4 - 1 **CHAPTER 4 MERCURY**  **AND OTHER HAP BENEFI TS ANALYSIS**  **4.1 Introduction**  This chapter provides an analysis of the benefits of the proposed Toxics Rule from mercury and reductions of other HAP. Our efforts at quantifying the toxics benefits of this rule focus on quantifying and estimating the welfare benefits of reducing mercury emissions because mercury is the only HAP controlled by this rule for which there are sufficient available analytic to ols to conduct a national
- scale benefits assessment. This analysis of the benefits of reduced mercury exposure from EGUs as a result of the rule is not changed from that provided for the proposed rule. It uses the same baseline and control cases for mer cury deposition as was used to estimate mercury benefits in the Mercury and Air Toxics Rule proposal. EPA determined that it was reasonable to not update the mercury benefits assessment for the final rule because of the small magnitude of the quantified me rcury benefits in the proposal, and the small difference (approximately 2 tons) in mercury emissions reductions between the proposed and final rules. It is not expected that mercury benefits would be substantially changed, and given the small magnitude of the benefits, any changes would not meaningfully affect the overall benefits of the rule, nor impact the benefit
- cost comparison. An assessment of how forecast EGU mercury emissions changed between the baseline used at proposal and the baseline used for the costs and co
- benefits analysis, and between the regulation as proposed and the regulation as finalized, is described in Appendix 5A. This analysis builds on the methodologies developed previously for the 2005 Clean Air Mercury Rule (CAMR). This is a na tional scale assessment which focuses on the exposures to methylmercury in populations who consume self
- caught freshwater fish (recreational fishers and their families). While there are other routes of exposure, including self
- caught saltwater fish and com mercially purchased fresh and saltwater fish, these exposures are not evaluated because ( 1) for self
- caught saltwater fish, we are unable to estimate the reduction in fish tissue methylmercury that would be associated with reductions in mercury deposition from U.S. EGUs, and ( 2) for commercially purchased ocean fish, it is nearly impossible to determine the source of the methylmercury in those fish, and thus we could not attribute mercury levels to U.S. EGUs. This benefits analysis focuses on reductions in lost IQ points in the population, because of the discrete nature of the effect, and because we are able to assign an economic value to IQ4 - 2

points. There are other neurological effects associated with exposures to methylmercury, including impacts on motor s kills and attention/behavior and therefore, risk estimates based on IQ will not cover these additional endpoints and therefore could lead to an underestimate of overall neurodevelopmental impacts. In addition, the NRC (2001) noted that “there remains some uncertainty about the possibility of other health effects at low levels of exposure. In particular, there are indications of immune and cardiovascular effects, as well as neurological effects emerging later in life, that have not been adequately studied. ” These limitations suggest that the benefits of mercury reductions are understated by our analysis ; however, the magnitude of the additional benefits is highly uncertain . In Section 4. 2, we discuss the potential health effects of mercury. Section 4. 3 provid es a discussion of mercury in the environment, including potential impacts on wildlife. Section 4. 4 describes the resulting change in mercury deposition from air quality modeling of the proposed Toxics rule. Section 4. 5 presents information on key data and assumptions used in conducting the benefits analysis. Section 4. 6 presents information on a dose
- response function that relates mercury consumption in women of childbearing with changes in IQ seen in children that were exposed prenatally. IQ is used as a surrogate for the neurobehavioral endpoints that EPA relied upon for setting the methylmercury reference dose (RfD). Section 4. 7 presents exposure modeling and benefit methodologies applied to a no
-threshold model (i.e., a model that assumes no threshold i n effects at low doses of mercury exposure). Section 4. 8 presents the final benefits and risk estimates for recreational freshwater anglers and selected high
- risk subpopulations. Section 4. 9 presents a qualitative description of the benefits from reduction s in HAPs other than mercury that will take place as a result of the Toxics Rule. For this benefits assessment, EPA chose to focus on quantification of intelligence quotient (IQ) decrements associated with prenatal mercury exposure as the endpoint for qua ntification and valuation of mercury health benefits. Reasons for this focus on IQ included the availability of thoroughly
- reviewed, high
- quality epidemiological studies assessing IQ or related cognitive outcomes suitable for IQ estimation, and the availab ility of well
- established methods and data for economic valuation of avoided IQ deficits, as applied in EPA’ s previous benefits analyses for childhood lead exposure. The quantitative estimates of human health benefits and risk levels provided in Section 4. 2 is a national
- scale assessment of economic benefits associated with avoided IQ loss due to reduced methylmercury (MeHg) exposure among recreational freshwater anglers . Modeled risk levels, in terms of IQ loss, for six high
- risk subpopulations as a means of estimating potential4 - 3

disproportionate impacts on demographic groups with traditionally subsistence or near
subsistence rates of fish consumption are presented in Chapter 7 Section 7.11 . The first analysis (Section 4. 2.1) estimates benefits from avoided IQ loss under various regulatory scenarios for all recreational freshwater anglers in the 48 contiguous U.S. states. The average effect on individual avoided IQ loss in 2016 is 0.00209 IQ points, with total nationwide benefits estimated between $0.5 and $6 .1 million. 1 In contrast, the subpopulations analyses (Section 7.12.2 ) focus on specific demographic groups with relatively high levels of fish consumption. For example, an African
-American child in the Southeast born in 2016 to a mother consuming fish at the 90th percentile of published subsistence
- like levels is estimated to experience a loss of 7.711 IQ points as a result of in
- utero MeHg exposure from all sources in the absence of a Toxics Rule. 2 The implementation of the Toxics Rule would reduce the ex pected IQ loss for this child by an estimated 0.176 IQ points. **4.2 Impact of Mercury o**  **n Human Health 4.2.1**  **Introduction** Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants in three forms: gaseous elemental Hg (Hg 0 ), oxidi zed 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 long enough to contribute to global scale deposition. Oxidized Hg and Hg P deposit quick ly from the atmosphere impacting local and regional areas in proximity to sources. Methylmercury (MeHg) is formed by microbial action in the top layers of sediment and soils, after Hg has precipitated from the air and deposited into waterbodies or land. On ce formed, MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger predatory fish may have MeHg concentrations many times, typically on the order of one million times, that of the concentrations in the freshwater body in wh ich they live.

1 Monetized benefits estimates are for an immediate change in MeHg levels in fish. If a lag in the response of MeHg levels in fish was accounted for, the monetized benefits could be significantly lower, depending on the length of the lag and the discount rate used. As noted in the discussion of the Mercury Maps modeling, the relationship between deposition and fish tissue MeHg is proportional in equilibrium, but the MMaps approach does not provide any information on the time lag of response. Depending on the watershed studied, the lag time between changes in mercury deposition and changes in the MeHg levels in fish has been shown to range from XX 2We do note that overall confidence in IQ loss estimates above approximately 7 points decreases because we begin to apply the underlying IQ loss function at exposure levels (ppm hair levels) above those reflected in epidemiological studies used to derive those functions. T he 39.1 ppm was the highest measured ppm level in the Faroes Island study, while ~86 was the highest value in the New Zealand study (USEPA, 2005) (a 7 IQ points loss is approximately associated with a 40 ppm hair level given the concentration
- response function we are using).4 -4

Although Hg is toxic to humans when it is inhaled or ingested, we focus in this rulemaking on exposure to MeHg through ingestion of fish, as it is the primary route for human exposures in the U.S., and potential health risks do not likely re sult from Hg inhalation exposures associated with Hg emissions from utilities. In 2000, the National Research Council (NRC) of the NAS issued the NAS Study, which provides a thorough review of the effects of MeHg on human health. There are numerous studies that have been published more recently that report effects on neurologic and other endpoints. **4.2.2 Neurologic Effects**  In its review of the literature, the NAS found neurodevelopmental effects to be the most sensitive and best documented endpoints and app ropriate for establishing an RfD (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neuropsychological tests. The NAS report (NRC, 2000) noted that studies in animals reported sensory effects as well as effects on brain development and memory functions and support the conclusions based on epidemiology studies. The NAS noted that their recommended endpoints for an RfD are associated with the ability of children to learn and to succeed in school. They concluded the following: “The population at highest risk is the children of women who consumed large amounts of fish and seafood during pregnancy. The committee concludes that the risk to that population is likely to be sufficient to result in an increase in the number of children who have to struggle to keep up in school. ”

**4.2.3 Cardiovascular Impacts**  The NAS summarized data on cardiovascular effects available up to 2000. Based on these and other studies, the NRC (2000) concluded that “Although the data base is not as extensive for cardiovascular effects as it is for other end points (i.e. neurologic effects) the cardiovascular system appears to be a target for MeHg toxicity in humans and animals. ” The NRC also stated that “ additional studies are needed to better characterize the effect of methylmercury exposure on blood pressure and cardiovascular function at various stages of life. ” Additional cardiovascular studies have been published since 2000. EPA did not to develop a quantitative dose
- response assessment for cardiovascular e ffects associated with MeHg exposures, as EPA finds there is no consensus among scientists on the dose
- response4 - 5

functions for these effects. In addition, there is inconsistency among available studies as to the association between MeHg exposure and various cardiovascular system effects. The pharmacokinetics of some of the exposure measures (such as toenail Hg levels) are not well understood. The studies have not yet received the review and scrutiny of neurotoxicity studies .
**4.2.4 Genotoxic Effects**  The Me rcury Study noted that MeHg is not a potent mutagen but is capable of causing chromosomal damage in a number of experimental systems. The NAS concluded that evidence that human exposure to MeHg caused genetic damage is inconclusive; they note that some ear lier studies showing chromosomal damage in lymphocytes may not have controlled sufficiently for potential confounders. One study of adults living in the Tapajós River region in Brazil (Amorim et al., 2000) reported a direct relationship between MeHg concen tration in hair and DNA damage in lymphocytes; as well as effects on chromosomes. Long-term MeHg exposures in this population were believed to occur through consumption of fish, suggesting that genotoxic effects (largely chromosomal aberrations) may result from dietary, chronic MeHg exposures similar to and above those seen in the Faroes and Seychelles populations.

**4.2.5 Immunotoxic Effects**  Although exposure to some forms of Hg can result in a decrease in immune activity or an autoimmune response (ATSDR, 1999) , evidence for immunotoxic effects of MeHg is limited (NRC, 2000) .
**4.2.6 Other Human Toxicity Data**  Based on limited human and animal data, MeHg is classified as a “ possible” human carcinogen by the International Agency for Research on Cancer (IARC, 1994 ) and in IRIS (USEPA, 2002) . The existing evidence supporting the possibility of carcinogenic effects in humans from low
- dose chronic exposures is tenuous. Multiple human epidemiological studies have found no significant association between Hg exposure and overall cancer incidence, although a few studies have shown an association between Hg exposure and specific types of cancer incidence (e.g., acute leukemia and liver cancer) (NAS, 2000) .4 - 6

**4.3 Impact**  **of Mercury on Ecosystems a nd Wildlife**  **4.3.1 Introduction**  Deposition of mercury to waterbodies can also have an impact on ecosystems and wildlife. Mercury contamination is present in all environmental media with aquatic systems experiencing the greatest exposures due to bioaccumulation. Bioaccumulati on refers to the net uptake of a contaminant from all possible pathways and includes the accumulation that may occur by direct exposure to contaminated media as well as uptake from food. Atmospheric mercury enters freshwater ecosystems by direct deposition and through runoff from terrestrial watersheds. Once mercury deposits, it may be converted to organic methylmercury mediated primarily by sulfate
- reducing bacteria. Methylation is enhanced in anaerobic and acidic environments, greatly increasing mercury t oxicity and potential to bioaccumulate in aquatic foodwebs. A number of key biogeochemical controls influence the production of methylmercury in aquatic ecosystems. These include sulfur, pH, organic matter, iron, mercury “aging,” and bacteria type and acti vity (Munthe et al.2007). Wet and dry deposition of oxidized mercury is a dominant pathway for bringing mercury to terrestrial surfaces. In forest ecosystems, elemental mercury may also be absorbed by plants stomatally, incorporated by foliar tissues and released in litterfall (Ericksen et al., 2003). Mercury in throughfall, direct deposition in precipitation, and uptake of dissolved mercury by roots (Rea et al., 2002) are also important in mercury accumulation in terrestrial ecosystems. Soils have signifi cant capacity to store large quantities of atmospherically deposited mercury where it can leach into groundwater and surface waters. The risk of mercury exposure extends to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians that receive mercury deposition or from aquatic systems near the forest areas they inhabit (Bergeron et al., 2010a, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 2010). Numerous studies have generated field data on the levels of mercury in a variety of wild species. Many of the data from these environmental studies are anecdotal in nature rather than representative or statistically designed studies. The body of work examining the effects of these exposures is growing but still incomplete given the complexities of the natural world. A large portion of the adverse effect research conducted to date has been carried out in the laboratory setting rather than in the wild; thus, conclusions about overarching ecosystem health and4 - 7

population effects are difficult to make at this time. In the sections that follow numerous effects have been identified at differing exposure levels. **4.3.2 Effects on Fis** **h** A review of the literature on effects of mercury on fish (Crump and Trudeau, 2009) reports results for numerous species including trout, bass (large and smallmouth), northern pike, carp, walleye, salmon and others from laboratory and field studies. The effects studied are reproductive and include deficits in sperm and egg formation, histopathological c hanges in testes and ovaries, and disruption of reproductive hormone synthesis. These studies were conducted in areas from New York to Washington and while many were conducted by adding MeHg to water or diet many were conducted at current environmental levels. The Integrated Science Assessment for Oxides of Nitrogen and Sulfur — Ecological Criteria (EPA, 2008) presents information regarding the possible complementary effects of sulfur and mercury deposition. The ISA has concluded that there is a causal relat ionship between sulfur deposition and increased mercury methylation in wetlands and aquatic environments. This suggests that lowering the rate of sulfur deposition would also reduce mercury methylation thus alleviating the effects of aquatic acidification as well as the effects of mercury on fish. **4.3.3 Effects on Birds**  In addition to effects on fish, mercury also affects avian species. In previous reports (EPA, 1997 and EPA, 2005) much of the focus has been on large piscivorous species, in particular the c ommon loon. The loon is most visible to the public during the summer breeding season on northern lakes and they have become an important symbol of wilderness in these areas (McIntyre and Barr, 1997). A multitude of loon watch, preservation, and protection groups have formed over the past few decades and have been instrumental in promoting conservation, education, monitoring, and research of breeding loons (McIntyre and Evers, 2000, Evers, 2006). Significant adverse effects on breeding loons from mercury hav e been found to occur, including behavioral (reduced nest
- sitting), physiological (flight feather asymmetry), and reproductive (chicks fledged/territorial pair) effects (Evers, 2008, Burgess, 2008) and reduced survival (Mitro et al., 2008). Additionally Ev ers et al. (2008) report that they believe that results from their study integrating the effects on the endpoints listed above and evidence from other studies the weight of evidence indicates that population
- level effects negatively impacting population vi ability occur in parts of Maine and New Hampshire, and potentially in broad areas of the loon’s range.4 - 8

Recently attention has turned to other piscivorous species such as the white ibis and great snowy egret. While considered to be fish
- eating generally th ese wading birds have a diverse diet including crayfish, crabs, snails, insects and frogs. These species are experiencing a range of adverse effects due to exposure to mercury. The white ibis has been observed to have decreased foraging efficiency (Adams a nd Frederick, 2008). Additionally ibises have been shown to exhibit decreased reproductive success and altered pair behavior at chronic exposure to levels of dietary MeHg commonly encountered by wild birds (Frederick and Jayasena, 2010). These effects incl ude significantly more unproductive nests, male/male pairing, reduced courtship behavior (head bobbing and pair bowing) and lower nestling production by exposed males. In this study a worst
- case scenario suggested by the results could involve up to a 50% reduction in fledglings due to MeHg in diet. These estimates may be conservative if male/male pairing in the wild resulted in a shortage of partners for females and the effect of homosexual breeding were magnified. In egrets mercury has been implicated in t he decline of the species in south Florida (Sepulveda et al., 1999) and Hoffman (2010) has shown that egrets experience liver and possibly kidney effects. While ibises and egrets are most abundant in coastal areas and these studies were conducted in south Florida and Nevada, the ranges of ibises and egrets extend to a large portion of the United States. Ibis territory can range inland to Oklahoma, Arkansas and Tennessee. Egret range covers virtually the entire United States except the mountain west. Insect ivorous birds have also been shown to suffer adverse effects due to current levels of mercury exposure. These songbirds such as Bicknell’s thrush, tree swallows and the great tit have shown reduced reproduction, survival, and changes in singing behavior. E xposed tree swallows produced fewer fledglings (Brasso, 2008), lower survival (Hallinger, 2010) and had compromised immune competence (Hawley, 2009). The great tit has exhibited reduced singing behavior and smaller song repertoire in an area of high contam ination in the vicinity of a metallurgic smelter in Flanders (Gorissen, 2005). While these effects were small and would likely have little effect on population viability in such a short
- lived species. **4.3.4 Effects on Mammals**  In mammals adverse effects of methylmercury exposure have been observed in mink and river otter, both fish eating species, collected in the wild in the northeast where atmospheric deposition from municipal waste incinerators and electric utilities are the largest sources (USEPA, 1999). For otter from Maine and Vermont maximum concentrations of Hg in fur nearly equal or exceed a concentration associated with mo rtality. C oncentration s of Hg in liver for mink in Massachusetts/ Connecticut and the levels in fur from mink in Maine exceed4 - 9

concentrations associated with acute mortality (Yates, 2005). Adverse sub
- lethal effects may be associated with lower Hg concentrations and consequently be more widesprea d than potential acute effects. These effects may include increased activity, poorer maze performance, abnormal startle reflex, and impaired escape and avoidance behav ior (Scheuhamme r et al., 2007). The studies cited here provide a glimpse of the scope of mercury effects on wildlife particularly reproductive and survival effects at current exposure levels . These effects range across species from fish to mammals and spatially across a wide area of the United States. The literature is far from complete however. Much more research is required to establish a link between the ecological effects on wildlife and the effect on ecosystem services (services that the environment provides to peop le) such as recreational fishing, bird watching and wildlife viewing . EPA is not, however, currently able to quantify or monetize the benefits of reducing mercury exposures affecting provision of ecosystem services adversely affected by mercury depostion . **4.4 Mercury Risk a**  **nd Exposure Analyses —**  **Data Inputs a**  **nd Assumptions**  **4.4.1 Introduction**  This section provides information regarding key data inputs and assumptions used in this assessment. The section begins with a description of the populations modeled in this assessment, follows with information about the data used to estimate MeHg concentrations in fish, and closes with a summary of the science and related assumptions used in this assessment to link changes in modeled mercury deposition to changes in fish tissue concentrations. **4.4.2 Data Inputs**  4.4.2.1 Populations Assessed For the National Aggregate Estimates of Exposed Populations in Freshwater Fishing Households The main source of data for identifying the size and location of the potentially exposed populations is the Census 2000 data, summarized at the tract
- level. There are roughly 64,500 tracts in the continental United States, with populations generally ranging between 1,500 and 8,000 inhabitants. For the national aggregate analysis of exposure l evels, the specific population of interest drawn from these data is the number of women aged 15 to 44 (i.e., childbearing age) in each tract. To predict populations in later years (2005 and 2016), we applied county
- level population growth projections for t he corresponding population category ( Woods and Poole, 2008 ) to the 2000 tract
- level data. To specifically estimate the portion of4 - 10

these populations that are pregnant in any given year, we applied state
- level 2006 fertility rate (live births per 1,000 wome n aged 15 to 44 years) data from U.S. Vital Statistics ( DHHS, 2009 ). Two main sources of national
- level recreation activity data are available and suitable for estimating the size and spatial distribution of freshwater recreational angler populations and a ctivities in the United States:

 the National Survey of Fishing, Hunting, and Wildlife
-Associated Recreation (FHWAR), maintained by the Department of the Interior (DOI) ( DOI and DOC, 1992, 1997, 2002 , 2007 ) and

 the National Survey of Recreation and the Environment ( USDA, 1994). **FHWAR Angler Data** . The FHWAR, conducted by the U .S. Census Bureau about every 5 years since 1955, includes data on the number and characteristics of participants as well as time and money spent on hunting, fishing, and wildlife watching. The most recent survey and report are for recreational activities cond ucted in 2006 (DOI and DOC, 2007 ). Data from this report were used to provide the most recent estimate of the percentage of the resident population in each state (16 years old or older) that engaged in freshwater fishing during the year. As shown in Table 4
- 1 , these percentages vary from 3% (New Jersey) to 27% (Minnesota). The methodology for assessing mercury exposures also requires a further breakdown of freshwater fishing activities into two categories: rivers (including rivers and streams) and lakes (including lakes, ponds, reservoirs, and other flat water). Data at this level of detail are not reported in the summary nat ional reports for the FHWAR; however, they are available from the FHWAR survey household
- level data. For this analysis, data from a previous analysis and summary of the 2001 FHWAR household
- level survey data ( EPA, 2005) were used to provide estimates of th e percentage of freshwater fishing days by residents in each state that were to either the lake or river category3 . As shown in Table 4
- 1 , the highest percentage going to lakes is in Minnesota (89%) and the highest to rivers is in Oregon (61%).

3Although the total number of fishing trips varies from year to year, there is little reason to expect that the ratio of river trips to lake trips would have changed significantly since 2001. For this reason, despite information on the type of waterbody visited being collected on the 2006 FWHAR survey, given resource and timetable limitations we did not update this input to the analysis.4 - 11

**Table 4 -**  **1 .**  **Summary of FWHAR State**  **- Level Recreational Fishing Characteristics** 

**State Freshwater Anglers as** 

**Percentage of State Population a**  **Percentage of Freshwater Fishing Trips b**  **Lakes Rivers**  Alabama 15.7% 59.9% 40.1% Arizona 7.0% 79.2% 20.8% Arkansas 19.5% 81.1% 18.9% California 4.1% 53.5% 46.5% Colorado 13.2% 63.7% 36.3% Connecticut 6.4% 58.7% 41.3% Delaware 5.0% 52.8% 47.2% Florida 7.9% 67.4% 32.6% Georgia 12.6% 70.4% 29.6% Idaho 18.4% 44.4% 55.6% Illinois 7.3% 76.4% 23.6% Indiana 12.3% 77.8% 22.2% Iowa 16.8% 55.1% 44.9% Kansas 14.8% 84.7% 15.3% Kentucky 17.5% 80.0% 20.0% Louisiana 14.2% 71.2% 28.8% Maine 19.4% 73.7% 26.3% Maryland 5.5% 40.7% 59.3% Massachusetts 5.1% 75.5% 24.5% Michigan 14.2% 85.6% 14.4% Minnesota 26.9% 89.0% 11.0% Mississippi 19.6% 79.0% 21.0% Missouri 18.9% 80.2% 19.8% Montana 22.8% 46.8% 53.2% Nebraska 12.3% 80.6% 19.4% Nevada 5.9% 80.5% 19.5% New Hampshire 8.9% 67.9% 32.1% New Jersey 3.1% 68.9% 31.1% (continued)4 - 12

**Table 4 -**  **1. Summary of FWHAR State**  **- Level**  **Recreational Fishing Characteristics (continued)**

**State Freshwater Anglers as** 

**Percentage of State Population a**  **Percentage of Freshwater Fishing Trips b**  **Lakes Rivers**  New Mexico 10.9% 56.1% 43.9% New York 4.7% 67.2% 32.8% North Carolina 10.7% 68.7% 31.3% North Dakota 17.3% 87.2% 12.8% Ohio 11.8% 78.8% 21.2% Oklahoma 18.8% 83.1% 16.9% Oregon 13.6% 39.0% 61.0% Pennsylvania 8.1% 44.0% 56.0% Rhode Island 4.4% 73.5% 26.5% South Carolina 14.2% 75.6% 24.4% South Dakota 14.6% 69.7% 30.3% Tennessee 13.8% 68.6% 31.4% Texas 9.7% 79.3% 20.7% Utah 15.6% 68.0% 32.0% Vermont 12.6% 71.1% 28.9% Virginia 7.5% 70.4% 29.6% Washington 9.5% 50.0% 50.0% West Virginia 19.7% 50.1% 49.9% Wisconsin 22.8% 79.5% 20.5% Wyoming 23.5% 64.0% 36.0%

a Based on FHWAR 2006 data for residents 16 years and older. b Based on FHWAR 2001 data for residents 16 years and older. **NSRE Angler Data** . The NSRE, formerly known as the National Recreation Survey (NRS), is a nationally administered survey, which has been conducted periodically since 1962. It is designed to assess outdoor recreation participation in the United States and elicit information regarding people ’ s opinions about their natural environment. The NSRE sample of freshwater anglers is smaller than the FHWAR sample, but it is nonetheless a useful resource because it provides a wide variety of information about fishing activities. Importantly, it includes relatively detailed information about the nature and location of recent freshwater trips. Because the4 - 13

sampling procedure is designed to be representative, inferences may be drawn about the relative popularity of particular types of freshwater bodies (e.g., lakes, rivers) among the general public and the average dista nce traveled to reach these sites. Although more recent NSRE surveys have been conducted in 2000 and 2009 , data from 1994 survey (NSRE , 1994) is used for this analysis because it contains the most detailed information regarding fishing trip destinations . T he NSRE 1994 elicited information from respondents about the most recent fishing trip. One of the main advantages of NSRE 1994 is that it includes geocoded data for reported fishing destinations. To specify the location of the last fishing trip, respondent s were asked to provide the name of the waterbody, the nearest town to the waterbody, and an estimate of the distance and direction from their home to the waterbody. Appendix B describes how these data were used in this analysis to estimate the percentage of freshwater fishing trips that were in different distance intervals from respondents ’ homes. Using the demographic data from the NSRE, these estimates were further differentiated according t o the income level and urban versus nonurban location of the res pondents. **4.4.3 Mercury Concentrations in Freshwater Fish**  4.4.3.1 Data Sources for Fish Tissue Concentrations To characterize the spatial distribution of mercury concentration estimates in freshwater fish across the country, we compiled data from three mai n sources, which are described below. **National Listing of Fish Advisory (NLFA) database.** The NLFA, managed by EPA ( http://water.epa.gov/scitech/swguidance/fishshellfish/fi shadvisories/ ), collects and compiles fish tissue sample data from all 50 states and from tribes across the United States. In particular, it contains data for over 43,000 mercury fish tissue samples collected from 1995 to 2007. **U.S. Geologic Survey (USGS) compilation of mercury datasets**  . As part of its Environmental Mercury Mapping and Analysis (EMMA) program, USGS compiled mercury fish tissue sample data from a wide variety of sources (including the NLFA) and has posted these data at http://emmma.usgs.gov/datasets.aspx . The compilation includes (1) state
- agency collected and reported data (including Delaware, Iowa, Indiana, Louisiana, Minnesota, Ohio, South Carolina, Virginia, Wisconsin, and West Virginia) from over 40,000 fish tissue samples, covering the period 1995 to 2007 and (2) over 10,000 fish tissue samples from several other sources, including the National Fish Tissue Survey, the National Pesticide Monitoring Program4 - 14

(NPMP), the National Contaminant Biomonitoring Program (NCBP), the Biomonitoring of Environmental Status and Trends (BEST) datasets of the USFWS and USGS ( http://www.cerc.cr.usgs.gov/data/data.htm ), and the Environmental Monitoring and Analysis Program (EMAP) ( http://www.epa.gov/emap/ ). **EPA ’**  **s National River and Stream Assessment (NRSA) study data** . These data include nearly 600 fish tissue mercury samples collected at randomly selected freshwate r sites across the United States during the period 2008 to 2009. 4.4.3.2 Approach for Compiling Fish Tissue Dataset for Exposure Analysis Data from these three datasets were combined into a single master fish tissue dataset covering the period 1995 to 2009. One problem encountered in combining these datasets is the potential duplication of samples in the NLFA and USGS state
- collected data. U nfortunately, these two datasets do not contain directly comparable and unique identifiers that allow duplicate samples to be easily identified and removed. Therefore, as an alternative, the samples from these two datasets were subdivided 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 excluded from the master data. The following criteria were also applied to exclude data from the master fish tissue dataset to be used in the analysis. Samples were excluded if they :

 did not include useable latitude
- longitude coordin ates for spatial identification;

 were located at sites outside the tidal boundaries of the continental United States (i.e., if they were not sampled from freshwater sites);

 did not come from fish species found in freshwater; or

 did not come from sampled fish that were at least 7 inches in length (i.e., unlikely to be consumed). Each remaining sample was then categorized as either a river or lake sample based on information about the sampling site location. First, specific character strings in the site names (e.g., “ river, ” “ creek, ” “ lake, ” “ pond, ” and “ reservoir” ) were used to classify sites. Second, remaining sites were categorized based on a GIS analysis that linked the sites ’ latitude
longitude coordinates to the nearest waterbody and its category.4 - 15

 The resulting master fish tissue mercury concentration dataset contains 26,940 sample concentration estimates from 3,876 river sites and 23,206 estimates from 2,167 lake sites.

 A new dataset was then created by spatially grouping and averaging the river and lake concentration estimates at the HUC
- 12 sub
-watershed level. First, all of the mercury sampling sites included in the master data were mapped and matched to the HUC
- 12 sub
-watersheds in which t hey are located. A total of 3,88 4 HUC- 12s in the continental United States (4.6%) contain at least one river or lake mercury sample. 4 S econd, site
- specific average mercury concentration values were generated by computing the mean concentration estimate at each site. Third, HUC
- level average lake concentration estimates were computed as the mean of the site
specific average lake concentrat ion estimates for each HUC containing at least one lake sampling site (1,396 HUCs). Fourth, HUC
- level average river concentration estimates were computed as the mean of the site
- specific average river concentration estimates for each HUC containing at least one river sampling site (2,655 HUCs). 4.4.3.3 Summary of Fish Tissue Mercury Concentration Estimates Used in the Exposure Analysis The resulting HUC
- level mercury concentration dataset is summarized in Table 4
- 2 . The average HUC- level mercury concentrat ion estimate for lakes is 0.29 ppm and for rivers is 0.26 ppm. The large standard deviations and ranges reported in the table also reflect the considerable spatial variation in lake and river concentration estimates across samples. As described below, the analysis uses this inter
- watershed spatial variation (rather than just the average point estimate across watersheds) to estimate mercury exposures However, in this analysis, exposure estimates were only generated for populations linked to these HUCs contai ning at least one river or lake mercury fish tissue sample.

4This number excludes 15 HUC
- 12s containing mercury samples. These HUC
- 12s were excluded from the ana lysis due to their proximity to potentially significant non
- air sources of mercury, including gold mines or non
- EGU mercury sources included in the 2008 Toxic Release Inventory.4 - 16

**Table 4 -**  **2 .**  **Summary of HUC -**  **level Average Mercury Fish Tissue Concentration Estimates N**  **a Mean**  **Std. Dev. Min**  **Max** Lake Fish Tissue Concentrations HUC- level average mercury concentration (ppm) 1,396 0.286 0.231 0.000 3.56 Number of lake samples per HUC 1,396 16.62 31.61 1 458 Number of lake sampling sites per HUC 1,396 1.55 1.97 1 33 River Fish Tissue Concentrations HUC- level average mercury concentration (ppm) 2,655 0.261 0.259 0.006 4.97 Number of river samples per HUC 2,655 10.15 22.45 1 288 Number of river sampling sites per HUC 2,655 1.46 1.10 1 16

a Number of HUC
- 12s with at least one river or lake sampling site. **4.5 Linking Changes in Modeled Mercury Deposition to**  **Changes in Fish Tissue Concentrations**  **4.5.1 Introduction**  In the United States, humans are exposed to MeHg mainly by consuming fish that contain MeHg. Accordingly, to estimate changes in human exposure EPA must analyze how changes in Hg deposition from U.S. coal
-fired power plants translate into changes in MeHg c oncentrations in fish. Quantifying the linkage between different levels of Hg deposition and fish tissue MeHg concentration is an important step in the risk assessment process and the focus of the material described in this section . To effectively estimate fish MeHg concentrations in a given ecosystem, it is important to understand that the behavior of Hg in aquatic ecosystems is a complex function of the chemistry, biology, and physical dynamics of different ecosystems. The majority (95 to 97 percent) of t he Hg that enters lakes, rivers, and estuaries from direct atmospheric deposition is in the inorganic form (Lin and Pehkonen, 1999). Microbes convert a small fraction of the pool of inorganic Hg in the water and sediments of these ecosystems into the organ ic form of Hg (MeHg). MeHg is the only form of Hg that biomagnifies in organisms (Bloom, 1992). Ecosystem
specific factors that affect both the bioavailability of inorganic Hg to methylating microbes (e.g., sulfide, dissolved organic carbon) and the activi ty of the microbes themselves (e.g., temperature, organic carbon, redox status) determine the rate of MeHg production and subsequent accumulation in fish (Benoit et al. , 2003). The extent of MeHg bioaccumulation is also affected by the number of trophic le vels in the food web (e.g., piscivorous fish populations)4 - 17

because MeHg biomagnifies as large piscivorous fish eat smaller organisms (Watras and Bloom, 1992; Wren and MacCrimmon, 1986). These and other factors can result in considerable variability in fish MeHg levels among ecosystems at the regional and local scale. **4.5.2 Use of Mercury Maps**  **to Project Changes in Fish Tissue Concentrations** To analyze the relationship between Hg deposition and MeHg concentrations in fish in freshwater aquatic ecosystems acro ss the U.S. for the national scale benefits assessment, EPA applied EPA’ s Office of Water’ s Mercury Maps (MMaps) approach (US EPA, 2001a) . MMaps implements a simplified form of the IEM - 2M model applied in EPA’ s Mercury Study Report to Congress (USEPA, 1997 ). By simplifying the assumptions inherent in the freshwater ecosystem models that were described in the Report to Congress, the MMaps model showed that these models converge at a steady
- state solution for MeHg concentrations in fish that are proportional to changes in Hg inputs from atmospheric deposition ( i.e., over the long term , fish concentrations are expected to decline proportionally to declines in atmos pheric loading to a waterbody). MMaps has several limitations: 1. The MMaps approach is based on the assumption of a linear, steady
- state relationship between concentrations of MeHg in fish and present day air deposition mercury inputs. We expect that this condition will likely not be met in many waterbodies because of recent changes in mercury inputs and other environmental variables that affect mercury bioaccumulation. For example, the US has recently reduced human
- caused emissions while international emissions have increased. 2. The requirement that environmental conditions remain constant over the time required to reach steady state inherent in the MMaps methodology may not be met, particularly in systems that respond slowly to changes in mercury inputs. 3. Many water bodies, particularly in areas of historic gold and mercury mining, contain signifi cant non - air sources of mercury. The MMaps methodology will yield biased results when applied to such waterbodies. As a simple illustrative example, if we have mercury deposition of 100 at a given location and a MeHg fish concentration of 6 in a local fish tissue sample , and a new emissions rule reduces deposition by half to 50, then, in the absence of other non
- air deposition sources, we would assume that the MeHg fish concentration is reduced by the same proportion, to 3 ((50 / 100) × 6). However, if tota l pre - control mercury loading to the system is actually 100 plus another unaccounted for source (for example, an additional 100 due to area gold mining), then the MeHg fish concentration of 6 is actually due to 200 in total mercury loading. In this case, r educing mercury air deposition from 100 to 50 would only reduce the total loading by 25%, to 150, which, based on the MMaps4 - 18 methodology, would result in a MeHg fish concentration of 4.5 ((150 / 200) × 6) rather than 3. In areas where non
- air sources of mercury load are unaccounted for, MMaps - based estimates of changes in MeHg fish tissue concentrations due to reduced mercury air emissions would therefore be biased high. 4. Finally, MMaps does not account for a calculation of the time lag between a reduction in mercury deposition and a reduction in the MeHg concentrations in fish and, as noted earlier, depending on the nature of the watersheds and waterbodies involved, the temporal response time for fish tissue MeHg levels following a change in mercury deposi tion can range from years to decades depending on the attributes of the watershed and waterbody involved 5 Research has suggested that fish tissue MeHg levels in some locations may display a multi
- phase response following a discrete change in mercury deposition, with the first phase lasting a few years to a decade or more and primarily involving changes in aer ial loading directly to the waterbody and the second phase lasting decade (to a century or more) and reflecting longer
-term changes in watershed erosion and runoff to the waterbody (Knights et al., 2009, Harris et al., 2007). This methodology therefore app lies only to situations where air deposition is the sole significant source of Hg to a water body, and where the physical, chemical, and biological characteristics of the ecosystem remain constant over time. EPA recognizes that concentrations of MeHg in fi sh across all ecosystems may not reach steady state and that ecosystem conditions affecting mercury dynamics are unlikely to remain constant over time. EPA further recognizes that many water bodies, particularly in areas of historic gold and Hg mining in w estern states, contain significant non
- air sources of Hg. Finally, EPA recognizes that MMaps does not account for the time lag between a reduction in Hg deposition and a reduction in the MeHg concentrations in fish. While acknowledging these limitations, E PA is unaware of any other tool for performing a national
- scale assessment of the change in fish MeHg concentrations resulting from reductions in atmospheric deposition of Hg. The following paragraphs provide additional details on the above limitations, as well as a brief assessment of the degree to which conditions match those assumptions. The MMaps model represents a reduced form of the IEM
- 2M and MCM models used in the Mercury Study Report to Congress (USEPA, 1997), as well as the subsequent Dynamic MCM (D
- MCM) model (Harris et al. , 1996). That is, the equations of these mercury fate and transport

5As noted in footnote 1 of this chapter,monetized benefits estimates are for a n immediate change in MeHg levels in fish (i.e., the potential lag period associated with fully realizing fish tissue MeHg levels was not reflected in benefits modeling). If a lag in the response of MeHg levels in fish were assumed, the monetized benefits could be significantly lower, depending on the length of the lag and the discount rate used. MMaps approach does not account for the time lag of response.4 - 19

models are reduced to steady state and consolidated into a single equilibrium equation equating the ratio of future/current air deposition rates to future/current fish tissue concentrations. Though plainly stated, the steady
- state assumption is a compilation of a number of individual conditions. For example, fish tissue data may not represent average, steady
- state concentrations for two major reasons:

 Fish tiss ue and deposition rate data for the base period are not at steady state. Where deposition rates have recently changed, the watershed or waterbody may not have had sufficient time to fully respond. The pool of mercury in different media could be sufficientl y large relative to release rates, and thus needs more time to achieve a new equilibrium. This is more likely to occur in deeper lakes and lakes with large catchments where turnover rates are longer and where the watershed provides significant inputs of me rcury.

 Fish tissue data do not represent average conditions (or conditions of interest for forecast fish levels). Methylation and bioaccumulation are variable and dynamic processes. If fish are sampled during a period of high or low methylation or bioaccum ulation, they would not be representative of the average, steady
- state or dynamic equilibrium conditions of the waterbody. This effect is significantly more pronounced in small and juvenile fish. Examples include tissue data collected during a drought or d uring conditions of fish starvation. Other examples include areas in which seasonal fluctuations in fish mercury levels are significant due, for example, from seasonal runoff of contaminated soils from abandoned gold and mercury mines or areas geologically rich in mercury. In such a case, MMaps predict ions would be valid for similar conditions (e.g. wet year/dry year, or season) in the future, rather than typical or average conditions. Alternatively, sufficient fish tissue would need to be collected to get an average concentration that represents a baseline dynamic equilibrium. Other ecosystem conditions might cause projections from the MMaps approach to be inaccurate for a particular ecosystem. Watershed and waterbody conditions can undergo significant chan ges in capacity to transport, methylate, and bioaccumulate mercury. Examples of this include regions where sulfate and/or acid deposition rates are changing (in turn affecting MeHg production independently of total mercury loading), and where the trophic status of a waterbody is changing. A number of other water quality parameters have been correlated with increased fish tissue concentrations (e.g. low pH, high DOC, lower algal concentrations), but these relationships are highly variable among different wat erbodies. MMaps will be biased when waterbody characteristics change between when fish were initially sampled, and the new conditions of the waterbody.4 - 20

As stated above, the relationship between the change in mercury deposition from air to the change in fis h tissue concentration holds only when air deposition is the predominant source of the mercury load to a waterbody. Due to this requirement in the model, the national application of the MMaps approach screened out those watersheds that either contained act ive gold mines or had other substantial non
- US EGU anthropogenic releases of mercury. Identification of watersheds with gold mines was based on a 2005 USGS data set characterizing mineral and metal operations in the United States. The data represent commod ities monitored by the National Minerals Information Center of the USGS, and the operations included are those considered active in 2003 (online link: http://tin.er.usgs.gov/mineplant/ ) . EPA considered the 25 th percentile US
- EGU emission level to be a reasonable screen for additional substantial non - US EGU releases to a given watershed. The identification of watersheds with substantial non - EGU anthropogenic loadings was based on a TRI
- net query for 2008 of non
- EGU mercury sources with total annual on
- site Hg loading (all media) of 39.7 pounds or more. This threshold value corresponds to the 25th percentile annual US
- EGU mercury emission value as characterized in the 2005 NATA. It should be noted that MMaps was designed to address an important, but very specific issue — that of eventual response of fish tissue to air deposition reductions. As such it responds to a need to understand how mercury reductions, independent of other changes in the environment, will impact fish contamination and human health. More complex models are required in cases where more complete descriptions are needed. A dynamic model is essential for modeling waterbody recovery during the period in which waterbody response lags reductions in merc ury loads. A dynamic model is also essential for understanding seasonal fluctuations, as well as year
-to -year fluctuations due to meteorological variability. Finally, a more complex model would be essential for assessing the impact of other watershed and w ater quality changes (e.g. , erosion, wetlands coverage, and acid deposition) that might affect mercury bioaccumulation in fish. These complex models are used to derive the MMaps approach, and are themselves based on a number of assumptions. While these ass umptions are considered reasonable given the state of the science of environmental modeling and mercury in the environment, the validity of assumptions inherent in both the MMaps approach and dynamic ecosystem scale models will need to be reevaluated as th e science of mercury fate and transport evolves. The MMaps methodology was peer reviewed by a set of national experts in the fate and transport of mercury in watersheds (US EPA, 2001a) . While two reviewers felt it could be used to predict future fish tissu e concentrations, a third cautioned it should not be considered a robust predictor until scientific data can be generated to validate the approach. Reviewers systematically identified a set of implicit assumptions that compose the steady state4 - 21

assumption i n the MMaps approach. They pointed out that due to evolving and complex nature of the science of mercury, some features of the complex models are assumptions themselves, and thus cannot be wholly relied upon as ultimate predictors of mercury fate and trans port. The reviewers pointed out that there is limited scientific information to directly verify this approach, and that some scientific data appears to refute individual components of the overall steady state assumption. One reviewer did perform a D
- MCM an d MMaps comparison, and found that, under these assumptions, MMaps model did produce comparable steady
- state results as the D
- MCM model. There was considerable discussion about how best to aggregate the data, to scale up to a deposition reduction requireme nt, from fish
- specific and waterbody specific information. The description of the approach and the methodologies as applied in this analysis are largely consistent with the peer review recommendations. The MMaps report (US EPA, 2001a) presented a national
- scale application of Mercury Maps to determine the percent reductions in air deposition that would be needed in watersheds across the country for average fish tissue concentrations to achieve the national MeHg criterion. In this national
- scale assessment, fish tissue concentrations were aggregated at the scale of large watersheds, thus presenting average results for each watershed. The use of other scales of aggregation, e.g., waterbody specific, is consistent with the MMaps approach to the degree to which different mercury loads can be discerned. **4.5.3 The Science of Mercury Processes and Variability in Aquatic Ecosystems**  The set of physical, chemical, and biological processes controlling mercury fate in watersheds and water bodies can be grouped into specific categories: mercury cycle chemistry; mercury processes in the atmosphere, soils and water; bioavailability of mercury in water; and mercury accumulation in the food web. The following is a review of these categories, discussing the related scienti fic developments that have added to our understanding of mercury processes. This review builds upon the work previously summarized in EPA’ s Mercury Report to Congress (USEPA, 1997). 4.5.3.1 Mercury Cycle Chemistry Mercury occurs naturally in the environmen t as several different chemical species. The majority of mercury in the atmosphere (95
- 97%) is present in a neutral, elemental state (Hg 0 ) (Lin and Pehkonen, 1999), while in water, sediments and soils the majority of mercury is found in the oxidized, dival ent state (Hg(II)) (Morel et al. , 1998). A small fraction (percent) of this pool of divalent mercury is transformed by microbes into MeHg (CH3Hg(II)/ MeHg) (Jackson, 1998). MeHg is retained in fish tissue and is the only form of mercury that biomagnifies i n aquatic food4 - 22

webs (Kidd et al. , 1995). As a result, MeHg concentrations in higher trophic level organisms such as piscivorous fish, birds and wildlife are often 104
- 106 times higher than aqueous MeHg concentrations (Jackson, 1998). Transformations among mercury species within and between environmental media result in a complicated chemical cycle. Mercury emissions from both natural and anthropogenic sources are predominantly as Hg(II) species and Hg 0 (Landis and Keeler, 2002; Seigneur et al. , 2004). Anthropogenic point sources of mercury consist of combustion (e.g., utility boilers, municipal waste combustors, commercial/industrial boilers, medical waste incinerators) and manufacturing sources (e.g., chlor
- alkali, cement, pulp and paper manufacturing) (USE PA, 1997). Natural sources of mercury arise from geothermic emissions such as crustal degassing in the deep ocean and volcanoes as well as dissolution of mercury from geologic sources (Rasmussen, 1994). 4.5.3.2 Mercury Processes in the Atmosphere The relat ive contributions of local, regional and long range sources of mercury to fish mercury levels in a given water body are strongly affected by the speciation of natural and anthropogenic emissions sources. Elemental mercury is oxidized in the atmosphere to form the more soluble mercuric ion (Hg(II)) (Schroeder et al. , 1989). Particulate and reactive gaseous phases of Hg(II) are the principle forms of mercury deposited onto terrestrial and aquatic systems because they are more efficiently scavenged from the at mosphere through wet and dry deposition than Hg0 (Lindberg and Stratton, 1998). Because Hg(II) species or reactive gaseous mercury (RGM) and particulate mercury (Hg(p)) in the atmosphere tend to be deposited more locally than Hg 0, differences in the specie s of mercury emitted affect whether it is deposited locally or travels longer distances in the atmosphere (Landis et al. , 2004). 4.5.3.3 Mercury Processes in Soils A portion of the mercury deposited in terrestrial systems is re
- emitted to the atmosphere. On soil surfaces, sunlight may reduce deposited Hg(II) to Hg 0, which may then evade back to the atmosphere (Carpi and Lindberg, 1997; Frescholtz and Gustin, 2004; Scholtz et al. , 2003). Significant amounts of mercury can be co
- deposited to soil surfaces in throughfall and litterfall of forested ecosystems (St. Louis et al. , 2001), and exchange of gaseous Hg 0 by vegetation has been observed (e.g., (Gustin et al. , 2004). Hg(II) has a strong affinity for organic compounds such that inorganic Hg in soils and we tlands is predominantly bound to dissolved organic matter (Mierle and Ingram, 1991). MeHg likewise forms stable complexes with solid and dissolved organic matter (Hintelmann and Evans, 1997). These complexes can dominate MeHg speciation under aerobic condi tions4 - 23

(Karlsson and Skyllberg, 2003). Truly dissolved and dissolved organic carbon (DOC)
- complexed Hg(II) and MeHg are transported by percolation to shallow groundwater, and by runoff to adjacent surface waters (Ravichandran, 2004). Sorbed Hg(II) and MeHg are transported by erosion fluxes to depositional areas on the watershed and to adjacent surface waters (e.g., (Hurley et al. , 1998). Concentrations of MeHg in soils are generally very low. In contrast, wetlands are areas of enhanced MeHg production and ac count for a significant fraction of the external MeHg inputs to surface waters that have watersheds with a large port ion of wetland coverage (e.g., St. Louis et al. , 2001). Accordingly, there is a positive relationship between MeHg yield and percent wetlan d coverage ( Hurley et al. , 1995). Hydrology exerts an important control on the magnitude and flux of MeHg in wetland ecosystems (Branfireun and Roulet, 2002), as well as the transport of inorganic mercury deposited in a given watershed to surface waters (B abiarz et al. , 2001). 4.5.3.4 Mercury Processes in Water In a water body, deposited Hg(II) is reduced to Hg 0 by ultraviolet and visible wavelengths of sunlight as well as microbially mediated reduction pathways (Amyot et al. , 2000; Mason et al. , 1995). In turn, Hg 0 is oxidized back to Hg(II), driven by sunlight as well as by “ dark” chemical or biochemical processes (Lalonde et al. , 2001; Zhang and Lindberg, 2001). Driven by wind and water currents, dissolved Hg 0 in the water column is volatilized, which can be a significant removal mechanism for mercury in surface waters and a net source of mercury to the atmosphere (Siciliano et al. , 2002). In the water column and sediments, Hg(II) partitions strongly to silts and biotic solids, sorbs weakly to sands, and c omplexes strongly with dissolved and particulate organic material. The abundance of various inorganic ligands (e.g., OH -, Cl -, S 2

-, DOC) in freshwater and saltwater ecosystems plays an important role in both oxidation and reduction of inorganic mercury as well as its bioavailability to methylating microbes. For example, reduction of Hg(II) is hypothesized to be a function of the predominance of Hg(OH) 2, which is inversely correlated with pH (Mason et al. , 1995). Reduction of Hg(II) to Hg 0 and subsequent volatilization from the water column is important because it effectively reduces the pool of inorganic mercury that could potentially undergo conversion to MeHg. Hg(II) and MeHg sorbed to solids settle out of the water column and accumulate on the surface of the benthic sediment layer. Surficial sediments interact with the water column via resuspension and bioturbation. The burial of sediments below the surficial zone can be a4 - 24

significant removal mechanism for contaminan ts in surface sedimen ts (e.g., Gobas et al. , 1998; Gobas et al. , 1995). The depth of the active sediment layer is a highly sensitive parameter for predicting the temporal response of different ecosystems to changes in mercury loading in environmental fate models. This is becau se the reservoir of Hg(II) potentially available for conversion to MeHg in the sediments is a function of the depth and volume of the active sediment layer. The compartment conducive for methylation is similarly affected (Harris and Hutchison, 2003; Sunder land et al. , 2004). Physical characteristics of different ecosystem types affect estuarine mixing and sediment resuspension, which also affect the production of MeHg in the water and sediments (Rolfhus et al. , 2003; Sunderland et al. , 2004; Tseng et al. , 2 001). 4.5.3.5 Bioavailability of Inorganic Mercury to Methylating Microbes The amount of bioavailable MeHg in water and sediments of aquatic systems is a function of the relative rates of mercury methylation and demethylation. In the water, MeHg is degrade d by two microbial processes and sunlight (Barkay et al. , 2003; Sellers et al. , 1996). Recent research has shown that demethylating Hg
- resistant bacteria may adapt to systems that are highly contaminated with total mercury, helping to explain the paradox o f low MeHg and fish Hg levels in these systems (Schaefer et al. , 2004). Mass balances for a variety of lakes and coastal ecosystems show that in situ production of MeHg is often one of the main sources of MeHg in the water and sediments (Benoit et al. , 199 8; Bigham and Vandal, 1994; Gbundgo
-Tugbawa and Driscoll, 1998; Gilmour et al. , 1998; Mason et al. , 1999). Sulfate
- reducing bacteria (SRB) are thought to be the principle agents responsible for the majority of MeHg production in aquatic systems (Beyers et al. , 1999; Compeau and Bartha, 1987; Gilmour and Henry, 1991). SRB thrive in the redoxocline, where the maximum gradient between oxic and anoxic conditions exists (Hintelmann et al. , 2000). Thus, in addition to the presence of bioavailable Hg(II), MeHg pro duction and accumulation in aquatic systems is a function of the geochemical parameters that enhance or inhibit the activity of methylating microbes, especially sulfur concentrations, redox potential (Eh) and the composition and availability of organic car bon. A number of factors affect the bioavailabilty of Hg(II). A strong inverse relationship between complexation of Hg(II) by sulfides and MeHg production has been demonstrated in a number of studies (Benoit et al. , 1999a; Benoit et al. , 1999b; Craig and B artlett, 1978; Craig and Moreton, 1986). Passive diffusion of dissolved, neutral inorganic mercury species is hypothesized as one of the main modes of entry across the cell membranes of methylating microbes (Benoit et al. , 1999a; Benoit et al. , 2003; Benoi t et al. , 1999b). Thus, the formation of4 - 25

neutral, dissolved mercury species such as HgCl 2, Hg(OH) 2, HgClOH, and HgS 0 (aq.), which depend on the availability of constituent ligands in the surface and interstitial waters, may strongly influence the availabili ty of inorganic mercury to SRB, although our understanding of the forms of mercury that are bioavailable to methylating microbes is currently incomplete (Benoit et al. , 2001; Benoit et al. , 1999a; King et al. , 2001). Additional detail is provided below on the relationship between sulfur deposition and mercury methylation. Changes in the bioavailability of inorganic mercury and the activity of methylating microbes as a function of sulfur, carbon and ecosystem specific characteristics mean that ecosystem chan ges and anthropogenic “stresses” that do not result in a direct increase in mercury loading to the ecosystem but alter the rate of MeHg formation may also affect mercury levels in organisms (Grieb et al. , 1990). Because mercury concentrations in fish can i ncrease even when there has been no change in the total amount of mercury deposited in the ecosystem, environmental changes such as eutrophication, which may alter microbial activity and the chemical dynamics of mercury within an ecosystem, must be conside red together with emission control strategies to effectively manage mercury accumulation in the food web. Recent research indicates that the bioavailability or reactivity of newly deposited Hg(II) may be greater than older “ legacy” mercury in the system (H intelmann et al. , 2002). These results suggest that lakes receiving the bulk of their mercury directly from deposition to the lake surface (e.g., some seepage lakes) would see fish mercury concentrations respond more rapidly to changes in atmospheric depos ition than lakes receiving most of their mercury from watershed runoff. The implications of these data are also that systems with a greater surface area to watershed area ratio that receive most of their inputs directly from the atmosphere (e.g., seepage l akes) may respond more rapidly to changes in emissions and deposition of mercury than those receiving significant inputs of mercury from the catchment area. **Sulfur and Mercury Methylation.** EPA’ s 2008 Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur –Ecological Criteria (Final Report) concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and increased mercury methylation in wetlands and a quatic environments. Specifically, there appears to be a relationship between SO 4 2 - deposition and mercury methylation; however, the rate of mercury methylation varies according to several spatial and biogeochemical factors whose influence has not been ful ly quantified (see Figure 4- 1 ). Therefore, the correlation between SO 4 2 - deposition4 - 26

**Figure 4 -**  **1 .**  **Spatial and Biogeochemical Fact ors Influencing MeHg Production**  and MeHg could not be quantified for the purpose of interpolating the association across water bodies or regions. Nevertheless, because changes in MeHg in ecosystems represent changes in significant human and ecological health risks, the association between sulfur and mercury cannot be neglected (EPA, 2008, Sections 4. 4.1 and 4.5). As research evolves and the computational capacity of models expands to meet the complexity of mercury methylation processes in ecosystems, the role of interacting factors may be better parsed out to identify ecosystems or regions that are more likely to generate higher co ncentrations of MeHg. Figure 4 - 2 illustrates the type of current and forward
- looking research being developed by the U.S. Geological Survey (USGS) to synthesize the contributing factors of mercury and to develop a map of sensitive watersheds. The mercury s core referenced in Figure 4 - 3 is based on SO 4 2 - concentrations, acid neutralizing capacity (ANC), levels of dissolved organic carbon and pH, mercury species concentrations, and soil types to gauge the methylation sensitivity (Myers et al. , 2007).4 - 27

**Figure 4** **- 2**  **. Preliminary USGS**  **Map o f Mercury Methylatio**  **n –** **Sensitive Watersheds Derived f** **rom More T han 55,000 Water Quality Sites aq**  **nd 2,500 Watersheds (Myers et**  **al. , 2007)**  Interdependent biogeochemical factors preclude the existence of simple sulfate
- related mercury methylation models (see Figure 4 - 2 ). It is clear that decreasing sulfate deposition is likely to result in decreased MeHg concentrations. Future research may all ow for the characterization of a usable sulfate
- MeHg response curve; however, no regional or classification calculation scale can be created at this time because of the number of confounding factors. Decreases in SO 4 2 - deposition have already shown promisi ng reductions in MeHg. Observed decreases in MeHg fish tissue concentrations have been linked to decreased acidification and declining SO 4 2 - and mercury deposition in Little Rock Lake, WI (Hrabik and Watras, 2002), and to decreased SO 4 2 - deposition in Isle Royale in Lake Superior, MI (Drevnick et al. , 2007). Although the possibility exists that reductions in SO 4 2 - emissions could generate a pulse in MeHg production because of decreased sulfide inhibition in sulfate
- saturated waters, this effect would likely involve a limited number of U.S. waters (Harmon et al. , 2007). Also, because of the diffusion and outward flow of both mercurysulfide complexes and SO 4 2 -,4 - 28

increased mercury methylation downstream may still occur in sulfate
- enriched ecosystems with increased organic matter and/or downstream transport capabilities. Remediation of sediments heavily contaminated with mercury has yielded significant reductions of MeHg in biotic tissues. Establishing quantitative relations in biotic responses to MeHg levels as a result of changes in atmospheric mercury deposition, however, presents difficulties because direct associations can be confounded by all of the factors discussed in this section. Current research does suggest that the levels of MeHg and total mercury in ecosystems are positively correlated, so that reductions in mercury deposited into ecosystems would also eventually lead to reductions in MeHg in biotic tissues. Ultimately, an integrated approach that involves the reduction of both sulfu r and mercury emissions may be most efficient because of the variability in ecosystem responses. Reducing SO X emissions could have a beneficial effect on levels of MeHg in many waters of the United States. 4.5.3.6 Mercury Accumulation in the Food Web Disso lved Hg(II) and MeHg accumulate in aquatic vegetation, phytoplankton, and benthic invertebrates. Unlike Hg(II), MeHg biomagnifies though each successive trophic level in both benthic and pelagic food chains such that mercury in predatory, freshwater fish i s found almost exclusively as MeHg (Bloom, 1992; Watras et al. , 1998). Thus, trophic position and food
chain complexity plays an important role in MeHg bioaccumulation (Kidd et al. , 1995). The chemical and physical characteristics of different ecosystems a ffect MeHg uptake at the base of the food chain, driving bioaccumulation at higher trophic levels. At the base of pelagic freshwater food
-webs, MeHg uptake by plankton is thought to be a combination of passive diffusion and facilitated transport (Laporte et al. , 2002; Watras et al. , 1998). Uptake of MeHg by plankton can be enhanced or inhibited by the presence of different ligands bound to MeHg (Lawson and Mason, 1998). Similarly, the assimilation efficiency of MeHg at the base of the food chain is also affected by the type of dissolved MeHg
- complexes in the water and sediments. This may be a function of differences in the ability of organisms to solubilize MeHg through digestive processes with different MeHg complexes (Lawrence and Mason, 2001; Leaner and M ason, 2002). The presence of organic ligands and high concentrations of DOC in aquatic ecosystems are generally thought to limit MeHg uptake by biota (Driscoll et al. , 1995; Sunda and Huntsman, 1998; Watras et al. , 1998). In fish, MeHg bioaccumulation is a function of several uptake (diet, gills) and elimination pathways (excretion, growth dilution) (Gilmour et al. , 1998; Greenfield et al. , 2001). As a result, the highest mercury concentrations for a given fish species correspond to smaller, long
- lived4 - 29

fish that accumulate MeHg over their life span with minimal growth dilution (e.g., (Doyon et al. , 1998). In general, higher mercury concentrations are expected in top predators, which are often large fish relative to other species in a waterbody. **4.5.4 Summary**  In the United States, humans are exposed to MeHg mainly by consuming fish that contain MeHg. Aquatic ecosystems respond to changes in mercury deposition in a highly variable manner as a function of differences in their chemical, biological and physical properties. Depending on the characteristics of a given ecosystem, methylating microbes convert a small but variable fraction of the inorganic mercury in the sediments and water derived from human activities and natural sources into MeHg. MeHg is the only fo rm of mercury that biomagnifies in the food web. Concentrations of MeHg in fish are generally on the order of a million times the MeHg concentration in water. In addition to mercury deposition, key factors affecting MeHg production and accumulation in fish include the amount and forms of sulfur and carbon species present in a given waterbody. Thus, two adjoining water bodies receiving the same deposition can have significantly different fish mercury concentrations. For this analysis, EPA used the Mercury Ma ps (MMaps) model to estimate changes in freshwater fish mercury concentrations resulting from changes in mercury deposition after regulation of mercury emissions from U.S. coal
-fired power plants. MMaps, a simplified form of the IEM
- 2M model applied in EPA’ s 1997 Mercury Study Report to Congress, is a static model that assumes a proportional relationship between declines in atmospheric mercury deposition and concentrations in fish at steady state. This means, for example, that a 50% decrease in mercury depo sition rates is projected to lead to a 50% decrease in mercury concentrations in fish. MMaps does not consider the dynamics of relevant ecosystem specific factors that can affect the methylation and bioaccumulation in fish in different water bodies over ti me, nor does it consider the inputs of non
- air sources to the watershed. In all cases, the MMaps model does not address the lag time of different ecosystems to reach steady state (i.e., when fish mercury concentrations reflect changes in atmospheric deposi tion). In addition, applying the MMaps model assumes that atmospheric deposition is the principle source of mercury to the waterbodies being investigated and environmental factors that affect MeHg production and accumulation in organisms will remain consta nt, allowing each ecosystem to reach steady state. While MMaps has several limitations, EPA knows of no alternative tool for performing a national
- scale assessment of such changes.4 - 30

**4.6 Analysis of the Dose** **- Response Relationship Between Maternal Mercury Bod**  **y Burden and Childhood IQ**  **4.6.1 Introduction**  In considering possible health endpoints for quantification and monetization, EPA reviewed the scientific literature on the health effects of mercury, including the “Toxicological Effects of Methylmercury, ” publ ished by the National Research Council (NRC) in 2000 (NRC, 2000). EPA chose to focus on quantification of intelligence quotient (IQ) decrements associated with prenatal mercury exposure as the initial endpoint for quantification and valuation of mercury he alth benefits. Reasons for this initial focus on IQ included the availability of thoroughly
- reviewed, high
- quality epidemiological studies assessing IQ or related cognitive outcomes suitable for IQ estimation, and the availability of well
- established metho ds and data for economic valuation of avoided IQ deficits, as applied in EPA’ s previous benefits analyses for childhood lead exposure. In the “ Peer Review of EPA’s Draft National
- Scale Mercury Risk Assessment” (SAB, 2011 available at: http://yosemite.epa.gov/sab/sabproduct.nsf/ea5d9a9b55cc319285256cbd005a472e/aaf67ae4 dd199409852578cb006bcb04!OpenDocument ) the Science Advisory Board noted that a number of measures of potential neurodevelopmental effects of methylmercury exist, some of which have greater sensitivity than IQ loss. However, none were viewed by the Panel as suitable for quantitative risk estima tion with a reasonable degree of scientific certainty at the present time, and none were recommended for incorporation into the analysis. IQ score has not been the most sensitive indicator of methylmercury’s neurotoxicity in the populations studied. The Fa roe Island study the most sensitive indicators were in the domains of language (Boston Naming), attention (continuous performance) and memory (California Verbal Learning Test), neuropsychological tests that are not subtests of IQ tests and are not highly c orrelated with global IQ. In the Seychelles study, the Psychomotor Development Index has been most sensitive measure and, while this is a component of the Bailey Scales of Infant Development, it is not highly correlated with cognitive measures. While the P anel agreed that the concentration - response function for IQ loss used in the risk assessment is appropriate, IQ loss is not a sensitive response to methylmercury and its use likely underestimates the impact of reducing methylmercury in water bodies. Epide miological studies of prenatal mercury exposure conducted in the Faroe Islands (Grandjean et al., 1997), New Zealand (Kjellstrom et al., 1989; Crump et al., 1998), and the Seychelles Islands (Davidson et al., 1998; Myers et al., 2003) have examined4 - 31

neurode velopmental outcomes through the administration of tests of cognitive functioning. Each of these studies included some but not all of the following tests: full
- scale IQ, performance IQ, problem solving, social and adaptive behavior, language functions, mot or skills, attention, memory and other functions. The NRC reviewed the studies and determined that “ Each of the studies was well designed and carefully conducted, and each examined prenatal MeHg exposures within the range of the general U.S. population exp osures” (NRC, 2000). As pa rt of previous analyses, EPA attempted to identify the appropriate dose
- response coefficients from the Faroe Islands, New Zealand, and Seychelles Islands studies, and devise d a statistical approach for combining those coefficients to provide an integrated estimate of the IQ dose
- response coefficient. F or this assessment, EPA used a more recently revised estimate of the IQ dose
- response function, based on a peer
- reviewed study by Axelrad et al. (2007) ( “the Axelrad study” ). The Axel rad study estimated a dose
- response relationship between maternal mercury body burden and subsequent childhood decrements in IQ using a Bayesian hierarchical model to integrate data from the Faroe Islands, New Zealand, and Seychelles Islands studies. The Axelrad study used a linear model that goes through the origin to fit population
level dose
- response relationships to the pooled data from the three studies. The application of a linear model should not be interpreted to suggest that any of the three studie s used have data showing health effects from MeHg exposure at or below the RfD. The RfD is an estimate of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects durin g a lifetime ( EPA, 2002). EPA believes that exposures at or below the RfD are unlikely to be associated with appreciable risk of deleterious effects. It is important to note, however, that the RfD does not define an exposure level corresponding to zero ris k; mercury exposure near or below the RfD could pose a very low level of risk which EPA deems to be non
- appreciable . It is also important to note that the RfD does not define a bright line, above which individuals are necessarily at risk of adverse effect. Use of a linear model that goes through the origin, rather than one that reflects a threshold effect is technically more simple and practical. It associates an increment of IQ benefit with a given reduction in exposure. A linear model allows us to estimat e the benefits of reductions in exposure due to power plants without a complete assessment of other sources of exposure. Other models would require information on the joint distribution of exposure from power plants and other sources to estimate the benefi ts of reducing the exposure due to power plants, which would require much more precise information about consumption patterns.4 - 32

**4.6.2 Epidemiological Studies of Mercury and Neurodevelopmental Effects**  The IQ dose
- response estimate is based on data from three major prospective studies investigating potential neurotoxicity of low
- level, chronic mercury exposure: the Faroe Islands study, the New Zealand study, and the Seychelles Child Development Study. In assembling the New Zealand sample, Kjellstrom et al. (19 89) ascertained the fish consumption of 10,930 of 16,293 pregnant women in the study area. They identified 935 women who reportedly consumed fish at least 3 times per week. Hair samples were obtained from these women, and 73 were found to have a hair mercu ry level of 6 parts per million (ppm) or greater. In this group, the mean was 8.3 ppm, with a range of 6 to 86 ppm, although only one woman had a level greater than 20 ppm. Each woman with 6 ppm hair mercury or greater was matched to 3 controls — one with ha ir mercury between 3–6 ppm, one with hair mercury less than 3 ppm and high fish consumption, and one with hair mercury less than 3 ppm and low fish consumption. Ethnic group, age, smoking, residence time in New Zealand, and child sex were also used to sele ct controls. The final study group included 237 children, including 57 fully matched sets of 4 children. Although children were assessed at 4 and 6 years of age, only the data collected at the older age is considered in this analysis, as the reliability an d validity of neurodevelopmental testing generally increases with child age. The Faroe Islands investigators assembled a birth cohort of 1,353 newborns recruited from three hospitals over a 21
- month period in 1986– 1987. In 1,022 women, two biomarkers of prenatal mercury exposure were collected: cord
- blood mercury, and maternal hair mercury at delivery. Neurodevelopmental assessments of 917 children were conducted at age 7 (Grandjean et al., 1997). For these 917 children, the geometric mean concentration of mercury in cord
- blood was 22.6 parts per billion (ppb) (inter
- quartile range 13.1–40.5 ppb, full range 0.9–351 ppb). The geometric mean concentration of mercury in maternal hair was 4.2 ppm (inter
- quartile range: 2.5–7.7 ppm, full range 0.2–39.1 ppm) (Budtz
-Jorgensen et al., 2004a). Neurodevelopmental assessments of the children were conduct ed at age 7 years (Grandjean et al., 1997). In assembling the Seychelles Child Development Study sample, investigators obtained ha ir samples from 779 pregnant women and ultimately enrolled a study sample consisting of 740 newborns. The mean maternal hair mercury level was 6.8 ppm (range 0.9–25.8 ppm) (Davidson et al., 1998). Neurodevelopmental assessments were conducted when the chil dren were 6.5, 19, 29, and 66 months, and at 9 years. The mean maternal hair mercury level for the4 - 33

643 children who participated in the assessment at age 9 years was 6.9 ppm (standard deviation 4.5 ppm) (Myers et al., 2003). **4.6.3 Statistical Analysis**  Previous statistical analysis conducted by Ryan (2005) produced a dose
- response relationship, integrating data from all three studies, with a central estimate of an IQ change of

− 0.13 IQ points (95% confidence interval − 0.28, − 0.03) for every ppm of mercury in maternal hair. Axelrad et al. (2007) conducted a more recent statistical analysis integrating data from the Faroe Islands, New Zealand, and Seychelles Islands studies to produce a single estimate of the IQ dose
- response relation ship, which is used in this RIA . Additional details of the analysis are reported in the Axelrad study and in i ts Supplemental Material (available at http://www.ehponline.org/docs/2007/9303/suppl.pdf ) . The informatio n is summarized below. The Axelrad study used a Bayesian hierarchical statistical model to estimate the integrated dose
- response coefficient. This is similar to the approach used by the NRC panel to calculate a benchmark dose value integrating data from al l three studies (NRC, 2000). The model makes use of dose
- response coefficients for IQ, and also considered all other cognitive endpoints reported in the three studies in an effort to obtain more robust estimates of the IQ relationship that account for with in - study (endpoint
-to - endpoint) variability as well as variability across studies. The Axelrad study assumed a linear relationship between mercury body burdens and neurodevelopmental outcomes, in keeping with the recommendation of the NRC committee (NRC, 2 000). In the New Zealand and Seychelles Islands studies, all information necessary for the model was obtained from the published papers, including linear regression coefficients (Crump et al., 1998; Myers et al., 2003). The Faroe Islands publications, howe ver, reported results with cord blood and maternal hair mercury transformed to the log scale and provided no results of linear models (Grandjean et al., 1997, 1999). A report by the Faroe Islands investigators (Budtz
-Jorgensen et al., 2005) provided the ad ditional details needed for the analysis. The Wechsler Intelligence Scales for Children (WISC) is a standard test of childhood IQ that was used in each of the three studies. The version of the test administered in the Seychelles Islands (3rd ed.; WISC
- III) was different from the earlier version used in New Zealand and the Faroe Islands (revised ed.; WISC
- R). In a sample of approximately 200 children, the correlation between the Full
- Scale IQ scores for the two versions was 0.89; thus the WISC
- R and4 - 34

WISC- III appear to measure the same constructs and generate scores with similar dispersion (Wechsler, 1991). The WISC
- R includes 10 core subtests and three supplementary subtests. For the Faroe Islands study, the investigators administered only three subtests of t he WISC
- R: Digit Span and Similarities (core subtests) and Block Design (a supplementary subtest). The Axelrad study used data for these three subtests to estimate an IQ – mercury coefficient for the Faroe Islands cohort. The Faroe Islands investigators fit data for these three subtests in a structural equation model (SEM) to estimate a standardized coefficient for a hypothetical Full
- Scale IQ (Budtz
Jorgensen et al., 2005). In the SEM analysis of IQ, the three WISC
- R subtests are viewed as representative of an underlying latent IQ variable. To estimate the association between mercury and IQ using information from the three studies, the Axelrad study used a hierarchical random
- effects model that includes study
-to study as well as endpoint–to–endpoint variabili ty. Axelrad et al. (2007) implemented the model with a Bayesian approach, using WinBUGS version 1.4 (http://www.mrc
- bsu.cam.ac.uk/bugs/). Although the Axelrad study’ s Bayesian analysis yields highest posterior density ( HPD ) intervals, the authors refer to these as confidence intervals to aid in the interpretation of results (Axelrad et al., 2007) . The integrated analysis produced a central estimate of
- 0.18 (95% CI, − 0.378 to − 0.009) IQ points for each part per million maternal hair mercury, similar to the results found for both the Faroe Islands and Seychelles studies, and lower than the estimate found in the New Zealand study. This central estimate was used as the basis for estimating IQ loss associated with prenatal MeHg exposure in this assessment. **4.6.4 Strengths and Limitations of the IQ Dose** **- Response Analysis**  The Axelra d study produced an estimate of the relationship between maternal mercury body burdens during pregnancy and childhood IQs that incorporates data from all three epidemiologic studies judg ed by the NRC to be of high quality and suitable for risk assessment. The statistical approach makes use of all the available data (including information on results for related tests of cognitive function), and can be used to produce population
- based estim ates of a health outcome that can be readily monetized for use in benefit
- cost analysis. 6

6There is limited evidence directly linking IQ and methylmercury exposure in the three large epidemiological studies that were evaluated by the NAS and EPA. Based on its evaluation of the three studies, EPA believes that children who are prenatally exposed to low concentrations of methylmercury may be at increased risk of poor4 - 35

There are several aspects of IQ as a metric for neurodevelopmental effects in this benefit
- cost analysis that are important to recognize. Full
- Scale IQ is a composite index that averages a child ’ s performance across many functional domains, providing a good overall picture of cognitive health. An extensive body of data documents the predictive validity of full
scale IQ, as measured at school age, and late outcomes such as academic and occupational success (Neisser et al., 1996). In addition, methods are readily available for valuing shifts in IQ and thus conducting a benefits analysis of interventions that shift the IQ distribution in a population. Methods for monetizat ion of the other tests administered in the three studies have not been developed. It is important to recognize, however, that full
- scale IQ might not be the cognitive endpoint that is most sensitive to prenatal mercury exposure. Significant inverse associa tions were found, in both the New Zealand and Faroe Islands studies, between prenatal mercury levels and neurobehavioral endpoints other than IQ. If the effects of mercury are highly focal, affecting only specific cognitive functions, taking full
- scale IQ as the primary endpoint for a benefits analysis might underestimate the impacts. In averaging performance over diverse functions in order to compute full
- scale IQ, the specific effects of mercury on only certain of these functions would be “ diluted, ” and t he estimated magnitude of the change in performance per unit change in the mercury biomarker would be underestimated. Moreover, it is well known that there may be substantial deficits in cognitive wellbeing even in individuals with normal or above average IQ. The criterion most frequently used to identify children with learning disabilities for the purposes of assignment to special education services is a discrepancy between IQ and achievement. Specifically, the child ’ s achievement in reading, math, or othe r academic areas is significantly lower than what would be expected, given his or her full
- scale IQ. Thus, there are deficits in cognitive functioning that are not captured by IQ scores. For example, two of the most sensitive endpoints in the Faroe Islands study were the Boston Naming Test, which assesses word retrieval, and the California Verbal Learning Test - Children, which assesses the acquisition and retention of information presented verbally. Depending on the severity of the deficits, a child who has deficits in either of these skills could be at a considerable disadvantage in the classroom setting and at substantial educational risk. Neither of these abilities is directly assessed by the WISC
- R or WISC- III, however, and so do not explicitly contribute to a child ’ s IQ score. Therefore, benefits performance on neuro behavioral tests, such as those measuring attention, fine motor function, language skills, visual
- spatial abilities (like drawing), and verbal memory. For this analysis, EPA is adopting IQ as a surrogate for the neurobehavioral endpoints that NAS and EPA r elied upon for the RfD.4 - 36

calculations relying solely on IQ decrements are likely to underestimate the benefits to cognitive functioning of reduced mercury exposures. In additions, impacts on other neurological domains (such as motor skil ls and attention/behavior) are not represented by IQ scores and thus are also excluded from the benefits analysis. As discussed above, the Faroe Islands study did not include testing for full
- scale IQ. For the Axelrad study, an estimate of a dose
- response coefficient for full
- scale IQ was estimated using the three subtests. While this extrapolation introduces some uncertainty, information has been presented that demonstrates a high correlation between the subtests and full
- scale IQ scores. While the Seychel les and New Zealand studies use maternal hair mercury as the exposure biomarker, the Faroe Islands study uses cord blood mercury. For purposes of the integrated analysis, it was necessary to express results from all three studies in the same terms. Several studies have examined the relationship between hair mercury and blood mercury, and have reported hair:blood ratios typically in the range of 200 to 300 (see ATSDR, 1999, pages 249–252 for a review). However, these studies generally do not use cord blood m ercury, which is t he exposure metric in the Faroe Islands study. One analysis found that mercury concentrations in cord blood are, on average, 70 percent higher than those in maternal blood (Stern and Smith , 2003). For c onversion of Faroe Islands data from cord blood mercury to maternal hair mercury, the Axelrad study used data specific to this population, indicating a median maternal hair:cord blood mercury ratio of 200 (Budtz
-Jorgensen et al., 2004a). One uncertainty concerning the New Zealand study is th e strong influence of one child in the study population with a particularly high maternal hair mercury level. Published analyses of the New Zealand study presented results with data for this child both included and excluded (Crump et al., 1998). In keeping with the conclusions of the NRC (2000), the integrated dose
response analysis in the Axelrad study made use of the dose
- response coefficients calculated with this child omitted. A sensitivity analysis using the New Zealand coefficient with this child incl uded results in an integrated dose
- response coefficient that is reduced in magnitude by 25 percent ( − 0. 1 25 versus a primary central estimate of − 0.18). Some uncertainty is also associated with the Seychelles study due to the exclusion of some members of th e cohort from the data reported by Myers et al. (2003) and used as input to this integrated dose
- response analysis. The Seychelles researchers did not include a small number of outliers (defined as observations with model residuals exceeding 3 standard dev iation units), and no results are available for the full cohort. However, the authors report4 - 37

that “ In all cases, the association between prenatal MeHg exposure and the endpoint was the same, irrespective of whether outliers were included ” (Myers et al., 2003). Finally, the integrated dose
- response analysis assumes the exposures assigned to each study subject are accurate representations of true exposure. In reality, there is likely to be some discrepancy between measured and actual exposures, for example, d ue to variation in hair length. Alternatively, the true exposure of interest may have been during the first trimester of pregnancy, whereas exposures in maternal hair and cord blood measured at birth reflect exposures later in pregnancy. Presence of exposu re measurement error could introduce a bias in the results, most likely towards the null (Budtz
-Jorgensen et al., 2004b). **4.6.5 Possible**  **Confounding from Long -**  **Chained Polyunsaturated Fatty Acids** Maternal consumption of fish during pregnancy exposes the fetus to long
- chain polyunsaturated fatty acids (LCPUFAs), believed to be beneficial for fetal brain development, and to the neurotoxicant MeHg (Helland et al. , 2003; Daniels et al. , 2004; Dunstan e t al. , 2006; Judge et al. , 2007). Reports from the Seychelles Islands study cohort have suggested a negative impact of MeHg exposure, accompanied by a simultaneous beneficial effect of omega
- 3 LCPUFAs on children ’ s development (Davidson et al. , 2008; Strai n et al. , 2008). It is unclear whether this result was evidence for independent influences of MeHg and LCPUFAs or effect modification. A recent study by Lynch et al. (2010) used varying coefficient models to characterize the interaction of mercury and nutr itional covariates (Hastie and Tibshirani, 1993), including omega
- 3 LCPUFAs, using data from the Seychelles Islands study. The Seychelles Islands study cohort of mother
- child pairs had fish consumption averaging 9 meals per week. Lynch et al. , (2010) asses sed maternal nutritional status for five different nutritional covariates known to be present in fish (n
- 3 LCPUFA, n - 6 LCPUFA, iron status, iodine status, and choline) and associated with children ’ s neurological development. The study also included prenata l MeHg exposure (measured in maternal hair). Lynch et al. , (2010) examined two child neurodevelopmental outcomes (Bayley Scales Infant Development
- II (BSID
- II) Mental Developmental Index (MDI) and Psychomotor Developmental Index (PDI)), each administered a t 9 and at 30 months. The varying coefficient models allowed the possible interactions between each nutritional component and MeHg to be modeled as a smoothly varying function of MeHg as an effect modifier. Iron, iodine, choline, and omega
- 6 LCPUFAs had li ttle or no observable modulation at different MeHg exposures. In contrast the omega
- 3 LCPUFA docosahexaenoic acid had beneficial effects on the BSID
- II PDI that were reduced or absent at higher MeHg exposures. The results from Lynch et al. (2010)4 - 38

suggest a potentially useful modeling method that could shed further light on the issue of interactions between nutritional covariates. A recent study by Rice et al. (2010) considered possible confounding in a probabilistic assessment of the health benefits of redu cing MeHg exposure in the United States. In deciding on a dose
- response relationship between MeHg exposure and effects on IQ loss, the authors chose to use the central estimat e from the Axelrad study, noting however that Axelrad et al. (2007) did not expli citly consider possible confounding of the MeHg
- IQ relationship by the concurrent consumption of LCPUFAs that might enhance cognitive development and bias downward the observed regression coefficient estimates from the Faroe Islands, New Zealand, and Seych elle s Islands studies. Rice et al. (2010) therefore multiplied the central estimate from Axelrad et al. (2010) by a n adjustment factor to offset the possible downward bias from inadequate confounder control. A factor of 1.5 was selected “to acknowledge the recent argument of Budtz
-Jorgensen et al. (2007) that the parameter estimates from the three epidemiological studies may be biased downward by a factor of approximately 2 because of failure to adeq uately control for confounding” (Rice et al., 2010). There remains uncertainty with respect to the nature and magnitude of potential confounding between LCPUFAs and MeHg, and the associated effects on childhood neurodevelopment due to maternal ingestion during pregnancy. Additional research is needed to provide further clarity on this issue, but recent studies such as those referenced above reinforce the view that fish consumption during pregnancy should be approached as a case of multiple exposures to nutrients and to MeHg, with a complex and potentially interact ive set of risks and benefits related to infant development. Due to the remaining uncertainty regarding the potential confounding between LCPUFAs and MeHg exposure, we have not incorporated any factors or other quantitative adjustments into this assessment . **4.7 Mercury Benefits Analysis**  **Modeling Methodology**  **4.7.1 Introduction**  This section describes the methodology used to model fishing behavior and associated MeHg exposure levels. The methodology incorporates data, assumptions, and analytical techniques alr eady described in previous s ecti ons. Sections 4. 7.2 and 4. 7.3 below describe elements of the methodology applied to develop a national
- scale estimate of benefits associated with avoided IQ loss among freshwater recreational anglers. Chapter 7 section 7.11 describes a variation of the methodology used to estimate risk levels (as measured by IQ loss) among modeled high
- risk subpopulations.4 - 39

**4.7.2 Estimation of Exposed Populations and Fishing Behaviors**  This section describes the methodology used to estimate the average daily ingestion of mercury (g/day) through noncommercial freshwater fish consumption (HgI) for selected populations of interest. Because the primary measurable health effect of concern — developmental neurological abn ormalities in children — occurs as a result of in
- utero exposures to mercury, the specific population of interest in this case is prenatally exposed children. To identify and estimate the size of this exposed population, the benefits analysis focuses on preg nant women in freshwater recreational angler households. Generally speaking, estimating mercury exposures for this exposure pathway and population of interest requires three main components: N i = size of the exposed population of interest i (annual numbe r of pregnant

women in freshwater angler households during the year), CHg i = average concentration (ppm) of methyl mercury in noncommercial freshwater

fish filets consumed by population i, and C i = average daily consumption rate (gm/day) of noncommercial f reshwater fish

by population i. The flow diagram in Figure 4 - 3 illustrates the approach used to estimate the first two components of this equation — Ni and CHg i. It shows the spatial scale of the data used to estimate these components and describes how these components are interrelated. For the third component — C i— recommendations from EPA’ s Environmental Exposure Factors Handbook (EPA, 1997) were used to estimate an average consumption rate estimate for recreationally caught freshwater fish.40

**Figure 4 -**  **3 .**  **Methodology for Estimating and Linking Exposed Populations and Levels of Mercury Exposure**41

First, 2000 Census data (U.S. Census Bureau, Census 2000 Summary File 3, Detailed Tables, United States) were used to define the size, age, gender distribution, and income of the populations within each census tract in the 48 contiguous U.S. states. 1. Estimating the number of pregn ant women (NP) living in the census tract as NP = NF * fs, ( 4. 1 ) where NF = number of females aged 15 to 44 in the tract (Census 2000) and fs = state - level general fertility rate (average number of live births in a year per 1,000 women aged 15 to 44) (200 6 Vital Statistics). 2. Estimating the annual number of prenatally exposed children in angler households (NPA) as NPA = NP*(NAs/N s ), ( 4. 2 ) where NA s = state - level number of angler residents ( FHWAR ) and

N s = adult population of state s (Census). Using Eq. (4.2) to estimate NPA implies that (1) the fraction of pregnant women in a state who are in freshwater angler households is equal to the fraction of households in the state that include freshwater anglers (i.e., pregnant women are no more or less likely th an the rest of the state population to live in households with freshwater anglers) and (2) the fraction of households in the state that includes freshwater anglers is equal to the fraction of adult residents in the state who are freshwater anglers. To esti mate NPA for years after 2000, it was assumed that state
- level fertility rates (fs ) and angler participation rates (NA s/N s ) would remain constant; however, the number of women of childbearing age in each block (NF) was increased based on county
- level popul ation growth projections (Woods and Poole, 2008 ). In other words, for the period 2000 to 2016, the estimated NPA for each census tract was assumed to increase at the same rate as the projected annual population growth rates for females 15 to 44 in their co rresponding counties.42

**Figure 4 -** **4 .**  **Linking Census Tracts to Demographic Data and Mercury Fish Tissue Samples**43

Fourth, to match exposed populations in each tract with mercury concentrations, we first divided the exposed population into four distinct demographic groups (i = 1 – 4): urban/low income, urban/high income, nonurban/low income, and nonurban/high income. To estimate the portion of households in each demographic group (p i for i = 1 – 4), tract
- level Census data were used to specify (1) the percentage of the population in each tract that resides in an urban area and (2) the percentage wi th household income less than $50,000 (i.e., the portion in the low
- income group). In addition, it was assumed that 1. each exposed individual in a census tract is associated with freshwater fishing in a single distance interval and a single waterbody type (i.e., all the fish they consume comes from the same distance and type of waterbody) , 7 and 2. the exposed populations in each census tract (rather than just the fishing trips) are distributed across the distance intervals and waterbody types according to the estimated proportions (i.e., parameters c, e, and p shown in Figure 4-4 ). More specifically, a maximum of 32 separate exposed subpopulations were defined for each census tract: NPA ijk = NPA * p i * e ij * c k (for all i, j, and k) ( 4. 3 ) for i = 1 – 4 demographic subgroup in the census tract, j = 1 – 4 distance interval, and k = lake or river. (See Figure 4- 3 for definitions of p i, e ij , and c k ). Using this approach, we were able to separately match each subpopulation NPA ijk with the census tract’ s avera ge mercury concentration for the corresponding distance and waterbody category (CHgjk ).

7An alternative would be to assume that all anglers in the census tract have the same distribution of trips across distance intervals and water types. This assumption would imply no variation in per
- capita mercury exposures within a census tract, but it would not affect the estimates of total exposure and total IQ losses in the tract.44

To approximate the percentage freshwater fishing trips (and exposed individuals) from each census tract matched to each waterbody type (c l or c r ), we used state
- level averages. These averages were calculated for each state, based on the portion of residents ’ freshwater fishing trips that are to each waterbody type, based on 2001 FHWAR data. Data from NSRE 1994 were used to approximate the percentage of freshwater fishi ng trips (and exposed individuals) matched to different distances from anglers ’ residential location. Four distance intervals were defined as 0– 10 miles, >10–20 miles, >20–50 miles, and >50– 100 miles. Based on self
- reported trip distance information from n early 2,000 respondents (see Appendix B for details), each of these distance categories was associated with roughly 20% of the reported trips in the NSRE sample. Four distinct demographic groups were also found to have significantly different average trave l distances for freshwater fishing in the NSRE sample: high
- income urban, high
- income rural, low
- income urban, and low
- income rural. An annual household income threshold of $50,000 (in 2000 dollars) was used to define high and low income, because it is clo se to the median value for both the NSRE sample and the U.S. population. The portion of trips for each demographic group (i = 1 – 4) to each distance interval (j = 1 – 4) is defined as e ij . The estimated values for e ij are reported in Appendix B . To estim ate average daily mercury ingestion rates for each exposed subpopulation n=ijk, we applied the following equation: HgI n = CHgFC n * C n = (CHg n * CCF) * C n ( 4. 4 ) where HgI = average daily mercury ingestion rate (μg/day); CHg = average mercury concentration in uncooked freshwater fish (ppm); CCF = cooking conversion factor: ratio of mercury concentration in cooked fish to

mercury concentration in uncooked fish (= 1.5); CHgFC = average mercury concentration in cooked freshwater fish (ppm); and C = average daily self
- caught freshwater cooked fish consumption rate

(gm/day) = 8 gm/day. To determine an appropriate daily fish consumption rate (C) for the analysis, EPA conducted an extensive review of existing literature characterizing self
- caught freshwater fish consu mption. Based on this review, it was decided that the ingestion rates for recreational45

freshwater fishers, specified as “ recommended ” in EPA’ s Environmental Exposure Factors Handbook (EPA, 1997) (mean of 8 gm/day and 95 th percentile of 25 gm/day), represen ted the most appropriate values to use in this analysis. These recommended values were derived based on ingestion rates from four studies conducted in Maine, Michigan, and Lake Ontario (Ebert et al. , 1992; Connelly et al. , 1996; West et al. , 1989; West et al. , 1993) , which measured annual average daily intake rates for self
- caught freshwater fish by all recreational fishers including consumers and non
- consumers of fish . The mean values presented in these four studies ranged from 5 to 17 gm/day, while the 95 th percent values ranged from 13 to 39 gm/day (Note: the 39 gm/day value actually represents a 96 th percent value). The EPA “ recommended values” were developed by considering the range and spread of means and 95 th percent ile values presented in the four st udies. EPA recognizes that using mean and 95 th percent ile consumption rates based on these four studies may not be representative of fishing behavior across the entire 48 - state study area and that regional trends in consumption may differ from the values u sed in this analysis. Moreover, rates of consumption by pregnant women in freshwater angler households may be different from those of the recreational fishers themselves. However, EPA believes that these four studies do represent the best available data fo r developing recreational fisher ingestion rates in the United States. Because the consumption rate estimate C is for cooked fish and the mercury concentrations are estimated for uncooked filet, a conversion factor (CCF) was applied to estimate mercury co ncentrations in cooked fish. Cooking fish tends to reduce the overall weight of fish by approximately one
-third (Great Lakes Sport Fish Advisory Task Force, 1993). Because volatilization of mercury is unlikely to occur during cooking, the overall amount of mercury will stay unchanged during cooking, and the concentration of mercury will increase by a factor of roughly 1.5 (Morgan, Berry, and Graves, 1997). **4.7.3 Estimation of Lost Future Earnings**  Estimating the IQ decrements in children that result from mothers’ ingestion of mercury required two steps. First, based on the estimated average daily maternal ingestion rate, the expected mercury concentration in the hair of exposed pregnant women was estimated as follows: CHgH n = (0 .08) - 1 * (HgI n/W), ( 4. 5 ) where CHgH = average mercury concentration in maternal hair (ppm) and46

W = average body weight for female adults below age 45 (= 64 kg). This conversion rate between average daily ingestion rate and maternal hair concentration is based on the one compartment model developed by Swartout and Rice (2000). The 2002 EPA Workshop on Methylmercury Neurotoxicity recommended that this one compartment model might be better suited than the PBPK model in mo deling dose
- response (EPA, 2002 ). The average body weight estimate (W) was based on EPA’ s Exposure Factor Handbook (E PA, 1997 ). Second, to estimate the expected IQ decrement in offspring resulting from in
- utero exposure to mercury through mothers’ fish consumption, the following dose
- response relationship was applied: dIQn = 0.18 * CHgH n , ( 4. 6 ) where dIQ = IQ decrement in exposed mother/child (IQ pts). The 0.18 dose
- response coefficient in this equation is based on the summary findings reported in Axelrad et al. (2007). The valuation approac h used to assess monetary losses due to IQ decrements is based on an approach applied in p revious EPA analyses (EPA, 2008). The approach expresses the loss to an affected individual resulting from IQ decrements in terms of foregone future earnings (net of changes in education costs) for that individual. These losses were estimated using the following equation: V n = VIQ * dIQi, ( 4. 7 ) where V = present value of net loss per exposed mother/child (2006 dollars) and VIQ = net loss per change in IQ point. The n et loss per IQ point decrement is estimated based on the following relationship: VIQ = (z * PVY) – (s * PVS), ( 4. 8 )47

where PVY = median present value of lifetime earnings, PVS = present value of education costs per additional year of schooling, z = percentage change in PVY per 1
- point change in IQ, and s = years of additional schooling per 1
- point increase in IQ. The estimate for PVY is derived using earnings and labor force participation rate data from the 2006 Current Population Survey (CPS) and as suming (1) an individual born today would begin working at age 16 and retire at age 67; (2) the growth rate of wages is 1% per year, adjusted for survival probabilities and labor force participation by age; and (3) lifetime earnings are discounted back to the year of birth. Using a 3% discount rate , the resulting present value of median lifetime earnings is $555,427 in 2006 dollars. Estimates of the average effect of a 1
- point increase in IQ on lifetime earnings (z) range from a 1.76% increase (Schwartz, 1994) to a 2.379% increase (Salkever, 1995). The percentage increases in the two studies reflect both the direct impact of IQ on hou rly wages and indirect effects on annual earnings as the result of additional schooling and increased labor force participation. The estimate for s is based on Schwartz (1994) who reports an increase of 0.131 years of schooling per IQ point. In addition to this positive net effect on earnings, an increase in IQ is also assumed to have a positive effect on the amount of time spent in school (s) and on associated costs (PVS). The range of estimate for s is based on Schwartz (1994) who reports an increase of 0 .131 years of schooling per IQ point and Salkever (1995) who reports an increase of 0.1007 years. The estimate for PVS is derived using an estimate of $16,425 per additional year of schooling in 1992 dollars (EPA, 2005), which is based on U.S. Department o f Education data reflecting both direct annual expenditures per student and annual average opportunity cost (i.e., lost income from being in school). We assume these costs are incurred when an individual born today turns 19, based on an average 12.9 years of education among people aged 25 and over in the United States. Discounting at a 3% rate to the year of birth results in an estimate of $13,453 per additional year of schooling in 2006 dollars. To incorporate (1) uncertainty regarding the size of z and ( 2) different assumptions regarding the discount rate, t he resulting value estimates for the average net loss per IQ point48

decrement (VIQ) are expressed as a range. Assuming a 3% discount rate , VIQ range s from $8,01 3 (using the Schwartz estimate for z and s ) to $11, 859 (using the Salkever estimate s ). With a 7% discount rate assumption, the VIQ estimates range from $8 93 to $1, 958 . **4.8 Mercury Benefits**  **and Risk Analysis**  **Results 4.8.1**  **Baseline Incidence** Applying the methodology described in Section 4. 7 , we first used GIS to link census tract centroids in the continental United States with HUC
- 12 watersheds containing mercury fish tissue sample data for 1995 to 2007. We found that, out of the 64,500 tracts in the 48
- state area, almost all of them are located with in 100 miles of at least one HUC
- 12 with freshwater mercury fish tissue sampling data. Therefore, very few tracts were entirely excluded from the analysis due to a lack of sampling data within 100 miles. Table 4
-4 reports the number of tracts linked to HUC
- level river or lake mercury concentration estimates within each distance interval. As expected, this number decreases as the size of the distance interval decreases. For example, 33% are within 10 miles of a HUC
- 12 containing a lake sample, and 52% are wi thin 10 miles of a HUC- 12 containing a river sample. Table 4
-4 also reports the average river and lake HUC
- level fish tissue mercury concentrations found within each distance interval. Assuming that the 1995 to 2007 samples are representative of baseline c onditions in 2005, the distance
- specific mean lake concentrations range from 0.26 to 0.3 ppm, and the mean river concentrations vary from 0.25 to 0.27 ppm. Table 4
-4 also reports corresponding river and lake mercury concentration estimates for a 2016 base case scenario. This scenario represents total mercury deposition from all global natural and anthropogenic sources based on projected 2016 conditions, including future anticipated regulations (e.g., Transport Rule). As described in Section 4. 4, CMAQ air q uality modeling runs were used to estimate average mercury deposition levels by HUC
- 12 sub
watershed under both the 2005 base case and the 2016 base case scenarios. For this analysis, it is assumed that HUC
- level fish tissue mercury concentrations would ch ange (between the two scenarios) by the same percentage as the change in modeled deposition levels. Overall, the mean concentrations decline by 6% to 9% in the 2005 base case compared with the 2016 base case scenarios. With these tract
- level mercury concen tration estimates, we then estimated the size of the exposed populations (NPA) in 2005 and 2016. These estimates are reported in Table 4
- 5 . As49

described in Section 4.7 .2 , a separate exposed population (NPAjk ) was estimated for each distance interval (j = 1 – 4) and waterbody (k = lake or river) combination at each tract. If mercury concentration data were not available for a specific distance
- waterbody combination, then the corresponding exposed population for the tract (NPAjk ) was not included in the analysis. Consequently, the exposed population estimates reported in Table 4
- 5 are best interpreted as lower
- bound estimates of the total exposed population. Excluding potentially exposed populations from the analysis because of missing/unavailable mercury concentration data reduced the total exposed population estimate by roughly 44%. These excluded populations include the portions of the tract
- level exposed p opulations that were matched with fishing trip travel distances that either (1) did not overlap with at least one HUC
- 12 with sampling data or (2) were greater than 100 miles (see Appendix C ). For 2005, the re were estimated to be 239,174 prenatally exposed children, and for 2016 the estimate is 244,286 prenatally exposed children.50 **Table 4 -** **4 .**  **Summary of Baseline Mer cury Fish Tissue Concentrations**  **2005 Base Case 2016 Base Case**  **Distance from Tract Centroid N**  **a Min**  **(ppm) Mean** 

**(ppm) Max** 

**(ppm) Median** 

**(ppm) Min** 

**(ppm) Mean** 

**(ppm) Max** 

**(ppm) Median** 

**(ppm)** Lake Sampling Sites 0– 10 miles 20,998 0.000 0.297 3.561 0.198 0.000 0.276 3.420 0.178 >10–20 miles 35,149 0.000 0.285 3.561 0.209 0.000 0.264 3.420 0.187 >20–50 miles 55,885 0.000 0.289 3.561 0.223 0.000 0.270 3.420 0.202 >50– 100 miles 61,820 0.000 0.264 2.333 0.241 0.000 0.247 2.251 0.227 River Sampling Sites 0– 10 miles 33,342 0.006 0.246 4.967 0.185 0.005 0.224 4.924 0.168 >10–20 miles 44,493 0.006 0.269 4.967 0.195 0.005 0.247 4.924 0.174 >20–50 miles 54,970 0.019 0.270 4.480 0.203 0.019 0.251 4.441 0.183 >50– 100 miles 62,868 0.023 0.267 4.967 0.214 0.022 0.251 4.924 0.192

a Number of tracts (out of 64,419) with at least one HUC
- 12 with sample data in the distance interval.51 **Table 4 -**  **5 .**  **Baseline Levels of Mercury Exposure and IQ Impacts Due to Freshwater Self -**  **Caught Fish Consumption**

**Number of Census**  **Tracts with Hg Samples**  **w/in 100 Miles**  **2005 Base Case 2016 Base**  **Case**

**Number of Prenatally Exposed**  **Children (NPA) Average** 

**Maternal Daily**  **Mercury Ingestion**  **(HgI) (μg/day)**  **Average IQ**

**Loss per Exposed**  **Child (dIQ) Total IQ** 

**Point Losses**  **Number of**

**Prenatally Exposed Children (NPA)**  **Average**

**Maternal Daily**  **Mercury Ingestion**  **(HgI) (μg/day)**  **Average**

**IQ Loss per**  **Exposed Child (dIQ)**  **Total IQ**

**Point Losses**  **State Mean**  **per Tract**  **Total in**

**State Mean**  **per Tract**  **Total in**

**State Total**  **63,978 3.74**  **239,174 3.04**  **0.11 25,544.9**  **3.82 244,286**  **2.84 0.10**  **24,419.4** AL 1,081 5.51 5,956 3.28 0.12 685.9 5.53 5,981 3.04 0.11 638.3 AR 623 6.45 4,017 3.80 0.13 537.1 6.55 4,084 3.66 0.13 525.9 AZ 1,097 3.17 3,476 2.21 0.08 269.8 3.75 4,117 2.18 0.08 316.3 CA 6,801 1.19 8,089 6.04 0.21 1,716.4 1.26 8,599 5.74 0.20 1,734.0 CO 1,045 3.53 3,693 1.20 0.04 155.3 3.92 4,101 1.18 0.04 169.8 CT 812 2.47 2,003 4.58 0.16 322.2 2.38 1,929 4.29 0.15 291.3 DC 181 2.23 404 1.67 0.06 23.7 2.03 367 1.35 0.05 17.4 DE 196 1.77 348 1.98 0.07 24.2 1.79 352 1.71 0.06 21.2 FL 3,144 3.28 10,299 5.24 0.18 1,897.5 3.71 11,651 5.17 0.18 2,118.9 GA 1,614 8.38 13,525 3.14 0.11 1,494.8 8.74 14,111 2.88 0.10 1,431.0 IA 791 6.39 5,052 1.21 0.04 215.3 6.18 4,888 1.15 0.04 197.5 ID 280 6.30 1,765 2.43 0.09 150.9 7.13 1,996 2.31 0.08 162.3 IL 2,950 2.33 6,884 1.83 0.06 442.3 2.32 6,831 1.49 0.05 356.9 IN 1,409 5.47 7,711 2.20 0.08 596.7 5.51 7,759 1.90 0.07 519.2 KS 716 2.08 1,490 2.38 0.08 124.8 2.06 1,478 2.34 0.08 121.8 KY 993 4.99 4,954 2.19 0.08 381.9 4.92 4,889 1.90 0.07 326.1 (continued)52 **Table 4 -**  **5 .**  **Baseline Levels of Mercury Exposure and IQ Impacts Due to Freshwater Self -**  **Caught Fish Consumption (continued)** 

**Number of Census**  **Tracts with Hg Samples**  **w/in 100 Miles**  **2005 Base Case 2016 Base Case** 

**Number of Prenatally Exposed**  **Children (NPA) Average** 

**Maternal Daily**  **Mercury Ingestion**  **(HgI) (μg/day)**  **Average IQ**

**Loss per Exposed**  **Child (dIQ) Total IQ** 

**Point Losses**  **Number of**

**Prenatally Exposed Children (NPA)**  **Average**

**Maternal Daily**  **Mercury Ingestion**  **(HgI) (μg/day)**  **Average**

**IQ Loss per**  **Exposed Child**  **(dIQ) Total IQ** 

**Point Losses**  **State Mean**  **per Tract**  **Total in**

**State Mean**  **per Tract**  **Total in**

**State** LA 1,103 6.91 7,623 3.82 0.13 1,022.9 6.59 7,269 3.77 0.13 962.6 MA 1,357 1.81 2,456 5.40 0.19 466.0 1.74 2,359 5.04 0.18 417.7 MD 1,210 2.23 2,703 2.16 0.08 204.8 2.35 2,840 1.76 0.06 176.2 ME 344 4.66 1,602 5.12 0.18 288.3 4.31 1,484 5.05 0.18 263.4 MI 2,701 3.89 10,520 2.72 0.10 1,005.0 3.79 10,234 2.37 0.08 854.0 MN 1,294 11.53 14,915 2.86 0.10 1,501.2 11.71 15,157 2.77 0.10 1,474.7 MO 1,311 3.66 4,796 1.80 0.06 302.7 3.75 4,911 1.70 0.06 294.2 MS 604 9.18 5,546 5.11 0.18 996.2 9.32 5,632 4.98 0.18 986.9 MT 267 3.62 965 2.40 0.08 81.5 3.68 984 2.38 0.08 82.3 NC 1,554 5.13 7,976 3.29 0.12 921.5 5.33 8,280 2.95 0.10 859.1 ND 224 2.89 647 3.43 0.12 78.1 2.79 626 3.41 0.12 74.9 NE 500 3.97 1,984 1.60 0.06 111.9 4.03 2,014 1.56 0.05 110.5 NH 272 3.68 1,001 5.53 0.19 194.5 3.71 1,010 5.39 0.19 191.2 NJ 1,930 1.02 1,965 3.28 0.12 226.5 1.00 1,936 2.98 0.10 202.7 NM 244 1.75 426 1.74 0.06 26.0 1.89 461 1.77 0.06 28.6 NV 471 1.70 803 3.78 0.13 106.8 2.09 985 3.60 0.13 124.8 NY 4,791 1.41 6,770 3.86 0.14 918.4 1.35 6,486 3.54 0.12 807.0 (continued)53 **Table 4 -**  **5 .**  **Baseline Levels of Mercury Exposure and IQ Impacts Due to Freshwater Self** **- Caught Fish Consumption**  **(continued)**

**Number of Census**  **Tracts with Hg Samples**  **w/in 100 Miles**  **2005 Base Case 2016 Base Case** 

**Number of Prenatally Exposed**  **Children (NPA) Average** 

**Maternal Daily**  **Mercury Ingestion**  **(HgI) (μg/day)**  **Average IQ**

**Loss per Exposed**  **Child (dIQ) Total IQ** 

**Point Losses**  **Number of**

**Prenatally Exposed Children (NPA)**  **Average**

**Maternal Daily**  **Mercury Ingestion**  **(HgI) (μg/day)**  **Average**

**IQ Loss per**  **Exposed Child (dIQ)**  **Total IQ**

**Point Losses**  **State Mean**  **per Tract**  **Total in**

**State Mean**  **per Tract**  **Total in**

**State** OH 2,923 4.11 12,015 1.61 0.06 678.8 3.93 11,489 1.30 0.05 527.0 OK 987 5.65 5,580 3.07 0.11 602.9 5.73 5,653 3.03 0.11 601.4 OR 754 5.14 3,877 2.80 0.10 382.1 5.43 4,095 2.81 0.10 404.3 PA 3,116 2.40 7,485 2.30 0.08 605.9 2.31 7,194 1.91 0.07 482.2 RI 233 1.55 361 6.01 0.21 76.2 1.53 356 5.15 0.18 64.5 SC 864 7.39 6,388 4.43 0.16 995.4 7.59 6,559 4.08 0.14 941.0 SD 225 3.29 740 1.77 0.06 45.9 3.20 719 1.72 0.06 43.6 TN 1,253 4.95 6,204 3.01 0.11 656.7 5.06 6,335 2.76 0.10 615.5 TX 4,310 3.97 17,127 2.83 0.10 1,701.2 4.32 18,633 2.67 0.09 1,748.9 UT 482 3.95 1,905 2.05 0.07 137.3 4.68 2,254 2.06 0.07 163.5 VA 1,524 3.66 5,580 2.61 0.09 512.7 3.82 5,820 2.19 0.08 448.7 VT 179 3.50 627 3.85 0.14 84.8 3.37 604 3.70 0.13 78.6 WA 1,315 3.67 4,823 1.69 0.06 287.2 3.90 5,133 1.68 0.06 302.8 WI 1,313 8.03 10,543 2.77 0.10 1,026.2 7.85 10,309 2.59 0.09 938.1 WV 466 6.53 3,042 2.10 0.07 224.3 6.10 2,840 1.66 0.06 166.1 WY 124 4.13 512 1.97 0.07 35.5 3.99 495 1.97 0.07 34.354

For each exposed population, we then estimated their average mercury ingest ion rate (HgI) using Equation (4. 4 ) and the IQ los s associated with this exposure level . As reported in Table 4 - 5 , in 2005, the average estimated mercury ingestion rate for the population of exposed pregnant women was 3.04 ug/day. For 2016, the ingestion rate was estimated to be 2.84 ug/day (6.6% lower). The corresponding average IQ loss per prenatally exposed chi ld was 0.11 in 2005 and 0.10 in 2016. Multiplying these average IQ losses by the size of the exposed population, the total loss in IQ points due to mercury exposures through consumption of self
caught freshwater fish was estimated to be 25,545 in 2005. For the 2016 base case, the total decrease in IQ points was estimated to be 24,419 (4.4% lower). **4.8.2 IQ Loss and Economic Valuation Estimates**  In addition to the base case scenarios described above , CMAQ air quality modeling runs were used to estimate averag e mercury deposition levels for three emissions control scenarios:

 **2005 EGU Zero -** **Out.** This scenario represents total mercury deposition from all global natural and anthropogenic sources except for U.S. EGUs based on current
- day conditions.

 **2016 EGU Zero -** **Ou t.**  This scenario represents total mercury deposition from all global natural and anthropogenic sources except for U.S. EGUs based on projected 2016 conditions, including future anticipated regulations (e.g., Transport Rule).

 **2016 Toxics Rule**  **.** This scenario represents total mercury deposition from all global natural and anthropogenic sources based on projected 2016 conditions, including future anticipated regulations (e.g., Transport Rule) and the Toxics Rule . For these three scenarios, it was again assumed that the HUC
- level fish tissue mercury concentrations would change (relative to the 2005 base case) by the same percentage as the change in modeled deposition levels. Mercury exposure and IQ loss estimates were then derived for these three scenarios, using the exposed population estimates for the relevant year (2005 or 2016) and the corresponding mercury concentration estimates for the relevant emission scenario (zero
- out or Toxics Rule ). In addition, the valuation methodology summarized in Section 4. 7.2 (i n particular, Equation [ 4. 7 ]) was applied to estimate the present value of IQ loss estimates for the two base case and three emissions control scenarios. To assess the aggregate benefits of reductions in EGU emissions, we evaluated five emission reduction scenarios .55

 2005 EGU zero - out (relative to 2005 base case)

 2016 base case (relative to 2005 base case)

 2016 EGU zero - out (relative to 2016 base case)

 2016 Toxics Rule (relative to 2005 base case)

 2016 Toxics Rule (relative to 2016 base case) The benefits of each emission reduction scenario are calculated as the difference (i.e., decrease) in total present value of IQ losses between the selected emission control scenario and the selected base case scenario. **4.8.3 Primary Results for National Analysis of Expos** **ures from Recreational Freshwater Fish Consumption**  Table 4
- 6 summarizes the aggregate national IQ and present
-value loss estimates for the two base case and three emission control scenarios. The highest losses are estimated for the 2005 base case. For the population of prenatally exposed children included in the analysis (almost 240,000, as reported in Table 4
- 5) , mercury exposures under baseline conditions during the year 2005 are estimated to have resulted in more than 25,500 IQ points lost . Assuming a 3% discount rate, the present value of these losses ranges from $ 210 million to $ 290 million. 8 . This range of total loss estimates is based on the range of per
- IQ- point value (VIQ) estimates summarized in Section 4.7.3. These losses represent expected presen t value of declines in future net earnings over the entire lifetimes of the children who are prenatally exposed during the year 2005. With a 7% discount rate , the present value range is considerably lower: $2 3 million to $ 51 million. The lowest losses are estimated to result from the 2016 zero
- out scenario, with total IQ losses of less than 24,000 among roughly 244,000 prenatally exposed children and present values of these losses ranging from $ 200 to $ 290 million (3% discount rate). For the five emission r eduction scenarios described above, Table 4
- 7 reports estimates of aggregate nationwide benefits associated with reductions in mercury exposures and resulting reductions in IQ losses. Most importantly, the benefits of the 2016 Toxics Rule

8 Monetized benefits estimates are for an immediate change in MeHg levels in fish. If a lag in the response of MeHg levels in fish were assumed, the mon etized benefits could be significantly lower, depending on the length of the lag and the discount rate used. As noted in the discussion of the Mercury Maps modeling, the relationship between deposition and fish tissue MeHg is proportional in equilibrium, b ut the MMaps approach does not provide any information on the time lag of response.56

**Table 4 -**  **6 .**  **Summar y Estimates of the Aggregate Size and Present Value of IQ Losses Under** 

**Alternative Base Case and Emissions Control Scenarios**

**Scenario Average IQ Loss per** 

**Prenatally Exposed Child**

**(dIQ) Total IQ Losses from** 

**One Year of Exposure Value of**  **Total IQ Losses in 2**  **016 (**  **millions of 2007$ )**  **3% Discount Rate 7% Discount**  **Rate** 2005 base case 0.1068 25,54 5 $210 to $310 $23 to $51 2005 EGU zero - out 0.0985 23,561 $190 to $290 $22 to $47 2016 base case 0.1000 24,419 $200 to $300 $22 to $49 2016 EGU zero - out 0.0971 23,722 $200 to $290 $22 to $48 2016 Toxics Rule 0.0979 23,90 9 $200 to $290 $22 to $48

**Table 4 -**  **7 .**  **Aggregate Benefit Estimates for Reductions IQ Losses Associated with**

**Alternative Emissions Reduction Scenarios**

**Emission Reduction Scenario**  **Decrease in Average IQ**  **Loss per Prenatally**  **Exposed Child (dIQ)**  **a Decrease in** 

**Total IQ Losses from**  **One Year of Exposure**  **Value of Total IQ Losses in 2016**  **( millions of 2007$**  **) 3% Discount Rate**  **7% Discount Rate** 2005 EGU zero - out (relative to 2005 base case) 0.00829 1 , 983 $16 to $24 $1.8 to $4.0 2016 base case (relative to 2005 base case) 0.00684 1 , 12 6 $9.3 to $14 $1.0 to $2.3 2016 EGU zero - out (relative to 2016 base case) 0.00285 697 $5.7 to $8.5 $0.6 to $1.4 2016 Toxics Rule (relative to 2005 base case) 0.00893 1 , 636 $13 to $20 $1.5 to $3.3 2016 Toxics Rule (relative to 2016 base case) 0.00209 51 1 $4.2 to $6.2 $0.47 to $1.0

a As reported in Table 4
- 5, the estimated number of prenatally exposed children is 239,174 in 2005 and 244,286 in 2016. scenario (relative to the 2016 base case) are estimated to range between $4. 2 million and $ 6.2 million (assuming a 3% discount rate), because of an estimated 511 point reduction in IQ losses. These benefits are 73% as large as the benefits of the 20 16 zero - out scenario (relative to the57

same 2016 base case). Relative to the 2005 base case, the benefits of the 2016 Toxics Rule scenario range from $13 million to $ 20 million (3% discount). Despite growth in the exposed population from 2005 to 2016, the c hanges from the 2005 base case to the 2 01 6 base case account for 69% of these benefits, while the changes from the 2016 base case to the 2016 Toxics Rule account for 31%. **4.8.5 Discussion**  **of Assumptions, Limitations, and Uncertainties** Uncertainty regarding the model results and estimates reported in Section 4.8 can arise from several sources. Some of the uncertainty can be attributed to model uncertainty. For example, to estimate exposures a number of different modeling approaches have been selected and com bined . The separate model components are summarized in Figure 4
-4 and equations (4.) to (4.8) , each of which simplifies potentially complex processes. The results, therefore, depend importantly on how these models are selected , specified , and combined . Another important source of uncertainty can be characterized as input or parameter uncertainties. Each of the modeling components discussed in this report requires summary data and estimates of key model parameters. For example, estimating IQ losses associate d with consumption of freshwater fish requires estimates of the size of the exposed population of interest, the average mercury concentrations in consumed fish, the freshwater fish consumption rate for the exposed population, and the concentration
- response relationship between mercury ingestion and IQ loss. All of these inputs are measured with some degree of uncertainty and can affect, to differing degrees, the confidence range of our summary results. The discussion below identifies and highlights some of the key model parameters, characterizes the source and extent of uncertainties associated with them, and characterizes the potential effects of these uncertainties on the model results. To organize this discussion, we discuss different components of the modeling framework separately. This section first discusses issues related to estimating the mercury concentrations and then those related to estimating the exposed population. After that, it discusses issues related to matching these two components and th en concludes by discussing the estimation of mercury ingestion through fish consumption. 4.8.5.1 Mercury Concentration Estimates As described in Sectio n 4.2.2, th e mercury concentration estimates for the analysis come from several different sources, includ ing fish tissue sample data from the National Listing of Fish Advisories (NLFA) and several other state
- and national
- level sources. These estimates58

were then used to approximate mercury concentrations across the study area. Some of the key assumptions, li mitations, and uncertainties associated with these estimates are the following:

 The fish tissue sampling data from various sources are subject to measurement and reporting error and variability. The NLFA is the largest and most detailed source of data on m ercury in fish; however, even this system was not centrally designed (e.g., by EPA) using a common set of sampling and analytical methods. Rather, states collected the data primarily to support the development of advisories, and the data are submitted volu ntarily to EPA. Each state uses different methods and criteria for sampling and allocates different levels of resources to their monitoring programs. In addition, there are uncertainties regarding the precise locations (lat/long coordinates) of some of the samples. The heterogeneity and potential errors across state sampling programs can bias the results in any direction and contribute to uncertainty.

 The fish tissue sampling data were assigned as either lake or river samples, based on the site name and/or the location coordinates mapped to the nearest type of waterbody. This process also involves measurement error and may have resulted in misclassifications for some of the samples. These errors are not expected to bias results, but they contribute to uncert ainty.

 The mercury concentration estimates used in the model were based on simple temporal and spatial averages of reported fish tissue samples. This approach assumes that the mercury samples are representative of “ local ” conditions (i.e., within the same HUC
- 12) in similar waterbodies (i.e., rivers or lakes). However, even though states use a variety of approaches to monitor and sample fish tissue contaminants, in some cases, the sampling sites are selected to target area s with high levels of angler activity and/or a high level of pollution potential. To the extent that sample selection procedures favor areas with relatively high mercury, the spatial extrapolation methods used in this report will tend to overstate exposure s. These approaches also implicitly assume that mercury concentration estimates are strongly spatially correlated, such that closer sampling sites (i.e., from the same HUC or distance interval) provide more information about mercury concentrations than more distant sites. To the extent that spatial correlation is weaker than assumed, this will increase the degree of uncertainty in the modeling results.

 To generate average mercury fish tissue concentration estimates, all available samples from the three main data sources (1995–2009) and from freshwater fish larger the 7 inches were included in the analysis. Smaller fish were excluded to better approximate concentrations in the types of fish that are more likely to be consumed, and samples from years before 19 95 were excluded to better represent more recent conditions. Even with these sample selection procedures, average concentration estimates from the retained samples may still under or overestimate actual concentrations in currently consumed fish.59

4.8.5.2 Ex posed Population Estimates The methods described in Section 4.7 to estimate the total exposed population of interest in 2005 and 2016 involve the following key assumptions, limitations, and uncertainties:

 The approach relies on data from the FHWAR to estim ate state - level freshwater angler activity levels, including freshwater fishing participation rates and lake
-to river trip ratios. Each of these data elements is measured with some error in the FHWAR, but they are based on a relatively large sample. More i mportantly the state
level averages are applied to each modeled census tract in the state; therefore, the model fails to capture within
- state variation in these factors, which contributes to uncertainty in the model estimates.

 The analysis also uses state
- level fertility rate data to approximate the rate of pregnancy among women of childbearing age in angler households for a smaller geographic area. The state
- level fertility rates from the National Vital Statistics are estimated with relatively little error; however, applying these rates to specific census tracts (and specifically to women in angler households) does involve considerably more uncertainty.

 The approach assumes that, in each census tract, the percentage of women who live in freshwater angler ho useholds (i.e., households with at least one freshwater angler) is equal to the percentage of the state adult population that fishes. Applying the state
- level participation rate to approximate the conditions at a block level creates uncertainty. More importantly, however, using individual
- based fishing participation rates to approximate household rates is likely to underestimate the percentage of women living in freshwater angler households. 9 Unfortunately, data on household participation levels in freshwat er fishing are not readily available.

 Census tract populations are only included in the model if they are matched to distance intervals and waterbody types that have spatial overlap with at least one HUC- 12 sub
- watershed containing a mercury concentrations estimate for that waterbody type . By design, this approach undercounts the exposed population (by roughly 40 to 45%) and, therefore, leads to underestimates of national aggregate baseline exposures and risks and underestimates of the risk reductions and b enefits resulting from mercury emission reductions.

 All of the tract
- level population estimates are based on Census 2000 data, which are projected forward to 2005 and 2016 using county
- level growth projections for the subpopulations of int erest from Woods and Poole (2008 ). Therefore, the 2005 and 2016 population estimates incorporate uncertainty from both the growth

9 For example, hypothetically if one out of every three members in each household fished, the population rate would be 33%, but the household rate would be 100%.60 projections themselves and from transferring the county
- level growth estimates to the tract level. The purpose of the analysis of potentially h igh risk subpopulations is not to estimate the size of the exposed population but rather to characterize the distribution of individual
- level risks in the subpopulations of interest. Nevertheless, the size and spatial distribution of the total population i n each group was used as a proxy for characterizing the spatial distribution of pregnant women in freshwater fishing households in each group. The main assumption underlying this approach is t hat the expected proportion of the subgroup ’ s population in eac h Census tract that consists of pregnant women in fishing households is the same across the selected census tracts. The main limitation of this assumption is that it does not allow or account for spatial variation in (1) the percentage of the subpopulation that are women of childbearing age, (2) the percentage of these women that are pregnant (i.e., fertility rate) and (3) the freshwater angler participation rates for the subgroups of interest. Unfortunately, spatially varying data for the last component (f ishing participation rates among the subpopulations of interest) are not readily available. This assumption is not expected to bias the results but it does contribute to uncertainty in the estimated distributions of individual
- level risks. 4.8.5.3 Matchin g of Exposed Populations to Mercury Concentrations The methods described in Section 4.7 to match the exposed population estimates with the corresponding mercury concentration estimates involve the following key assumptions, limitations, and uncertainties:

 For the aggregate benefits analysis, tract
- level exposed populations are assigned to waterbody types based on state
- level ratios of lake
-to - river fishing days (from the FHWAR ). They are further assigned to distance intervals based on observed travel distan ce patterns in national fishing data (NSRE , 1994). Both of these assignment methods involve uncertainty, but particularly the second method because it is based on much more aggregate data and on a much smaller and more dated sample of anglers. This approac h does not take into account the physical characteristics of the area in which the population is located. In particular, the allocation of exposures to lakes or rivers at different distances from each census tract does not take into account the presence or number of these waterbodies in each distance interval. Using these state and national level estimates to represent conditions at a local (i.e., census tract) level increases uncertainty in the model results, but it is not expected to bias the results in e ither direction.61

 For the analysis of potentially high
- risk populations, these methods and assumptions were slightly modified. In particular, because these analyses focus on low
- income and/or subsistence fishing populations, all trips were assumed to occur within 20 miles of the census tract. Unfortunately, it is difficult to evaluate the accuracy of this restriction due to limited data on travel distances for the subgroups of interest. One potentially important factor that is not included for matching popul ations and mercury concentrations is the effect of fish consumption advisories on fishing behavior. Evidence summarized in Jakus, McGuinness, and Krupnick (2002) suggests that awareness of advisories by anglers is relatively low (less than 50%), and even t hose who are aware do not always alter their fishing behavior. Nonetheless, anglers are less likely to fish in areas with advisories. Unfortunately, we were not able to reliably quantify the reduction and redistribution of fishing trips in either model to account for fish advisories. By excluding these effects, the model estimates are likely to overstate mercury exposures. 4.8.5.4 Fish Consumption Estimates One of the most influential variables in both modeling approaches is the rate of self
caught freshwat er fish consumption. The following key assumptions, limitation, and uncertainties are associated with the methods used:

 For the aggregate analysis we have assumed 8 g/day for the general population in freshwater angler households (based on recommendations in EPA’ s EFH). Unfortunately, data are not available to reliably vary this rate with respect to characteristics of the population across the entire study area. Uncertainty regarding the true average fish consumption rate has a direct effect on uncertainty for the aggregate exposure and benefit estimates. Because a single consumption rate is applied uniformly across the entire exposed population and because it is a multiplicative factor in the model, the two uncertainties are directly proportional to one another. The recommended 8 g/day rate is based on four studies with mean estimates ranging from 5 g/day (37% less than 8) to 17 g/day (113% more than 8). If it is assumed that this range of estimates represents the uncertainty in the mean freshwater fish cons umption rate for the study population, then the resulting uncertainty range for the estimated mean mercury ingestion level (and resulting IQ loss) will also be between − 37% and +113% of the mean mercury ingestion level.

 To analyze the distributions of indi vidual
- level risks in potentially high risk subpopulations, we applied empirical distributions of fish consumption rates for specific subpopulations . One of the main limitations of this approach is that these empirical distributions are based on relatively small and localized samples. In particular, the estimated distribution of consumption rates for low
- income African American subsistence/recreational fishers in the Southeastern U.S. (see Table 4
- 3) is based on a very small sample (N=39) drawn from one loc ation (Columbia, SC). The62 sample sizes for the other groups, particularly the Hispanic (N= 45) and Laotian (N=54) populations are also small; therefore, there is considerable uncertainty regarding how well these empirical consumption rate distributions ref lect actual rates of consumption in the subpopulations of interest. Another related and potentia lly influential variable in the modeling approach is the assumed conversion factor for mercury concentrations between uncooked and cooked fish. Studies have found that cooking fish tends to reduce the overall weight of fish by approximately one -third (Great Lakes Sport Fish Advisory Task Force, 1993 ) without affecting the overall amount of mercury. But these conversion rates depend on cooking practices and types of fish. Uncertainty regarding this conversion factor also has a proportionate effect on the modeling results. 4.8.5.5 Measurement and Valuation of IQ Related Effects The models for estimating and valuing IQ effects involve three main steps. The first step is translating maternal mercury ingestion rates to mercury levels in hair. The second st ep is translating differences in hair mercury concentrations during pregnancy to IQ changes in offspring. The third step is translating IQ losses into expected reductions in lifetime earnings. As discussed below, each of these steps also involves the follo wing assumptions, limiations, and uncertaint ies: .
 The conversion of mercury ingestion rate to mercury concentration in hair is based on uncertainty analysis of a toxicokinetic model for estimating reference dose (Swartout and Rice, 2000). The conversion fa ctor was estimated by considering the variability and uncertainty in various inputs used in deriving the dose including body weight, hair
-to - blood mercury ratio, half
- life of MeHg in blood, and others. Therefore, there is uncertainty regarding the conversi on factor between hair mercury concentration and mercury ingestion rate. Although, the median conversion factor (0.08 μg/kg
- day/hair
- ppm) is used, the 90% confidence interval is from 0.037 to 0.16 μg/kg day/hair
- ppm. Any change in the conversion factor wil l proportionately affect the benefits results because of the linearity of the model.

 The dose
- response model used to estimate neurological effects on children because of maternal mercury body burden is susceptible to various uncertainties. In particular, t here are three main concerns. First, there are other cognitive end
- points that have stronger association with MeHg than IQ point losses. Therefore, using IQ points as a primary end point in the benefits assessment may underestimate the impacts. Second, blo od
-to - hair ratio for mercury is uncertain, which can cause the results from analyses based on mercury concentration in blood to be uncertain. Third, uncertainty is associated with the epidemiological studies used in der iving the dose
- response models.63

 With regard to the relationship between prenatal methylmercury exposure and childhood IQ loss, we expect greater uncertainty in associated estimates of IQ loss as exposure levels increase beyond those observed in the primary studies (i.e., Faroe Islands, New Ze aland, Seychelles Islands studies) used to derive the dose
- response function. In particular, high
- end total exposure estimates for some of the subsistence
- level fishing subpopulations included in this assessment likely exceed levels observed in the three p rimary studies.

 To parameterize the dose
- response relationship between maternal hair concentrations and IQ loss for this analysis, we applied the results of an integrative study by Axelrad et al. (2007) . The implications of applying this study include the following:

– This approach may confound potentially positive cognitive effects of fish consumption and, more specifically, omega
- 3 fatty acids. Results from Rice (2010) offer a reasonable, but highly uncertain, estimate for offsetting the possible downward b ias resulting from the positive confounding effects of fatty acids. Rice ’ s high coefficient reflects the central estimate of Axelrad but adjusted upwards by a factor of 1.5 to “ acknowledge the recent argument of Budtz
Jorgensen (2007) that the parameter estimates from these three epidemiological studies (Faroe Islands, Seychelles Islands, New Zealand ) may be biased downward by a factor of approximately 2 because of failure to adequately control for confounding. ” A third study, Oken (2008), analyzes a cohort in Massachusetts and also seems to support a higher “Axelrad
- plus” coefficient range due to evidence of fatty acid confounding (i.e., positive cognitive effects of fatty acids in fish may have previously led to underestimates of mercury
attributable IQ lo ss). This study offers further qualitative support for a higher
end estimate but is limited by the fact that it did not control for the children ’ s home environment, which is generally a significant factor in early cognitive development.

– The dose
- response c oefficient from the Axelrad et al. study is sensitive to the exclusion of one outlier data point from the Seychelles study . Including the outlier would reduce the effect size by about 25 percent. If this outlier actually reflects the true response for a su bset of the populations, then risks (as modeled) could be biased high specifically for this subpopulation

– Because the dose
- response coefficient is applied uniformly across the entire exposed population and is a multiplicative factor in the model, the uncertainty in this parameter has a directly proportional effect on the reported risk and benefit estimates. In other words, adjusting the absolute value of the dose
- response coefficient upward by a factor of 1.5 (i.e., based on Rice , 2010) would yield reductio ns in IQ losses and benefits from mercury emission reductions that are also greater by a factor of 1.5.64

 The valuation of IQ losses is based on a unit
-value approach developed by EPA, which estimates that the average effect of a 1
- point reduction in IQ is t o reduce the present value of net future earnings. Three key assumptions of this unit
-value approach are that (1) there is a linear relationship between IQ changes and net earnings losses, (2) the unit value applies to even very small changes in IQ, and (3 ) the unit value will remain constant (in real present value terms) for several years into the future. Each of these assumptions contributes to uncertainty in the result. In particular the unit value estimate is itself subject to two main sources of uncert ainty.

– The first source is directly related to uncertainties regarding the average reductions in future earnings and years in school as a result of IQ changes. The average percentage change estimates are subject to statistical error, modeling uncertaintie s, and variability across the population. To address these uncertainties we have included in the analysis and reported results a range of values for this parameter, based on statistical analyses by Salkever (1995) and Schwartz (1994 ) .

– The second main sour ce of uncertainty is the estimates of average lifetime earnings and costs of schooling. Both of these estimates are derived from national statistics from the early 1990s, but they are also subject to statistical error, modeling uncertainties, and variabili ty across the population. It is also worth noting that the lost future earnings estimates do not include present value estimates for nonwage/nonsalary earnings (i.e., fringe benefits) and household (nonmarket) production. Based on the results of Grosse et al. (2009 ) , i nclud ing these fact ors would increase the present value of median earnings (both explicit and implicit) by a factor of roughly 1.9. However, it is not known whether IQ changes have a similar effect on these other (implicit) earnings. 4.8.5.6 Unquantified Benefits In addition to the uncertainties discussed above associated with the benefit analysis of reducing exposures to MeHg from recreational freshwater angling, we are unable to quantify several additional benefits, which adds to the uncerta inties in the final estimate of benefits. Table 4
- 20 displays the health and ecosystem effects associated with MeHg exposure that are discussed in Secti on 4.2.2 for whi ch we are currently unable to quantify. We note that specifically with regard to health effects, the NRC (2000) provided the following observation:
“ Neurodevelopmental effects are the most extensively studied sensitive end point for MeHg exposure, but there remains some uncertainty about the possibility of other health effects at low levels of exposure. In particular, there are indications of immune and cardiovascular effects, as well as neurological effects emerging later in life, that have not been adequately studied. ”65

**Table 4 -** **8 .**  **Unquantified Health and Ecosystem Effects Associated with Exp osure to Mercury**  **Category of Health or Ecosystem Effect Potential Health or Ecosystem Outcomes**  Neurologic Effects Impaired cognitive development Problems with language Abnormal social development Other Health Effects a Associations with genetic, autoimmune and cardiovascular effects Ecological Effects a Survival, reproductive, behavioral, and neurological effects in wildlife (birds, fish, and mammals)

a These are potential effects and are not quantified because the literature is either contradictory or incomplete. In addition to the health and ecosystem effects that we are not able to quantify, we are currently unable to quantify exposures to other segments of the U.S. population including consumption of commercial seafood and freshwater fish (produced domestically as well as imported from foreign sources) and consumption of recreationally caught seafood from estuaries, coastal waters, and the deep ocean. These consumption pathways impact additional recreational anglers who are not modele d in our benefits analysis as well as the general U.S. population. Reductions in domestic fish tissue concentrations can also impact the health of foreign consumers (consuming U.S. exports). Because of technical/theoretical limitations in the science, EPA is unable to quantify the benefits associated with several of these fish consumption pathways. For example, reductions in U.S. power plant emissions will result in a lowering of the global burden of elemental mercury, which will likely produce some degree of reduction in mercury concentrations for fish sourced from the open ocean and freshwater and estuarine waterbodies in foreign countries. In the case of mercury reductions for fish in the open ocean, complexities associated with modeling the linkage betwe en changes in air deposition of mercury and reductions in biomagnification and bioaccumulation up the food chain (including open ocean dilution and the extensive migration patterns of certain high
consumption fish such as tuna) prevent the modeling of fish obtained from the open ocean. In the case of commercial fish obtained from foreign freshwater and estuarine waterbodies, although technical challenges are associated with modeling long
- range transport of elemental mercury and the subsequent impacts to fis h in these distant locations, additional complexities such as accurately modeling patterns of harvesting and their linkages to commercial consumption in the United States prevent inclusion of foreign
- sourced freshwater and estuarine fish in the primary ben efits analysis. Finally, with regard to commercially
- produced freshwater fish sourced in the United States (i.e., fish from catfish, bass, and trout farms), we are unable to accurately quantify66

effects from this consumption pathway because many of the fish farms operating in the United States use feed that is not part of the aquatic food web of the waterbody containing the fish farm (e.g., use of agricultural
- based supplemental feed). In addition, many of the farms involve artificial “ constructed ” waterbody environments that are atypical of aquatic environments found in the regions where those farms are located, thereby limiting the applicability of Mercury Maps’ assumption in linking changes to mercury deposition to changes in mercury fish tissue concentrat ions (e.g., waterbodies may have restricted or absent watersheds and modified aquatic chemistry, which can effect methylation rates and impact time scales for reaching steady
- state mercury fish tissue concentrations following reductions in mercury depositi on). Some research indicates that the recycling of water at fish farms can magnify the mercury concentration because the system does not remove mercury as it is recycled, while newly deposited mercury is added to the system. Thus, additional research on aq uaculture farms is necessary before a benefits analysis can be conducted. Exclusion of these commercial pathways means that this benefits analysis, although covering an important source of exposure to domestic mercury emissions (recreational freshwater ang lers), excludes a large and potentially important group of individuals. Recreational freshwater consumption accounts for approximately 10 to 17% of total U.S. fish consumption, and 90% is derived from commercial sources (domestic seafood, aquaculture, and imports) (EPA, 2005). In conclusion, several unquantified benefits associated with this analysis add to the overall uncertainty in estimating total benefits. To the extent that the proposed rule will reduce mercury deposition from power plants over estuari ne areas, coastal, and open ocean waters, there would be a subsequent reduction in mercury fish tissue concentrations in these different waterbodies and an associated benefit from avoided decrements in IQ and other known health and ecosystem effects. **4.8.6 Overall Conclusions**  4.8. 6.1 Total Baseline Incidence of IQ Loss: Self
- Caught Fish Consumption among Recreational Freshwater Anglers
 Out of 64,500 census tracts in the continental U.S., 63,978 are located within 100 miles of at least one HUC
- 12 watershed w ith freshwater mercury fish tissue sampling data, and therefore were included in the modeling of IQ loss among recreational freshwater anglers.67

 Approximately 240,000 prenatally exposed children were modeled, with an average IQ loss of 0.11 and 0.10 IQ points, respectively, from self
- caught freshwater fish consumption for the 2005 and 2016 base case scenarios.

 The highest estimated state
- specific average IQ loss among children of freshwater recreational anglers is 0.21 IQ points under the 2005 base case scenario, in both California and Rhode Island.

 Total estimated IQ loss from self
- caught freshwater fish consumption among children of recreational anglers is estimated at 25,555 and 24,419 IQ points, respectively, for the 2005 and 2016 base case scenarios.

 The present economic value of baseline IQ loss for 2005 ranges from $ 210 million to $ 310 million, assuming a 3% discount rate, and from $ 23 million to $5 1 million, assuming a 7% discount rate.

 The present economic value of baseline IQ loss for 2016 ranges from $ 200 million to $ 300 million, assuming a 3% discount rate, and from $ 22 million to $ 49 million, assuming a 7% discount rate. 4.8. 6.2 Avoided IQ Loss and Economic Benefits due to Regulatory Action: Self
- Caught Fish Consumption among Recreational Fresh water Anglers
 Eliminating all mercury air emissions from U.S. EGUs in 2016 would result in an estimated 0.00893 fewer IQ points lost per prenatally exposed child from self
- caught freshwater fish consumption, as compared with the 2005 base case scenario.

 Th e present economic value of avoided IQ loss from eliminating all mercury air emissions from U.S. EGUs in 2016 is estimated at a range of $5.7 million to $8.5 million, assuming a 3% discount rate, and $0.6 million to $1.4 million, assuming a 7% discount rat e.

 Reduced mercury air emissions due to implementation of the Toxics Rule in 2016 would result in an estimated 0.00209 fewer IQ points lost per prenatally exposed child from self
- caught freshwater fish consumption, as compared with the 2016 base case scena rio.

 The present economic value of avoided IQ loss from reduced mercury air emissions due to implementation of the Toxics Rule in 2016 is est imated at a range of $4.2 million to $6.2 million, assum ing a 3% discount rate, and $0.47 million to $1 million, as suming a 7% discount rate.68

**4.9 Benefits Associated with Reductions in Other HAP than Mercury**  Even though emissions of air toxics from all sources in the U.S. declined by approximately 42 percent since 1990, the 2005 National
- Scale Air Toxics Assessment (NATA) predicts that most Americans are exposed to ambient concentrations of air toxics at levels that have the potential to cause adverse health effects (U.S. EPA, 2011d). 10 The levels of air toxics to which people are exposed vary depending on where peopl e live and work and the kinds of activities in which they engage. In order to identify and prioritize air toxics, emission source types and locations that are of greatest potential concern, U.S. EPA conducts the NATA. 11 The most recent NATA was conducted for calendar year 2005 and was released in March 2011. NATA includes four steps: 1) Compiling a national emissions inventory of air toxics emissions from outdoor sources 2) Estimating ambient and exposure concentrations of air toxics across the United State s 3) Estimating population exposures across the United States 4) Characterizing potential public health risk due to inhalation of air toxics including both cancer and noncancer effects Based on the 2005 NATA, EPA estimates that about 5 percent of census tracts nationwide have increased cancer risks greater than 100 in a million. The average national cancer risk is about 50 in a million. Nationwide, the key pollutants that contribute most to the overall cancer risks are formaldehyde and benzene. 12 , 13 Secondary formation (e.g., formaldehyde forming from other emitted pollutants) was the largest contributor to cancer risks, while stationary, mobile and background sources contribute almost equal portions of the remaining cancer risk.
10The 2005 N ATA is available on the Internet at http://www.epa.gov/ttn/atw/nata2005/. 11The NATA modeling framework has a number of limitations that prevent its use as the sole basis for setting regulatory standards. These limitations and uncertainties are discussed on the 2005 NATA website. Even so, this modeling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the decision making process. U.S. EPA. (2011) 2005 National
- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2005/ 12 Details on EPA’s approach to characterization of cancer risks and uncertainties associated with the 2005 NATA risk estimates can be found at http://www.epa.gov/ttn/atw/nata1999/riskbg.html#Z2
. 13 Detai ls about the overall confidence of certainty ranking of the individual pieces of NATA assessments including both quantitative (e.g., model
-to - monitor ratios) and qualitative (e.g., quality of data, review of emission inventories) judgments can be found at http://www.epa.gov/ttn/atw/nata/roy/page16.html.69

Noncancer health effects can result from chronic, 14 subchronic, 15 or acute 16 inhalation exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive systems. According to the 2005 NATA, about th ree -fourths of the U.S. population was exposed to an average chronic concentration of air toxics that has the potential for adverse noncancer respiratory health effects. Results from the 2005 NATA indicate that acrolein is the primary driver for noncancer respiratory risk. Figure 4 - 5 and Figure 4 6 depict the estimated census tract
- level carcinogenic risk and noncancer respiratory hazard from the assessment. It is important to note that large reductions in HAP emissions may not necessarily translate into sig nificant reductions in health risk because toxicity varies by pollutant, and exposures may or may not exceed levels of concern. For example, acetaldehyde mass emissions are more than double acrolein emissions on a national basis, according to EPA’s 2005 Na tional Emissions Inventory (NEI). However, the Integrated Risk Information System (IRIS) reference concentration (RfC) for acrolein is considerably lower than that for acetaldehyde, suggesting that acrolein could be potentially more toxic than acetaldehyde . 17 Thus, it is important to account for the toxicity and exposure, as well as the mass of the targeted emissions. Due to methodology and data limitations, we were unable to estimate the benefits associated with the hazardous air pollutants that would be reduced as a result of these rules.. In a few previous analyses of the benefits of reductions in HAPs, EPA has quantified the benefits of potential reductions in the incidences of cancer and non
- cancer risk (e.g., U.S. EPA, 1995 ). In t hose analyses, EPA relied on unit risk factors (URF) developed through risk assessment procedures. 18 These URFs are designed to be conservative, and as such, are more likely to represent the high end of the distribution of risk rather than a best or most li kely estimate of risk. As the purpose of a benefit analysis is to describe the benefits most likely to occur from a
14Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database (http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approxima tely 10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal species). 15 Defined in the IRIS database as r epeated exposure by the oral, dermal, or inhalation route for more than 30 days, up to approxima tely 10% of the life span in humans (more than 30 days up to approximately 90 days in typically used laboratory animal species) . 16 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 17 Details on the derivation of IRIS values and available supporting documentation for individual chemicals (as well as chemical values comparisons) can be found at http://cfpub.epa.gov/ncea/iris/compare.cfm
. 18The unit risk factor is a quantitative estimate o f the carcinogenic potency of a pollutant, often expressed as the probability of contracting cancer from a 70
-year lifetime continuous exposure to a concentration of one μg/m 3 of a pollutant.70

reduction in pollution, use of high
- end, conservative risk estimates would overestimate the benefits of the regulation. While we used high
- e nd risk estimates in past analyses, advice from the EPA’s Science Advisory Board (SAB) recommended that we avoid using high
- end estimates

**Figure 4 -**  **5 .**  **Estimated Chronic Census Tract Carcinogenic Risk from HAP Exposure f** **rom Outdoor Sources (2005 NATA)** 71

**Figure 4 -**  **6 .**  **Estimated Chronic Census Tract Noncancer (Respiratory) Risk from HAP Exposure f** **rom Outdoor Sources (2005 NATA)** in benefit analyses (U.S. EPA
- SAB, 2002 ). Since this time, EPA has continued to develop better methods for ana lyzing the benefits of reductions in HAPs. As part of the second prospective analysis of the benefits and costs of the Clean Air Act (U.S. EPA, 2011a), EPA conducted a case study analysis of the health effects associated with reducing exposure to benzene i n Houston from implementation of the Clean Air Act (IEc, 2009 ). While reviewing the draft report, EPA’s Advisory Council on Clean Air Compliance Analysis concluded that “the challenges for assessing progress in health improvement as a resul t of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure - response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the c hallenges of tracking health progress for diseases, such as cancer, that have long latency periods” (U.S. EPA
- SAB, 2008 ).72

In 2009, EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose
- response relationships (Gwinn et al., 2011 ). In summary, monetization of the benefits of reductions in cancer incidences requires several important inputs, including central estimates of cancer risks, estimates of exposure to carcinogenic HAPs, and estimates of the value of an avoid ed case of cancer (fatal and non
fatal). Due to methodology and data limitations, we did not attempt to monetize the health benefits of reductions in HAPs in this analysis. Instead, we provide a qualitative analysis of the health effects associated with th e HAPs anticipated to be reduced by these rules and we summarize the results of the residual risk assessment for the Risk and Technology Review (RTR). EPA remains committed to improving methods for estimating HAP benefits by continuing to explore additiona l concepts of benefits, including changes in the distribution of risk. Available emissions data show that several different HAPs are emitted from oil and natural gas operations, either from equipment leaks, processing, compressing, transmission and distri bution, or storage tanks. Emissions of eight HAPs make up a large percentage the total HAP emissions by mass from the oil and gas sector: toluene, hexane, benzene, xylenes (mixed), ethylene glycol, methanol, ethyl benzene, and 2,2,4
-trimethylpentane (U.S. EPA, 2011a). In the subsequent sections, we describe the health effects associated with the main HAPs of concern from the oil and natural gas sector: benzene, toluene, carbonyl sulfide, eth yl benzene, mixed xylenes, and n
- hexane. These rules combined are a nticipated to avoid or reduce 58,000 tons of HAPs per year. With the data available, it was not possible to estimate the tons of each individual HAP that would be reduced. EPA conducted a residual risk assessment for the NESHAP rule (U.S. EPA, 2011c). The results for oil and gas production indicate that maximum lifetime individual cancer risks could be 30 in
- a - million for existing sources before and after controls with a cancer incidence of 0.02 before and after controls. For existing natural gas transmiss ion and storage, the maximum individual cancer risk decreases from 90
- in - a - million before controls to 20
- in - a - million after controls with a cancer incidence that decreases from 0.001 before controls to 0.0002 after controls. Benzene is the primary cancer r isk driver. The results also indicate that significant noncancer impacts from existing sources are unlikely, especially after controls. EPA did not conduct a risk assessment for new sources affected by the NSPS. However, it is important to note that the ma gnitude of the HAP emissions avoided by new sources with the NSPS are more73

than an order of magnitude higher than the HAP emissions reduced from existing sources with the NESHAP. **4.9.1 Hazards**  Emissions data collected during development of this proposed ru le show that HCl emissions represent the predominant HAP emitted by industrial boilers. Coal
- and oil
-fired EGUs emit lesser amounts of HF, chlorine, metals (As, Cd, Cr, Hg, Mn, Ni, and Pb), and organic HAP emissions. Although numerous organic HAP may be e mitted from coal
- and oil
-fired EGUs, only a few account for essentially all the mass of organic HAP emissions. These organic HAP are formaldehyde, benzene, and acetaldehyde. Exposure to high levels of these HAP is associated with a variety of adverse heal th effects. These adverse health effects include chronic health disorders (e.g., irritation of the lung, skin, and mucus membranes, effects on the central nervous system, and damage to the kidneys), and acute health disorders (e.g., lung irritation and con gestion, alimentary effects such as nausea and vomiting, and effects on the kidney and central nervous system). We have classified three of the HAP as human carcinogens and five as probable human carcinogens. The following sections briefly discuss the main health effects information we have regarding the key HAPs emitted by EGUs. 4.9.1.1 Acetaldehyde Acetaldehyde is classified in EPA’ s IRIS database as a probable human carcinogen, based on nasal tumors in rats, and is considered toxic by the inhalation, ora l, and intravenous routes. 19 Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. Department of Health and Human Services (DHHS) in the 11 th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC. 20, 21 The primary

19 U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrat ed Risk Information System File of Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0290.htm. 20 U.S. Department of Health and Hum an Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 21 International Agency for Research on Cancer (IARC). 1999. Re
- evaluation of some organic chemicals, hydrazine, and hydrogen peroxide. IARC M onographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.74

noncancer effects of exposure to acetaldehyde vapors include irritation of the eyes, skin, and respiratory tract. 22 4.9.1.2 Arsenic Arsenic, a naturally occurring element, is found throughout the environment and is considered toxic thr ough the oral, inhalation and dermal routes. Acute (short
-term) high
- level inhalation exposure to As dust or fumes has resulted in gastrointestinal effects (nausea, diarrhea, abdominal pain, and gastrointestinal hemorrhage); central and peripheral nervous system disorders have occurred in workers acutely exposed to inorganic As . Chronic (long
-term) inhalation exposure to inorganic As in humans is associated with irritation of the skin and mucous membranes. Chronic inhalation can also lead to conjunctivitis, irritation of the throat and respiratory tract and perforation of the nasal septum. 23 Chronic oral exposure has resulted in gastrointestinal effects, anemia, peripheral neuropathy, skin lesions, hyperpigmentation, and liver or kidney damage in humans. Inorganic As exposure in humans, by the inhalation route, has been shown to be strongly associated with lung cancer, while ingestion of inorganic As in humans has been linked to a form of skin cancer and also to bladder, liver, and lung cancer. EPA has classified inorganic As as a Group A, human carcinogen . 24 4.9.1.3 Benzene The EPA’ s IRIS database lists benzene as a known human carcinogen (causing leukemia) by all routes of exposure, and concludes that exposure is associated with additional health effects, incl uding genetic changes in both humans and animals and increased proliferation of bone marrow cells in mice. 25 , 26, 27 EPA states in its IRIS database that data indicate a causal

22 U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrated Risk Information System File of Acetaldehyde. Research and Development, National Center for Env ironmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0290.htm. 23Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Arsenic. Atlanta, GA: U.S. Department o f Health and Human Services. Available on the Internet at <http://www.atsdr.cdc.gov/mhmi/mmg168.html#bookmark02> 24 U.S. Environmental Protection Agency (U.S. EPA). 1998. Integrated Risk Information System File for Arsenic. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at: http://www.epa.gov/iris/subst/0278.htm. 25 U.S. Environmental Protection Agency (U.S. EPA). 2000. Integrated Risk Information System File for Benzene. Researc h and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at: http://www.epa.gov/iris/subst/0276.htm. 26 International Agency for Research on Cancer, IARC monographs on the evaluation of carcin ogenic risk of chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs, International Agency for Research on Cancer, World Health Organization, Lyon, France, p. 345–389, 1982. 27 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. ( 1992) Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony
- stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691–3695.75

relationship between benzene exposure and acute lymphocytic leukemia and suggest a relationship between benzene exposure and chronic non
- lymphocytic leukemia and chronic lymphocytic leukemia. The IARC has determined that benzene is a human carcinogen and the DHHS has characterized benzene as a known human carcinogen. 28, 29 A number of advers e noncancer health effects including blood disorders, such as preleukemia and aplastic anemia, have also been associated with long
-term exposure to benzene. 30, 31 4.9.1.4 Cadmium Breathing air with lower levels of Cd over long periods of time (for years) res ults in a build
- up of Cd in the kidney, and if sufficiently high, may result in kidney disease. Lung cancer has been found in some studies of workers exposed to Cd in the air and studies of rats that inhaled Cd . The U.S. DHHS has determined that Cd and Cd compounds are known human carcinogens. The IARC has determined that Cd is carcinogenic to humans. EPA has determined that Cd is a probable human carcinogen. 32 4.9.1.5 Chlorine The acute (short term) toxic effects of Cl 2 are primarily due to its corrosive properties. Chlorine is a strong oxidant that upon contact with water moist tissue (e.g., eyes, skin, and upper respiratory tract) can produce major tissue damage. 33 Chronic inhalation exposure to low concentrations of Cl 2 (1 to 10 parts per million, ppm) may cause eye and nasal irritation, sore throat, and coughing. Chronic exposure to Cl 2, usually in the workplace, has been reported to cause corrosion of the teeth. Inhalation of higher c oncentrations of Cl 2 gas (greater than 15 ppm) can rapidly lead to respiratory distress with airway constriction and accumulation of fluid in the lungs (pulmonary edema). Exposed individuals may have immediate onset of rapid breathing, blue discoloration of the skin, wheezing, rales or hem optysis (coughing up blood or
28 International Agency for Research on Cancer (IARC). 198 7. Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Supplement 7, Some industrial chemicals and dyestuffs, World Health Organization, Lyon, France. 29 U.S. Department of Health and Human Services National Toxicology Progra m 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 30Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193 – 197. 31 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541–554. **32**Agency for Toxic Substances and Disease Registry (ATSDR). 2008. Public Health Statement for Cadmium . CAS# 1306 - 19 - 0. Atlanta , GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at < http://www.atsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15 >. 33Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Chlorine. Atlanta, GA: U.S. Department of Health and Human Services. http://www.a tsdr.cdc.gov/mmg/mmg.asp?id=198&tid=36.76

blood
- stain sputum). Intoxication with high concentrations of Cl 2 may induce lung collapse. Exposure to Cl 2 can lead to reactive airways dysfunction syndrome (RADS), a chemical irritant
induced type of asthma. Dermal exposure to Cl 2 may cause irritation, burns, inflammation and blisters. EPA has not classified Cl 2 with respect to carcinogenicity. 4.9.1.6 Chromium Chromium may be emitted in two forms, trivalent Cr ( Cr+3 ) or hexavalent Cr ( Cr+6 ). The respiratory tract is the major target organ for Cr+6 toxicity, for acute and chronic inhalation exposures. Shortness of breath, coughing, and wheezing have been reported from acute exposure to Cr+6, while perforations and ulcerations of the septum, bro nchitis, decreased pulmonary function, pneumonia, and other respiratory effects have been noted from chronic exposures. Limited human studies suggest that Cr+6 inhalation exposure may be associated with complications during pregnancy and childbirth, but th ere are no supporting data from animal studies reporting reproductive effects from inhalation exposure to Cr+6

. Human and animal studies have clearly established the carcinogenic potential of Cr+6 by the inhalation route, resulting in an increased risk of lung cancer. EPA has classified Cr+6 as a Group A, human carcinogen. Trivalent Cr is less toxic than Cr+6

. The respiratory tract is also the major target organ for Cr+ 3 toxicity, similar to Cr+6

. EPA has not classified Cr+ 3 with respect to carcinogenicity. 4.9.1.7 Formaldehyde Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. 34 EPA is currently reviewing recently published epidemiological data. After reviewing the currently available epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as “sufficient, ” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was characterized as “strong. ” 35 EPA is reviewing the recent work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as part of a reassessment of the human hazard and dose
- response associated with formaldehyde. For maldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial

34 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 35 International Agency for Research on Ca ncer (2006) Formaldehyde, 2
- Butoxyethanol and 1
-tert - Butoxypropan - 2 - ol. Monographs Volume 88. World Health Organization, Lyon, France.77

lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also cause airway inflammation — including eosinophil infiltration into the airways. There are several studies that suggest that formaldehyde may incr ease the risk of asthma — particularly in the young. 36, 37 4.9.1.8 Hydrogen Chloride Hydrogen chloride is a corrosive gas that can cause irritation of the mucous membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour . 38 The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most seriously exposed pe rsons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl can lead to RADS, a chemically
- or irritant- induced type of asthma. Children may be more vulnerable to corrosive agents than adults because of the relatively smaller diameter of their airways. Children may also be more vulnerable to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classifie d for carcinogenic effects . 39 4.9.1.9 Hydrogen Fluoride Acute (short
-term) inhalation exposure to gaseous HF can cause severe respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (long
-term) oral exposure to fluoride at low levels has a beneficial effect of dental cavity prevention and may also be useful for the treatment of osteoporosis. Exposure to higher levels of fluoride may

36Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Formaldehyde. Atlanta, GA: U.S . Department of Health and Human Services, Public Health Service. http://www.atsdr.cdc.gov/toxprofiles/tp111.html 37WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint sponsorship of the United Nations E nvironment Programme, the International Labour Organization, and the World Health Organization, and produced within the framework of the Inter
- Organization Programme for the Sound Management of Chemicals. Geneva. 38Agency for Toxic Substances and Disease Re gistry (ATSDR). Medical Management Guidelines for Hydrogen Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available online at http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02. 39 U.S. Environmental Protection Agency (U.S. EPA). 1995. Integrated Risk Information System File of Hydrogen Chloride. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at .http://www.epa.gov/iris/subst/0396.htm.78

cause dental fluorosis. One study reported menstrual irregularities in women occupationally expo sed to fluoride via inhalation. The EPA has not classified HF for carcinogenicity40
. 4.9.1.10 Lead The main target for Pb toxicity is the nervous system, both in adults and children. Longterm exposure of adults to Pb at work has resulted in decreased perfo rmance in some tests that measure functions of the nervous system. Lead exposure may also cause weakness in fingers, wrists, or ankles. Lead exposure also causes small increases in blood pressure, particularly in middle
- aged and older people. Lead exposure may also cause anemia. Children are more sensitive to the health effects of Pb than adults. No safe blood Pb level in children has been determined. At lower levels of exposure, Pb can affect a child ’ s mental and physical growth. Fetuses exposed to Pb in the womb may be born prematurely and have lower weights at birth. Exposure in the womb, in infancy, or in early childhood also may slow mental development and cause lower intelligence later in childhood. There is evidence that these effects may persist beyond childhood. 41 There are insufficient data from epidemiologic studies alone to conclude that Pb causes cancer (is carcinogenic) in humans. The DHHS has determined that Pb and Pb compounds are reasonably anticipated to be human carcinogens based on limi ted evidence from studies in humans and sufficient evidence from animal studies, and the EPA has determined that Pb is a probable human carcinogen. 4.9.1.11 Manganese Health effects in humans have been associated with both deficiencies and excess intakes of Mn . Chronic exposure to high levels of Mn by inhalation in humans results primarily in central nervous system effects. Visual reaction time, hand steadiness, and eye
- hand coordination were affected in chronically
- exposed workers. Manganism, characterize d by feelings of weakness and lethargy, tremors, a masklike face, and psychological disturbances, may result from chronic exposure to higher levels. Impotence and loss of libido have been

40 U.S. En vironmental Protection Agency. Health Issue Assessment: Summary Review of Health Effects Associated with Hydrogen Fluoride and Related Compounds. EPA/600/8
- 89/002F. Environmental Criteria and Assessment Office, Office of Health and Environmental Assessment, Office of Research and Development, Cincinnati, OH. 1989. 41Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Public Health Statement for Lead. CAS#: 7439
- 92 - 1. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service . Available on the Internet at < http://www.atsdr.cdc.gov/ToxProfiles/phs13.html>.79

noted in male workers afflicted with manganism attributed to inhalat ion exposures. The EPA has classified Mn in Group D, not classifiable as to carcinogenicity in humans . 42 4.9.1.12 Nickel Respiratory effects have been reported in humans from inhalation exposure to Ni . No information is available regarding the reproductive or developmental effects of Ni in humans, but animal studies have reported such effects. Human and animal studies have reported an increased risk of lung and nasal cancers from exposure to Ni refinery dusts and nickel subsulfide. The EPA has classified nic kel subsulfide as a human carcinogen and nickel carbonyl as a probable human carcinogen . 43 , 44 The IARC has classified Ni compounds as carcinogenic to humans . 45 4.9.1.13 Selenium Acute exposure to elemental Se , hydrogen selenide, and selenium dioxide (SeO 2 ) by inhalation results primarily in respiratory effects, such as irritation of the mucous membranes, pulmonary edema, severe bronchitis, and bronchial pneumonia. One Se compound, selenium sulfide, is carcinogenic in animals exposed orally. EPA has classifi ed elemental Se as a Group D, not classifiable as to human carcinogenicity, and selenium sulfide as a Group B2, probable human carcinogen. **4.10 References**  Adams, Evan M., and Frederick, Peter C. Effects of methylmercury and spatial complexity on foraging b ehavior and foraging efficiency in juvenile white ibises (Eudocimus albus). Environmental Toxicology and Chemistry. Vol 27, No. 8, 2008. Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological Profile for Mercury. U.S. Department of Health and Human Services, Public Health Service, Atlanta, GA. Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte. 2000. Cytogenetic damage related to low levels of methyl mercury contamination in the Braz ilian Amazon. An. Acad. Bras. Ciênc. 72(4): 497
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42 U.S. Environmental Protection Agency. Integrated Risk Information System (IRIS) on Manganese. National Center for Environmental Assessment, Office of Research and Developme nt, Washington, DC. 1999. 43 U.S. Environmental Protection Agency. Integrated Risk Information System (IRIS) on Nickel Subsulfide. National Center for Environmental Assessment, Office of Research and Development, Washington, DC. 1999. 44 U.S. Environmental Pro tection Agency. Integrated Risk Information System (IRIS) on Nickel Carbonyl. National Center for Environmental Assessment, Office of Research and Development, Washington, DC. 1999. 45 Nickel (IARC Summary & Evaluation , Volume 49, 1990), http://www.inchem.o rg/documents/iarc/vol49/nickel.html80

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- Induced Photochemical Production of Dissolved Gaseous Mercury in Freshwater. Environmental Science and Technology, 35: 928 - 935.4A- 1 **APPENDIX 4A ANALYSIS OF TRIP**  **TRAVEL DISTANCE FOR RECREATIONAL FRESHWA** **TER ANGLERS** As described in Section 3. 7 . 7 , the method used to estimate exposures to mercury in freshwater fish requires information about how far individuals typically travel for freshwater fishing. This appendix de scribes the data and methods used to analyze travel distance patterns by freshwater anglers, and it reports the results that were used to estimate exposures. **4A.1 Data**  To conduct an analysis of trip travel distance for freshwater anglers, we used data fro m the NSRE 1994. As described previously, this 16,000
- person survey elicited information on water- based recreation activities — specifically boating, fishing, swimming, and wildlife viewing — during the previous year. Respondents were asked about their most recent trip taken in each of the four categories. Of particular interest to this analysis is data concerning fishing trip characteristics for all respondents who fished in freshwater bodies during the previous year. Of the 3,220 respondents who had reporte d fishing, 2,482 visited either a lake, pond, river, or stream on their most recent trip. The fishing module elicited location information about most recent fishing trip taken during the preceding 12 months. This trip was recorded as either a single
- or m ultiday trip to a specific water body ( “site ” ) identified by the respondent. Subsequently, a series of questions were asked to gather location data on the specific site visited, including the site name, the state in which the site was located, and the name of the city or town nearest the site. To identify potential determinants of travel distance for a freshwater fishing trip, we analyzed the 2,384 available responses to the following survey question: “What was the one way travel distance, in miles from you r home, to your destination on *site*? ” Table C- 1 presents summary statistics for travel distance, which are reported separately for single
- day, multiday, and aggregated trips. As would be expected, median travel distance varied according to trip type, fro m 20 miles for a single
- day trip to almost 140 miles for a multiday trip. Across both trip types, the average travel distance was slightly less than 100 miles. **4A.2 Analysis of Travel Distance Data**  The influence of multiple demographic characteristics on travel distance was tested using multivariate regression analysis. Table C- 2 reports descriptive statistics for the anglers included in this analysis. As indicated by the table, over 90 percent of th e sample is white; males comprise a higher percentage of the sample (62 percent) than females. More than half4A- 2

**Table 4A** **- 1**  **. Reported Trip Travel Distance for Freshwater Anglers (Miles**  **) N**  **Mina P5**  **P25 P50**  **Mean P75**  **P95 Max**  All trip types 2 , 384 0 2 10 20 91.9 45 125 3 , 000 Single
- day trips only 1 , 791 0 2 10 20 41 45 125 1 , 100 Multiday trips only 586 3 18 70 138 248.2 300 850 3 , 000

a Seven respondents reported traveling 0 miles for their most recent trip; all were described as single
- day trips. Note: Ninety- eight respondents who visited freshwater bodies on their most recent fishing trip did not report the travel distance. **Table 4A** **- 2**  **. Demographic Characteristics of Freshwater Anglers**  **a**
**N Frequency**  Gender 2 , 267 62% Male Race 2 , 250 91% White 4% Black 2% Hispanic 2% Other Education 2 , 262 11% Less than high school degree 34% High school degree/equivalent 55% Some college or more Work status 2 , 263 75% Employed Geography 2 , 237 23% Urban 37% Suburban 41% Rural Region 2 , 205 13% Northeast 33% South 31% Midwest 23% West

a In total, 2,384 respondents reported information on trip travel distance to a freshwater destination. Note: Values may not add to 100 percent due to rounding.4A- 3

the sample had completed at least some college and three
-fourths of the sample reported being employed. The survey asked respondents to classify their place of residence as either rural, suburban, or urban. Approximately 40 percent described their area as rural, 37 percent as suburban, and 23 percent as urban. Respondents were assigned to a U.S. Census geographic region by matching their zip code to a corresponding state. The states were then aggregated to th e appropriate Census region (http://www.census.gov/geo/www/us_regdiv.pdf). The majority of respondents resided in the South and Midwest, followed by the West and Northeast. Table C- 3 presents additional characteristics on the demographic distribution of t he sample. The average age of respondents was 38 years, while household size averaged approximately three members, with less than one person under the age of six. Respondents ’ average weekly leisure time was 28 hours. However, this varied significantly across the sample, from zero to 168 hours. In the survey, family income is reported as a categorical variable, with respondents selecting the income range that reflected family income in the previous year. The midpoint of this range was taken to produce a con tinuous income variable. Subsequently, this value was converted to (2000$) using the consumer price index. Median (mean) income was estimated to be $57,325 ($66,496) annually. **Table 4A** **- 3**  **. Demographic Characteristics of Freshwat**  **er Anglers N**  **Mean SD**  **Min Max**  Age 2 , 245 38.4 14.5 16 92 Household size 2 , 255 3.1 1.5 1 10 Persons < 6 yrs 2 , 270 0.3 0.7 1 5 Persons > 16 yrs 2 , 254 2.2 0.9 0 7 Weekly leisure time (hrs) 2 , 025 27.7 23.9 0 168 Family income (2 , 000$) 1 , 851 66, 496 57 , 324 8, 938 208, 547 Multivariate regression analysis was used to identify determinants of travel distance to freshwater fishing sites. The dependent variable in this analysis was the miles traveled to the most recent freshwater fishing site. The explanatory variables included several demographic and geographic characteristics of the respondents. Separate regressions were conducted for the full sample (1), single
- day trips only (2), and multiday trips only (3). The results are reported in Table C-4 . Family income was estimated4A-4

**Table 4A** **- 4**  **. OLS Regression Results for Determinants of Reported Trip Travel Distance (Miles**  **) (1)**  **Full Sample (both single**  **** **and multiday trips) (2)**  **Single -**  **Day Trips Only**  **(3) Multiday**  **Trips Only Variable Description**  **Coefficient t** **- stat**  **Coefficient t** **- stat**  **Coefficient t** **- stat**  CONSTANT 0.6966 1.54 1.7954 3.89** 2.2493 3.26** AGE 0.0044 1.83* 0.0011 0.44 0.001 0.28 GENDER 0.0572 0.83 0.0173 0.25 0.1446 1.39 EDUC 0.1729 2.48** 0.1552 2.21** 0.128 1.22 MINORITY − 0.0437 − 0.36 0.0228 0.19 − 0.1391 − 0.76 FAMILY INCOME (log) 0.187 4.41** 0.0827 1.92* 0.1759 2.78** URBAN 0.3491 3.95** 0.2799 3.12** 0.2121 1.62* SUBURBAN 0.3422 4.48** 0.193 2.50** 0.4298 3.67** NEAST − 0.0387 − 0.36 − 0.2549 − 2.42** 0.1525 0.89 MIDWEST 0.3856 4.65** 0.1 1.21 0.4923 3.63** WEST 0.6103 6.73** 0.3374 3.59** 0.3239 2.32** R 2 = 0.077 R 2 = 0.041 R 2 = 0.112 N = 1,798 N = 1,360 N = 434 ** = significant at 5 percent level. * = significant at 10 percent level. to have a positive and highly significant effect in all three models. Dummy variables for urban and suburban location were also found to have positive and highly significant effects in al l models. These results suggest that wealthier anglers and those living in or near metropolitan areas tend to travel further to fishing sites, relative to less
- wealthy anglers and those living in rural areas. In models (1) and (2) dummy variables for the M idwest and West regions also had positive and highly significant effects on trip travel distance, relative to the South region. The Northeast region did not have a statistically significant effect on distance traveled. Education was estimated to be positiv ely and significantly related to distance traveled in the first and second models. (Note that the respondent’ s level of education, recorded in the survey as a categorical variable, was recoded as a continuous variable for the regression analysis.) Neither age, race, nor gender had significant effects (at a 5 percent level) on travel distance in any of the models.4A- 5

**4A.3 Summary Results Applied in t**  **he Population Centroid Approach** Given the high significance of geographic area and family income across the regre ssions, nonparametric results (frequency distributions) were generated for four mutually exclusive subgroups of respondents and five travel distance categories. The result s are reported in Table C- 5 . Respondents were categorized into the four following gro ups:

 G1: family income > $50,000 (in 2000 dollars) and urban or suburban resident

– (N = 452 for single
- day trips)

– (N = 649 for single
- and multiday trips)
 G2: family income < $50,000 and urban or suburban resident

– (N = 329 for single
- day trips)

– (N = 417 for single
- and multiday trips)
 G3: family income > $50,000 and rural resident

– (N = 295 for single
- day trips

– (N = 376 for single
- and multiday trips)
 G4: family income < $50,000 and rural resident

– (N = 309 for single
- day trips)

– (N = 386 for single
- and multiday trips) These categories were selected because they match categories that can be easily identified in Census data and because they split the sample into roughly similar group sizes. Travel distance was categorized into ranges reported in the first column of Table C- 5 . The results are consistent with those generated from the regression analysis. Among respondents on single
- day trips, the number that traveled longer distances (greater than 100 miles) increased from the low
- income rural cohort (5 percent) to th e higher
- income urban/suburban cohort (11 percent). The same pattern holds for those taking either a single
- or multiday trip. The number traveling longer distances more than doubled, from 11 percent among low
- income rural respondents to 27 percent among h igh
- income urban/suburban respondents. These results indicate higher
- income urban/suburban anglers travel greater distances to freshwater destinations than lower
- income urban/suburban anglers and rural anglers. As described in Section 3. 7 , the trip frequen cy estimates reported in Table C- 5 for the full sample were used in the population centroid approach to weight exposures to mercury in4A- 6

fish according to distance from the Census tract centroid, income levels in the tract, and whether the tract is predomina ntly rural or urban/suburban. **Table 4A** **- 5**  **. Travel Distance Frequencies by Demographic Group (Percentage in**  **Each Distance**

**Category)**

**Travel Distance (mi) (G1)**  **High -**  **Income and Urban/Suburban**  **Resident (G2)** 

**Low -**  **Income and Urban/Suburban**  **Resident (G3)** 

**High -**  **Income and**  **Rural Resident (G4)** 

**Low -**  **Income and Rural Resident**  Single
- day trips only (N = 1,385) N (N = 452) (N = 329) (N = 295) (N = 309) Distance < 10 mi 23% 32% 31% 34% >10 mi to 20 mi 18% 23% 22% 24% >20 mi to 50 mi 31% 20% 28% 26% >50 mi to 100 mi 17% 19% 14% 11% Distance >100 mi 11% 6% 5% 5% Full sample (both single
- and multiday trips) (N = 1,828) N (N = 649) (N = 417) (N = 376) (N = 386) Distance <10 mi 16% 26% 24% 29% >10 mi to 20 mi 13% 18% 18% 21% >20 mi to 50 mi 24% 18% 25% 25% >50 mi to 100 mi 19% 19% 16% 14% Distance >100 mi 27% 18% 17% 11%