EPA’s approach of excluding watersheds with significant 

non-air loadings of mercury as a method to reduce uncertainty associated with application of the 

Mercury Maps approach. Are there additional criteria that should be considered in including or 

excluding watersheds?

Estimating risk including HQ and IQ loss (presented within section 1.3)

This section describes how exposure estimates generated for the high-end fish consuming populations 

modeled in the analysis are translated into risk estimates for those populations (in the form of both 

MeHg RfD-based HQs and IQ losses). This section also includes a detailed discussion of the 

concentration-response function used in modeling IQ loss.
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Question 11: Please comment on the specification of the concentration-response function used in 

modeling IQ loss. Please comment on whether EPA, as part of uncertainty characterization, 

should consider alternative concentration-response functions in addition to the model used in the 

risk assessment. Please comment on the extent to which available data and methods support a 

quantitative treatment of the potential masking effect of fish nutrients (e.g. omega-3 fatty acids 

and selenium) on the adverse neurological effects associated with mercury exposure, including 

IQ loss. (detail on the concentration-response function used in modeling IQ loss can be found in 

section 1.3 of the Mercury Risk TSD).

Discussion of key sources of uncertainty and variability (section 1.4)

This section describes the extent to which the risk assessment design reflects consideration for 

potentially important sources of variability associated with the type of exposure being modeled. It also 

discusses sources of uncertainty associated with the analysis, including the nature and potential 

magnitude of their impact on risk estimates (Note, also that an important part of the analysis – the 

sensitivity analyses completed primarily to examine the potential impact of uncertainty related to the 

Mercury Maps approach – are discussed in section 2.7 of the Mercury Risk TSD). 

Question 12: Please comment on the degree to which key sources of uncertainty and variability 

associated with the risk assessment have been identified and the degree to which they are 

sufficiently characterized.

Discussion of analytical results (section 2)

This section presents estimates generated as part of the risk assessment, including important 

intermediate calculations as well as the risk estimates themselves – subsections include: (a) estimates of 

mercury deposition over watersheds (section 2.3), (b) characterization of changes in fish tissue MeHg 

levels based on modeling the impact of changes in mercury deposition (section 2.4) and (c) presentation 

of MeHg RfD-based HQ estimates and IQ loss risk estimates (section 2.6). Key observations from the 

analysis are presented in section 2.8.

Question 13: Please comment on the draft Mercury Risk TSD’s discussion of analytical results 

for each component of the analysis. For each of the components below, please comment on the 

extent to which EPA’s observations are supported by the analytical results presented and 

whether there is a sufficient characterization of uncertainty, variability, and data limitations, 

taking into account the models and data used.

 Mercury deposition from U.S. EGUs 

 Fish tissue methyl mercury concentrations 

 Patterns of Hg deposition with HG fish tissue data

 Percentile risk estimates

 Number and frequency of watersheds with populations potentially at risk due to U.S. EGU 

mercury emissions

Question 14: Please comment on the degree to which the final summary of key observations in Section 

2.8 is supported by the analytical results presented. In addition, please comment on the degree to which 

the level of confidence and precision in the overall analysis is sufficient to support use of the risk 

characterization framework described on page 18.

United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC