# 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 Unitsii EPA-452/R-11-009

December 2011



## Revised Technical Support Documen t: National-Scale Assessment of Mercury Risk to Populations with High Consumption of Self-caught

Freshwater Fish

In Support of the Appropriate and Ne cessary Finding fo r 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 Carolinaiii DISCLAIMER

This document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Ag ency. 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 Recomme ndations 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 c oncentrations 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 scenario s to include in the analysis ............................ 31 Identify subsistence fisher populations ..................................................................................... 3 2

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

1.4.4 Estimating total fish consumption-rela ted 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-attribut able risk (2016 scenario) ..................................... 43

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

1.4.6.2 Additional research supporting the propor tionality 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 Appropr iate and Necessary Finding ................................................ 48

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

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

2.1 Key design elements to consider when re viewing 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 w ith 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 per centage of watersheds with populations potentially atrisk due to U.S. EGU mercur y emissions (2016 scenario) ................................................... 83

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

2.7 Discussion of key sources of va riability 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 ........................................................................................................ 120v List of Tables

Table 1-1 Summary Statistics fo r the 2011 MFT HUC-level Data ............................................. 25 Table 1-2 Summary of 75 th Percentile Hg Concentrations in Fish Tissue Samples by Number of Observations and Number of Sites per HUC ................................................................................ 27 Table 1-3 Summary of 50 th Percentile Hg Concen trations in Fish Tissu e 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 w ith 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 additiona l 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/m 2 ) for the

2005 and 2016 scenarios.* ..................................................................................................... ....... 64 Table 2-2 Comparison of percent of total mercur y deposition attributable to U.S. EGUs for 2005

and 2016.* .................................................................................................................... ................. 64Table 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 Ti ssue Mercury Concentrations Across Datasets

Used in the March Version and Curren t Version of the Risk Assessment ................................... 73 Table 2-5 Comparison of total and U.S. EGU-a ttributable 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 se t of female subsistence fish consumer scenarios included in the analysis (2016 s cenario) (for both total and U.S. EGU incremental RfD-based HQ)* .......................................................................................................................... ................... 81 Table 2-8 Watersheds with potentially at-risk pop ulations based on consideration for risk based on U.S. EGU mercury deposition and resulting e xposure 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 contributi on 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 ei ther consideration for (a) U.S. EGU percent contribution to total risk OR (b) risk when U. S. EGU mercury depositi on 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 specifie d contribution to tota l 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 c onsidering 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 depos ition is considered al one, without taking into account deposition from other sources (2016 scenario) ............................................................... 90vi Table 2-14 Key sources of variabi lity associated with the analysis and degree to which they are reflected in the design of the analysis ....................................................................................... .... 93 Table 2-15 Key sources of uncertain ty 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 Ch aracterization 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-wis e Procedure Used to Develop 2010 Mercury Fish Tissue (MFT) Dataset Used in the 2010 Natio nal-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 Characteriz ing Frequency of Sample Sizes Across HUCs Included in the Risk Assessment (illustrates fraction of HU Cs with small sample size of 1-2) ........................... 28 Figure 1-8 LOESS (locally-weighted scatter pl ot smoothing) Least-Square Regression of 75 th
Percentile HUC-level Fish tissue Hg Le vels 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/m 2 ) for the 2005 Scenario ........................... 56 Figure 2-2 Total Mercury Deposition by HUC (μg/m 2 ) for the 2016 Scenario ........................... 57 Figure 2-3 U.S EGU-Attributable Mercury Deposition by HUC (μg/m 2 ) for the 2005 Scenario

............................................................................................................................... ........................ 58 Figure 2-4 U.S EGU-Attributable Mercury Deposition by HUC (μg/m 2 ) for the 2016 Scenario 59 Figure 2-5 Mercury Wet Depos ition by 12km Grid Cell (μg/m 2 ) for the 2005 Scenario ............ 60 Figure 2-6 Mercury Dry Depos ition by 12km Grid Cell (μg/m 2 ) for the 2005 Scenario ............. 61 Figure 2-7 Mercury Wet Deposit ion by 12km Grid Cell (μg/m 2 ) for the 2016 Scenario ............. 62 Figure 2-8 Mercury Dry Depos ition by 12km Grid Cell (μg/m 2 ) 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 Con centrations for 2005 Scenario (HUC12-level 75 th
percentile values, ppm) ....................................................................................................... .......... 69 Figure 2-12 Total Fish Tissue Mercury Concentr ations Projected for 2016 Scenario (HUC12-

level 75 th percentile values, ppm)* ............................................................................................... 70 Figure 2-13 EGU-Attributable Fish Tissue Me rcury Concentrations for 2005 Scenario ............. 71 Figure 2-14 EGU-Attributable Fi sh Tissue Mercury Concentrati ons Projected for 2016 Scenario

............................................................................................................................... ........................ 72vii 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 Tissu e Concentrations Versus Total Hg Deposition for the 3,141 Watersheds In cluded in the Risk Assessment ................................. 78 Figure 2-18 Cumulative distribut ion plots of U.S. EGU-attrib utable Hg deposition over the 3,141 watersheds used in modeling the high-end fe male consumer population as contrasted with all 88,000 watersheds (2016 Scenario). ........................................................................................ 78viii Executive Summary

The Technical Support Document is a revise d version of the TSD for EPA’s Nationalscale Mercury Risk Assessment completed in Ma rch 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 revi ew to EPA's Administrator on the scientific and technical aspects of environmental issues. The SAB esta blished a 22-member panel with representation from academic institutions, industry, federal agencies, and state governments. The SAB supported the overall design of th e risk assessment, confirmed EPA’s analytical assumptions, and concluded that the risk assessment should pr ovide “an objective, r easonable, and credible determination” of the potentia l public health hazard. The SAB made many recommendations for improving this TSD, which SAB organized into th ree general themes: (1) improve clarify of the document regarding methods and presentation of results, (2) expand disc ussion 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 waters heds 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 th en bioaccumulates as MeHg in fish. The primary pathway of concern from a public he alth 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 pr egnant are of concern.) Depending on the level of prenatal exposure, ch ildren born to those women ma y then experience a range of neurodevelopmental effects including decrements on a number of neuropsy chological measures.

We focused on consumption from inland freshw ater 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, particul arly for waterbodies w ith elevated U.S. EGUattributable mercury deposition such as the Ohio River Vall ey. We focused on subsistence fishing populations because they typically have substantially higher cons umption rates of selfcaught fish than recreational fishers and theref ore, experience higher risk. Because we do not

have data available on the distri bution of subsistence fi shing populations in al l watersheds in the U.S., we modeled a hypothetical female subsiste nce consumer at those watersheds where we

have fish tissue data and where we believe subsistence fishing activ ity 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 merc ury deposition for the continental U.S.

2. Interpolate mercury deposition to watersheds

3. Estimate 75 th percentile fish tissue merc ury concentrations at wate rsheds with fish tissue

data

4. Project 75 th percentile fish tissue mercury concen trations for the 2016 scenario, which reflects projected Hg deposition without regu lation of mercury emissions from U.S.

EGUs

5. Define female subsistence consumer scenarios

6. Identify watersheds with subs istence 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 be tween 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 75 th percentile fish tissue sample because we believe it is likely that subsistence fishers may target larger fish (with somewhat higher MeHg levels) to supplement fa mily meals. Although we can only calculate risk for the 4% (3,100) of the 88,000 watersheds in the c ontinental U.S. for which we have fish tissue mercury data, these data cover 48 states includi ng many states with high levels of mercury deposition and fish tissue mercury concentrations . We excluded watersheds with potentially significant non-air s ources of mercury.

In estimating risk for the most comprehensiv e 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 h ealth effects. We compare modele d 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, s upported by the SAB, considers su ch 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 le ast 5% of the risk and (b) numb er and percent of watersheds modeled where U.S. EGU-sourced mercury consid ered alone, without taking account deposition from other sources, results in an HQ>1. The combination of these two estimates provides thex total number and percent of watersheds where fe male 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 bodi es. 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) refini ng and improving the technical pres entation in general, including addition of a sample calculation figure clarifyi ng how the watershed-level risk estimates are generated, (c) expanding the discussion of how th e 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 appli cation 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 vari ability and uncertainty, an d (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 metric s, and reflecting consumption rates ranging

from the 90 th percentile to the 99 th percentile, we estimate that 22% to 29% of the watersheds modeled have populat ions that are potentially at -risk due to U.S. EGU Hg

emissions in 2016.

o Based on U.S. EGU-attributable depositi on 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 99 th 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 populatio ns that are potentially at-risk due to

total deposition with at least 5% of that tota l deposition attributable to U.S. EGUs (based on the 99 th % 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 merc ury concentrations. On average, for thesexi 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 fracti on of modeled watersheds would still have populations at-risk due to deposition from gl obal 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-sc ale risk assessment for mercury (Hg) that informed the March 2011 finding that it is appr opriate and necessary to regu late electric utility steam generating units in the United States (U.S. EGUs), pursuant to Section 11 2(n)(1)(A) of the Clean Air Act (CAA). That-risk assessment is docum ented in the Technical Support Document: National-Scale Mercury Risk A ssessment Supporting the Appropr iate 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-rev iewed, and that the results of the peer review and any EPA response to them would be publis hed 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 Administrato r on the scientific and technical aspects of

environmental issues. The SAB established a 22 -member peer review pa nel with representation from academic institutions, industry, federal agen cies, and state governments. The peer review charge questions and results of the peer review provided in lette r 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 recomme ndations for strength ening the nationalscale risk assessment and developed a revise d version of the risk assessment reflecting implementation of a number of the recommenda tions 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 revi sed version of the risk assessment. While the underlying technical approach used in assessi ng 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 revi sed 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 prov ided by the SAB peer review panel, and an overview of the changes to th e 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 ove rview 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 pr ovide an overview of the analytical approach used in the analysis, with subsections addressi ng 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.pdf2 walks the reader through the proce ss 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 toge ther in generating watershed-le vel 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 pr esented at the beginning of that section). Section 3 presents a summary of key observations fr om 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 appr oach 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 or iginal SAB comment in italics. 2

 The watershed-focus of the risk as sessment should be clearly stated . We revised the technical approach section to more clearly st ate 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 measur ed fish tissue mercury (Hg) data and (b) it is reasonable to assume that high-end fish ing activity could occur. We emphasize the point that the analysis is not a representa tive population-weighted assessment of risk. Rather, it is based on evalua ting exposure scenarios.

 Because IQ does not fully capt ure 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 th e MeHg RfD-based hazard quotient metric . We

modified the structure of the Revised TSD accordingly, including discussion of alternative neurodevelopemental endpoints (bes ides IQ) reflected in the MeHg RfD (see section 1.3).

 Clarify the rationale for using an HQ at or abo ve 1.5 as the basis for selecting potentially impacted watersheds . We revised this discussi on accordingly (see section 1.4.5).

 Additional detail should be provided on the c haracteristics of the fi sh tissue Hg dataset, including its derivation and the distribution of specific attrib utes 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 resu lted 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 addre ssed 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 ch ild-bearing age who consume subsistence-le vels of fish that they catch or are caught by relatives/acquaintances and sh ared 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 gi ven hydrologic unit code (HUC) 4 , number of species reflected across HUCs). We included additional figur es and tables describing the derivation of the HUC-level fish tissue Hg da taset 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 summari zing the distribution of key attributes within that dataset highlight ed by the SAB (e.g., distribution of fish tissue sample size and number of species across the HUC-level es timates). 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 filteri ng on size (i.e., the dataset used in the risk assessment, versus all fish combined).

 Determine whether there is additional (mor e recent) fish tissue data for key states including Pennsylvania, New Jersey, Kent ucky and Illinois where U.S. EGUs Hg deposition may be more significant. We expanded the fish ti ssue dataset by incorporating additional (newer) fish tissue data from the National Listing of Fish Advisory (NLFA) (which included additional data for four st ates including MI, NJ, PA , and MN). We also

obtained additional data for Wisconsin (see section 1.4.2).

 Regarding the fish tissue dataset us ed in the risk assessment, clarify which species of Hg is reflected in the underlying samples and discus s 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 to tal 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 uncer tainty (see Table 2-15 entry B in section 2.7) to include the potential for different sa mpling 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 char acterizing 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 presente d in Table 2-15 entry D of section 2.7 to

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

 While there is support fo r 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 tissu e 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 us e of the median fish tissue Hg value (as

contrasted with the 75 th percentile value) in generating risk estimates : We also included a sensitivity analysis that used the 50 th 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 75 th percentile value used in the core analysis (see section 2.6)

 The SAB was generally supportive of the cons umption 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 rate s for South Carolina reflect small sample sizes, the consumption surveys underlying th e studies are older and behavior may have changed, consumption rates may reflect seas onality 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 consump tion 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 a nnual-average daily consumption rates and that they represent as prepared estimates. We al so expanded the explanation of the exposure calculations to more completely describe th e 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 smalle r than 7 inches in length from analysis . We

described the rationale for the 7-inch cutoff for edible fish used in th e risk analysis (see section 1.4.2).

 The SAB was generally supportive of the approac h 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 scen arios (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 deposit ion and changes in Me Hg fish tissue levels

. We expanded our discussion of uncertainty on the proportionality assumption and added citations for the more recent studies suppor ting 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 com paring measurements of wet deposition with CMAQ-based estimates, the pres entation of Hg deposition estimates should include maps of wet and dry Hg deposition in addition to the to tal 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 (poly unsaturated 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 fr eshwater fish consumption . We expanded the discussion of5 uncertainty on modeling IQ loss to address this issue (see Table B-2 entry B in Appendix B).

 A dditional sources of variability should be disc ussed in terms of the degree to which they are reflected in the design of the risk a ssessment 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 effe cts 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 un certainty should be discussed in terms of their potential impact

on risk estimates. These include:

o Emissions inventory (including the non-E GU segment) used in projecting total and U.S. EGU-attributable Hg depositi on. This includes the projection of

reduction in U.S. EGU emi ssion for the 2016 scenario.

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

scenarios.

o Ability of the Mercury M aps-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 es timate “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 populatio ns and concerns that this could lowbias the risk estimates. We expanded the discussion of sources of uncer tainty 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 ut ility steam generating units in the United States (U.S. EGUs), pursuant to Secti on 112(n)(1)(A) of the Clean Air Act (CAA). The appropriate and necessary finding is based, in part, on an asse ssment of the potential public health hazard6 associated with Hg emitted from U.S. EGUs (s ee Section III of the preamble to the proposed Mercury and Air Toxics Standa rd (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 analys is 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 consump tion of freshwater self-caught fish have the potential to experience signifi cant 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 fo llowing policy-relevant questions were developed to guide the design of the risk assessment:

(a) What is the nature and magnitude the pot ential risk to public health in the United States for individuals experienci ng reasonable high-end exposure to Hg in freshwater, self-caught fish that is attributable to cu rrent U.S. EGU Hg

emissions?

(b) What is the nature and magnitude of th e potential risk for this same group of individuals based on projected U.S. E GU Hg emissions in 2016 considering potential reductions in EGU Hg emissi ons 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 isol ated exposure pathway, but rather is combined with Hg emitted from other sources (domestic and international) in contaminating fish. Therefore, to consid er U.S. EGU contributions to exposure and risk associated with the consumpti on 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 ofimportant technical factors related to airsourced Hg, including, in particular , Hg released from U.S. EGUs:

 While Hg exposure and risk can occur thr ough a variety of pathways, the dominant human exposure pathway associated with ambient air emissions is through the consumption of fish that have bioaccumulate d Hg originally deposited to watersheds following atmospheric release and transport. De position of Hg to watersheds includes Hg originating from local/regional sources, comb ined 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 anthr opogenic and natural sources. Generally oxidized (divalent) and particle-bound Hg w ill 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 provi des 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, th is 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 (includi ng 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-attrib utable deposition and Hg in fish, we have not included this commercial consumption pa thway in the quantitative risk assessment. 10

 The type of fish consumption likely to re sult 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 regi ons 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 (Hg 0 ), oxidized Hg compounds (Hg +2 ), and particle-bound Hg (Hg P ). Elemental Hg does not quickly deposit or chemically react in the atmosphere, resulting in residence times that are l ong enough to co ntribute 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 me rcury levels in mothers (a surrogate for exposure in utero ) ormercury 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 smal l. Therefore, particularly in the cont ext of estimating indi vidual 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 furt her off the U.S. coast, where total mercury loading is likely to also be dominated by non-U.S. anth ropogenic 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 re lative to the average levels in the continental U.S., several factors make modeling how changes in mercury depositio n affect fish tissue Hg concentrations in these nearcoastal areas and the Great Lakes challe nging and uncertain. Speci fically, the size of these waterbodies relative to inland lakes and rivers and the potential for fish to ha ve larger habitats makes it difficult to quantify the EGU contribution to fish tissue Hg concentrations in these lo cations. Due to the greater un certainty associated with modeling near coastal watersheds, we have elected not to si mulate this pathway in the risk assessment. Because we have not assessed this pathway, this ri sk assessment may underestimate the number of at-risk watersheds in the U.S.8 higher in 2016 (see Section 2.4). Therefore, e fforts to identify areas with high U.S. EGUattributable MeHg exposures a nd 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 questi ons and technical fact ors 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 refl ects emissions after imposition of CAA requirements. The available emissions data (see Section 2.3) sugge st that current 2010

U.S. EGU emissions are closer to levels reflected in th e 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 re gulations U.S. EGU emissions are not likely to be substan tially reduced between 2010 and 2016 (although the CAA requires the Agency to consider onl y federal CAA requirements in estimating

future HAP emissions and attendant risks asso ciated 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 greates t exposure and risk from consuming fishimpacted by Hg emitted from U.S. EGUs. This translates into a focus on women of childbearing age who consume subs istence-levels of fish ca ught 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 scenar ios, we focus on those self-caught fish consumers with the highest intake rates a nd 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-consumi ng fisher populations that reflect different SES groups and are active in different regi ons of the country (e.g., Laotians, Great Lakes Tribal populations, Black and Wh ite anglers active in the So utheast - see Section 1.4.3). As noted earlier in section 1.1 for the analys is, we focus on women of child-bearing age

11 Deposition estimates for the 2005 scenario are used in s caling 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 leve ls 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 fe male subsistence fish consumer risk at watersheds where there is the potential for th is 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 watershe ds in the U.S. where we have fish tissue Hg data and where we believe high fish-cons uming 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 th ese high-end fisher populations, the risk estimates that are generated are not population-weighted a nd instead are given a uniform weight for each watershed-level risk estimate generated. 14 This focus on subsistence fishing activity and associated c onsumption reflects the fact th at 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 distri bution of risk across all types of fishers potentially active at watersheds wh ere we have Hg fish tissue data.

 Exclude commercial fish consumpti on from the quantitative risk analysis. Although risk associated with commercial fish consump tion may be a potential public health concern under certain circumstances, the relatively lo w contribution of U.S. EGU Hg to this source of dietary fish (relati ve to non-US Hg emissions), leads us to exclude this consumption pathway from the risk assessm ent 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 Lake s, 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 li nkage between U.S. EGU Hg deposition and Hg exposure and risk for this dietary pathwa y 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 estim ates 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 subsisten ce fish consumer scenario (which is assessed across all watersheds with fish tissue Hg data), the potential for th e 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 populatio n specifically at inland fres hwater waterbodies, (b) for this population, the fraction that focus their activity at i ndividual watersheds, and target somewhat larger fish to supplement their diet, and (c) for this subgroup, the fracti on of female consumers of childbearing age who either fish themselves and consume at this level, or obtain and re gularly consume fish provided by fishers who focus their fishing efforts at the individual watershed.. However, cu rrently available information does not allow us to estimate each of these subgroups of high-consuming fishers. Sp ecifically, while we have data on the frequency of recreational angling within the U.S., th is covers general recreational fishing and not subsistence fishing. In this analysis, we have focused on a subset of female subs istence-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 no t possible at this point to definitively state at which waterbodies they are active or to enum erate them for purposes of generating population-weighted risk distributions.10  Include estimates of total ri sk from all Hg deposition sour ces, 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 ris k, 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 resulti ng from other sources of Hg. These two risk metrics reflect the cumulative burden of Hg exposures and incremen tal 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 po pulations. 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 discu ssed in greater detail in sections 1.4 and 1.4.6, we generate risk estimates at the watershed-le vel 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 subsisten ce fishers. As noted earlier, limitations in quantifying the number of high-c onsuming fishers active across th e set of modeled watersheds prevents us from generating population-weighted risk distributions. However, we do use the watershed-level risk estimates (with uniform we ighting across watersheds) to generate risk

15 When exposures are to be compared to the EPA’s refere nce 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 re flecting total exposure is calculated, we can then consider the U.S. EGU incremental contribution to th at total risk. However, U.S. EGU incremental risk in the form of an HQ should not be consid ered 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 su ch as motor skills and atten tion/behavior and therefore, risk estimates based on IQ will not cover these additi onal 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 Nami ng 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 identif y specific risk per centiles, (e.g. the 95 th percentile of risk across watersheds). In this context, the distribu tion of modeled subsisten ce fisher risk estimates represents the range of risk across modeled watersheds base d on assessing the same subsistence fisher scenario at each watershed (without popul ation-weighting). Percen tile risk estimates obtained from this distribution are for the populati on of watersheds, rather than the population of

subsistence fishers.

Reflecting the goals of the analysis presen ted 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 tota l risk attributable to U.S. EG Us 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 signi ficance of HQ estimates, we considered total MeHg exposures above the RfD to repres ent 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 stag es is also described below (throughout this document, this will be referred to as the “risk characterization framework”):

Stage 1 – Identify watersheds with populations po tentially 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 poten tially 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 consider ed 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 extrap olations from animals to humans, and for database deficiencies. In addition, there is no evidence for a biological threshold obse rved 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 sugges ts 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 contribu ted 5 percent or more to Hg deposition in the watershed. EPA believes this is a conservative approach given the increasing risks associated with incr emental exposures above the RfD.

b) Risk focusing on U.S. EGU depositi on 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 c ontributed 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 considerwatersheds 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 exposur e which, when considered separate from total Hg exposure, represents a potential public health hazard.

This framework allows us to identify wa tersheds where U.S. EGU-related exposure considered separately, or as a portion of total ri sk, represents a potential public health hazard. More specifically, it allows us to estimate th e 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 Ch aracterization Framework

1.4 Overview of Analytical Approach

This section provides an overview of the anal ytical approach for this risk assessment, which we illustrate in Figure 1-2. In this figure, we identify the subsect ions 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. EG U 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 : watersheds where U.S.

EGU deposition, if considered in
isolation, could result in MeHg

exposure that exceeds the MeHg RfD.

Stage 2 : total number of watersheds with populations potentially at

‐ risk due to Stage 1a

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



### Stage 2 = A + B ‐ C Some watersheds are identified in

both stages (these are not double

‐ counted in the total Stage 2 number). 

# B C A14 Figure 1-2 Flow Diagram of Risk Analysis (for the 2016 ai r 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 esti mated 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

decision data input analysis step KEY: 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 modeli ng 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 wi th 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 spat ial resolution of the mercury deposition modeling. We interpolated th e gridded mercury deposition estimates to deposition for

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

13. Characterize measured fish tissue Hg concentrations (75 th percentile) at each watershed : We estimate the 75 th percentile fish tissue Hg concen trations 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 fi sh 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 75 th percentile fish tissue Hg con centrations for the 2016 scenario : We used the assumption of a proportional relationship be tween Hg deposition and fish tissue Hg concentrations together with deposition es timates for the 2005 and 2016 scenarios to project fish tissue Hg concentrations for th e 2016 scenario. Specifically, we used the ratio of 2016 deposition to 2005 deposition at a given watershed to adjust the 75 th 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 fr eshwater waterbodies.

Our literature review identified seven female s ubsistence fish consumer scenarios that we selected to include in the analysis: (1) typi cal female subsistence fish consumer, (2) low income White fishers in the southeast, (3) lo w income Black fishers in the southeast, (4) low income Hispanic fishers, (5) Vietnamese fishers, (6) Laotian fi shers, and (7) Tribal fishers (Chippewa active near the Great Lakes). (See section 1.4.3).

16. Identify watersheds with subsis tence fisher population activity: For the typical female subsistence fish consumer scenario, we assume subsisten ce 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 us e demographic data to determine if at least

25 individuals with SES attributes matc hing those of the s ubsistence 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 cons umption 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 scenar io. In each case, at the high end (e.g., 90 th to 99 th
percentiles), consumption rates reached subs istence levels, ranging from an 8oz selfcaught fish meal every few days to an 8oz m eal every day and higher in some cases. (See section 1.4.3).

18. Estimate total fish consumption-rela ted 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 po tentially active at a given watershed. These estimates of exposure use the projected 75 th percentile fish tissue Hg concentrations for 2016 described in Step 4 above. We were caref ul 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 c onsumed). 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 Me Hg RfD to generate HQ estimates for total MeHg exposure. (See section 1.4.5)

20. Estimate of U.S. EGU-attrib utable risk (2016 scenario) : We used the sameproportionality assumption discussed in Step 4, together with total and U.S EGUattributable Hg deposition estimates (gener ated 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 gi ven watershed calculate the frac tion 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 resp onse of aquatic freshwaterecosystems to changes in Hg deposition focused on watersheds with dimensions closest to HUC12. In each of the studies, researchers used waters heds reflecting a fairly refined spatial scale

18 Given the assumption that fish tissue Hg concentrati ons and exposure and risk are linearly related, the proportionality assumption (together with the ratio of U.S. EGU-related to total Hg depo sition 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 a pplied 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 75 th percentile fish tissue Hg concentration (projected for the 2016 scenario) that was

attributable to U.S. EGUs at each watershed and then us ed 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 th at, at least in the context of these studies, researchers believed that the relationship betw een changes in Hg deposition and changes in MeHg levels in aquatic biota coul d be effectively explored at th e level of these more spatially

refined watersheds. Each of the st udies is briefly summarized below.

The Knights et al., 2009 study focused on ch aracterizing 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 wate rbodies 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 th e article). Given that the majo rity 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 link ing 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, Cana da), 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 meas urement of Hg in various media and biota to see how long it took for the loaded Hg to im pact 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 invol ved 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 wate rshed reflected in these two studies of Hg loading response, use of a more refined spatial sc ale (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 th e 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. Conve rsely, 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 th erefore, 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 charac terize fish tissue Hg concentrations at the watershed level using measured fish tissue Hg data. This process i nvolved three tasks: (a) develop the database of fish tissue Hg concentrat ions based primarily on st ate-level data, using a number of filtering steps that ar e described below, (b) use the fi sh tissue database to generate percentile estimates (75 th and 50 th percentiles) for each watershed containing fish tissue Hg18 measurement data and (c) filter the set of watershe ds with fish tissue Hg percentile estimates to exclude locations potentially impacted by non-atmospheric Hg sources.

The SAB recommended that EPA evaluate whethe r 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 revi sed 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 TS D, 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 fo r a handful of states. We have also included

additional detail on the deriva tion of the fish tissue Hg dataset in response to an SAB recommendation. We describe each step to devel op the dataset ultimately used in this risk assessment, which is illustrated in Figures 1-3 through 1-6. We al so provide the number of fish tissue Hg measurements and watershed-level estima tes 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 w ith fish tissue samples from three main

sources:

 National Listing of Fish A dvisory (NLFA) database. The NLFA, managed by EPA (http://water.epa.gov/sc itech/swguidance/fishshellfish/ fishadvisories/), collects and compiles fish tissue sample data from all 50 states and from trib es across the United States. In particular, the 2010 vers ion 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) co mpilation of mercury datasets. As part of its Environmental Mercury Mapping and Analys is (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/da tasets.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, c overing the period 1995 to 2007 (referred to as “USGS States” in Figure 1-3)

(2) over 6,000 fish tissue samples from several ot her sources, includi ng the National Fish Tissue Survey, the National Pesticide M onitoring 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 and19 Analysis Program (EMAP) (http://www.ep a.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 databa se were obtained from state fish advisory programs managed by the health department, natu ral resource department or environmental protection agency in each state. In many cases, these data were not sampled randomly but instead reflect protocols intended to ta rget (1) areas know to support re creational and or commercial fishing and (2) areas believed to have elevated le vels of chemical contamination (i.e., MeHg) in

fish. 19 Therefore, the state-level data do not pr ovide a representative characterization of the distribution of MeHg concentratio ns in fish tissue at waterbodies across the state and are instead, likely to be biased towards locati ons with higher Hg fish tissue con centrations 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 da ta 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 wa tersheds 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 literatur e 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 tissu e Hg concentration is MeHg (see section 1.4.4).

Data from the four datasets shown in Fi gure 1-3 were combined into a single “2010 Master” Hg fish tissue (MFT) sample datase t covering the period 1995 to 2009. One problem encountered in combining these datasets is the potential duplication of sa mples in the NLFA and

19 We reviewed approaches used by Pennsylvania and Wi sconsin for determining where to collect fish tissue Hg measurements. These states were chosen because they eith er have relatively high fish tissue Hg concentrations (Wisconsin), or relatively elevated U.S. EGU-relate d Hg deposition (Pennsylvania). Pennsylvania identifies sampling locations based on (a) a need to verify or delist specific locations for fish advi sories, (b) provide additional data points for their Water Quality Network and (c) res pond 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/ser ver.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/consumpti on/1990-2005mercurysummary.pdf ). While the details of these two strategies do differ somewhat, generally both states ar e targeting areas either suspected of ha ving 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 an d instead, are likely to favor coverage of more heavily impacted watersheds (at least for the fraction of samples coll ected to cover areas of potential concern with regard to mercury impacts).20 USGS-States data. Unfortunately, these two data sets 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 subdivi ded 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-refere nced (i.e., did not include latitude and longitude information)

(b) excluded samples with missing date information

(c) excluded samples that were not from freshwat er fish species or freshwater locations (i.e., excluded estuarine locations),

(d) excluded samples from fish sma ller than 7 inches in length. 20 Additional discussion of uncertainty related to th is filtering step is presen ted later in this section.

These filters excluded a total of 21,674 sample s 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 samp ling data for 1995 to 2009, we decided to

restrict data to 2000 to 2009 in the risk asse ssment (the SAB supported this decision). We excluded fish tissue samples that likely refl ected Hg deposition levels from the 1990’s when anthropogenic emissions in the U.S. were hi gher than after 2000. We recognize the complex spatial and temporal nature of the response of fi sh tissue Hg concentrations to changes in Hg deposition and loading and acknow ledge 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 init ial dataset by 27,522 observa tions (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 thes e 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 in ches 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 databa se 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 y ears. The composition and processing of these “augmentation” data are shown in Figure 1-4. Al l of these data were provided to EPA by the individual states, but they have not yet been in corporated into the most recent versions of the

NLFA. The main criteria for sele cting these particular 5 states ar e: (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 thes e critical descriptors) and (2) the states are located in areas of the country with relative ly 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. Theref ore, 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 Merging22 formats. They were then combined into the “A ugmentation Master” datase t 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 locatio ns east of the Mi ssissippi 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 includ e a measure of fish length; th erefore, all of the samples

were retained and assumed to be above the 7 inch threshol d. In the March assessment, these samples were excluded, however, in order to provide a more complete spatial co verage 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 Screening Data Merging

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 percen tile 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 th is process, we used th e latitude and longitude information from each sampling location to identi fy 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 tissu e Hg concentrations, supported by the MMaps study is most supportable in those situations wher e 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 signifi cant non-air mercury loadings was also completed in generating the 2010 MFT database. At this stage, to restri ct 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) Augmentation MFT Sample Dataset

(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

watersheds24 using a USGS data set characte rizing mineral and metal operati ons 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 thos e considered active in 2003. We identified watersheds with substantial non-EGU anthropog enic 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 25 th percentile annual U.S.-EGU Hg emission

value as characterized in the 2005 NATA. 25 The 25 th 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 unknow n. The SAB also commented that while other screening criteria could be applied, such as rem oving watersheds near ur ban areas with potential waste runoff, these would be unlikely to substant ially change the results. In addition, applying additional screening criteria woul d 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 anthr opogenic emissions of Hg) resulted in 15 HUC12 watersheds, containing a total of 230 observati ons, 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), whic h represents a 33% increase in the number

of watersheds assessed since the Ma rch TSD, as summarized in Figure 1-5.

As shown in Figure 1-5, this approach yiel ded 3,141 HUC-level sets of fish tissue Hg estimates for the augmented (full) dataset used in the revised nationa l-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 (25 th , 50 th , 75 th and 90 th ) fish tissue Hg values for each sampling site within a HUC and (b) for the m ean, 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 75 th percentile fish tissue Hg value for that HUC, we would first compute the 75 th percentile values at each sampling site and then take the average of

those two 75 th percentile values. If there is only on e sampling site in a given HUC, then we simply compute the mean or percen tile of interest from the measur ements at that site and that statistic is used to represent the HUC. This appr oach 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 representativ e 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 measurem ents 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 m ean 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-speci fic 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 75 th percentile fish tissue Hg value does not use a sing le fish tissue Hg value in instances where there are multiple sites within a watershed. Instead, we calculate the 75 th percentile fish tissue Hg for each site (i.e., with each value representing a specific sampling site, which often tr anslates into a distinct wa terbody) and then take the average of those values, reflecting fishing activity that is di stributed 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 75 th percentile fish tissue value at each watershed as the main basis for exposure and risk characterization. 28 Selection of the 75 th percentile value was based on the assumption that a subset of su bsistence fishers would favor larger fish which have the potential for higher bioa ccumulation (i.e., use of a median or mean value could low-bias likely catch-related Hg levels). There is uncerta inty associated with this assumption and should fishers at a particular wa tershed favor fish that are either larger or smaller than the type of fish

reflected in the 75 th percentile sample, risk estimates co uld be biased accordingly. Uncertainty

related to use of the 75 th percentile fish tissue Hg value in exposure modeling (specifically, potential bias in th e estimation of the 75 th 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 H UC12s with Fish Tissue Mercury Data (for 2010 MFT, Augmentation MFT and the Combined 2011 MFT Datasets)

Uncertainty related to low sample size for a subs tantial fraction of the HUC12s and exclusion of fish <7 inches in length in calcula tion 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 75 th percentile estimate for those watersheds . To examine the potential magnitude of

low bias in the 75 th percentile estimates, we have summarized the distribution of 75 th percentile

28 We note that the 75 th 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 75 th 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 subsiste nce 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 Locations27 fish tissue values (as used in the risk assessmen t) for various sample-si ze-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 freque ncy of different sample sizes across the HUCs included in the risk assessment (F igure 1-7) and (b) a LOESS (locally-weighted scatter plot smoothing)-based fitted curve relating the 75 th percentile HUC-lev el 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 assessm ent, with some watersheds having up to 30 or more distinct sampling sites.

While supporting the use of the 75 th percentile fish tissue Hg va lue in the risk assessment, the SAB recommended a sensitivity analysis involv ing use of a median fish tissue Hg value in place of the 75 th percentile value. This se nsitivity analysis is discu ssed in section 2.6. However, we have provided fish tissue summary statistics here for median fish ti ssue statistics to support comparison with the 75 th percentile values (Table 1-3).

Table 1-2 Summary of 75 th 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 percen tile HUC-level fish tissue Hg concentration - ppm)

Mean N 25 th 50 th 75 th 90 th 95 th

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 25 th 50 th 75 th 90 th 95 th 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 75 th percentile values for HUCs with lower sample sizes. Specifically, in Table 1-2, the mean 75 th percentile HUC-level value increases across strata as the sample size increases. This sugge sts that as the number of samples increases,

estimates of the 75 th percentile will be more likely to appr oach 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 75 th percentile HUC-level trend) has a positive slope, denoting that higher 75 th

29 The LOESS procedure involves fitting of a least-squa res 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 cu rve presented in Figure 1-8 reflects exclusion of two

outliers.28 percentile values are associated w ith higher HUC-level sample sizes. 30 Interestingly, the mean

50 th percentile HUC-level statistic across sample si ze strata (see Table 1-3) also tends to be higher with more samples, but the trend is not as substantial or c onsistent as for the 75 th
percentile estimates. This is expected because th e 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 genera ting median estimates, but it may underestimate higher-end values

such as the 75 th percentile. Uncertainty related to sa mple size and potential bias in the 75 th
percentile HUC-level fish tissue Hg concentrations is also disc ussed 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 num ber of sampling sites is not directly correlated with the number of observations at a given wate rshed (e.g., there are waters heds 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 Charact erizing 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 75 th 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

75 th Percentile HUC-level Fish tissue Hg Levels Against HUC-level Sample Size

Table 1-3 Summary of 50 th 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 percen tile HUC-level fish tissue Hg concentration - ppm)

Mean N 25 th 50 th 75 th 90 th 95 th

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 25 th 50 th 75 th 90 th 95 th 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 th e risk estimates could be high-biased for this Number of observations per watershedWatershed‐level75thpercentilefishtissueHglevel(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 highe r 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 fi sh consumption, it is re asonable (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 gr eater exposure and risk. Given this goal, it is

reasonable to assume that a subset of subsiste nce fishers could focus on larger fish (i.e., > 7 inches) as they attempt to supplement their di et. By basing exposure and risk modeling on the

75 th 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 de gree 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 75 th percentile sample, which is the value used in the risk assessment. The resu lts 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 75 th percentile HUC-level estimates using only the fish larger than 7 inches with the 75 th percentile estimates base d on all fish combined. For example, if we compare the average of the HUC-level 75 th percentile values when only fish >7 inches in length were included (0.32 ppm) with the average of the HUC-level 75 th percentile values when all lengths are incl uded (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 75 th percentile values (e.g., the 95 th percentile of the HUC-level 75 th percentile values are 0.89 ppm and 0.86 ppm, for the >7 inch and all le ngths included, datasets, respectivel y). One likely reason that the

exclusion of fish smaller than 7 inch es did not have a large impact on the 75 th 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 an alysis (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 concen trations is small, we do not carry forward this sensitivity analysis through the calc ulation of risks, as the impact on risk is also likely to be

small.31 Table 1-4 Comparison of HUC-Level Fish Ti ssue 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 25 th % 50 th % 75 th % 90 th % 95 th % 99 th % (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

25 th % 3,147 0.19 0.08 0.14 0.25 0.40 0.52 0.89

50 th % 3,147 0.25 0.10 0.19 0.32 0.50 0.67 1.06

75 th % 3,147 0.32 0.13 0.25 0.42 0.67 0.89 1.36

90 th % 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 75 th percentile fish tissue Hg concentr ations are generated for each of the watersheds, we then adjust these estimates to represent 75 th 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 w ith Figure 1-2), projecti on 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 scenar ios. Specifically, for a given watershed, we multiply the 75 th 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. Deta il 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 subsisten ce fisher scenarios included in this analysis requires three tasks: (a) identif y subsistence fisher populations, (b) assess where they might be32 active and (c) define fish consumption rates for female subsistence fish consumers associated with those populations. These thr ee 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 poten tial to consume fish caught at inland freshwater locations, since these populations are expected to experience the greatest U.S. EGU-attributable risks. Therefore, in reviewi ng studies of fishing be havior, we prioritized surveys characterizing fishing activity for populati ons with these characteristics. In addition, we needed studies that provided st atistically rigorous estimates of annual-average daily fish consumption rates. Subsistence levels of fi sh consumption are likely only experienced by relatively small fractions of the study populations surveyed (e.g., 90 th to 99 th percentile consumption rates).

Although we reviewed many studies, we ultimatel y identified three studies that met our criteria for characterizing subsistence-level sel f-caught freshwater fish consumption. These three studies included several subsistence populations differentiated by ethni city and SES status including: (a) White and Black popul ations (including female and lo w 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 popul ations (Chippewa and Ojibwe) active around the Great La kes (Dellinger et al., 2004).

These studies were used to characterize beha vior for female subsistence fish consumers

assumed to be associated with the subsisten ce fisher populations described in the studies. 31
Specifically, based on these studies, we charac terized behavior for seven distinct female

subsistence fish consumer scenarios which were included in the risk assessment. These scenarios are listed (and briefly de scribed) in Table 1-5.

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

subsistence fish consumer Scenario s 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 ther e 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 subsisten ce 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 fish er populations might be active

The following approach was used to identify th e 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 Lake s), we assumed that hi gh-end fishing behavior could be generalized beyond the specific geograp hic 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. wher e U.S. EGU-attributable deposition is higher and where we have more measured Hg fish tissue da ta. In deciding how to extend coverage for each fisher population, we considered several factors including (a) th e potential for high-end fishing activity to be culturall y-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 econom ic need. Because we assumed that fishing activity is culturally related, we generalized fishing activity by Hispanics, Laotians and Vietnamese beyond California, where the study pr oviding 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 part icular subsistence fisher population might be active, we then identified th e specific subset of wa tersheds within that region where the high-end fishing activity (a nd consumption by female subsistence fish

consumers associated with those subsistence fi shers) might occur. This task was challenging34 because we do not have data characterizing the number and location of subsistence fishers for any of the fishing populations under considera tion. Therefore, we developed and applied a source population construct to guide iden tification of areas (watersheds) where a given subsistence scenario might be active. 32 This approach requires th at in order for a subsistence fisher scenario to have the potential to be activ e at a given watershed, that watershed must be in close proximity to a source population matching th e ethnic/SES composition of the subsistence fishers being represented. In this case, we specifi ed 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 th e Laotian subsistence fi sher scenario was only assessed at those watersheds intersecting U.S. Cens us 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 belo w the poverty line. The SAB peer review panel agreed that the criterion of us ing 25 persons within a source p opulation to identify watersheds with the potential for subsistence fishing activit y is a reasonable approach. The specific approach used to extrapolate coverage regi onally 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 fe male 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 extra polated fishing activity for the Chippewa/Ojibwe

outside of their ceded territori es, although we do assume that all watersheds within those territories with fish tissue Hg data have the potential for fish ing activity by this group.

The typical female subsistence fish consumer scenario differs from the other scenarios in

that it is not SES-differentiated a nd 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 scenar io 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 scenar ios 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 th e 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 po pulation, while in the case of the high-consuming female angler; we did consider a source popula tion based on poverty. Because the typi cal female subsistence scenario does not use a source population (and is applied uniformly to a ll watersheds with fish tissue Hg data except for those35 subsistence fish consumers scenario to all waters heds 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 wate r bodies that could be fished by subsistence anglers (leading to an underest imation of the percent of watersheds where Hg exposure from U.S. EGU sources is a risk). As mentioned earlie r, Table 1-5 lists the se ven female subsistence fish consumer scenarios and pr esents 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 subsisten ce fish consumer scenarios to include in the analysis and determining where those scenario s should be applied, next we defined high-end (subsistence) self caught fish consumption rates fo r those scenarios. The three studies referenced in Table 1-6 provided either subsiste nce-level consumption rates at the 90 th to 99 th percentile, or the statistical parameters nece ssary to calculate those percen tiles (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 consum ption 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 cons umption 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 not es relevant to their interpretati on in the context of this risk assessment. Of particular interest was whet her 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 ultimatel y, calculation of exposure and risk. This issue is discussed in gr eater length in the next section.

The fish consumption rates presented in Tabl e 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 ar e 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 magnit ude, they could be experienced by individuals purposefully supplementing their diet due to economi c need or due to high levels of recreational fishing activity. While these scenarios could repres ent either subsistence or high-end recreational fishing, the resulting exposure and risk characte rization would be similar. For typical female subsistence fish consumers, as mentioned earlier, this scenario is appl ied uniformly across all watersheds with fish tissue Hg data, and whether we define cons umption as subsistence or highend recreational fishing does not a ffect the exposure and risk char acterization. In either case, the

excluded due to potential impacts from non-air depos ition), 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 pr ovided, 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 in dividual residing in the vi cinity 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 se lf-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 consum ption modeled in the analysis.

A subset of these “supporting” stud ies is briefly described below:

 A study by Burger et al., 1999, examined recr eational and subsistence fishing activity along the Savannah river in Georgia to dete rmine 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 ca se 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 co nsumption rates could be high enough to approach subsistence levels. For example, a ~200 g/day fish consumption rate represented the 98 th percentile for Whites, but only the 92 nd percentile for Blacks. This study supports the presence of high-e nd 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, educati on), 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 id entified for fishing popul ations in FL, MN (specifically for Tribes) and CT (for Asian populations, alth ough it is not clear whether the rates for Asians are for self-caught fi sh 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 th e overall population likely engaging in highend subsistence levels of self -caught fishing behavior. Th is study supports the existence of subsistence fishing populations in FL a nd 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 im portant to differentiate between high-end recreational and necessity-based subsistence activity, sin ce the associated populations at any given watershed could differ, leading to different population-weig hted 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 th at subsistence behavi or 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.38Table 1-6 Fish consumption rates and additional behavior-related information forsubsistence populations included in theanalysis(A) Fish consumingpopulations covered bystudy (and referenceinformation)(B) Overview ofstudy(C) Assumption regarding wherethe subsistence fisher scenario(and associated female subsistencefish consumers) might be active(D) Self-caught fish consumption rates(mean, 90th, 95th, 99th) g/day and notes ontype of consumption rate(e.g., temporal averaging period and aspurchased versus as consumed)(E) Notes on consumption rateinformation relevant to the riskassessmentHigher self-caught fishconsuming populations(White, Black and female)surveyed in SouthCarolinaCitation: Dailyconsumption of wild fishand game: Exposures ofhigh end recreationalists,Burger et al., InternationalJournal of EnvironmentalHealth Research, 12:4, p.343-354, July, 2002Random survey ofparticipants in thePalmettoSportsmen’s Classicin Columbia SC(1998). Populationinterested infishing/hunting (notgeneral population –represents outdoorenthusiasts in SC)- the Black and White fisherpopulations were extrapolated tocover watersheds modeled for riskin the Southeastern states. Therationale for this was that fishingactivity by thesetwo groups couldbe generalized in this region of thecountry. These scenarios were onlyassessed for watersheds in theSoutheast located within U.S.Census tracts with at least 25individuals from that ethnic groupbelow the poverty line.- given the focus of the riskassessment on consumption bywomen (in considering exposuresfor pregnant women in particular),we extrapolated the typical femaleconsumer scenario to all watershedsin the continental U.S. and, giventhe more generalized nature of thisscenario (no ethnic or SESdifferentiation), we assessed thescenario for all watersheds includedin the risk assessment (i.e., we didnot apply the source populationcriterion used for the otherscenarios).- Black: 171, 446, 557, NC *- White: 38.8, 93, 129, 286- female: 39.1, 123, 173, 373* the sample size for this population is only39, reducing overall confidence in a 99thconsumption rate (therefore, this high-endpercentile was not included in the riskassessment)Consumption rates are annual-average valuesexpressed as meals or portions (as prepared)(median, 75th, 90th, 95th, and 99th). Surveyasked respondents by month for number ofmeals of different type of fish and serving size(here models were used to demonstratedifferent meal or serving sizes). Authorscould then use thisto estimate monthlyconsumption rates and convert these into anannual average.Sample size is variable – out of 458respondents, 39 are Blacks, 149 arefemale and 98 are low income –Black n is relatively smaller than theother groups, which increasesuncertainty in higher percentilevalues provided for this group.The authors point out that theseresults highlight the considerablespread between high-end consumersand more typical behavior (95thpercentile is more than 10X greaterthan the mean or median intake ratefor wild-caught fish).Results are also provided for lowincome (0-20K$ annual income).These consumption rates arerelatively high particularly for thehigher percentiles (90th, 95thand 99thrates are: 285, 429 and 590 g/day).This observation forms the basis forour decision to assess a number ofthe subsistence populations only forwatersheds located in U.S. Censustracts containing members of sourcepopulations below the poverty linefor the White and Black populations.39(A) Fish consumingpopulations covered bystudy (and referenceinformation)(B) Overview ofstudy(C) Assumption regarding wherethe subsistence fisher scenario(and associated female subsistencefish consumers) might be active(D) Self-caught fish consumption rates(mean, 90th, 95th, 99th) g/day and notes ontype of consumption rate(e.g., temporal averaging period and aspurchased versus as consumed)(E) Notes on consumption rateinformation relevant to the riskassessmentHigher self-caught fishconsuming ethnicpopulations includingHispanics, Laotians andVietnamese surveyed inCaliforniaCitation: Contaminatedfish consumption inCalifornia’s Central ValleyDelta (Shilling et al.,Environmental Research110, p. 334-344 (2010)Study looks atsubsistence fishingactivity amongethnic groupsassociated withmore urbanizedareas near theSacramento and SanJoaquin rivers in theCentral Valley inCA.- the Hispanic fishing scenario wasextrapolated to cover watershedslocated in U.S. Census tracts with atleast 25 low income members of theethnic populations (e.g., theHispanic consumption rates wouldbe applied to the subset of the 3,141watersheds located in U.S. Censustracts with at least 25 low incomeHispanic individuals).- the Laotian and Vietnamesefishing scenarios were extrapolatedto cover watersheds located in U.S.Census tracts with at least 25members of the underlying ethnicgroup.- Hispanic: 25.8, 98, 155.9, NC*- Lao: 47.2, 144.8, 265.8, NC*- Vietnamese: 27.1, 99.1, 152.4, NC** 95thpercentile values were provided in thestudy. 90thpercentile values were calculatedusing Crystal Ball (based on the median andstandard deviations provided) assuming a lognormality of the consumption ratedistributions. 99thpercentile consumption rateswere not provided (or derived) for any of thesepopulations due to small sample sizes of thestudy populations.Consumption rates are annual-averageestimates (cooked weight- as filets) based onthe fact that they compare their average valuesagainst other rates from the literatureincluding rates used by the EPA in theregulatory context - all of which are annualaverages. Survey used different sized modelsof cooked fish filets.The authors note that many of theseethnic groups relied on fishing inorigin countries and bring thatpractice here (e.g., Cambodian,Vietnamese and Mexican). Theauthors also note that fishconsumption rates reported here forspecific ethnic groups(specificallySoutheast Asian) are generally inline with rates seen in WA and ORstudies.40(A) Fish consumingpopulations covered bystudy (and referenceinformation)(B) Overview ofstudy(C) Assumption regarding wherethe subsistence fisher scenario(and associated female subsistencefish consumers) might be active(D) Self-caught fish consumption rates(mean, 90th, 95th, 99th) g/day and notes ontype of consumption rate(e.g., temporal averaging period and aspurchased versus as consumed)(E) Notes on consumption rateinformation relevant to the riskassessmentHigh-end self-caught fishconsuming Chippewa andOjibwa Tribal populationsactive in the vicinity of theGreat Lakes.Citation: Exposureassessment and initialintervention regarding fishconsumption of tribalmembers in the UpperGreat Lakes Region in theUnited States. Dellinger ,Environmental Research95 (2004) p. 325-340This studycontrasted selfreported fishconsumption ratesby Tribes in theGreat Lakes areawith “actual” fishconsumption ratescollected for asubset of theoriginal studypopulation (147 of822 from 4 Tribalpopulation/locationcombinations). Thestudy found thatactual fishconsumption rateswere lower thanreported values.Activity only assumed to occur inareas ceded to the Tribes covered inthe study (regions in the vicinity ofthe Great Lakes). Because fishingactivity is highly variable acrossTribes (and closely associated withheritage cultural practices) we havenot extrapolated fishing behavior forthese Tribes outside of the specificpopulations and regions covered.- reported value for all Tribal areas (in thestudy) combined: 62, 136.2, 213.1, 492.8All higher percentiles (90th– 99th) werederived using Crystal Ball (based on medianand standard deviations and an assumption oflog-normally distributed variability inconsumption rates)Consumption rates appear to be annualaverage values. Study includes reference toquerying for the number of fish meals in ayear, which suggests that estimates are annualaverages (but it is not explicitly stated).Ingestion rates are in-line with other high-endconsumer rates cited in this table. It alsoappears that estimates are for amountconsumed (i.e., mealweight), based on thefact that the "actual" estimates provided in thearticle focused on this type of consumptionrate.While the “actual” consumptionrates collected for a subset of thefamilies were far lower than thereported values (often an order ofmagnitude smaller), a number offactors resulted in a decision to usethe reported values rather than theactual values in the risk assessment.First, and most importantly, thesample size is very small for the“actual” analysis with n’s rangingfrom 12 to 54 individuals(representing a smaller number ofassociated families) for the differentsurvey groups. These smallsampling rates reduce theprobability of capturing individualswith higher consumption rates in thebroader population. It also appearsthat the actual values may coverwalleye specifically and not includeall fish, which could bias thesevalues downward. There is concernthat, even if consumption rates havedecreased, actual heritage culturalpractices could still exist (or therecould be a desire to return to thoserates), in which case, risks levelsassociated with those higherhistorical consumption rates couldbe important to assess. And finally,the high-end percentile consumptionrates derived based on reportedmean consumption rates (andstandard deviations) are in-line withsubsistence consumption rates seenfor other populations in the U.S.Therefore, these Tribal high-end fishconsumption rates would generalcomport with subsistence fishconsumption activity and thereforeare considered reasonable to includein the risk assessment.41 1.4.4 Estimating total fish consumption-rel ated MeHg exposure (2016 scenario)

Next, we estimated total exposure to Hg for the set of seven female subsistence fish consumer scenarios being evaluate d (for the 2016 scenario). To ge nerate estimates of total Hg exposure, we combined the 75 th 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 conve rsion factor between total Hg m easurements 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 consum ption, it is important to verify that the linkages between factors used in modeling exposu re (i.e., the variables in the exposure equation listed above) are conceptually correct. For example, we need to c onvert the total Hg levels in fish to MeHg levels in order to calculate the HQ, wh ich 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 bi ased risk estimates. Below we discuss each exposure factor, including the data used to specify that factor and the linka ge of that factor to others in the exposure equation. The n, we present the e xposure equation.

Mercury fish tissue concentration (FTC): We calculated the 75 th 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 in to 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 pres ented 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 fi sh 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 prepara tion. Cooking fish typically increas es 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 concentrati on per unit fish due to preparation/cooking). This fact or is based on the Morgan et al., 1997 study, which estimated

factors of between 1.1 and 1.5 for walle ye and 1.5 and 2.0 for lake trout.

The SAB recommended that additional studie s 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 c onclude that they do not support a lower42 preparation/cooking loss adjustment factor. The Farias study appears to suggest that preparing fish by Manaus residents in the Amazon coul d decrease Hg concentrations. Although this initially suggests that th e 1.5 factor used in the risk assessment is biased high, a closer read of the Farias study suggests that the au thors may have measured non-fish components added to dishes (e.g., onions, heavy breading etc) in post-cook ing measurements, whic h 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 diffe rent types of fish meals after preparation rather than pre and post-c ooking 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 cons umption rates (a) are annua l-averages and not rates reflecting elevated consumption during shorter s easonal periods and (b) represent “as consumed” rather than “as purchased” values. Based on the revi ew of the three studies we concluded that the consumption rates are annual average daily consum ption 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 assessme nt, we completed a literature review focusing on the issue of cooking/preparation of fish and the effect on Hg con centration. 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 fo r each female subsistence fish consumer scenarios at each watershed (for 2016), we comp are these exposure estimates to the MeHg RfD to generate HQ estimates. The MeHg RfD is 0.0001 mg/kg-day (equivale nt 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 estimat es, 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 exposur es 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 fact or to consider in interpreting HQ’s is the precision underlying these risk es timates. While precision associat ed with the exposure estimates may be higher, the RfD is only reported to one sign ificant digit, which limits the precision in the HQ estimate. Consequently, we interpret an HQ of 1.5 or greater as repres enting 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 di scussion to the appendices. We discuss the approach used to generate IQ loss estimates including the con centration-response function in Appendix A and we provide the results and appr oach used to interpret the public health significant of IQ loss in Appendix B.

1.4.6 Estimation of U.S. EGU-attri butable 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-attribut able risk is at the core of the 2-stage risk characterization framework (see Se ction 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 fi sh tissue Hg concentrations. 39 As noted in section 1.4.2.1, this proportionality assumption states that, under stea dy 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 proportional ity assumption is supported by the Mercury Maps

38 Note, that for the U.S. EGU increm ental 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 sugg ests that, for those specific watersheds, we need to also consider whether total risk (i.e., the HQ re flecting 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 beca use incremental exposures abov e the RfD increase the risk.

39 Because risk at the HUC-level is linear ly related to the fish tissue Hg concen tration (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 th e March TSD) support a proportional relationship between changes in Hg deposition and changes in fish tissue Hg concen trations, although with important caveats related to the temporal re sponse 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 em issions from U.S. EGU sources and subtracting the results from total Hg deposition to isol ate the U.S. EGU contri bution. The CMAQ modeling for this analysis is described in section 1.4.6.3.

Hg deposition estimates at each watershed c oupled with the proportionality assumption

described above can be used to estimate U.S. E GU-attributable HQ risk for the 2016 scenario. To

estimate the U.S. EGU-attributab le 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 completi ng the estimate of U.S. EGU-attributable risk

for 2016 is outlined below:

Generate U.S. EGU-attri butable HQ risk estimate s 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 ge nerated as described in section 1.4.5.
2016 EGU Hg dep: 2016 CMAQ-based projec tions 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 C ongress, the Mercury Maps model showed that these models converge at a steady-state solution for MeHg con centrations in fish that are proportional to changes in Hg inputs from atmospheric deposit ion. This steady-state so lution only applies in situations where air deposition is the only si gnificant 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 us ed to estimate the U.S. EGU-attributable fraction of risk would ideally only be applied to watersheds wher e these criteria have been met. Application of the proportionality assumption in situations wher e 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 c onstant over time. EPA further recognizes that many waterbodies, particularly in areas of historic gold and Hg mi ning 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 th ere are other modeling t ools 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 resu lts produced by the two model approaches should be equivalent. We did not have all of the da ta inputs (water chemistr y, methylation potential, etc) that would be required to run the R-MCM, and given th e SAB recommendation, and their comment that “it is unlikely that substantia l additional insight woul d be gained with the alternative model framework,” we elected to use the proportiona lity assumption as supported by the Mercury Maps modeling and the peer-revie wed literature. Because the Mercury Maps approach only applies in those watersheds wher e aerial deposition is the do minant source of Hg loading, we excluded watersheds with substant ial 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 uncertain ties 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 ri sk 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 de position for the period during which the fish tissue samples were collected (2000 to 2009) have not re mained constant. In addition, those fish tissue concentrations may actually refl ect patterns of Hg deposition fr om earlier time periods (e.g., the 1990s) when Hg emissions from U.S. sources were experiencing s ubstantial decreases. 41 In

40 As described below, we have excluded those watershe ds containing gold mines or with other non-EGU related anthropogenic Hg emissions ex ceeding specified thresholds.

41 The discussion of model uncertainty provided in the Technical Support Document describing the Mercury Maps analysis (MMAPs TSD, U.S. EPA, 2 001) also addresses the fact that pr oportionality 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 thes e loadings effectivel y 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 ex cluded areas likely impacted by active gold mines, we believe that this issue has been largely ameliorated in th e 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 no t 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 th e Mercury Maps based proportionality assumption. However, we beli eve that the Mercury Maps approach for apportioning fish tissue Hg concentr ations is still appropr iate 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 subs tantial non-air loading of Hg from industrial activity and mines, we did not consider municipa l sewage emissions of Hg to watersheds. This introduces additional uncertainty into the analysis, since watersheds could have a substantial fraction of Hg loading originati ng from municipal sewage treatment . In these instances, failure to

consider this source of non-air lo ading 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 publ ications have supported the finding of a linear relationship between Hg loading and accu mulation in aquatic biot a (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 beco me 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 foll owing reductions in Hg deposition to a watershed, we are likely to see a 2-stage response includi ng: (a) an initial more rapid phase of reduction (ranging from months to a few year s) reflecting a decrease in dire ct loading to the waterbody and subsequent reductions in fish ti ssue Hg concentrations related to decreased water column Hg levels and (b) a second slower phase of reducti on (years to decades or more) reflecting longer term changes in the rate of erosion/runoff lo ading to the waterbody and the potential buffering effect of historical reservoirs of Hg in sedime nt. These findings suggest th at we might not see an equivalent fractional reduction in risk matchi ng 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 st eady 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 st ate, or near steady st ate conditions are met, regardless of how long that takes, then the te mporal response is not a factor and we have increased confidence in applying th e 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 depositi on from U.S. EGUs using the CMAQ model. The Community Multi-scale Air Qual ity (CMAQ) model v4.7.1 (www.cmaq -model.org) is a state of

the science three-dimensional Eulerian “one-atm osphere” 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 photochemi cal oxidants, ozone, primary and secondary PM concentrations, and air toxics over regional and urban spatial sc ales for given input sets of meteorological conditions and emissions. Mercur y oxidation pathways are represented for both the gas and aqueous phases in addition to aque ous phase reduction reactions (Bullock and Brehme, 2002). Mercury estimates from CMAQ have been compar ed to observations and other mercury modeling systems in several peer review ed 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 Qual ity Modeling TSD (U.S. En vironmental 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 simula tions, all other model inputs are the same

in both simulations. Other North American emissi ons of criteria and toxi c pollutants (including mercury) are based on a 2006 Canadian invent ory and 1999 Mexican inventory (U.S. EPA, 2011b).

Global emissions of criteria and toxic pollutant s (including mercury) are included in the modeling system through boundary condition inflow . The lateral boundary and initial species concentrations are provided by a three-dimens ional 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 interval s and an initial concentration field for the 36 km CMAQ si mulations. 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 gl obal model GEOS-CHEM simulation using a 2000 based global inventory as described in (Selin et al., 2007). A compar ison 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 me rcury emissions from Asia between 2000 and 2006.

Given these consistent emissions estimates fr om Asia, the 2005 boundary inflow to the 36 km CMAQ domain was not adjusted. Mercury bounda ry conditions are the same for both the 200548 and 2016 simulations based on the consistency in Asian mercury emissions between 2000 and 2006, the declining ambient mercury concentratio ns in the northern hemisphere since 2000 (Slemr et al, 2011), and the large uncertainties surrounding projected gl obal 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 ke y differences between the watershed-level risk assessment completed in sup port of the 2005 revision rule and the current

risk assessment. These differences include both technical factors relate d to the design of the assessments, as well as differences in the interp retation of potential public health significance of the risk estimates generated. Key differe nces 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 reso lution, whereas in the 2005 analysis, we used CMAQ modeling with a 36 km grid cell resolution. Th e 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 refine d treatment of the inte rsection of aerial Hg deposition and measured fi sh tissue concentrations at the watershed level.

Application of more re fined HUC12 watersheds: The current analysis us es HUC12 watersheds as the basis for risk estimation (these watersheds ty pically 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 potenti al for capturing areas of elevated aerial Hg deposition (combined with meas ured 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 defi ned and considered more ubiquitous: Based on an extensive review of available literature, we identified st udies characterizing high-end self-caught fish consumption for a wide variety of source popul ations (e.g., Hispanic, Vietnamese, Whites and Blacks in the southeast, Great Lakes Tribal populations). A lthough in many cases, it was necessary to extrapolate high-end fishing act ivity to regions beyond those covered in the underlying studies, we believe that the litera ture supports the plausibility of high-end subsistence-like fishing activity across the watershe ds included in the analysis. Additionally, the variety of studies identifying self -caught fishing activity at subsis tence 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 Dete rmining Effectiveness of Utility Emission Controls.49 regions of the country, supports as sessing this type subs istence 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 subs istence fisher activity for the following reasons: (a) it characterized regi onal or local activity that could not be readily extrapolated more broadly, (b) fishing activity queried includ ed 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 (95 th and

99 th 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. Specificall y, while many of the studies of subsistence-like activity are regional in nature, wh en considered together, we now conclude that they support modeling subsistence- like fishing activity more broa dly 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 wa s 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 75 th percentile fish tissue Hg concentration reflecting the potential for high-end subsisten ce fishers to target larger fi sh, 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 conser vative approach (i.e., resulting in higher risk, other factors equal).

Calculation of RfD-based HQ estim ates 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 incrementa l 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 fro m other sources of Hg and (b) the percent of

43 These NW Tribal fishing estimates are subject to consider able uncertainty when extrapol ated 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. Ther e is also significant uncertainty in extrapolating this type of highly-specific cultural-based fishing activity to ot her 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 an alysis 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 subsis tence 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 th ese 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 consumptio n 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 cu rrent analysis, we highli ght 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, fo r the 2005 analysis both of the risk metrics used (i.e., the IDI and the comparison of U.S. EGU-re lated fish tissue concentrations against EPA’s water quality criterion expressed as a Hg fish tis sue value) essentially considered the U.S. EGU portion of risk in isolation. Thes e risk metrics in the 2005 analysis were not contrasted with the much larger fraction of total Hg-related risk a ssociated 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 walk s 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 intermed iate 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 measurem ents available for a given watershed (after filtering), generate the 75 th percentile fish tissue Hg concentration. We provide the median, 75 th and 90 th 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 deposit ion/2005 deposition). (see section 1.4.6.3).

 Step 3: Multiply the ratio fr om Step 2 by the fish tissue Hg concentration to project the fish tissue Hg concentration value to repres ent the 2016 scenario for this watershed. This step involves application of the propor tionality 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 discussi on of this exposure calculation).

 Step 5: Compare the exposure estimate genera ted 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 betwee n the calculation steps outlined in Figure 1-9 and the discussion of variability and uncertainty presented in sec tion 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 dir ectly or indirectly multiple (if not all) of the calculations steps involved in generating risk estimates. Careful consider ation of the analytical appro ach 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 sp ecific 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. Comp ute 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 th e proportionality assumption and uses the ratio developed in step 6 (see section 1.4.6).52Figure 1-9 Sample Calculationfor watershed-level Risk HQIR=FTC*MCF*FCR*CAFBWExample– LowerKettleCreekinnorthcentralPennsylvania(HUC12ID:20502030106)IR:dailyMeHg intakerate(ug/kg‐day)FTC:MeHg fishtissueconcentration(ug/gorppm)MCR:mercuryconversionfactor– fractionfishtissuemercurythatisMeHg (unitless)FCR:fishconsumptionrate(g/day)FPCAF:fishpreparation/cookingadjustmentfactorRfD:methylmercury RfDFracEGU:fractiondep fromUSEGUFishtissue(ppm)Median:0.2475th:0.3290th:0.346samplesPROJECTEDFishtissue(ppm)2016Mean:0.2075th:0.2690th:0.28TotalHgdepoverwatershed(CMAQ):2005=30.422016=25.19Ora~17%reduction95th%consumptionrate:173g/dayFishpreparation/cookingadjustmentFactor:1.5Bodyweight:64gIR=1.01ug/kg‐dayMeHg RfD:0.1ug/kg‐dayTotalHQ=IR=1.01=10.1RfD 0.12016Hgdepoverwatershed(CMAQ):total=25.19USEGU=1.26USEGUis5%FracEGU =0.050USEGUrisk=HQ*FracEGU=10.1*0.050=0.504(i.e.,<1)123457Mercuryconversionfactor:0.95#:calculationstep653 2 Discussion of An alytical Results

This chapter provides a discussion of the results of the 2016 simulation, including both

risk estimates as well as intermediate calculati ons and inputs associated with generating those risk estimates. EPA’s projection of total Hg em issions 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). Th is 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 du e to regulations and federal enforcement actions that achieve Hg redu ctions as a co-benefit of controls for NOx and

SO 2 emissions. Given these estimates of tota l Hg emissions, characte rization of “current conditions” is better represente d by the 2016 Scenario than the 2005 Base Case, since total Hg emissions for the former is equal to our projec tion of recent emissions. By contrast, the 2005 analysis reflects total Hg emissions (52.9 tons) wh ich are significantly hi gher 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 estim ates for the 2005 scenario (although the 2005 Hg deposition estimates are used in scaling fish tissu e Hg concentrations to represent concentrations in 2016).

In this section, we provide a brief overvie w 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 indi vidual 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 concentrati ons (section 2.3), (c) re lationship between Hg deposition and Hg fish tissue concentrations (secti on 2.4) and (d) the MeHg RfD-ba sed 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 di scuss 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 informati on 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 fishi ng activity at inland fr eshwater bodies. The analysis is not intended to capture more ge neralized recreational fishing activity or to reflect self-caught fisher exposur e associated with saltwater fishing or fishing in the Great

46 The modeling conducted for the March risk assessment in cluded a representation of the proposed Cross State Air Pollution Rule, which was finalized in June 2011. We have evaluated differences in pr ojected 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-e nd 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 da ta and (b) this type of subsistence fisher activity could potentia lly occur. Therefore, the risk assessment is watershed-focused, does not provide population-representa tive 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 count ry not covered by the analysis, including a

substantial number of watersheds with relativ ely 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 proportio nality assumption to link changes in Hg deposition (over watersheds) to changes in Hg fish tissue leve ls. This approach assumes that near steadystate conditions are met, which may take year s 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-attr ibutable 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 discu ssion 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 ge nerate distributions of populatio n-weighted risk for specific scenarios assessed (e.g., low income Hispanic fish ers, or Tribal fisher s in the vicinity of the Great Lakes). However, we do believe, base d on surveys of their behavior, that this type of subsistence-like activity could r easonably be expected to occur across some fraction of the 3,141 watersheds included in th e 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 wate rsheds that meet the risk characterization

criteria outlined in the risk characte rization 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 the55 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 sec tions 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 observa tions 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 pr esenting CMAQ modeling results for total Hg deposition (μg/m 2 ) at the watershed-level, for th e 2005 and 2016 scenario s respectively.

 Figures 2-3 and 2-4: Maps pr esenting CMAQ modeling results for U.S. EGU-attributable Hg deposition (μg/m 2 ) 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/m 2 ) 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 depositi on modeling results have been included to allow readers to consider patte rns of deposition over specif ic 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, 50 th , 75 th , 90 th , 95 th and 99 th percentiles) for total Hg deposition and U.S. EGU-attributable deposition for the 2005 and 2016 scenarios.

 Table 2-2: Summary of statistics (mean, 50 th , 75 th , 90 th , 95 th and 99 th percentiles) for U.S. EGU-attributable deposition as a percen t of total deposition for the 2005 and 2016

scenarios.

 Table 2-3: Summary of statistics (mean, 50 th , 75 th , 90 th , 95 th and 99 th percentiles) for percent reduction of (a) total Hg depositi on, and (b) U.S. EGU-attributable deposition, based on comparison of the 2016 scen ario against the 2005 scenario.

Mercury Risk TSD. The figures in th e March TSD were intended to show annual total mercury (Hg) deposition per unit area (in units of μg/m 2 ) by watershed, however they actually disp layed intermediate calculations that had not been adjusted by the waterbody-specifi c surface areas. We corrected the figure s 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.56Figure 2-1 Total Mercury Deposition by HUC (μg/m2) for the 2005 Scenario57Figure 2-2 Total Mercury Deposition by HUC (μg/m2) for the 2016 Scenario58Figure 2-3 U.S EGU-AttributableMercury Deposition by HUC (μg/m2) for the 2005 Scenario59Figure 2-4 U.S EGU-AttributableMercury Deposition by HUC (μg/m2) for the 2016 Scenario60Figure 2-5 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario61Figure 2-6 Mercury Dry Depositionby 12km Grid Cell (μg/m2) for the 2005 Scenario62Figure 2-7 Mercury Wet Depositionby 12km Grid Cell (μg/m2) for the 2016 Scenario63Figure 2-8 Mercury Dry Depositionby 12km Grid Cell (μg/m2) for the 2016 Scenario64 Table 2-1 Comparison of total and U.S. EG U-attributable mercury deposition (μg/m 2 ) 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 th e 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 99 th percentile for total Hg deposition will not be

the same watershed as the watershed with the 99 th 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 th e 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 th e 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 va lues 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 regardi ng 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 La kes have elevated U.S. EGUattributable deposition, many of the highest areas (and larg est 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 genera lly dominated by sources other than U.S. EGUs and the contribution from U.S. EGUs decrea ses between the 2005 and 2016 scenarios : On average., U.S. EGUs contribute 5% of to tal 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 scenar ios) originates from other U.S. sources of Hg emissions and from international sour ces (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 95 th
percentile watershed (see Tables 2-2 and 2-3).

 The contribution of U.S. EGU-attributable de position to total depos ition varies across watersheds and can represent a relativel y large fraction in some instances : In the 2005 scenario, while on average, U.S. EGUs only c ontributed 5% of total Hg deposition in the U.S., this contribution ranged up to 30% for the 99 th percentile watershe d (see Table 2-2). While overall U.S. EGU –attributable deposi tion decreased substantially between the 2005 scenario and the 2016 scenario, U.S. EG Us contributed 11% of total Hg deposition

for the 99 th percentile watershed in 2016 ( (see Table 2-2).

2.3 Fish Tissue Mercury Concentrations

This section characterizes the s ubset of U.S. watersheds with fish tissue Hg data included

in the current the risk assessment. This datase t 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 w ith 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 75 th percentile fish tissue sample for each watershed. As discussed in Sections 1.4.2.1 and 1.4.6, we used the proportionality assump tion together with Hg deposition coverages

49 Controls on PM precursors, including directly emitted PM and SO 2 , 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 (tot al and U.S. EGU-related) to project fish tissue Hg concentrations for the 2016 scenario a nd to estimate the U.S. EGU-attributable fraction of total fish Hg.

We provide figures and tables to summarize the fish tissu e data. As discussed in Section 2.2, most of the areas with elevated U.S. EGU- attributable Hg deposit ion 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 presen t a set of bulleted obs ervations based on the figures and tables. The set of figur es and tables presented include:

 Figure 2-9: Map of 3,141 watershe ds with fish tissue sampling data used in the current version of the risk assessment, including (a) wa tersheds included in the previous version of the risk assessment (2010 MFT) and (b) addi tional fish tissue da ta incorporated to

enhance the current version of the risk a ssessment (augmentation). This map illustrates the uneven coverage of the fish tissue data set 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 fi sh tissue data show ing 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 Fi sh Tissue Mercury Concentrations for the 2005 and 2016 scenarios respectively.

 Figures 2-13 and 2-14: Ma ps presenting EGU-Attribut able 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 th e 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 asse ssment) and (c) the 2011 MFT dataset (the dataset used in the current revised versi on 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 addi tion, this table also presents the percent reduction (between the 2005 and 2016 scen arios) 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.67Figure 2-9 Set of 3,141 HUC12s with Fish TissueMercury Data Used in the Risk Assessment** Includes2010 Mercury Fish Tissue (MFT)dataset used in the March version ofthe risk assessment as well as theaugmentationdataset incorporated for the currentversion of the risk assessment.68Figure 2-10 Fish Tissue Measurement Sampling Frequencyfor HUCs with Fish Tissue DataIncluded in the Risk Assessment69Figure 2-11 Total Fish Tissue Mercury Concentrations for 2005 Scenario (HUC12-level 75thpercentile values, ppm)70Figure 2-12 Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12-level 75thpercentile values,ppm)** Values estimated by adjusting fish tissue Hg data presented in Figure 2-12 to reflect different patterns of Hg deposition in2016 (relative to 2005).71Figure 2-13 EGU-Attributable Fish TissueMercury Concentrations for 2005 Scenario(HUC12-level 75thpercentile values, ppm)** Scales are different between the EGU-attributableand total Hg fish tissue concentrations maps.72Figure 2-14 EGU-Attributable Fish Tissue Mercury Concentrations Projected for 2016 Scenario(HUC12-level 75thpercentile 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 75 th 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-a ttributable 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

tissue concentration Used for 2005 scenario Projected for 2016 scenario

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 99 th percentile for total Hg fish concentration for the 2005

scenario could occur in a different HUC than the 99 th 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 signi ficantly improved co verage for Wisconsin, Minnesota, Pennsylvania and New Jersey: Although the additional fish tissue Hg data74 significantly expanded coverage for watershe ds in areas with elevated U.S. EGUattributable Hg deposition (Pe nnsylvania) and for areas with relatively elevated fish tissue Hg concentrations (Minnesota and Wisconsin), the additional data did not substantially change the overa ll distribution of fish tissu e Hg concentrations. This conclusion is based on a comparison of percenti les across the different datasets (i.e., 2010 MFT, augmentation, and 2011 MFT datase ts) as presented in Table 2-4.

 U.S. EGUs contribute a larger fraction to tota l Hg fish tissue levels in the U.S. than they do to total Hg deposition (in terms of percen t), 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 depositi on in the U.S. (for th e 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 Tabl e 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 we st. By providing greater coverage for the eastern half of the country, the Hg fish ti ssue sampling data generally provides greater coverage for regions with potentially greater U.S. EGU-attributable risk.

 Relative to the combined impact of other s ources, 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 c ontribution 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 tota l 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. EG Us contribute up to 43% of total Hg fish tissue levels (for the 99 th percentile watershed). Under the 2016 Scenario, this pattern is reduced, but U.S. EGUs can st ill contribute up to 16% of total fish tissue Hg concentrations (again, for the 99 th 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 pa tterns of CMAQ modeled Hg de position 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 area s with elevated U.S.75 EGU-attributable deposition. To address these qu estions, we developed a series of figures including:

 Figures 2-15 and 2-16: Maps co mparing (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 th e fish tissue dataset used in the March TSD (2010 MFT dataset) and the augmented da taset. These maps show the degree of overlap between fish tissue Hg data and area s of elevated U.S. EGU-attributable Hg deposition (for both 2005 and 2016 scenarios). 51

 Figure 2-17: Plot comparing th e fish tissue Hg concentratio ns 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 wa tershed level.

 Figure 2-18: Cumulative distri bution 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 contin ental U.S. Given emphasis in the risk assessment on the 2016 scenario, we only provide d 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 depo sition 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 ex ceeding the MeHg RfD. In other words, we identified a deposition level associated generally with watersheds having high U.S. EGU-attribut able 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. Howeve r, 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-attri butable deposition as a means for identifying ar eas of potential elevated risk is subject to uncertainty, since it does not also consider the baselin e 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 attempti ng to identify a bright line for classifying areas of the country with regard to the magnitude of elev ated U.S. EGU-attribut able Hg deposition.76Figure 2-15 Comparison of Locations ofWatersheds with Fish Tissue Hg Data* withPattern of U.S. EGU-attributable HgDeposition (2005 scenario)* Fish tissue Hg data includes (a) 2010 MFT data used in March version of the risk assessment and (b) augmentation dataadded for this revised version.77Figure 2-16 Comparison of Locations ofWatersheds with Fish Tissue Hg Data* withPattern of U.S. EGU-attributable HgDeposition (2016 scenario)* Fish tissue Hg data includes (a) 2010 MFT data used in March version of the risk assessment and (b) augmentation dataadded 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 Waters heds 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 hi gh-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 assessmentCMAQ‐based totalHgdepositionbyHUC(2005Scenario)(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 1.8 279 We made the following observations based on th e information presented in Figures 2-15 through 2-18 regarding how estimates of Hg depos ition 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-attribut able Hg deposition. The augmented dataset significantly expanded coverage in key area s. However, the number of “at-risk” watersheds may still be substant ially 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, th e 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 substa ntially 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 waters heds (i.e., the highes t total Hg deposition watersheds do not always have the highest fish tissue Hg concentrations). This is not unexpected because the relationship between to tal Hg deposition and total Hg fish tissue levels is highly dependent on the methylati on 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 collect ed 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 sugge sts that watersheds where fish tissue Hg data were collected tended to have highe r 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 thesecumulative 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 mo st 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 s upported by a number of studies. While this scenario is emphasized in summarizing risk estimates, we also provide risk estimates fo r 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 2 016 air quality scenario, since as discussed in section 2.3, U.S. EGU-related Hg emission levels re flected in this scenario are closest to recent

conditions. The remainder of this s ection is organized as follows:

 Overview of percentile risk estimates generated for the fi sher scenarios (Section 2.5.1): We provide percentile risk estimates for the typical female subsistence fish consumer scenario at the national level and summarize pe rcentile risk estimates for the other SESdifferentiated female subsistence fish consum er scenarios assessed in the analysis. These percentile risk estimates allow us to compar e risk (total and U. S. EGU-attributable)

across the female subsistence fish consumers s cenarios assessed in the risk assessment.

 Overview of the number (and frequency) of wa tersheds with populations potentially atrisk due to U.S. EGU-attribut able Hg deposition (section 2.5.2) : This set of risk estimates provides the main input to the risk ch aracterization framew ork (see section 1.3). Specifically, watersheds with populati ons 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 poten tial public health

concern.

To support the discussion of risk estimates, we present a series of tables summarizing

those estimates and a list of observa tions based on this information.

2.5.1 Overview of percentile risk estimates (2016 scenario)

This section compares risk pe rcentiles (for total and U.S. EGU-attributable risk) across

the set of female subsistence fish consumer scen arios 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 scen arios 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 sp ecific 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 estimat es separately for each

female subsistence fish consumer scenario. This means that the total and U.S. EGU-attributable risk for a particular percentile ar e not matched by watershed (i.e., the 90 th 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 s ubsistence 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 re latively high compared to general recreational angler rates but do not represen t true subsistence rates.

Table 2-6 provides risk percentiles for RfD-ba sed HQs for all of the female subsistence

fish consumer scenarios assessed in the analysis for the 2016 scenario.

Table 2-6 Percentile risk esti mates for the full set of fema le 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 (90 th ) 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

(99 th ) NC NC

Low income Hispanic female subsisten ce fish consumer evaluated nationally

54 Presentation of non-matched percentile risk estimates repr esents a change from the March TSD. In that version, we used risk bands to match trends in total risk to a sp ecific U.S. EGU-attributable risk percentile. However, given

that total and U.S. EGU-attributable risk are not closely correlated, we de termined 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

(99 th ) 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

(99 th ) 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

(99 th ) 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 90 th
percentile total and U.S. EGU-attribut able 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 99 th percentile consumption rate for this population due to insufficient sample size in the underlying study. Consequently, risk estimates for the 99 th 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 subsis tence 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-a ttributable) generated for the typical female subsistence fish consumer scenario are gene rally higher than estimates generated for the Hispanic and Vietnamese scenarios. Risk estim ates for the typical female subsistence fish consumer scenario are also higher than ri sks for the Tribal population for U.S. EGUattributable risk although th e Tribal populations tend to ha ve higher total risks. Given emphasis on U.S. EGU-attributable risk in the policy context, these trends would suggest83 that the typical female subsistence fish cons umer scenario provides general coverage for the other three scenarios (Hispanic, Vietname se and Tribal), since the typical female subsistence population tends to have higher U. S. EGU-attributable risk estimates.

 Risk estimates for the typical female subsis tence fish consumer (assessed at the nationallevel) may not provide full coverage for risk s experienced by low income Blacks and low income Whites in the Southeast, or for L aotians assessed at the national-level: U.S.

EGU-attributable risk estimates for the Sout heastern low income White and low income

Black scenarios and for the Laotian scenario are higher than those fo r the typical female

subsistence fish consumer. In th e case of low income Whites, this likely reflects the fact that some regions in the Sout heast (specifically South Caroli na) 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 th e typical female subsistence fish consumer scenario (see Table 1-6). While both th e 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 a ll 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 ha ve greater overall confidence in risk estimates generated for th e typical female subsistence fish consumer scenario because it is assessed at a larger number of watersheds with broader spatial coverage). However, the higher risks estimat es for these two Southeastern subsistence

fisher scenarios should also be considered in the context of interpreti ng 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 high er 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). Sp ecifically, risk estimates generated for the Laotian scenario may reflect relatively rare localized interactions between fish tissue Hg concentrations and U.S. EGU-related Hg depos ition, 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. EG U mercury emissions (2016 scenario)

This section discusses risk estimates base d 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 identify ing watersheds where: (a) U.S. EG Us 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 cons idered 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-r isk populations described above comprise Stage 1a of the 2-Stage approach, while the second category comprises Stage 1b (see Figure 1-1). The84 combination (i.e., mathematical union) of th ese two groups of watersheds with at-risk populations comprises the set of watersheds re presented in Stage 2 of the framework.

In summarizing risk estimates in this sect ion, we focus on estimates generated using the

current risk assessment that reflects refinement s 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 pr esent the Stage 2 risk estimates, we also include a summary of Stage 2 estimates from the March vers ion of the Mercury Risk TSD (then referred to as “stage 3” result s) for comparison.

With regard to the HQ estimates, any contribu tion of Hg from EGUs to watersheds with potential exposures exceeding the MeHg RfD re presents 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 populat ions are all based on the underlying risk estimates generated for the typical female subsis tence fish consumer scenario. The estimates of watersheds with potentially at -risk populations are summarized in tables described below, followed by a set of observations re garding these risk estimates.

 Tables 2-7: Identifies watersheds with potentially at-risk populations based on consideration for different degree s of U.S. EGU c ontribution (i.e., ≥ 5, ≥ 10, ≥ 15 and

≥ 20%) at watersheds where total risk is cons idered to represent a potential public health hazard (i.e. HQ>1). For reference purposes, th e table also identifie s 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 pe rcent 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 poten tially at-risk populations based on U.S. EGUattributable deposition consid ered 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 cate gories of watersheds w ith potentially at-risk populations (i.e., mathematical union of the St age 1 and 2 estimates presented in Tables 2-7 and 2-8, see Figure 1-1 for explanation). Table 2-9 considers th e number and percent of watersheds that have (a) U.S. EGUs cont ributing to total risk of an HQ>1, OR (b) an HQ>1 based on considering U.S. EGU Hg de position 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-ris k populations based on consideration for various degrees of U.S. EGU contribut ion to total risk (2016 scenario)

EGU risk

threshold Number and percentage of HUCs meeting risk threshold

criteria (RfD-based HQ > 1)*

2016 analysis

90 th percentile fish consumption 95 th percentile

fish consumption 99 th 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 fo r 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 a nd 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

90 th fish consumption 95 th percentile

fish consumption 99 th 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 po tentially at-risk populat ions based on either consideration for (a) U.S. EGU percent contri bution 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

90 th percentile fish consumption 95 th percentile fish consumption 99 th 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 fo r 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 populat ions 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 ta king into account deposition from other sources (2016 scenario)

EGU risk

threshold Number and percentage of HUCs meeting risk threshold criteria

90 th percentile fish consumption 95 th percentile fish consumption 99 th 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 fo r the same watershed across a given row).

We made several observations regarding wate rsheds with potentia lly at-risk populations due to U.S. EGU–attributable Hg deposition ba sed on the information in Tables 2-7 through 2- 10, oriented around the 2-Stage ri sk characterization framework.

 Depending on the percentile fish consump tion rate, between 3 and 24% of those watersheds with total risk HQs >1 have U.S. EG Us 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 falli ng into this category (with the 24% value reflecting risk modeled using the 99 th percentile fish consumption rate for the high-end female consumer – see Table 2-7). It is impor tant when considering this risk metric to reiterate that any exposure above the MeHg RfD represents a poten tial 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 mo deled 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 depos ition. The low end of the range reflects the 95 th percentile consumption rate, and the high end reflects the 99 th
percentile consumption rate (aga in for the typical female subs istence fish consumer).

 Depending on the percentile fish consum ption rate, between 22 and 29% of the

watersheds are at-risk based on at least one of the risk characte rization criteria (2016 scenario) : Combining the two categories of watershe ds with populations at-risk due to U.S. EGU Hg emissions summarized in the la st two bullets, we get a total estimate ranging from 22 to 29% of watersheds at-risk. These estimates are sensitive to the87 specification of the percent U.S. EGU contribu tion. 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 thre shold also reflect the different fish consump tion rates considered for the high-end female consumer (i.e., 90 th , 95 th and 99 th 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 im plemented since the March TSD have not substantially affected overall Stage 2 results (formerly known as “Stage 3” results in the March ve rsion 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 conc entrations (see sections 1.4.2.1 and 1.4.6) and (b)

use of the watershed-level 75 th percentile fish tissue Hg concen tration as the basis for generating watershed-level risk estimates (see section 1.4.2 ). Given the emphasis pl aced 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 pr oportionality assumption, we conducted two sensitivity analyses focused on concerns that the risk assessment may have included some watersheds that are disproportionately imp acted 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 representi ng 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 fi sh 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 concer ns 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 25 th percentile of total Hg depos ition (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 su ggested 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 analys is 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 concer n 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 activ ity that could contribute non-air Hg loading. We note that, in the March TSD, we excluded SC from this sensitiv ity 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 fact or). 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 depositi on so we have increased confidence that aerial deposition plays a significan t role in loading).

To address uncertainty in use of the watershed-level 75 th percentile fish tissue Hg concentration in risk modeling, we conducted a se nsitivity analysis usi ng the watershed-level median fish tissue Hg. This sensitivity analys is, which was recommended by SAB, considers the

situation in which subsistence fi shers do not necessarily target the larger (higher trophic level) fish and consequently could catch fish w ith lower fish tissue Hg concentrations.

The results of both sensitivity analyses are presented in terms of their impact on the 2- Stage Risk Characterization Framew ork 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 pr esents core results, followed by results for each of the three sensitivity analyses (this allows the re sults 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 co ntribution as dimensioned

below )*

90 th percentile fish consumption 95 th percentile fish consumption 99 th 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 25 th 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 co ntribution as dimensioned

below )*

90 th percentile fish consumption 95 th percentile fish consumption 99 th percentile fish consumption

≥ 15% 20 (3%) 20 (3%) 20 (3%)

≥ 20% 9 - 9 - 9 -

Sensitivity analysis C (generate risks usin g median watershed-le vel 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 )*

90 th percentile fish consumption 95 th percentile

fish consumption 99 th 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 25 th percentile with regard to total Hg deposition)

≥ 1.5* 9 - 25 (3%) 104 (13%)
Sensitivity analysis C (generate risks usin g median watershed-le vel 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 pres ented 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) ri sk when U.S. EGU mercury deposition is considered alone, without taking into acco unt 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 contribu tion as dimensioned below)*

90 th percentile fish consumption 95 th percentile fish consumption 99 th 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 25 th 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 usin g median watershed-le vel 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 se nsitivity analyses include:

 Generating risk estimates excluding watersheds located in four states (AL, SC, ME and MN): This sensitivity analysis resulted in di fferent effects on the Stage 1a and Stage 1b risk estimates, although neither was substant ially different than the core estimates.

Estimates of the number of watersheds w ith at-risk populations due to U.S. EGUs contributing at least 5% to total risk (Sta ge 1a) demonstrated a range of moderate increases compared with the core analysis, depending on the “percent contribution” category considered (see Table 2-11). Si milarly, estimates of the percentage of91 watersheds with at-risk populations when c onsidering U.S. EGU Hg deposition (without other sources – Stage 1b) based on dropping out th e four states was 9% as compared with 10% when considering all states (for the 99 th percentile fish consum ption 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 watersh eds falling in the top 25 th
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 fo r 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 (w ithout taking into account deposition from other sources – Stage 1b) is 13% as compared with 10% when considering all watersheds (for the 99 th percentile fish consumption rate - see Table 2-11). This sensitivity analysis suggests that the risk estimates would be some what higher (particularl y for the tail of the risk distribution) if we focus on those wa tersheds receiving th e 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 wa tershed-level fish tissue Hg concentration (instead of the 75 th percentile as used in the core analysis): These sensitivity analysis results are notably different for the two categories of risk metr ic considered. Estimates of the number of watersheds with at-risk popul ations due to U.S. EGUs contributing ≥ 5% are essentially the same as the core risk esti mates. However, estimates of the percentage

of watersheds with at-ris k 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 concentrati on 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 75 th percentile. The SAB peer-review panel concluded that “using the 75 th
percentile of fish tissue values as a reflec tion of consumption of larger, but not the largest, fish among sport and subsistence fi shers 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 un certainty associated with the risk assessment. Regarding variabilit y, we focus on identifying the key sources of variability associated with modeling risk for th e scenarios included in the analysis and then discuss the degree to which those sources are reflected in the desi gn of the risk assessment and implications for the risk estimates generated. Th e risk assessment has been designed to reflect critical sources of variability to the extent allowed by availabl e methods and data and given the resources and time available. Key sources of vari ability associated with the analysis include:92  The pattern of total and U.S. EGU-attributab le 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 tissu e Hg concentrations used by States which results in varying degrees of spatial coverage of fish tissu e Hg concentrations across the

states.

 The response of watersheds to Hg loading in cluding variation in the temporal response and degree to which fish tissue Hg concentr ations change in response to altered Hg loadings.

 The degree of subsistence fishing activit y 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 body weight 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 iden tifying potentially signi ficant sources of uncertainty impacting the analysis. Then we char acterize (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 particul ar source of uncertainty). The results of this qualitative assessment are pr esented in Table 2-15. The SAB commented extensively on the sources of variability and un certainty that should

be addressed in the revised vers ion of this section and those r ecommendations are reflected in

Tables 2-14 and 2-15.93Table 2-14 Key sources of variability associated with the analysisand degree to which they are reflected in the design of theanalysisSource of variabilityDescriptionDegree to which source is reflected in design of risk analysis and implicationsfor the risk estimates generated(A) 2005 Hg emissions Emissions approacheshave been applied to create a 2005Hg inventory for separate categories of emissions (e.g.,Coal utilities, Portland Cement, Electric Arc Furnaces).Within each category, types of variability for total Hginclude (1) annual variability related to product demand,(2) sub-annual (e.g., hourly) temporal variability fromproduction related fluctuations at each facility, (3)variability of emissionscontrol systems over timedepending on the control device age and process stagessuch as startup/shutdown and operating conditions evenduring standard operations, (4) fuel characteristicsincluding the Hg content of the fuel, and (5) differences insimilar pieces ofequipment that are relevant whenemission factors are applied to a class of process (e.g., notall fluidized bed boilers will have the same emissionspotential). Further, the speciation characteristics of thetotal Hg has variability basedon many of the same factorsas for total Hg (e.g., production parameters, controlsystems, fuels, physical properties of individual units)The analysis accounts for annual variabilityin that estimates of Hg emissions weredeveloped for the particular 2005 year as the midpoint of the fish tissue dataprojected to 2016 to estimate risks in the future baseline. The analysis attempted toestimate sub-annual variability through useof (a) hourly EGU demand data that isdeveloped from several years of hourly Continuous EmissionsMonitoring (CEM)heat input dataand (b) monthly, day-of-week, and diurnal temporal allocation factorfor non-EGU sources. The approach forestimating 2005 EGU emissions was tied to1999 control assumptions and fuels, whichcreates some uncertainty described inTable 2-15. The variability associated with different pieces of equipment in a classof process was not accounted for explicitly, but the Hg emission factors developedfor EGU boilers and other unit types weredeveloped to represent, on average,emissions from a given unit type by averaging emissions test data over a many unitsat facilities within a given class. The emission factors used to compute Hg for U.S.EGUs were assigned based onboiler type, fuel (coal or oil), and control deviceconfiguration. The same characteristics were used to assign speciation fractions.(B) 2016 Hg emissions The sources of variability identified for the 2005emissions also apply to 2016; however, they are addresseddifferently because of the differences in approachassociated with estimating emissions for a future year.Annual variability is accounted for by the Integrated Planning Model’s 2016 estimateof Hg emissions. This model accounts forexpected electricity demand, controls inplace before 2016, and forecasts of fuel prices resulting in predicted choices of fuels.Further, the model predicts the Hg content ofthe fuel used in each unit in the futurebased on forecasts of which coal mine willsupply coal to each unit. Thus, IPM doesa more complete job than is done in 2005 for including control andfuel information.As in 2005, sub-annual variability uses a) hourly EGU demand data that is developedfrom several years of hourly ContinuousEmissions Monitoring (CEM) heat inputdata and (b) monthly, day-of-week, and diurnal temporal allocation factor for nonEGU sources. IPM further accounts for differences in equipment using EmissionsModification Factors (EMFs), which adjustthe Hg emissions based on the boilertype and control deviceconfiguration. The EMFs arenot specific to each unit, butrather were developed to represent, on average, emissions changes due to boiler andcontrol device types.Finally, the speciation fractionsused in 2005 were also appliedin 2016, but the fractions were assigned based on the 2016 unit type, fuel type, andcontrol devices, rather than the base year configuration.(C) Pattern of total andU.S. EGU-attributableHg deposition acrossPatterns of annual deposition of Hg including total (allsource) and estimates of the U.S. EGU fraction (bywatershed) displays considerable spatial variability acrossBy extrapolating CMAQ grid cell results at the more spatially refined HUC12watershed level, we retain the greater degree of spatial resolution in characterizingHg deposition obtained through the use ofthe 12k CMAQ grid cell simulations.94Source of variabilityDescriptionDegree to which source is reflected in design of risk analysis and implicationsfor the risk estimates generatedwatersheds in the US(including variation inthe global inflow of Hginto the U.S.)the U.S. based on the results of 12 km grid cell CMAQmodeling (see Section 2.4).This variability includeschanges over time in global-scale ambient mercuryconcentrations due to changes in global emissions andchemistry.Regarding variation in the global inflowof Hg, the global GEOS-CHEM simulationdoes provide ambient Hg inflow estimatesused in the CMAQ model domain thatvary spatially in the horizontal direction and verticallyfrom the surface through thetroposphere. The Hg inflow concentrations also vary for each day of the year andwithin each day. However, the ambient speciated Hg inflow is kept constant between2005 and 2016 scenarios.(D) Patterns of fishtissue Hgconcentrations acrosswatersheds within theU.S.Mercury fish tissue measurements can displayconsiderable spatial variability across watersheds (seesection 2.3).We have fish tissue measurements for roughly 4% of the watersheds in the U.S. (i.e.,3,141 watersheds with measured valuesout of 88,000 based on data from 2000-2010). These measurementsare concentrated in the Eastern part of the U.S., althoughthere are some measurements in almost all states. While our measured fish tissuelevels do generally provide some degree ofcoverage for areas with elevated Hgdeposition (in terms of both total and U.S. EGU-attributable), this coverage is limitedand there are a large number of watershedswith high total and U.S. EGU-attributableHg deposition for which we do not have fish tissue measurements (see section 2.4).(E) Sampling protocolsfor measuring fishtissue Hgconcentrations used byStates differ, whichresults in varyingdegrees of spatialcoverage of fish tissueHg concentrationsacross the states.States utilize varied approaches for targeting waterbodiesfor fish sampling and Hg measurement. In virtually allcases, sampling strategies are not intended to provide arepresentative characterization of fish tissue Hgconcentrations across the state and instead are intended toidentify and then target areas suspected of having higherHg levels, or higher fishing activity.The sampling protocol used by states in collecting fish tissue Hg measurements isdirectly reflected in the fish tissue dataset used in the risk assessment. Therefore, anypotential bias in the collection of fish tissue data (i.e., tendency to capture morehighly impacted waterbodies) is reflected in that dataset. However, having fish tissueHg data that are potentially biased towards more heavily Hg-impacted waterbodies isnot problematic given the focus of the riskassessment. Specifically, given that we areattempting to capture reasonable high-end subsistence fisher risk, having fish tissuedata biased towards more highly impacted waterbodies is actually preferable, since itincrease the potential that we will identify areas of higher exposure and risk relatedto the subsistence fisher scenario (it would only be problematic if we were attemptingto generate a representative picture of more generalized recreational fisher risk).(F) Response ofwatersheds to Hgloading includingvariation in thetemporal response anddegree to which fishtissue Hgconcentrations changein response to alteredHg loadings.The response of fish tissue Hg concentrations to mercurydeposition can vary greatly depending on a number offactors (e.g., role of watershed in loading to waterbody,methylation potential of the waterbody, rates of sulfatedeposition, nature of aquaticbiotic foodweb including mixof upper-level trophic fish etc). These factors can alsoaffect the temporal profile of that response.Variation in methylation potential is reflected directly in the variation in themeasured Hg levels in fish across the different sampling locations. This variation isvery important, and in fact is a primary determinant of variation in fish tissue Hgconcentrations across watersheds. This will have a large impact on the variability intotal Hg exposure, and as shown in Figure2-17, results in a low correlation betweentotal Hg deposition and fish tissue Hg acrosswatersheds. However,this variabilitywill not have an impact on the attribution of exposure at any specific watershed,because the scientific literature supports a linear relationship between changes indeposition of Hg at a watershed and changes in fish tissue Hg at the same watershed.Furthermore, because we are not predicting temporal trends in fish tissue levels andinstead consider a future point in time (once near-steady state conditions arereached), variation in the temporal profile of changes in fish tissue levels related todifferences in methylation potential of different watersheds is also minimized as afactor in the analysis.(G) Differences in thespatial distribution andStudies reviewed in developing the approach for thisanalysis suggests thatthere can be considerable variationSurveys of high- self-caught fish consuming populations allow us to clearly definethis type of activity for specific areascovered by those surveys (e.g., Hispanic,95Source of variabilityDescriptionDegree to which source is reflected in design of risk analysis and implicationsfor the risk estimates generatedextent of subsistencefishing activity bydifferent groups acrosswatersheds in the U.S.in the nature of high-end self-caught fisher populationsacross regions of the country. This variation reflects ethnicand cultural practices and can also be driven be socioeconomic status (SES)related factors (e.g., poverty, levelsof income). In addition, wewould expect that access tofreshwater vs saltwater fishinglocations would also play arole.Vietnamese and Laotian fishing activity in specific regions of California, high-endfishing activity by Blacks and Whites in South Carolina and Tribal activity near theGreat Lakes). However, available studies onthis type of high-end fishing activity atinland freshwater bodies do not provide comprehensive coverage for all regions inthe U.S. Therefore, we extrapolated coverage from areas reflected in these studies toother portions of the U.S. There is uncertainty associated with this process. Asdiscussed in Table 2-15 (entry E) a lackof comprehensive survey information onsubsistence fishing activity for all regions inthe U.S. is a limitation of the analysis.This also prevents us from generatingpopulation-weighted estimates of risk.(H) Subsistence fishingbehavior including:consumption rates,species harvested,degree to whichactivity is focused onone or morewaterbodies anddifferences incooking/preparationpractices. Variation inbodyweight can alsoimpact exposure andrisk estimates.Subsistence fisher populations can display significantvariability in their fishing behavior, which can impactoverall exposure and risk. Variation in consumption rateshas a direct impacton exposure, as do differences in thetypes of fish (size and species) potentially targeted.Tendency to focus fishing activity in one spot, versusdistributing it across differentwaterbodies can also impactrisk. Specifically, activitydistributed across multiplewaterbodies will tend to dilute out locations with higherfish tissue Hg concentrations, while focused activitymeans that one or more fishers may get all of their fishfrom the more impacted lake. Differences incooking/preparation can also impact Hg levels on a perunit of fish basis, which, when combined with “as cooked”consumption rates can effected modeled exposure andrisk. Body weight will also affect the calculation ofexposure, when standardizedby body weight (e.g., aheavier individual ingesting the same amount of fish willhave a lower body-weighted adjusted exposure than alighter individual ingesting the same amount of fish).While we have fairly comprehensive information on consumption rates for the set offemale subsistence fish consumer scenarios we included in the analysis, we do nothave information (for those fishers) characterizing several of the other behaviorrelated factors including typesof species and size of fish targeted and the degree towhich fishers target a single waterbody,versus distribute their activity acrossmultiple waterbodies. However, given the focus of this assessment on capturingreasonable high-end fish consumption by subsistence fishers, we have modeled thesescenarios assuming that (a) they tend to favor somewhat larger fish (use of the 75thpercentile watershed-level fish tissue Hg concentration) and (b) they focus theirfishing activity on a single watershed.While we do not know how representativethese types of higher-exposure related behaviors are within the subsistence fisherpopulations covered in the risk assessment, wedo believe it is reasonable to assumethat some subset of eachpopulation could engage in thistype of behavior. Withregard to fish cooking/preparation, we have not considered variability in thisbehavior, although the factors used (1.5 – see section 1.4.4), is a value that reflectsthe range of adjustment factors seen withdifferent types of fish preparation methodsfor different types of fish. Similarlyfor body weight, we have used a centraltendency factor and not explicitly modeled variability in this factor.96Table 2-15 Key sources of uncertainty associated with the analysis, the nature of their potential impact on risk estimates, anddegree to which they are characterized as part of the analysisSource ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisCharacterizing the pattern of fish tissue Hg concentrations across watersheds in the U.S.(reflects small fraction of total watersheds with fish tissue measurement data,differences in state-level protocols for collection of fish tissue data, substantial number of watersheds with relatively low sample sizes (e.g., 1-2 samples), uncertainty associated withfiltering watersheds to excludelocations with potentially significant non-air Hg impacts)(A) Relatively smallfraction of U.S.watersheds withmeasured fish tissueHg dataFish tissue Hg measurements areavailable for 3,141 of the ~88,000watersheds in the U.S. (i.e., ~4%of the watersheds). This relativelylow coverage raises concerns thatthe risk assessment may missareas of elevated U.S. EGU Hgdeposition and/or areas withelevated fish tissue Hgconcentrations, both of whichcould result in a low-biasedassessment of U.S. EGU-relatedrisk.Supplemental data for Pennsylvania that has beenincorporated into the fish tissue dataset to enhance thecurrent version of the risk assessment reducessomewhat concerns over missing areas of high U.S.EGU deposition (Pennsylvania has high U.S. EGUdeposition and in the March version of the riskassessment had very little coverage by fish tissue Hgdata). Similarly, additional data for Wisconsin andMinnesota reduces concerns over missing areas withelevated fish tissue Hg concentrations. However, thereis still the potential that areas of high U.S. EGU Hgdeposition and/or areas with high fish tissue Hgconcentrations have been excluded from the analysis. Inaddition, there is still an overall low bias in ourestimates of the number ofwatersheds with specifiedlevels of risk since only a fraction of watersheds havefish tissue Hg measurements.We have semi-quantitatively examined the issue ofpotentially missing Hg deposition hot spots by presentingFigures 2-15 and 2-16, which show coverage by fish tissueHg data for areas of the country with elevated U.S. EGUHg deposition. Examination of these maps suggests, forexample, that while Pennsylvania is now fairly wellcovered by available fish tissue data, Illinois, Kentuckyand Ohio are less well covered. Itis also important to pointout that even while general coverage for high U.S. EGUHg deposition areas by measured fish tissue data hasimproved for this version of the risk assessment, thepotential still exists thatsubstantial numbers of highimpact watershedshave not been included in the riskassessment due to a lack of fish tissue sampling at thoselocations.(B) Differences instate-level protocolsfor collection of fishtissue data whichresults in differingdegrees of spatialcoverage across statesand differing degreesof potential bias in thesamples collectedStates use different strategies fordetermining where fish tissuesamples will be takengeographically as well as thetypes of fish thatwill be measured(and the methods of collection).Generally, however, there is likelyto be a bias towards targetingwaterbodies suspected of havingelevated fish tissue Hgconcentrations for most states.Differences in the strategies usedto collect fish tissue data togetherwith the potential for biasfavoring more highly impactedwaterbodies, introducesuncertainty into the riskassessment. Specifically, there isBecause the goal of the risk assessment is to capturerisk for subsistence fishers that are likely to experienceelevated U.S. EGU-attributable risk, having fish tissueHg concentrations that likely reflect targeting of morehighly impacted waterbodies(i.e., biased towardshigher risk locations) is notproblematic and in fact, ispreferable. If it is reasonable to assume that subsistencefisher activity could occur at these sampled watershedsthat have relatively higher fish tissue Hg concentrations,then having risk estimates based on these measurementsstrengthens the analysisfrom a science policystandpoint. By contrast, ifthe analysis had beendesigned to generate a fully representative populationweighted assessment of subsistence fisher risk, thenbias in the fish tissue Hgconcentrations would beproblematic (since it would high-bias an assessmenttargeted at capturing “typical” risk for these highconsumers).Given that potential high-bias in the characterization offish tissue Hg concentrations across the watershedsincluded in this risk assessment is not problematic andmay in fact be preferable, we did not attempt to quantifythe impact of this factor on risk estimates.97Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysislikely to be varying degrees ofhigh-bias in the characterizationof fish tissue Hg concentrationsacross states.(C) Substantialnumber of watershedswith relatively lowsample sizes (e.g., 1-2samples)Only 28% of the watersheds withfish tissue Hg data have at least10 samples (~41% of thewatersheds have only 1 or 2samples). Generating 75thpercentile risk estimates forwatersheds with a small numberof samples (e.g., <10 to 20) issubject to uncertainty and will onaverage understate the true 75thpercentiles across watersheds,although the bias in the estimateof the 75thpercentile for anyspecific watershed is unknown.The potential that a substantial fraction of the 3,141watersheds included in the risk assessment could havelow bias in the 75thpercentile fish tissue Hgconcentrations used in modeling risk means that in turn,risk estimates for a substantial number of watershedscould be low-biased. For the very low samples sizes(e.g., 1-2 samples per watershed), it is likely that, as atrend, the estimates of the 75thpercentile values areactually more likely representative of the centraltendency, e.g. the median.We examined trends in the watershed-level 75thpercentileestimates across different strata of watersheds, where thosestrata were based on samplesize (see Table 1-2). Asdiscussed in section 1.4.2, the average of the 75thpercentile estimates increases substantially across thesestrata (i.e., as the number of samples available tocalculated a 75thpercentile increases, the trend in 75thpercentiles also increases). This suggests that other factorsequal (see below), there is a distinct potential for low biasin the 75thpercentile fish tissue Hg levels used in riskcharacterization for those watersheds with lower samplesizes. The important caveat to this is that this observationassumes that there is no correlation between absolute levelof fish tissue Hg concentrations and sample size acrosswatersheds. This may not bethe case if, for example,waterbodies with higher absolute fish tissue Hgconcentrations are also targeted more for sampling (whichis likely the case). Therefore, the potential for low-bias inthe 75thpercentile estimates for watersheds with 1-2samples probably needs to be softened slightly since thereis the potential for these watersheds to have lower “actual”measured fish tissue Hg levels.(D) Filteringwatersheds to excludelocations withpotentially significantnon-air Hg impactsAs described in section 1.4.2, wefiltered watersheds (with fishtissue Hg data) to exclude thosepotentially impacted by non-airHg sources including gold minesand any industrial sourcesmeeting a specified Hg releasethreshold. However, we did notfilter out watersheds located nearlarge urban areas which have thepotential to release Hg throughwaste water treatments effluent.Recall that the proportionalityassumption used as the basis forgenerating U.S. EGU-relevantexposure and risk does not hold inIn those instances where risk is modeled for a watershedassuming that air Hg deposition is dominant, when inreality, municipal wastewater discharge may becontributing significantly to Hg loading, then estimatesof U.S. EGU-attributable risk for that watershed couldbe biased high. In this case,we would not have assigneda portion of risk to the municipal discharges and insteadwould have over-estimated the U.S. EGU-attributablefraction of risk.Although we did not explicitly consider the issue of urbanwastewater Hg loading in our sensitivity analyses, two ofthe sensitivity analyses did examine the broader issue ofuncertainty in filtering our watersheds to exclude areaswith substantial non-air Hg loading (se section 2.6). Thesetwo sensitivity analyses suggested that the risk estimatesgenerated in the core analysis are relatively robust evenwhen we (a) exclude a set of states that may havesubstantial non-air Hg loadingsacross their watersheds (orhave higher methylation potentials in their waterbodies) or(b) focus risk modeling on those watershed in the upperquartile with regard total Hg loading (and therefore aremore likely to be the dominant source of Hg to awatershed).98Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisinstances where non-air Hgloading is substantial.Characterizing subsistence fishing activity across areas of high U.S. EGU mercury deposition(includes assessing potential locations forsubsistence fishing activity as well asdetails regarding actual fishingand fish consumption behavior)(E) Predicting whichwatersheds are likelyto experiencesubsistence fishingactivityThere is uncertainty associatedwith estimating which of thewatersheds for which we havefish tissue Hg data are likely toexperience subsistence fishingactivity associated with the femalesubsistence fish consumerscenarios considered in theanalysis. For the typical femalesubsistence fish consumerscenario, we assumed that activitycould occur at any of thewatersheds with fish tissue data.However, for the remainder of thefemale subsistence fish consumerscenarios, we used the concept of“source population” (see section1.4.3) to guide identification ofwatersheds with the potential forsubsistence fisher activity bythese specific SES-differentiatedgroups of fishers.Because the goal of the analysis is to determine whetherthere is the potential for significant risk for femalesubsistence fish consumers and not to generaterepresentative population-weighted risk distributions forthose populations, the importance of rigorouslyassessing where these populations are active (and thenumbers of fishers at each location) is reducedsomewhat. However, bias could be introduced into theanalysis if we have modeled risk for high-impactwatersheds when in reality there is no likelihood ofsubsistence fishing activity at those locations. Thepotential impact of this sourceof uncertainty is difficultto assess. However, as discussed above, because we arenot attempting to generate population-weighted riskdistributions, it is not expected to substantially impactthe risk assessment.We did not explicitly examinethis source of uncertaintyrelated to where specific subsistence fisher populationsmay be active.(F) Focus on risks tosubsistence fishingpopulations andassociated femalesubsistence fishconsumers (concernthat analysis does notcover high-endrecreational anglerrisk and risk by otherpotentially highconsuming groups)The SAB raised concerns that byfocusing on subsistence fishers,we may have not providedcoverage in the risk assessmentfor other high-consumingpopulations including the subsetof recreational anglers whofrequently fish and consume thefish they catch, and thus have nearsubsistence level consumptionrates.The typical female subsistence fish consumer scenariois actually characterized (in terms of consumption rates)using a survey of recreational anglers conducted inSouth Carolina (Burger et al., 2002). This population ischaracterized as “subsistence” for purposes of thisanalysis due to the magnitude of the upper percentileconsumption rates used (i.e., the 90thto 99thpercentilevalues) which are considered subsistence since theyrepresent a substantial contribution to dietary intake ofprotein. Furthermore, given that in this revised analysis,the typical female subsistence fish consumer scenariowas assessed at all watersheds with fish tissue Hg data(excluding those filtered due to high-non air Hgimpacts), this scenario would provide completecoverage for high-end recreational anglers, both interms of consumption rate and potential location ofWe did not examine this issue of providing coverage forhigh-end recreational anglers because, as discussed in thecell to the left, the typical female subsistence fishconsumer scenario provides coverage for a high-endrecreational angler in the context of this assessment (i.e.,given the goal of assessingsubsistence-level fishconsumption at each watershed without populationweighting).99Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisactivity. There is no systematic bias introduced into theanalysis due to its focuson female subsistence fishconsumer scenariosthat is consistent with the overallgoal of the analysis (i.e., to characterize risk for themost highly impacted fisher populations).(G) Characterizingsubsistence fisherbehavior (includingconsumption rates,types/size of fishtargeted and thedegree to whichactivity is focused atspecific waterbodies)There is uncertainty associatedwith characterizing subsistencefisher behavior includingconsumption rates,types and sizeof fish targeted and the degree towhich fishers target specificwatersheds rather than distributetheir activity across multiplewatersheds.There are relatively comprehensive survey dataavailable for characterizingfish consumption rates forthe subsistence scenarios included in the analysis. Infact, the subsistence fisher scenarios included in theanalysis were selected, in large part, based on theavailability of survey data to characterize consumptionrates. In addition, the consumption rates display areasonable degree of consistency in terms of magnitudeacross the studies (see Table 1-6). Therefore, we believethat there is relatively low uncertainty associated withcharacterizing this aspect ofsubsistence fisher behaviorand little concern for the introduction of bias into theanalysis.57However, the other critical aspects (degree to whichfishers target specific sizes and species of fish and thedegree to which their activity is focused at one or morewatersheds) are not well characterized in the literatureand therefore are subject to considerable uncertainty.However, because the analysis is focused on modelingreasonable high-end risk, theimportance of uncertaintyassociated with these additional behavior-related factorsis reduced substantially. Specifically, if it is reasonableto assume that a subset of subsistence fishers wouldGiven that the focus of this analysis is on assessing risk forpopulations likely to experience the highest reasonableU.S. EGU-attributable risk, we concluded that because it isreasonable to assume thatsome fraction of high-endfishing populations could focus their activity on a singlewatershed (and could favor largerfish), we can model thisbehavior in our analysis without introducing substantialuncertainty. If these assumptions regarding behavior arerelaxed (i.e., fishers were assumed to eat smaller fish anddistribute their activity across multiple watersheds), thenrisk will be reduced.* However, we did not explicitlymodel these alternative behavioral profiles given our focuson capturing reasonable estimates of high-end risk.* The observation that distributing fishing activity betweenwatersheds will reduce risk needs additional clarification.While distributing fishing activity between watershedsmay or may not impact centraltendency estimates of riskacross a group of fishers, high-end risk (risk forindividuals at the upper tailof a simulated distribution)will be lower, since instances of having an individual fishat a single high Hg watershed will be removed, withfishing activity at that high Hg watershed now beingaveraged with activity at other less-impacted watersheds.57Concerns have been raised by the SAB that some of the studies providing the consumption rates are older and that therefore, these studies may not capture current fishingpractices. In addition, concerns were raised that sampling frequencies characterizing higher consumption levels for some of thefishing populations were relatively low (e.g., Blackfishers in the Burger et al., 2002 study). The sources of uncertainty described here would havea potentially significant impact on the risk assessment if we were attempting togenerate 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 the study being old, orhaving insufficient survey samples to fullcharacterize specific high-end percentilesof consumption would be a significant concern. However, when the goal of the analysis is toidentify 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 generate a representativepopulation-weighted picture of risk), then the potential impact of these sources of uncertainty is reduced. Concerns were alsoraised by the SAB that the fish consumption surveyscould reflect seasonally-differentiated consumption rates (i.e.,higher rates may only occurred for part of the year during increased fishing activity). Regarding this issue ofseasonality in the consumption rates, ourevaluation of all three studies suggests thatthey provide annualized consumption rates (see Table 1-6) and therefore, we believe that thelikely impact of this sourceof uncertainty is limited.100Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysistarget larger fish and focus their fishing activity at aspecific watershed, then uncertainty in these two factorsis largely ameliorated.(H) Estimating unitconcentration ofMeHg in cooked fish(application ofadjustment factors forthe fraction of totalHg that is MeHg infish and cooking/preparation).Two adjustment factors areapplied to the 75thpercentile fishtissue Hg concentration at a givenwatershed to generate an estimateof MeHg in the cooked fishserving (in ppm). These include aHg conversionfactor forestimating the fraction of Hg infish that is MeHg and afishpreparation/cooking adjustmentfactor for adjusting the MeHgconcentration in the fish to reflectpreparation of the fish. There isuncertainty associated with bothof these factors.Mercury conversion factor:the factor used in thisanalysis (0.95) reflects consideration for the range ofvalues provided in the Mercury Study Report toCongress (U.S. EPA, 1997) (i.e.,≥0 .90) (see section1.4.4). It would be possible to include a sensitivityanalysis considering 0.90 and 0.99, but given that thisfactor is linearly related toboth exposure and risk, theoutcome would be similarlymodest: approximately 5%lower and higher risk, respectively for a givenwatershed-specific estimate. Given availableinformation, we do not believe that any systematic biasis introduced into the analysis due to the use of thisparameter value.Fish preparation/cooking adjustment factor:The factorused in this analysis (1.5)reflects consideration for therange 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 laketrout - see section 1.4.4). Again, as with the Hgconversion factor, we could consider a range of factorsas a sensitivity analysis, but again, given that exposureand risk are linearly related to this factor, the outcomewould is predictable. In this case, the impact on riskcould be more pronounced, since the potential factorsrange from 1.0 to 2.0. The SAB identified severalalternative studies for characterizing this factor,suggesting that those studies would support a smaller(or no) positive adjustment of the MeHg levels in fishreflecting preparation. However, as discussed in section1.4.4, close analysis of these studies resulted in aconclusion that they in fact, did not support applicationof alternative (lower) factors. Furthermore, givenavailable information, we do not believe that anysystematic bias is introducedinto the analysis due to theuse of this parameter value.Uncertainty in these factors was not quantitativelyassessed as part of the sensitivity analysis, however, asnoted in the cell to the left, the linear relationship betweenboth factors and exposure and risk allows us to readilydetermine the magnitude of impact that uncertainty inthese factors could have on risk at the watershed-level.Application of proportionality assumptionin generating estimates of the U.S.EGU-attributable fraction of risk(I) Fish tissuemeasurement dataConcerns that the proportionalityassumption linking Hg depositionIf air deposition patterns from the 1990s are reflected insome of the Hg fish tissue measurements we are usingWe did not quantitatively assess this potential source ofuncertainty and its impact on risk. However, our decision101Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisused in the analysismay still reflectearlier historicalpatterns of Hgdeposition (from the1990’s) more so thandeposition from thesimulation period(i.e., ~2005)and fish tissue Hg concentrationsmay not hold if fish tissue Hgconcentrations actually reflect anearlier (higher Hg deposition)period. In this case, bias could beintroduced into estimates ofreduction in fish tissue Hgconcentrations (based on theproportionality assumption) sincethe effect of changes in currentHg deposition on fish tissue Hgconcentrations could beoverstated (i.e., there could be aneffective buffering of the fishtissue Hg concentrations by theresidual effects of earlier higherHg deposition).the implications can vary depending on the nature ofthat difference. If fish tissue levels for a watershed stillreflect higher 1990 depositionvalues, then there may bean upward bias to the estimates of U.S. EGUattributable risk, since in reality we would expect theunderlying fish tissue levels todecrease as the impact ofthose earlier higher deposition values dissipates.Conversely, if total deposition remains the same, butonly the source distribution has changed since the1990’s, then the effect on our risk assessment may notbe that significant, sincewe are making projectionsbased on the current source-mix (or future source-mix)assuming near steady-state assumptions are reachedgiven those specific sourcemixes. Given currentlyavailable information, we cannot evaluate the interplayof these two factors and the way in which they cometogether to introduce potential bias into estimates ofU.S. EGU-attributable risk (we can only discuss each asa source of uncertainty as has been done here).to use only fish tissue samples from after 1999 is intendedto reduce this source of uncertainty.(J) Watersheds candisplay substantiallydifferent methylationpotentials, resulting indifferences in theimpact of a unitchange in Hgdeposition on fishtissue HgconcentrationsDifferences in methylationpotential cause variation in fishtissue Hg concentrations acrosswaterbodies (even when thosewaterbodies have similar rates ofHg loading). In addition,differences in methylationpotential can also translate intodifferent temporal profiles for theresponse of fish tissue Hgconcentrations to changes in Hgloading. Failure to reflect thesesources of variability inmethylation potential canintroduce uncertainty into theanalysis.Differences in methylation potential across watersheds,when present, are likely to be reflected in the underlyingfish tissue Hg concentrations themselves. Therefore,these methylation differences will not to have asubstantial impact on our analysis and are not expectedto introduce systematic bias (the time sequence ofchanges in fish tissue levelswill depend on differencesin methylation potentials, but we are not attempting topredict these temporal profiles).We did not quantitatively assess uncertainty related to thisissue. Our determination is that this is not an importantsource of uncertainty for our risk estimates.(K) Factors related tomethylation inwatersheds (e.g.,sulfate deposition,pH) have notremained constantThis source of uncertainty isdistinct from the issue ofmethylation potential discussedabove (here we talk about factorswhich can interact with theunderlying methylation potentialAs with methylation potential discussed above, anychanges in factors affecting the underlying methylationpotential of a waterbody would eventually be reflectedin fish tissue Hg concentrations and therefore notintroduce systematic bias into the analysis. For thisreason, our concern over thisissue is reduced. However,We did not quantitatively assess uncertainty related to thisissue.102Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisover time, resulting invariation in themethylation potentialof watersheds overtime.to increase or decrease it). If thesefactors (pH and sulfate depositionfor example), have changed for agiven watershed, then themethylation rate of that waterbodymay also change, therebyimpacting the degree to whichfish bioconcentrate and ultimately(for higher trophic levels)bioaccumulate Hg. Changes inmethylation factors can alsoimpact the temporal profile forchanges in fish tissue Hgconcentrations.if changes in methylation factors are continuous andextend into the current simulation period, then there ismore concern, since there could be more of a gradualchange in methylation across our simulation period (achange that would not be fully reflected in the fishtissue Hg values we have in the dataset). However, it isdifficult to characterize the magnitude of the potentialimpact of this source of uncertainty on risk estimates,including the direction and magnitude of any bias, giventhe complex interplay of the factors involved (i.e.,potential for ongoing changes in pH and sulfate toproduce methylation rates that vary over time).(L) Effort to excludewatersheds withsubstantial non-air Hgdeposition58Despite efforts to excludewatersheds with substantial nonair sources of Hg loading (seesection 1.4.2), some watershedswith substantial non-air impactsmay have been retained in theanalysis.The potential that we may have failed to excludewatersheds with significantnon-air Hg loadings couldintroduce high-bias into our estimates of U.S. EGUattributable risk, since we would overstate the role ofU.S. EGUs in contributing to risk, by overlooking theother non-air sources.We completed two sensitivity analyses exploring this issueand its potential impact on risk including: (a) an analysisof risks when watersheds falling in LA, SC, MN, and ME,are excluded (there is concern that these four states mayhave significant non-air Hgreleases and/or increasedmethylation potential) and (b) an assessment of risk basedonly on those watersheds falling in the upper 25thpercentile with regard to total Hg deposition (i.e.,estimating risk when we focus on those watersheds wherewe are more confident that aerial Hg deposition plays adominant role). The sensitivityanalysis results suggest thatexclusion of the four states where we have concerns overnon-air Hg loading does not substantially impact risk.However, when we estimated risk focusing on thewatersheds with the highest overall Hg loading (the upper25thpercentile), we did see a moderate increase in riskestimates.(M) Potential that themodeling frameworkis not sufficientlyrefined from a spatialstandpoint to captureelevated levels of HgIf the overall approach for linkingHg deposition (specifically thefraction attributable to U.S.EGUs) is not sufficiently spatiallyrefined, then areas of high U.S.EGU Hg impact and resulting riskWe believe the level of uncertainty associated withprecision in modeling exposure and specifically theability to identify U.S. EGU-related risk “hot spots”(again, using SAB terminology to refer to areas ofelevated Hg-related risk) is moderate. CMAQ modelingis provided at the 12km grid resolution which matchesThe issue of spatial scale in capturing high-end subsistencefisher risk has not been quantitatively assessed as part ofthe risk assessment.58For the March version of the Mercury RiskTSD, we also explored the issue of whether the proportionality assumption was moreapplicable to stationarywaterbodies (lakes/ponds) then flowing waterbodies (rivers/streams). However, for this version of the Mercury Risk TSD, reflecting recommendations made bySAB that the proportionality assumption can be readily applied foreither category of waterbody, we have not repeated that sensitivity analysis, or discussed thissource of uncertainty.103Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisdeposition (“hotspots” as termed inthe SAB comments)(Note, this source ofuncertainty alsoinvolves CMAQmodeling)may not be reflected in the riskassessment. This issue speaks tospatial precision in (a) the CMAQair quality modeling, (b) scale ofthe watersheds (and degree towhich subsistence activity isfocused within specificwatersheds) and (c) applicabilityof the proportionality assumptionin relating changes in Hgdeposition to changes in fishtissue Hg concentrations (orrelating fraction of total Hgdeposition coming from U.S.EGUs to the fraction of fish tissueHg attributable to U.S. EGUs).well the watershed scale used in the analysis (i.e., theHUC12). The HUC12 watersheds, in turn, represent asmall watershed which is appropriate if we assume thatthe subsistence fisher being modeled focuses theiractivity primarily at the same waterbody (i.e., within agiven HUC12 watershed). Therefore, given the focus ofthis analysis on a reasonable highly-exposedsubsistence fisher (i.e., anindividual who focuses theiractivity at a given waterbody), then the modelingframework, including the spatial scale ofthe variousmodeling elements (CMAQ modeling, watershed, fishtissue data), would seem appropriate. For this reason,we believe there is little concern for the introduction ofbias into the risk assessment due to the precision of themodeling and our ability to capture potential hot spots.Factors relating to the estimation of mercurydeposition over watersheds using the CMAQ model(e.g., estimating Hg emissions from U.S. EGUs and other sources, chemistryassociated with Hg fate and transport, prediction of wetand dry deposition, and global inflow of Hg into the U.S.)(N) Hg 2005 U.S.EGU emissions,uncertainties andsources of bias2005 Hg emissions from EGUsused for this analysis are from the2005 inventory developed for theNational Air Toxics Assessment(NATA). These data werecalculated by EPA by scaling Hgdata from the 1999 NationalEmissions Inventory (NEI) usingthe ratio of 2005 heat input to1999 heat input. Thus,uncertainties and bias in the 2005data fall into two classes – thosethat are due tothe 1999 estimatesand those resulting from thescaling approach used to estimate2005 emissions.The 1999 NEI datauncertaintiesinclude uncertainties traditionallyassociated with emissionsestimation, such as uncertaintiesin the accuracy of test data, theapplicability of the test data, therepresentativeness of emissionThe approach taken for the 1999 NEI used estimatesdeveloped from the data collected for the ICR done forthe Clean Air Mercury Rule. These data werepreferentially used over other estimates available fromstates, local agencies, or tribes. While these approachesinclude some uncertainties, they used best availablepractices and data available from the very rich datasource of the 1999 ICR. Ingeneral, emissions estimatesrelying on test data will result in the best availableemission estimates for sources with such data, and theuncertainties associated with measurement error areconsidered negligible in comparison to the other sourcesof uncertainty. Estimates based on emission factors willhave greater uncertaintybut will not significantlycontribute to bias unless the emission factors are notrepresentative. Care was taken to apply the mostrepresentative emission factors data to the sources in thecases where this was needed. A great deal was knownabout throughput at these sources from the ICR data,and so that is not considered a large source ofuncertainty. These uncertainties can cause bothoverestimates andunderestimates of emissions, butsince there is no way to assess that, it is not possible toquantify the potential impact(including the directionThe impact of this source of uncertainty was notquantitatively evaluated.104Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisfactors for specific units, theoversimplification occurring whenemission factors are used ratherthan continuous monitoring, andthe accuracy of throughputestimates.The 2005 approach addsuncertainty as well as bias toemissions estimates because itdoes not include the impacts ofcontrols added to sources between1999 and 2005 or changes infuels, including the Hg content ofthe fuel. In addition, uncertaintiesin the throughput informationused to scale the emissions canaffect the emissions estimates.Lastly, the temporal andspeciation representation ofemissions in the 2005 modelinputs is another source ofuncertainty. However, as noted incolumn to right, the net effect ofhigh-bias in 2005 emissionswould be to low-bias riskestimates for the 2016 scenario.and magnitude of bias) on risk estimates.The 2005 scaling approachwill tend to overestimateemissions in 2005 because of additions of controltechnologies and coal used between 1999 and 2005 thatwould have reduced emissions. These reductions werenot included because only the changes to throughputwere considered in the approach. This most likelycauses uncertainties that result in a high bias(overestimate) for the emissions in 2005. Since the2005 deposition results are usedin the denominator ofthe risk scaling approach to predict risk in 2016,overestimates of 2005 emissionsare expected to resultin an underestimate of risk in 2016. The uncertaintiesassociated with the heat inputdata in 2005 are very low,since most units included in the mercury estimates hadCEM data to record and report heat input for 2005.The speciation approach used test data from the 1999data collection, which assigned speciation factors basedon boiler type, fuel type, and control configuration.While imperfect, these data will tend to minimizeinaccuracies in speciation assigned since the datasetused to calculate the fractions was fairly large, and wascertainly the most comprehensive Hg speciation dataavailable. Finally, the temporal allocation approach waskept the same between 2005 and 2016 so that any smallimpacts of these assumptions on deposition results willcancel out using the scaling approach for risk.(O) Hg 2016 U.S.EGU emissions,uncertainties andsources of biasThere are three key variables thatfigure in the modeling of 2016 Hgemissions from U.S. electricgenerating units (U.S. EGUs): themercury content of the fuelcombusted (essentially variousranks and grades of coal), theextent of coal consumed, and typeand performance characteristics ofthe controls to reduce mercuryemissions at a given U.S. EGU.Uncertainty may figure in each ofthese key variables.As a result of uncertainty, the 2016 mercury emissionsfrom EGUs may be over orunder-estimated. However,the extent of the impact of uncertainty is considerablyreduced (as is concern over bias that may impact riskestimates) due to the numberof directly measured andquality assured data points that are used to characterizethe variables described in the previous column, by themature nature of the technologies for mercury emissioncontrol, and by the commensurate experience andexpertise in characterizing their performance as a resultof more than a decade of in-field operation of thecontrols.The impact of this source of uncertainty was notquantitativelyevaluated.105Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysis(P) Hg Non-EGUemissions, generaluncertainties andconsideration ofpotential biasThe 2005 NATA inventory fornon-EGU sources is a compilationof state data and data associatedwith sector-specific rulemakingactivities at EPA. Uncertaintiesinclude uncertainties traditionallyassociated with emissionsestimation, such as uncertaintiesin the accuracy of test data, theapplicability of the test data, therepresentativeness of emissionfactors for specific units, theoversimplification occurring whenemission factors are used ratherthan continuous monitoring, andthe accuracy of throughputestimates. In general, the databased on source tests andindividual source reporting are ofhigher quality with lessuncertainty. In addition,speciation and temporal allocationconsiderations add sources ofuncertainty. These uncertaintiesare also applicable to the 2016emissions, since these emissionsare derived by scaling the 2005emissions based on expectedreductions between 2005 and2016.Mercury emissions based on facility test data have beenused whenever possible for the 2005 NATA inventory,focused on the categories with the highest emissions.For example, emissions sourceswith 2 or more tons in2005 include Portland cement, electric arc furnaces,boilers, chemical manufacturing, hazardous wasteincineration, chlor-alkali plants, gold mining, andmunicipal waste combustors.For these sources, whichrepresent 36 tons out of 47 tons non-EGU Hgemissions, 65% of these sources used data developedfor rules associated with these sectors. The rule dataarein large part based on testdata and have otherwisehad extensive review by industry experts and thefacilities themselves. In combination with the U.S.EGU data, emissions from these higher quality datasources comprises 76% of the total anthropogenic U.S.Hg inventory. Thus, EPA’s approach reflects aninventory with much lower uncertainty than if it hadbeen based solely on more generic emission factors.Speciation data used forthis analysis are highlyuncertain, but reflect best available information. Sincethe analysis was not quantifying the impact of non-EGUsources, the speciation of non-EGU sources tends tocontribute less to the impact on the analysis.Additionally, while temporalallocation uncertainties arealso high, the temporal allocation approaches for eachsector are used consistently in 2005 and 2016, so theirimpacts are lessened throughthe use of the scalingapproach.There are no known sources of bias related toestimation of emissions from non-EGU sources in thisanalysis. Every attempt was made to use the bestavailable emissionsdata in 2005 and future yearprojections to 2016.However, a possible (but not likely) source of non-EGUemissions underestimates isfrom natural sources.Public comments on EPA’s approach suggest that somenatural sources were not considered. However, EPAdoes include natural elemental Hg emissions fromvolcanoes, oceans, and land. Further estimates ofrecycled Hg emissions from oceans and land areThe impact of this source of uncertainty was notquantitativelyevaluated.106Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisincluded. One source of uncertainty is the treatment ofHg from fires. While EPA did not explicitly includethese emissions in the analysis as fires, the modelcalibration approach used to estimate the recycled landemissions captures such emissions (Seigneur, et. al.2004). To the extent that there is any underestimationbias, it would reduce the fractional contribution of U.S.EGU sources, however, given the low likelihood ofthese sources of bias, the impacts on risk are expectedto be small.(Q)Hg Emissions,changes inuncertainties and biasbetween 2005 and2016The sources of change between2005 and 2016 emissions are theU.S. EGU emissions and thestationary non-EGU emissions.All other emissions sources(mobile and non-anthropogenic)stayed constant between the twoyears. For U.S. EGUs, differentsources of uncertainties exist inthe two different approaches for2005 and 2016 emissions, asdescribed above. The possiblechanges in uncertainties from2005 to 2016 are caused by thechanges in methods.For non-EGU sources,uncertainties in 2016 emissionscan result from assumptions aboutemissions reductions associatedwith other EPA regulations aswell as predicting future activitylevels associated with economicor other changes.For EGUs, some types of uncertainties could be greaterin 2016 than in 2005 since the emissions are forecast tothe future, for example, the future expected electricitydemand and coal usage. 2005 emissions estimates havedifferent uncertainties and it is not possible to determinewithout extensive uncertaintyanalysis which sourcesare greater than others. Asdescribed above, a bias mostlikely exists in the 2005 data that does not exist in the2016 data, resulting in a possible underestimate of riskfrom the deposition-based scaling approach used for2016 risk estimates.For non-EGUs, several source categories reducedemissions from 2005 to 2016 as a result of other EPAregulations. While the reductions are estimated, theuncertainties are relatively low given the extensiveanalyses done to develop the regulations and sincecompliance with EPAemissions reductionsrequirements tends to be veryhigh. Uncertainties areintroduced because EPA does not include co-benefits ofreductions from controls for other pollutants such asPM2.5, which might lead tooverestimation of the 2016non-EGU emissions, lesseningthe impacts of U.S.EGUs, and therefore lowering the risk estimates fromEGUs in our analysis. Uncertainty is also introduced inthis analysis for non-EGU sources because the possiblegrowth or reduction in emissions associated witheconomic changes is not included in the 2016 estimates.However, it is not possible to determine the impact ofexcluding such considerations since non-EGU industryspecific forecasts are highly uncertain themselves, thereis a complex relationshipbetween economic changes,regulations on emissions,and new source requirementsThe impact of this source of uncertainty was notquantitativelyevaluated.107Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisthat can reduce emissions.(R) Uncertainty inlocation of 2016emissions reductionsUncertainties in the 2016 scenarioregarding the specific geographiclocations of reductions in EGUderived mercury deposition as afraction of total mercurydepositionThe upstream U.S. EGU sources of Hg emissions areprovided by EPA’s power sector model, the IntegratedPlanning Model (IPM). The uncertainty in the extent ofprojected Hg emissions fromthese sources is treatedabove (see entry “O”). Here the focus is on the questionof uncertainty in the location of the 2016 emissionsreductions from the standpoint of which EGUs areprojected to operate and so serve as sources of Hgemissions. IPM is an extremely detailed bottom-updeterministic linear programming representation of theU.S. power sector. There isno stochastic component inthe model that would allow one to characterize thelikelihood that the locationof EGU sources and theextent of emissions from these sources are shifting.Therefore, we are not able at this time, to evaluate thepotential for systematic bias being introduced into riskestimates through this stepin our modeling. However,the model’s rigorous adherence to economic andengineering fundamentals anddetailed representation ofall factors affecting power system operation aredesigned to limit uncertainty and ensure that themodel’s locational projections are reasonable.The impact of this source of uncertainty was notquantitativelyevaluated.(S) Global inflow ofmercury into thecontinental UnitedStatesThere is considerable uncertaintyin the global emissions inventoryfor mercury and given the longresidence time of elementalmercury it is possible that inflowinto the modeling domain mayreflect deficiencies in the globalemissions inventory. Some studiessuggest global mercury emissionswould increase without controlimplementation (Pacyna et al.,2009; Streets et al., 2009). Recentmeasurements of ambientmercury at remote locations from1975 to 2008 suggest globalmercury burden is steadilydecreasing (Slemr et al., 2011).Given this conflicting informationand the uncertainty in projectingMercury deposition may beover or under-estimated(i.e., potential bias introduced is not clear in terms ofdirection or magnitude).This analysis did not address this source of uncertainty.Mercury inflow was adjusted in another study and wasshown to have a larger impact on modeled dry depositioncompared to wet deposition. Boundary conditionperturbations within realistic bounds resulted in minimallychanged distributions of total modeled mercury deposition(Pongprueksa et al., 2008). Thisstudy also determined thatmodel spin-up of one week is necessary to removeinfluence of initial conditions (Pongprueksa et al., 2008).The CMAQ modeling in this analysis had spin-up of morethan one week making initial condition influencenegligible.108Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisthe current global emissioninventory, boundary inflow iskept constant between 2005 and2016.(T) Photochemicalmodel prediction ofmercury wetdeposition estimatesThe uncertainty in the widevariety of model inputs such asemissions, meteorology, globalinflow to the modeling domain,and chemistry manifest in modelestimates of mercury wetdeposition.Mercury wet deposition maybe under-estimated duringthe summer season and over-estimated during thewinter (i.e., potential biasintroduced is not clear interms of direction or magnitude). Model performance isdescribed in “Air QualityModeling Technical SupportDocument: Point Source Sector Rules” (USEPA,2011b).Model estimated weekly mercury wet deposition iscompared to observation data to assess model skillsimulating this component ofmercury deposition. Mercurywet deposition measurements are weekly totals taken atsites that are part of the Mercury Deposition Network(http://nadp.sws.uiuc.edu/MDN/) which operates under theNational Atmospheric Deposition Program. This isgenerally consistent with other published studies that usecoarser grid resolution, older versions of CMAQ and olderemission inventories (Bullocket al., 2008; Lin et al.,2007).(U) Photochemicalmodel prediction ofmercury drydeposition estimatesThe uncertainty in the widevariety of model inputs such asemissions, meteorology, globalinflow to the modeling domain,and chemistry manifest in modelestimates of mercury drydeposition.Mercury dry deposition may be over or under-estimated(i.e., potential bias introduced is not clear in terms ofdirection or magnitude).This analysis did not address this source of uncertainty.There is a lack of dry deposition observation data thatmakes a direct quantitative or even qualitative comparisonto modeled estimates impossible. Other studies haveshown differences in estimated dry deposition based onchanges in ambient mercury, reaction rate changes, andchanges to the dry deposition scheme (Bullock et al., 2009;Lin et al., 2007; Pongprueksaet al., 2008; Ryaboshapko etal., 2007a; Ryaboshapko et al., 2007b). However, giventhat these studies are based on earlier versions of CMAQor other models that are not routinely used for regulatorypurposes and that mercury chemistry and dry depositionhas changed through model versions, it is not clear that drydeposition estimates in the version of CMAQ used for thisanalysis would be comparable to earlier studies.(V) MercuryChemistryThe complete set of mercuryoxidation and reduction reactionshas not been identified by thescientific community.Mercury deposition may beover or under-estimated(i.e., potential bias introduced is not clear in terms ofdirection or magnitude).This analysis did not address this source of uncertainty.Other studies have shown differences in estimated totalmercury deposition based onchanges in reaction rates(Bullock et al., 2009; Lin et al., 2007; Pongprueksa et al.,2008; Ryaboshapko et al., 2007a; Ryaboshapko et al.,2007b). However, given thatthese studies are based onearlier versions of CMAQ orother models that are notroutinely used for regulatory purposes and that mercurychemistry and deposition has changed through modelversions it is not clear that total mercury depositionestimates in the version of CMAQ used for this analysiswould be comparable to earlier studies.109Source ofuncertaintyDescriptionNature of potential impact on the exposure and riskestimatesDegree to which the potential impact of the source ofuncertainty is characterized as part of the analysisApplication of the MeHg RfD in generating hazard quotient (HQ) risk estimates(W) Degree to whichthe RfD as calculatedprovides coverage forlow SES statusgroups which may beat greater risk foradverse health effectsfollowing MeHgexposureGiven a number of factors (e.g.,nutritional deficiencies, reducedaccess to health care and healthrelated information), there isconcern that the MeHg RfD maynot provide sufficient coveragefor these low SES populations.The RfD is defined as the amount of the substance ofconcern that can be consumed without expectation ofharm for a lifetime by populations including sensitivesubpopulations. The calculated RfD for MeHg includesan uncertainty factor to account for humanpharmacokinetic variability (3 fold) and uncertaintyand variability in pharmacodynamics. This may besufficient to account for increased sensitivity to IQdecrements or adverse effects on neurobehavioralfunctions in low socio-economic status populations.There are no published analyses by EPA or other partiesthat would permit estimation of uncertainty for thisfactor and the potential for resulting bias beingintroduced into the analysis. Two of the humanpopulations on which the RfD was based (the Faroeseand the Seychellois) are relatively homogenous forsome aspects of SES.We did not explore this potential source of uncertaintyquantitatively. However, as noted in the cell to the left dueto the method used in calculating the RfD, concerns thatthe RfD may not provide coverage for low SESpopulations is reduced.(X) The MeHg RfDwas derived based onsaltwater fishconsumption (whichcan involve higherlevels of nutrients thatameliorate theadverse effects ofMeHg). However, theRfD is now beingapplied in the contextof freshwater fishconsumption (whichcan involve lowerlevels of thesenutrients).Recent studies (e.g. Oken et al2008; Choi et al 2008; ) point tothe potential for nutrients in fish(particularly marine fish) toameliorate some of the observedadverse effects of MeHg when coexposure occurs. However, therewas no correction for potentialconfounding by nutrients inmarine fish and mammals incalculation of the benchmarkdoses used in the RfD derivation.Therefore, there is the potentialthat these benchmark doses maybe underestimates, in which casethe HQ estimates based on theRfD could be biased low,particularly in the case offreshwater fish which may havelower levels of these nutrients.Failure to consider the effects of fish nutrients as acovariate or confounder fornerodevelopmental effectsassociated with MeHg exposure could result in HQestimates being biased low. However, currently,available information does not support a rigorousadjustment of the RfD to address potential confoundingby fish nutrients.We did not explore this potential source of uncertaintyquantitatively.110 3 Summary of Key Observations

This section provides key polic y-relevant observati ons drawn from discussions presented in Sections 2.1 through 2.7. It is important to emphasize, that th e 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 c onclusionary 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 asse ssment (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 e ndpoint may not fully capture the range of neurodevelopmental effects a ssociated with MeHg exposur e, 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 cont ribute 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-rela ted Hg deposition is higher in the eastern part of the country with elev ated 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 scen arios, reflecting mainly co-benefits from implementation of criteria pollutant cont rols 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 contri bute 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 tissu e levels with total Hg deposition (as

characterized at the watershed-le vel) suggests that there is no t 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. Howe ver, we still conclude that, generally, our111 coverage for high U.S. EGU impact areas rema ins 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 hea lth concern) could be substan tially larger than estimated.

 Based on application of the 2-stage risk char acterization 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 u nder 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 in to account mercury deposition from other sources, would produce an HQ >1 or watershe ds where total HQ (reflecting Hg from all sources) is > 1 and U.S. EGUs make at le ast at 5% contribution to that-risk).

 Comparison of risk estimates generated for the typical female subsistence fish consumer scenario with estimates generated for the ot her six SES-differentiated female subsistence

fish consumer scenarios incl uded in this risk assessmen t results in the following observations: (a) total and U.S. EGU-attributab le risks for the Hispanic and Vietnamese scenarios are generally lower than for the typical female subs istence 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 tota l risks are generally higher for the Tribal scenario and (c) U.S. EG U-attributable risks for the Laotian and low

income southeastern White and Black scen arios are generally higher than for the typical female subsistence fish consumer scenario, although total risk s can be higher or lower depending on the scenario. These tr ends 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 covera ge for the Laotian and low income

southeastern White and Black s cenarios, particularly in term s 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, tota l 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 subs tantial 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 assump tion is unlikely to substantially effect an assessment of whether Hg emissions from U.S. EGUs constitute a public health concern.

Use of watershed-level 50 th percentile fish tissue Hg con centrations (instead of the 75 th
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 75 th
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(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 me tric has been de-emphasized in presenting

risk estimates due to concerns that it ma y not fully capture the range of adverse neurodevelopmental effects a ssociated with MeHg exposur e. 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 rela ted to those risk estimates is pr esented 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 w ith the same exposure estimates used in generating HQ estimates (i.e., estimates of Me Hg exposure generated for a given subsitence

fisher scenario - see section 1.4.4 for derivation of these exposu re estimates). However, these estimates of body weighted-adjusted MeHg exposure need to be converted into an equivalent

maternal hair concentration sin ce the IQ loss function uses hair Hg as the dose measure. To do that, we use a dose-to-hair convers ion factor (DHCV) of 12.5 (units ppm per unit μg/kg-day) that converts ingested dose (IR) to ha ir Hg concentration in ppm. Th e DHCV factor is based on a one compartment toxicokinetic model used for derivi ng the MeHg RfD by Swar tout 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-re sponse (CR) function relating material hair Hg levels to IQ points lo st 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 integrat es data from the three key epid emiological 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 possibili ty that neurological deficits re lated to Hg exposure through fish consumption could be masked to some degree by th e neurologically-beneficia l effects of fish oil consumption. Some authors have su ggested 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 Es timates (IQ lo ss 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 signifi cance of the IQ loss risk estimates, based on recommendations provided by the Clean Air Science Advisory Co mmittee (CASAC) in the context of the last National Ambient Air Quality Standard (NAAQS) re view 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 per centiles for the IQ loss risk metric for children born to members of the typical female subsistence fish consumer scenario fo r the 2016 scenario.

 Table B-2: identifies and disc usses key sources of uncertain ty 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 estima tes 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 th e 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 th e 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 first119 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 ourestimate 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 quantitativel y 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 th e 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 Pa nel 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 provide d by EPA to the SAB are include d 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

. Th e goal of th is

draft document is to characteriz e 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 asse ssment, 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 Documen t.

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 representat ives 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 sough t advice from the SAB on key components of i t s analysis. In response, t he 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. The re 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. IntelligenceQuotient ( 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

en dpoint 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 endpoint s as a supplement to
the main analysis. A lthough the SAB considers the number o f 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. T he 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 impose d 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 “ a ppropriate and n ecessary f inding ” 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, b ased 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 d ocument 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 Paneli NOTICE

This report has been written as part of the activities of the EPA Science Advisory Board (SAB), a public advisory group providing extramural scienti fic 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

r eviewed 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

. M ention of trad e names of commercial products does not constitute a

recommendation for use. Reports of the SAB are posted on the EPA web site 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 Rese arch 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 C ollege, 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 Wisconsin

Madison, 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, TXiii Dr. Jana Milford , Professor, Departm ent 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, Departmen t 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 & Occ upational 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, Mi nnesota 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 , Profes sor, 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. Timoth y 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, Pittsbur gh,

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, WAv 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 As sociate 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 Eng ineering, 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, H ubert 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, Bozem an, MTvi 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 Res ources, 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 Instit ute 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 Se gerson
, 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, Departmen t 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 Definition s 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 75 th 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 Cha rge Questions

................................ ................................ ..................... A

- 1viii ACRONYMS, ABBREVIATIONS AND DEFINITIONS OF T ERMS

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 (GEO S) 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 P olyunsaturated F atty A cid

RfD Reference Dose

R

- MCM Regional Mercury Cycling Model

SAB Science Advisory B oard

TRI Toxic Release Inventory

TSD Technical Support Document1 

### 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 determin ation, 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 an d 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 consider s 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 focuse s 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 f or watersheds with data on meth ylmercury 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 2 016 “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, t he Panel reviewed background materials provided by the Office of Air Quality Planning and Standa rds, 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 highlight s the main finding s

. Detailed

responses t o the individual charge questions are provided in the body of the report

.

T he 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 view s the risk assessment favorably, concluding tha t
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 trans parent 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 discus sion provided by EPA representatives

. Re s ponses to charge questions indicate where improvements need to be made, and a summary of the most critical recommendations is provided

in section 4

. T he SAB’s support for the risk assessment is contingent on its rec ommendations being fully

considered in the revision of the assessment.

Overall design

T he 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

. T he report’s introduction should make clear from the start that the analysis is a determination of potential exposure at the scale of watersheds.

C ritical health endpoints besides IQ loss ; u s e of an IQ loss metric benchmark ; and c oncentration

response function used in modeling IQ loss

T he SAB support s 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 me tric 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 note s 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 in to 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 appen dix where IQ loss is discussed along with other possible endpoints not included in

the primary assessment.

While the SAB agree s that the concentration

- response function for IQ loss used in the risk assessment has validity, IQ loss is not a sensitive resp onse endpoint for methylmercury and its use likely underestimates the impact of reducing methylmercury in water bodies. T he SAB agree s that if IQ loss

were retained in the risk assessment despite these reservations , a loss of one or two points on average i n a population would be an appropriate benchmark

. The SAB agree s 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 agree s 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 beca use 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 agree s that HUC12 watersheds provide the appropri ate 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 r obust. Further, the finer resolution of HUC12 watersheds is better suited to follow deposition patterns of a single source such as an EGU and increase s the likelihood that measured deposition within a watershed is homogeneous. The SAB note s that one disadv antage of smaller3 watershed size is that

, within a given watershed, the number of fish samples with methylmercury data is

diminished

. During the public meeting, t he SAB question ed 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

. EP A 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 agree s 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

avai lable 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 note s advantages and d isadvantages of the Agency decision

to limit fish tissue concentration data to the period after 1999 but agree s with this approach

, given that older data might not be representative of conditions during the 2005 reference deposition year

. The SAB

is concer ned 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. W ith further development, this approach could be used for a national scale assessment such as this in the

future but the S AB does not recommend it for the current assessment.

Use of the 75th percentile fish tissue methylmercury valu e

As a means of selecting methymercury fish concentrations representative of larger, but not the largest, edible fish, the risk assessment uses the 75 th percentile fish concentration for watersheds with one or more fish concentration value. The SAB consider s this percentile reasonable but is concern ed that over half of the watersheds in the assessment have only four or fewer fish sample s with meth ylmercury

concentration

, and a significant number of these have a single fish sample

. The SAB note s that in

watersheds where only a few fish samples are available, the 75th percentile concentration and exposure most likely will be underestimated

. This shou ld 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 source s of fish methylmercury concentration data more fully, including the state sampling program s that provide most of the data

. Discussion of sampling programs should include the types and sizes of

fish obtained , as well as uncertainties ass ociated 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

- cau ght 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 the4 suitability of data for inclusion in the risk assessment (e.g., in terms of providing annual average intakes

of the edible porti on 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 agree s 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 possi ble, 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 agree s with the Mercury Maps approach used in the an alysis 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

accumu lated in fish, and reductions in atmospheric mercury deposition should lead to decreases in methylmercury concentrations in biota. The SAB note s 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 exi sting 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 agree s 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 u ncertainties 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 uncertai nty 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 i mportant sources of variability and uncertainty is considered incomplete.

Inclusion of several additional sources of variability and uncertainty is recommended. The SAB note s 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 f ive 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 w h ere 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 crit ical 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 CA A ( Clean Air

Act ) requ irements ? 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: National

Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for Coal and Oil

Fired Electric Generating Units

- March 2011 , developed to sup port a proposed rule published in the Federal Register on March 16, 2011 to regulate emissions of h azardous a ir p ollutants from for coal

- and

oil

- fired Electric Utility Steam Generating Units (EGUs). Section 112(n)(1) of the Clean Air Act requires EPA to d etermine whether it is “appropriate and necessary” to regulate hazardous air pollutants

emissions from EGUs under section 112. Th e “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 P anel to peer review the draft Technical

Document. The P anel 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 dr aft 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 me thods and findings. The Technical Support Document needs to better explain what was done and why, translat e 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 asse ssment 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 provide s responses and s pecific suggestions and recommendations for re vising 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 Q uestions

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 mo re highly exposed).

Response:

3.2. C ritical 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

mercur y 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 methyl mercury exposure via fish consumption and then compare s 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 methyl mercury in fish tissue samples , as well as est imates 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 recommendat ions 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. T he Introductory section should ma ke 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

asses sment)?

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

. Th e 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 identify ing the impact of consuming fish from water bodies with unacceptable levels of methylmercury because IQ will likely result in an

underestimat ion of potential neurobehavioral impacts , compared to analys e s u sing the hazard quotient ( HQ )

. T hus, th e SAB consid ers 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 variabilit y. 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, t he 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 Techni cal 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 test s 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 th is i ndex is a component of the Bailey Scales of Infa nt Development, it is not highly correlated with cognitive measures ( Davidson et al. 2008 )

.

Additionally, t he use of IQ, or any neuropsychological measure, distracts from the main goal of the document. The analysis in the document emphasizes the number o f 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. Th is is because IQ underestimates hazard, as noted above.

The SAB recommends that EPA reframe the document’s discussion of IQ . EPA should incorporat e 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 potenti al health

- related outcome. The a ppendix 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

del ays (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 G randjean 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 methyl mercury.

A recent analysis by van Wijngaarden et al. (20 06) 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 di scussion of markers of health impacts of lowering mercury deposition and reducing intake by subsistence fishers.9 One SAB member suggest s the use of blood markers of selenium

- dependent enzyme function, noting

that methylmercury irreversibly inhibits seleni um

- dependent enzymes that are required to support vital

but

- 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 Documen t 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 be nchmark 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 200 7)

. 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 magnitu de 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). T o 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 d etail 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 drainag e 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 watershed10 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 note s , however

, th at 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 docum ent acknowledge s 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 Easter n 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

water sheds 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 methyl mercury 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

c ollected from rivers versus lakes, and whether from single or multiple sites

.

3.5. M easured fish tissue mercury concentrations

Qu estion 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 methyl mercury concentrations and human exposure to methyl mercury 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 in11 the methyl form

. Although empirical data available are largel y supportive of this assumpti on, 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 . Ho wever, a s 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 hav e 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 acro ss watersheds)

, should be better detailed in the report.

There are advantages and disadvantages to using fish methyl mercury data prior to 1999 for the risk

assessment. The advantage is that considerable fish data were obtained prior to 1999 and the use o f these

data could increase the information available for the national risk assessment. The disadvantage is that

fish methyl mercury concentrations may have changed since 1999 and these older data may not be representative of conditions during the 2005 refe rence deposition year. Unfortunately

, there are few high quality time series data of fish methyl mercury concentrations

, so it is difficult to quantify the extent to

which fish methyl mercury concentrations have change d since the 1990s. As a result

, the SAB

recommends that the EPA utilize fish methyl mercury data collected since 1999 for the risk assessment.

Given the spatial distribution of mercury deposition from EGUs and the density of fish methyl mercury measurements (Figure 2

- 15), there are some states th at receive what the Technical Support Document

terms “relatively elevated ” mercury deposition from U.S. EGU emissions and have limited fish methyl mercury 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 methyl mercury 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 methyl mercury 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 (US GS) compilation of methyl mercury 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 enti rely 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 . P articularly strong

s ampling 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

t he 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 t han attempting to make inferences to the larger population of all 88,000 HUC12 watersheds.

Researchers have developed empirical models for fish methyl mercury concentrations using water chemistry and land cover data (Chen et al. 2005; Driscoll et al. 2007; and Watras et al. 1998)

. These12 empirical relationships have been used to estimate methyl mercury concentrations for different fish species at sta te and regional spatial scales

. Such an empirical modeling approach could be used to provide more compreh ensive estimates of fish methyl mercury concentrations across water resources and potentially improve the extent of future mercury risk assessments. However, i f 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 methyl mercury concentration. The SAB is not recommending that this approach be used for the

current risk assess ment. Rather

, the EPA might consider use of empirical modeling to improve the

information available related to fish methyl mercury concentrations in future assessments.

To strengthen the Technical Support D ocument 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 th e coverage for the mercury risk

assessment

.

3.6. Use of the 75 th 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 tiss ue 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

p opulation exposures at the watershed level?

Response: Using the 75 th 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

p ublished 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 methyl mercury 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 75 th percentile fish tissue methyl mercury concentration (on y axis) to the number of fish samples available for any given watershed. The estimate

of the 75 th percentile appears to increase with increasing sample size, thus suggesting that the 75 th
percentile fish tissue concentration for watersheds with few fish samples is un derestimated.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 wit h smoothing parameter set to 0.2.

The SAB recommend s inclusion of a graph depicting the number of tissue samples available for analysis by tissue concentration. The SAB also recommend s 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 a n underestimate of the number of watersheds at risk. Furthermore, the SAB

recommends that EPA should also conduct a sensitivity analysis using th e median fish tissue concentration to better represent the distribution of fish tissue methylmercury levels w h ere 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 acknowledge s 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 collectio n 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 recommend s that the document describe more clearly the source of

the fish methyl mercury 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 fis h 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, of15 the size of fish sampled in water sheds with few fish tissue samples available on estimated mercury concentrations. The SAB also recommends that the Technical Support Document clarify that the 75 th
percentile represents available fish tissue data that may or may not represent the fish in the watershed or

the fish consumed

.

3.7. Co nsumption 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 bod ies 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 use s sources that reported daily consumption for populations of low socioeconomic status African

- and European

- Americans f emales as the target population for the risk assessment. In addition, consumption rates from a study that targeted Laotian

and 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 recommend s that the Technical Support Document acknowledge that

, while several estimates of fish consumption rate s 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 focuse s 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 i s 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, i t is unclear whether the risk assessment use s annualized fish consumption rates and whether fish consumption is based on concentrations that are “ as caught ” or “ as prepared .” T he 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 utilize s existing data to identify consumption rates and target populations that are representative of the most highly exposed susceptible populations.16 Regarding alternative approaches, th e SAB note s 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 ( Knobel o ch et al. 2005). This would tend to underestimate ri sks 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 exp lain 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 identi fy 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 agree s 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 agree s that it is a reasonable approach, and that other approaches may not be as

effe ctive 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 subsis tence fisher is potentially active at the watershed. No major concerns are raised by the SAB concerning this issue. However, the SAB recommend s that the Technical Support Document clarify how many census tracts were eliminated due to the use of this cut

po int. 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 proportion s 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 M aps 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 link17 modeled estimates of mercury deposition to monitored MeHg fish tissue levels for all the watersheds evaluated? (additiona l 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 sen sitivity analyses conducted in the risk assessment addresse s two specific L imitations/uncertainty associated with Mercury Maps (MMaps) approach and proportionality assum ption

. The risk assessment’s qualitative characterization of the limitations and uncertainty in the application of Mercury Maps approach is appropriate. The SAB recommend s that

EPA also summarize quantitative estimates of the uncertainty published in the e xisting 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 wate rsheds with methyl mercury 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 literatu re 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 de position (e.g., M u nthe 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 wat ersheds 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 associ ated 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 an d 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 signific ant 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 con stant 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 t hat are significant relative to the air deposition load to that watershed are set aside from analysis in this risk assessment.

Since the Mercury Map s 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 ) . The se studies suggested that mercury deposited directly to aquatic ecosystems can become quickly available to biota and accumulated in fish, and that r eductions 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 o f the Mercury Maps to both flowing and stationary freshwater bodies to verify if the two scenarios would produce different

results. The results of these s ensitivity analyses suggest that uncertainty related to the Mercury Maps approach is unlikely to subst antially 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 re solution 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 i s 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. ant hropogenic 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, w hich 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) w ill 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 Ru le (36

- km resolution). M odeling results at finer resolution can be used to better resolve deposition pattern s near point sources. The confidence in applying the 12

- km resolution CMAQ results for estimating fish tissue methylmercury changes and its

associat ed 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 agree s 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 sh ould 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. S ubstant ial data support the Mercury Maps and the R

- MCM steady

state 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 criteri a 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 exclude s 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 i dentification 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

thresho ld value corresponds to the 25 th percentile annual U

. S

. EGU mercury emission value as

characterized in the 2005 National Air Toxics Assessment . The EPA team consider s the 25th percentile

U

. S

. EGU emission level to be a reasonable screen for additional subs tantial 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 s hould 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. C oncentration

- 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 supp ort 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. D etail on the

concentration

- response function us ed 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. Th e 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 20 05) 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 effect s of other

neurotoxicants. T hese 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 alo ng 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

re ducing 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 coef ficient 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 en ough 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 Sto kes

- Riner et al. (2011), demonstrated that

maternal hair mercury was associated with p rotein d isulfide i somerase only after controlling for the

effects of maternal omega

- 3 polyunsaturated fatty acid (PUFA) status. Controlling for omega

- 3 PUFAs steepened th e 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 becaus e 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 might21 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 po int s out that m ethylmercury is a potent inhibitor of multiple families of selenium

dependent enzymes that are required by the brain and endocrine system (Carvalho et al. 2008; 201 1 ; Seppanen et al. 200 4 ; Ralston and Raymond 2010). Therefore, the adverse ef fects 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 stud y 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 the re is disagree ment in the scientific community regarding the significance of these observations to humans.

The same SAB member also suggest s that since selenium abundance is largely observed to be inversely related to mercury bioaccumulation (Chen et al. 2001; Paulsso n 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 m ay interact to exacerbate mercury exposure risks in watersheds with low selenium

availabil ity. This SAB member thinks that special consideration should be given to evaluating potential health risks from consumption of fish with high mercury contents that o riginate 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 concentrati ons 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 men tioned 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 ris k 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

75 th : 0.19

90 th : 0.32

Samples: 44

PROJECTED

Fish tissue ( ppm ) 2016

Mean: 0.13

75 th : 0.16

90 th : 0.27 Total Hg dep

over watershed (CMAQ):

2005 = 39.55

2016 = 33.07
Or a 14.8% reduction 95 th % 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 define s 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 qu alitative 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 t o 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 SA B ’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 impact23 

### 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

75 th : 0.42

90 th : 0.47

5 samples

PROJECTED

Fish tissue ( ppm ) 2016

Mean: 0.25

75 th : 0.40

90 th : 0.44 Total Hg dep

over watershed (CMAQ):

2005 = 15.46

2016 = 14.63
Or a 5.4% reduction 95 th % 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 t o the explicit discussion of variability and uncertainty, the SAB suggests that language be used throughout the Technical Support Document to clarif y the scope of the results vis

- à

- vis variability and uncertainty in data and methods

. For example, the Techn ical Support Document should cite the evaluation of uncertainty in the CMAQ and MMAPs source documents. Notwithstanding the uncertainties in the approach , the SAB consider s the approach presented in the Technical Support

Document sound and reasonable.

Variability. The SAB note s 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 h elpful 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 v ariability 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 f ish 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 foll owing 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 75 th percent iles, 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

com plete 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 modif ied version of the National Emissions Inventory (NEI). Appendix F should discuss the uncertainties in inventory components ; whether and how the uncertainty change s 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 c learly 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

- deri ved 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 sp ots”

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 f or 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 ch aracterize 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 resp onses 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 pe r25 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 sampli ng 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 relie s upon a single older study to derive an adjustment factor o f 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 popu lations (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 su ch 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 con sumption 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 examine d.

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, tak ing 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 analy tical results provided to26 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 support s EPA’s plan to include updated figures from the memorandum in EPA’s Technical Support Document as replacements

for F igures 2

- 1 to 2

- 4 in the March 2011 draft so that they correctly reflect total annual mercury depo sition per square

- meter by watershed

. The SAB recommend s that the spatial patterns of simulated deposition shown in Figure 2

- 1 to 2

- 4 be better explained and that EPA should characteriz e 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

r ecommend s that EPA explain these difference s 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

Ne twork.

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 deposit ion 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 re sults 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 recommend s that EPA clarify the text to improve the description of the analytical results for each bulleted ob servation 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 focus ed 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 tot al fish tissue MeHg associated with U.S. EGUs on average, for a subset of watersheds, they can make a substantially larger contribution.27 Although t here 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 wate rsheds (~ 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 ris k 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 percentag es of EGU

- contribution to fish methyl mercury 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 h igher.

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 proporti onal 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 U nited States . These figures unnecessarily cut off the western

continental United States. While the SAB understand s 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 nat ional 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 methyl mercury concentrations in this region (see Figure 2

- 6), as well as the model predicted changes in fish methyl mercury 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, Loui siana and Indiana all have abundant data availability compared to most states. EPA

should discuss how this data availability bias affect s the analytical results

. The SAB recommends that

• The fish tissue MeHg sampling data (summarized at the watershed

- level) provides limited cov erage 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 high light 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, t he 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 portio n 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 importa nt 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 methyl mercury and that variability of ecosystem factors that control methylation p otential (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 an d 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 EGU

derived 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 co nstitutes “relatively elevated U.S. EGU Hg deposition” is different in the two maps . T he red area in Fig ure 2

- 15 is characterized as an average deposition rate of 3.79 and for Fig ure 2

- 16, 1.29 ug/m 2 . 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 bas is 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 se en 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. EGU

related 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/m 2 , respective ly). 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. T he SAB suspects that the deposition rate th reshold 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 an y 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 incremen t in fish concentration that is greater than 0.038 ppm methyl mercury , a concentration that does not correspond to a constant mercury deposition rate because the concentration varies among watersheds in accordance with the proportionality principle describe d in the risk assessment. However, the SAB note s that the average

mercury deposition rate that produces this incremental methyl mercury 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 s cenario red area,

rather t han assum e 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 uncertainti es of high values in Tables

2

- 5 and 2

- 7. The SAB ha s 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 t he 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 excee ds 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 s ection 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 T ables 2

- 6 and 2

- 7, EPA should consider reporting consumption rates and put ting the percentiles in parentheses rather than reporting the percentiles and having the rates in parentheses

.

In Table 2

- 15 and other places, th e mean i s 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 : T he 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 f ocuses on subsistence fishing populations as a target population likely to be the most severely impacted by methyl mercury consumption in fish. There is scant evidence documenting the prevalence or extent of subsistence fishing in the United States . Some

SA B 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 suggest s 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 de position...” 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 relat ion 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 depositio n 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 exp anded 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 availabl e 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 s tudy 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 stud y , but the manner in which it highlights the key findings could be improved. T he 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) W hat 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) H ow is risk estimated for both the current and futu re 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 i n 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. While t here are numerous unquantified sources of variability and uncertainty that are contained in the numerical estimates of potential risk, t he variability and uncertainty

do not contradict this basic finding.

Question 15: D espite 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 sub question originally posed by EPA for Charge Question 14 , which read as

follows: “ In addition, please comment on the degree to which the level of confidenc e 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

, afte r 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 R ecommendations

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 f rom 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 source s 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 ar e 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 incorporat e IQ and other neuropsychological measures as supplemental information and focus on HQ as the primary critical health endpoint

. It is not suggested t hat 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 potenti al 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 neuropsychologic al tests (as discussed by van Wijngaarden et al. 2006), presented in the overall context of the weight of evidence. Question 2: Critical health end points besides IQ

• The SAB recommends that the Technical Support Document acknowledge and discuss alternative (to HQ) quantitative measures but does not recom mend 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 recommen ds 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

m ethylmercury 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

an alysis 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 watershe ds 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.

• T he SAB reco mmends 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 genera l 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 infor mation 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.

• T he 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 consumpti on 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 est imates 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 as suming 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 distr ibution 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 summariz ed 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 uncertain ty analysis should be explicitly stated. Question 10: Exclusion of watersheds with significant non

- air loadings

• I Q 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 alon g 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, t he 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 outcom es.

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 wei ght) 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 ass essment. 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 o f 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 Em issions 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 A lternative future scenario forecast s. Appendix F should more clearly describe the

variables th at were held constant versus factors that were varied between the two

scenarios.

o Regarding u ncertainty in locati on of 2016 emissions reductions. D ue 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 the36 2016 scenario. Appendix F should address the uncertainties in the 2016 scenario regarding the specific geographic locations of reductions in EGU

- derived mercury depositi on as a fraction of total mercury deposition.

o U se 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 r epresentativeness 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 t he 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

i ntroduced 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 effe cts 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 doe s 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 SA B 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 concentr ation

- 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 examine d.

 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 1 3 : 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 t hat 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 ma ps 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 a nd 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 earl ier 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 tabl es.

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 o n 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

p attern 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 subs tantively 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 methylmercur y 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 lowe r in the 2016

sce nario 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 s ame 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 sh ould 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 s hould 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 reporti ng 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 pot entially 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 asses sment 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 s ources 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 study40 

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- 6268.A

- 1 

### Appendix A: Agency Cha rge Questions

Background and Charge for the SAB Review of EPA’s Technical Support Document: National

Scale 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 hea lth 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 m etal HAP, organic HAP, and acid gas HAP. S ection 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 nec essary 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 complete d the required study in 1998. (Utility Air Toxics Study, 1998). Based in part on the results of that study , EPA made a finding i n 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: Nati onal

- 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 imposi tion 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 identi fying 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 r isk 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 ris k (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 t hat 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 tran slate 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 Merc ury 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 merc ury 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 transpor t. 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 reso lution. 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 emi ssions 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 post

combustion emissions controls. Emissions of mercur y 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, pollut ant inflow into the photochemical modeling domain, 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 on e

- 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 Mercur y 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 Qualit y (CMA Q) 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 descriptionA

- 3 of emissio ns 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 child

bea ring age at inland fresh water bodies; the consumption rates used ranged from the 90 th to 99 th
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 Appe ndix 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 fis hing activity (resulting in higher self

caught 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 eit her 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 an d 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 merc ury 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 assess ment, 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 sci entific 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 RfD

based HQ, we considered any exposu re 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 mor e 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 i n 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 S upport Document: Point Source Sector Rules (EPA

- 454/R

- 11

- 003), Research Triangle Park, North Carolina.A

- 4 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. Th e 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 subgrou ps) 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 exposur e, 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 h air 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 Is lands).

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 T SD).

Figure 1 provides a conceptual diagram for the key steps in the risk assessment.A

- 5 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, an d 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 im pacts, 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 c omparison 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 transpo rt algorithms are documented in several peer reviewed publications. 12

, 13

, 14

11 U.S. Environmental Pr otection Agency (U.S. EPA). 2002 . Integrated Risk Inf ormation 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 descrip tion 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: 182 8

– 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.A

- 6 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 near

subsistence 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 high

end 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 EGU

attributable 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 EGU

attributable fraction Mercury RfD for

HQ estimation IQ loss concentration

response function

Factor translating Hg ingestion dose into Hg hair

concentrations (in ppm) Modeling IQ loss Exposure modeling

Risk modeling

decision d ata input a nalysis step KEY:A

- 7 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 h elp 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 eleme nts 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 r isk 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 exposure s.

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 ov erview 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 poi nts 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 fo r 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.A

- 8 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 a pproximately 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 est imates 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 M eHg 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 75 th 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 woul d 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 f ish 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 D efining 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 t he self

- caught fish consumption rates used in modeling exposure for these study populations.A

- 9 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 assoc iated 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

wate rsheds 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 wat ersheds) 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 w ith 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 C MAQ 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 model ing 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 i ncludes a detailed discussion of the

concentration

- response function used in modeling IQ loss.A

- 10 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 unce rtainty 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

sectio n 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 associat ed 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

sensi tivity 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 th e 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 suf ficient 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 StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsHealth and Environmental Impacts DivisionResearch Triangle Park, NCEPA-452/R-11-009December 2011