4-1

CHAPTER 4

MERCURY AND OTHER HAP BENEFITS 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 tools 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 mercury 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 mercury 

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 national 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 commercially 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 IQ 
4-2

points. There are other neurological effects associated with exposures to methylmercury, 

including impacts on motor skills 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 provides 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 in 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 reductions 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 

quantification 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 availability 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 potential 
4-3

disproportionate impacts on demographic groups with traditionally subsistence or nearsubsistence 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.1In 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.2The implementation of the Toxics Rule would reduce the 

expected IQ loss for this child by an estimated 0.176 IQ points.

4.2 Impact of Mercury on 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 (Hg0), oxidized Hg compounds (Hg+2), and particle-bound 

Hg (HgP). Elemental Hg does not quickly deposit or chemically react in the atmosphere, 

resulting in residence times that are long enough to contribute to global scale deposition.

Oxidized Hg and HgP deposit quickly 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. Once 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 which they live.

 

1Monetized 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. The 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 result 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 appropriate 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 effects associated with 

MeHg exposures, as EPA finds there is no consensus among scientists on the dose-response 
4-5

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 Mercury 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 

earlier 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 concentration 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 and 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. Bioaccumulation 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 toxicity 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 activity (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 significant 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 and 
4-7

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 Fish

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 changes 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 relationship 

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 common 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 have 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 Evers 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 viability 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 these 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 and 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 include 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 the 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. 

Insectivorous 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. Exposed 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 contamination 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 mortality. Concentrations of Hg in 

liver for mink in Massachusetts/ Connecticut and the levels in fur from mink in Maine exceed 
4-9

concentrations associated with acute mortality (Yates, 2005). Adverse sub-lethal effects may be 

associated with lower Hg concentrations and consequently be more widespread than potential 

acute effects. These effects may include increased activity, poorer maze performance, 

abnormal startle reflex, and impaired escape and avoidance behavior (Scheuhammer 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 people) 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 and Exposure Analyses—Data Inputs and 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 levels, 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 the corresponding population category 

(Woods and Poole, 2008) to the 2000 tract-level data. To specifically estimate the portion of 
4-10

these populations that are pregnant in any given year, we applied state-level 2006 fertility rate 

(live births per 1,000 women 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 

activities 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 conducted 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 national 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 the 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%).

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

State

Freshwater Anglers as

Percentage of State Populationa

Percentage of Freshwater Fishing Tripsb

Lakes Rivers

 

3

Although 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

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)

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

State

Freshwater Anglers as

Percentage of State Populationa

Percentage of Freshwater Fishing Tripsb

Lakes Rivers
4-12

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 the 

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 distance traveled to reach these sites. Although more recent 

NSRE surveys have been conducted in 2000 and 2009, data from 1994 survey (NSRE, 1994) is 
4-13

used for this analysis because it contains the most detailed information regarding fishing trip 

destinations.

The 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, respondents 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 to the income level and urban 

versus nonurban location of the respondents.

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 main sources, which are 

described below. 

National Listing of Fish Advisory (NLFA) database. The NLFA, managed by EPA 

(http://water.epa.gov/scitech/swguidance/fishshellfish/fishadvisories/), collects and compiles 

fish tissue sample data from all 50 states and from tribes across the United States. In particular, 

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 Program 

(NPMP), the National Contaminant Biomonitoring Program (NCBP), the Biomonitoring of 

Environmental Status and Trends (BEST) datasets of the USFWS and USGS 

(http://www.cerc.cr.usgs.gov/data/data.htm), and the Environmental Monitoring and Analysis 

Program (EMAP) (http://www.epa.gov/emap/). 
4-14

EPA’s National River and Stream Assessment (NRSA) study data. These data include 

nearly 600 fish tissue mercury samples collected at randomly selected freshwater 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. Unfortunately, 

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 coordinates 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’ latitudelongitude coordinates to the nearest waterbody and its category.

 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 
4-15

the HUC-12 sub-watersheds in which they are located. A total of 3,884 HUC-12s in 

the continental United States (4.6%) contain at least one river or lake mercury 

sample.4Second, 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 sitespecific average lake concentration 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 concentration 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 

containing at least one river or lake mercury fish tissue sample.

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

This number excludes 15 HUC-12s containing mercury samples. These HUC-12s were excluded from the analysis 

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

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 

concentrations 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 the 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 organic form of Hg 

(MeHg). MeHg is the only form of Hg that biomagnifies in organisms (Bloom, 1992). Ecosystemspecific factors that affect both the bioavailability of inorganic Hg to methylating microbes (e.g., 

sulfide, dissolved organic carbon) and the activity 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 levels in the food web (e.g., piscivorous fish populations) 

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 across 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 
4-17

concentrations are expected to decline proportionally to declines in atmospheric 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 

significant 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 total 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, reducing mercury air deposition from 100 to 50 would 

only reduce the total loading by 25%, to 150, which, based on the MMaps 

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 deposition can range from years to decades depending on the attributes 

of the watershed and waterbody involved5Research has suggested that fish tissue 

 

5

As noted in footnote 1 of this chapter,monetized benefits estimates are for an 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. 
4-18

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 aerial 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 applies 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 fish 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 western 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, EPA 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 

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 tissue 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 sufficiently 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 mercury.
4-19

 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 

bioaccumulation, 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 during 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 predictions 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 changes 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 waterbodies. MMaps will be biased 

when waterbody characteristics change between when fish were initially sampled, and the new 

conditions of the waterbody.

As stated above, the relationship between the change in mercury deposition from air to 

the change in fish 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 

active 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 commodities 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 25th 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 
4-20

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 mercury 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 water 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 

assumptions 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 the 

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 tissue 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 state 

assumption in 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 transport. 

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 and 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 requirement, 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.
4-21

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 scientific 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 environment as several different chemical species. The 

majority of mercury in the atmosphere (95-97%) is present in a neutral, elemental state (Hg0) 

(Lin and Pehkonen, 1999), while in water, sediments and soils the majority of mercury is found 

in the oxidized, divalent 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 in aquatic food 

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 Hg0(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) (USEPA, 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).
4-22

4.5.3.2 Mercury Processes in the Atmosphere

The relative 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 atmosphere 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 Hg0, differences in the species 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 Hg0, 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 Hg0by 

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 

wetlands 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 conditions 

(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 account for a significant fraction of the external MeHg 

inputs to surface waters that have watersheds with a large portion of wetland coverage (e.g., 

St. Louis et al., 2001). Accordingly, there is a positive relationship between MeHg yield and 

percent wetland 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 
4-23

the transport of inorganic mercury deposited in a given watershed to surface waters (Babiarz 

et al., 2001).

4.5.3.4 Mercury Processes in Water

In a water body, deposited Hg(II) is reduced to Hg0by ultraviolet and visible wavelengths 

of sunlight as well as microbially mediated reduction pathways (Amyot et al., 2000; Mason 

et al., 1995). In turn, Hg0is 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 Hg0in 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 complexes strongly with dissolved and particulate organic material. 

The abundance of various inorganic ligands (e.g., OH-, Cl-, S2-, 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 Hg0and 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 a 

significant removal mechanism for contaminants in surface sediments (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 because 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; Sunderland 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., 2001).
4-24

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 

degraded 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 of 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., 

1998; 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 production 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 carbon.

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 Bartlett, 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; Benoit et al., 1999b). Thus, the formation of 

neutral, dissolved mercury species such as HgCl2, Hg(OH)2, HgClOH, and HgS0(aq.), which 

depend on the availability of constituent ligands in the surface and interstitial waters, may 

strongly influence the availability 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 changes and anthropogenic “stresses” that do not result in a direct increase in 
4-25

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 

increase 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 considered 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 (Hintelmann 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 deposition 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 lakes) 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 aquatic environments. Specifically, there appears to be a 

relationship between SO4

2-

deposition and mercury methylation; however, the rate of mercury 

methylation varies according to several spatial and biogeochemical factors whose influence has 

not been fully quantified (see Figure 4-1). Therefore, the correlation between SO4

2-

deposition 
4-26

Figure 4-1. Spatial and Biogeochemical Factors Influencing MeHg Production

and MeHg could not be quantified for the purpose of interpolating the association across 

waterbodies 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 

concentrations 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 score referenced in Figure 

4-3 is based on SO4

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 of Mercury Methylation–Sensitive Watersheds Derived 

from More Than 55,000 Water Quality Sites aqnd 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 allow 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 SO4

2-

deposition have already shown promising reductions in MeHg. 

Observed decreases in MeHg fish tissue concentrations have been linked to decreased 

acidification and declining SO4

2-

and mercury deposition in Little Rock Lake, WI (Hrabik and 

Watras, 2002), and to decreased SO4

2-

deposition in Isle Royale in Lake Superior, MI (Drevnick 

et al., 2007). Although the possibility exists that reductions in SO4

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 SO4

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 sulfur and mercury emissions may be most efficient because of 

the variability in ecosystem responses. Reducing SOX 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

Dissolved 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 is found 

almost exclusively as MeHg (Bloom, 1992; Watras et al., 1998). Thus, trophic position and foodchain complexity plays an important role in MeHg bioaccumulation (Kidd et al., 1995). The 

chemical and physical characteristics of different ecosystems affect 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 Mason, 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-lived 
4-29

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 form 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 Maps (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 deposition 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 time, 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 deposition). 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 constant, 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 Body 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,” published 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 health 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 methods 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 estimation 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 Faroe 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 correlated 

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 Panel 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. 

Epidemiological 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 examined 
4-31

neurodevelopmental 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, motor 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 exposures” (NRC, 2000).

As part of previous analyses, EPA attempted to identify the appropriate dose-response 

coefficients from the Faroe Islands, New Zealand, and Seychelles Islands studies, and devised a 

statistical approach for combining those coefficients to provide an integrated estimate of the IQ 

dose-response coefficient.

For 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 

Axelrad 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 populationlevel 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 studies 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 during 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 risk; 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 estimate 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 benefits 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. (1989) 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 mercury 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 hair 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 select 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 and 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 conducted at age 7 years (Grandjean 

et al., 1997).

In assembling the Seychelles Child Development Study sample, investigators obtained 

hair 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 children 

were 6.5, 19, 29, and 66 months, and at 9 years. The mean maternal hair mercury level for the 
4-33

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 relationship, which is used in this RIA. Additional details of the analysis are 

reported in the Axelrad study and in its Supplemental Material (available at 

http://www.ehponline.org/docs/2007/9303/suppl.pdf). The information 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 all 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 within-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, 2000). 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, however, 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 additional 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 and 
4-34

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 the 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 (BudtzJorgensen 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-tostudy as well as endpoint–to–endpoint variability. 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 Axelrad 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 judged 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 estimates of 

a health outcome that can be readily monetized for use in benefit-cost analysis.6

 

6

There 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 poor 
4-35

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 fullscale 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 monetization 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 associations 

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 the 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 other 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 neurobehavioral 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 relied 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 skills 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 Seychelles 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 mercury, which 

is the 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 conversion 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 the 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 doseresponse 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 

included results in an integrated dose-response coefficient that is reduced in magnitude by 25 

percent (−0.125 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 the 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 

deviation units), and no results are available for the full cohort. However, the authors report 
4-37

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, due 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 exposure 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 et 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; Strain 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 nutritional 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) assessed 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 prenatal 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 at 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 little 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 reducing 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 estimate from the Axelrad study, noting however that Axelrad et al. 

(2007) did not explicitly 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 Seychelles Islands studies. Rice et al. (2010) therefore multiplied the central estimate from 

Axelrad et al. (2010) by an 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 adequately 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 interactive 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 already described in previous sections. 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 abnormalities 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 

pregnant women in freshwater recreational angler households. 

Generally speaking, estimating mercury exposures for this exposure pathway and 

population of interest requires three main components: 

Ni = size of the exposed population of interest i (annual number of pregnant 

women in freshwater angler households during the year),

CHgi = average concentration (ppm) of methyl mercury in noncommercial freshwater 

fish filets consumed by population i, and

Ci = average daily consumption rate (gm/day) of noncommercial freshwater 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 CHgi. It shows the spatial scale of the data used to 

estimate these components and describes how these components are interrelated. For the 

third component—Ci—recommendations from EPA’s Environmental Exposure Factors 

Handbook (EPA, 1997) were used to estimate an average consumption rate estimate for 

recreationally caught freshwater fish. 
4

0

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 pregnant 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) (2006 Vital Statistics).

2. Estimating the annual number of prenatally exposed children in angler households 

(NPA) as

NPA = NP*(NAs/Ns), (4.2)

where

NAs = state-level number of angler residents (FHWAR) and

Ns = 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 than 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 estimate NPA for years after 2000, it was assumed that state-level fertility rates (fs) 

and angler participation rates (NAs/Ns) would remain constant; however, the number of women 

of childbearing age in each block (NF) was increased based on county-level population 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 corresponding counties.
4

2

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 (pi 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 with 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:

NPAijk = NPA * pi * eij * ck (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 pi, eij, and ck).

Using this approach, we were able to separately match each subpopulation NPAijk with 

the census tract’s average mercury concentration for the corresponding distance and 

waterbody category (CHgjk). 

 

7

An 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 (cl or cr), 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 fishing 

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 nearly 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 travel 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 close 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 eij. The estimated values for eij are reported in Appendix B.

To estimate average daily mercury ingestion rates for each exposed subpopulation n=ijk, 

we applied the following equation:

HgIn = CHgFCn* Cn= (CHgn* CCF) * Cn (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 

consumption. Based on this review, it was decided that the ingestion rates for recreational 
45

freshwater fishers, specified as “recommended” in EPA’s Environmental Exposure Factors 

Handbook (EPA, 1997) (mean of 8 gm/day and 95th percentile of 25 gm/day), represented 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 95th percent values ranged from 13 to 39 gm/day (Note: the 

39 gm/day value actually represents a 96th percent value). The EPA “recommended values”

were developed by considering the range and spread of means and 95th percentile values 

presented in the four studies. EPA recognizes that using mean and 95th percentile 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 

used 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 for 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 concentrations 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: 

CHgHn = (0.08)-1* (HgIn/W), (4.5)

where

CHgH = average mercury concentration in maternal hair (ppm) and
46

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 modeling dose-response 

(EPA, 2002). The average body weight estimate (W) was based on EPA’s Exposure Factor 

Handbook (EPA, 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 * CHgHn, (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 approach used to assess monetary losses due to IQ decrements is based 

on an approach applied in previous 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:

Vn = 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 net 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 assuming (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 hourly 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 of 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, the resulting value estimates for the average net loss per IQ point 
48

decrement (VIQ) are expressed as a range. Assuming a 3% discount rate, VIQ ranges from 

$8,013 (using the Schwartz estimate for z and s) to $11,859 (using the Salkever estimates). With 

a 7% discount rate assumption, the VIQ estimates range from $893 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 within 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 within 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 conditions 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 quality 

modeling runs were used to estimate average mercury deposition levels by HUC-12 subwatershed 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 change (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 concentration estimates, we then estimated the size of 

the exposed populations (NPA) in 2005 and 2016. These estimates are reported in Table 4-5. As 
49

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 populations 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, there were 

estimated to be 239,174 prenatally exposed children, and for 2016 the estimate is 244,286 

prenatally exposed children.
5

0

Table 4-4. Summary of Baseline Mercury 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 

State Losses

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)
5

2

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 

State Losses

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)
5

3

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 

State Losses

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 34.3
54

For each exposed population, we then estimated their average mercury ingestion rate 

(HgI) using Equation (4.4) and the IQ loss 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 child 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 selfcaught 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 average 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-Out. 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 (in 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 Exposures 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 present 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: $23 

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 reduction 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

 

8Monetized 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 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.
56

Table 4-6. Summary 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 2016 

(millions of 2007$)

3% Discount Rate

7% Discount 

Rate

2005 base case 0.1068 25,545 $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 23,909 $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 1,126 $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 511 $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 2016 zero-out scenario (relative to the 
57

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 changes from the 2005 base case to the 2016 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 combined. 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 associated 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 then concludes by discussing 

the estimation of mercury ingestion through fish consumption.

4.8.5.1 Mercury Concentration Estimates

As described in Section 4.2.2, the mercury concentration estimates for the analysis 

come from several different sources, including fish tissue sample data from the National Listing 

of Fish Advisories (NLFA) and several other state- and national-level sources. These estimates 
58

were then used to approximate mercury concentrations across the study area. Some of the key 

assumptions, limitations, 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 mercury 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 voluntarily 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 uncertainty.

 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 areas 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 exposures. 

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 1995 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 Exposed 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 estimate state-level freshwater 

angler activity levels, including freshwater fishing participation rates and lake-toriver 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 importantly the statelevel 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 households (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 freshwater 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 benefits 

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 interest 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 high 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 in 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 that the expected proportion of the 

subgroup’s population in each 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 (fishing 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 Matching 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 

distance 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 approach 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 either 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 populations 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 those 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 selfcaught freshwater 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 consumption 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 individual-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 location (Columbia, SC). The 
62

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 reflect actual 

rates of consumption in the subpopulations of interest. 

Another related and potentially 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 step 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 following assumptions, limiations, and 

uncertainties:.

 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 factor 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 conversion 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 will 

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, there 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, blood-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 deriving 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 Zealand, 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 primary 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 bias 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 BudtzJorgensen (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 mercuryattributable IQ loss). This study offers further qualitative support for a higherend 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 coefficient 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 subset 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

reductions 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 to 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 uncertainty. 

– 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 

uncertainties, 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 source 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 variability 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), including 

these factors 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 uncertainties in the final estimate of benefits.

Table 4-20 displays the health and ecosystem effects associated with MeHg exposure 

that are discussed in Section 4.2.2 for which 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 Exposure 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 EffectsaAssociations with genetic, autoimmune and cardiovascular effects

Ecological EffectsaSurvival, 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 modeled 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 between 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 highconsumption 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 fish 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 benefits 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 quantify 
66

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 concentrations (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 

deposition). 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 aquaculture 

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 anglers), 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 estuarine 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 with 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 $51 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 Freshwater 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.

 The 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 rate.

 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 scenario.

 The present economic value of avoided IQ loss from reduced mercury air emissions 

due to implementation of the Toxics Rule in 2016 is estimated at a range of $4.2 

million to $6.2 million, assuming a 3% discount rate, and $0.47 million to $1 million, 

assuming 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 people 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 

States

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 NATA 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/

12Details 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.

13Details 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 acute16 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 three-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 4-5and Figure 46 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 significant 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 National 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 

those 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 likely 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 

approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used 

laboratory animal species).

15Defined in the IRIS database as repeated exposure by the oral, dermal, or inhalation route for more than 30 days, 

up to approximately 10% of the life span in humans (more than 30 days up to approximately 90 days in typically 

used laboratory animal species).

16Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.

17Details 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 of 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/m3

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-end 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 from 

Outdoor Sources (2005 NATA)
71

Figure 4-6. Estimated Chronic Census Tract Noncancer (Respiratory) Risk from HAP 

Exposure from Outdoor Sources (2005 NATA)

in benefit analyses (U.S. EPA-SAB, 2002). Since this time, EPA has continued to develop better 

methods for analyzing 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 in 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 result 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 challenges 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 avoided case of cancer (fatal and nonfatal). 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 the 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 additional 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 

distribution, 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, ethyl benzene, mixed 

xylenes, and n-hexane. These rules combined are anticipated 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 transmission 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 risk 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 magnitude of the HAP emissions avoided by new sources with the NSPS are more 
73

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 rule 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 emitted 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 health 

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 congestion, 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, oral, 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 11th Report on Carcinogens and is 

classified as possibly carcinogenic to humans (Group 2B) by the IARC.20,21 The primary 

 

19U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrated 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.

20U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

available at: http://ntp.niehs.nih.gov/go/16183.

21International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine, 

and hydrogen peroxide. IARC Monographs 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 through 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, including 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 

 

22U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrated 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.

23Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Arsenic. Atlanta, 

GA: U.S. Department of Health and Human Services. Available on the Internet at 

<http://www.atsdr.cdc.gov/mhmi/mmg168.html#bookmark02>

24U.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.

25U.S. Environmental Protection Agency (U.S. EPA). 2000. Integrated Risk Information System File for Benzene. 

Research and Development, National Center for Environmental Assessment, Washington, DC. This material is 

available electronically at: http://www.epa.gov/iris/subst/0276.htm.

26International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic 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. 

27Irons, 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 adverse 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) results 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 Cl2 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 Cl2 (1 to 10 parts per million, ppm) may cause eye and nasal irritation, sore 

throat, and coughing. Chronic exposure to Cl2, usually in the workplace, has been reported to 

cause corrosion of the teeth. Inhalation of higher concentrations of Cl2 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 hemoptysis (coughing up blood or 

 

28International Agency for Research on Cancer (IARC). 1987. 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.

29U.S. Department of Health and Human Services National Toxicology Program 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.

31Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541–554.

32Agency 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.atsdr.cdc.gov/mmg/mmg.asp?id=198&tid=36.
76

blood-stain sputum). Intoxication with high concentrations of Cl2 may induce lung collapse.

Exposure to Cl2 can lead to reactive airways dysfunction syndrome (RADS), a chemical irritantinduced type of asthma. Dermal exposure to Cl2 may cause irritation, burns, inflammation and 

blisters. EPA has not classified Cl2 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, bronchitis, 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 there 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+3toxicity, 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.

Formaldehyde 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 

 

34U.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.

35International Agency for Research on Cancer (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 increase 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 persons 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 

classified 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 Environment 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 Registry (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.

39U.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 

exposed 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 performance 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 limited 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, characterized 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 

 

40U.S. Environmental 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 inhalation 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 nickel 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 (SeO2) 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 classified 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 behavior 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 Brazilian Amazon. An. Acad. Bras. Ciênc. 72(4): 497-507.

 

42U.S. Environmental Protection Agency. Integrated Risk Information System (IRIS) on Manganese. National Center 

for Environmental Assessment, Office of Research and Development, Washington, DC. 1999.

43U.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.

44U.S. Environmental Protection Agency. Integrated Risk Information System (IRIS) on Nickel Carbonyl. National 

Center for Environmental Assessment, Office of Research and Development, Washington, DC. 1999.

45Nickel (IARC Summary & Evaluation , Volume 49, 1990), 

http://www.inchem.org/documents/iarc/vol49/nickel.html
80

Amyot, M., Lean, D.R.S., Poissant, L. and Doyon, M.-R., 2000. Distribution and transformation of 

elemental mercury in the St. Lawrence River and Lake Ontario. Canadian Journal of 

Fisheries and Aquatic Sciences, 57 (Suppl. 1): 155-163.

ATSDR (1999). Toxicological Profile for Mercury. Agency for Toxic Substances and Disease 

Registry.

Axelrad, D. A.; Bellinger, D. C.; Ryan, L. M.; Woodruff, T. J. (2007). Dose-response relationship of 

prenatal mercury exposure and IQ: an integrative analysis of epidemiologic data. 

Environmental Health Perspectives. 2007, 115, 609–615.

Babiarz, C.L. et al., 2001. Partitioning of total mercury and methylmercury to the colloidal phase 

in freshwaters. Environmental Science and Technology, 35(24): 4773-4782.

Barkay, T., Miller, S.M. and Summers, A.O., 2003. Bacterial mercury resistance from atoms to 

ecosystems. FEMS Microbiology Reviews, 27: 355-384.

Benoit, J.M., Gilmour, C.C. and Mason, R.P., 2001. The influence of sulfide on solid-phase 

mercury bioavailability for methylation by pure cultures of Desulfobulbus propionicus 

(1pr3). Environmental Science and Technology, 35(1): 127-132.

Benoit, J.M., Gilmour, C.C., Heyes, A., Mason, R.P. and Miller, C., 2003. Geochemical and 

Biological Controls over Methylmercury Production and Degradation in Aquatic Systems, 

Biogeochemistry of Environmentally Important Trace Metals. ACS Symposium Series 

835.

Benoit, J.M., Gilmour, C.C., Mason, R.P. and Heyes, A., 1999a. Sulfide controls on mercury 

speciation and bioavailability to methylating bacteria in sediment pore waters. 

Environmental Science and Technology, 33(6): 951-957.

Benoit, J.M., Gilmour, C.C., Mason, R.P., Riedel, G.S. and Reidel, G.F., 1998. Behavior of mercury 

in the Patuxent River estuary. Biogeochemistry, 40: 249-265.

Benoit, J.M., Mason, R.P. and Gilmour, C.C., 1999b. Estimation of mercury-sulfide speciation in 

sediment pore waters using octanol-water partitioning and implications for availability 

to methylating bacteria. Environmental Toxicology and Chemistry, 18(10): 2138-2141.

Bergeron, CM., Bodinof, CM., Unrine, JM., Hopkins, WA. (2010a) Mercury accumulation along a 

contamination gradient and nondestructive indices of bioaccumulation in amphibians. 

Environmental Toxicology and Chemistry 29(4), 980-988.

Bergeron, CM., Bodinof, CM., Unrine, JM., Hopkins, WA. (2010b) Bioaccumulation and maternal 

transfer of mercury and selenium in amphibians. Environmental Toxicology and 

Chemistry 29(4), 989-997.
81

Beyers, D.W., Rice, J.A. and Clements, W.H., 1999. Evaluating biological significance of chemical 

exposure to fish using a bioenergetics-based stressor-response model. Canadian Journal 

of Fisheries and Aquatic Sciences, 56: 823-829.

Bigham, G.N. and Vandal, G.M., 1994. A drainage basin perspective of mercury transport and 

bioaccumulation: Onondaga Lake, New York, Twelfth International Neurotoxicology 

Conference, Hot Springs, Arkansas USA.

Bloom, N.S., 1992. On the chemical form of mercury in edible fish and marine invertebrate 

tissue. Canadian Journal of Fisheries and Aquatic Sciences, 49: 1010-1017.

Branfireun, B. and Roulet, N., 2002. Controls on the fate and transport of methylmercury in a 

boreal headwater catchment, northwestern Ontario, Canada. Hydrology and Earth 

System Sciences, 6(4): 785-794.

Brasso, Rebecka L., and Cristol, Daniel A. Effects of mercury exposure in the reproductive 

success of tree swallows (Tachycineta bicolor). Ecotoxicology. 17:133-141, 2008.

Budtz-Jorgensen E, Grandjean P, Jorgensen P, Weihe P, Keiding N (2004a). Association between 

mercury concentrations in blood and hair in methylmercury-exposed subjects at 

different ages. Environmental Research, 95(3):385-93.

Budtz-Jorgensen E, Keiding N, Grandjean P (2004b). Effects of exposure imprecision on 

estimation of the benchmark dose. Risk Analysis, 24(6):1689-96.

Budtz-Jorgensen, E.; Grandjean, P.; Weihe, P. (2007). Separation of risks and benefits of 16 

seafood intake. Environmental Health Perspectives. Vol. 115, 323-327.

Burger, J. (2002). Daily consumption of wild fish and game: Exposures of high end 

recreationalists, International Journal of Environmental Health Research, 12:4, p. 343-

354.

Burgess, Neil M., and Meyer, Michael W. Methylmercury exposure associated with reduced 

productivity in common loons. Ecotoxicology. 17:83-91, 2008.

Carpi, A. and Lindberg, S.E., 1997. Sunlight-mediated emission of elemental mercury from soil 

amended with municipal sewage sludge. Environmental Science and Technology, 31(7): 

2085-2091.

Compeau, G.C. and Bartha, R., 1987. Effect of salinity on mercury-methylating activity of sulfate 

reducing bacteria in estuarine sediments. Applied and Environmental Microbiology, 53: 

261-265.

Connelly, N.A., B.A. Knuth, and T.L. Brown. 1996. “Sportfish Consumption Patterns of Lake 

Ontario Anglers and the Relationship to Health Advisories.” North American Journal of 

Fisheries Management 16:90-101.
82

Craig, P.J. and Bartlett, P.D., 1978. The role of hydrogen sulphide in environmental transport of 

mercury. Nature, 275: 635-637.

Craig, P.J. and Moreton, P.A., 1986. Total mercury, methyl mercury and sulphide levels in British 

estuarine sediments-III. Water Research, 20(9): 1111-1118.

Cristol D. A., Brasso R. L., Condon A. M., Fovargue R. E., Friedman S. L., Hallinger K. K., Monroe 

A. P., White A. E. (2008) The movement of aquatic mercury through terrestrial food 

webs. Science 320, 335–335.

Crump KS, Kjellstrom T, Shipp AM, Silvers A, Stewart A (1998). Influence of prenatal mercury 

exposure upon scholastic and psychological test performance: Benchmark analysis of a 

New Zealand cohort. Risk Analysis, 18:701-713.

Crump, Kate L., and Trudeau, Vance L. Mercury-induced reproductive impairment in fish. 

Environmental Toxicology and Chemistry. Vol. 28, No. 5, 2009.

Daniels, J.L., Longnecker,M.P., Rowland, A.S., et al., (2004). Fish intake during pregnancy and 

early cognitive development of offspring. Epidemiology 15, 394–402.

Davidson PW, Myers GJ, Cox C, Axtell C, Shamlaye C, Sloane-Reeves J, Cernichiari E, Needham L, 

Choi A, Wang Y, Berlin M, Clarkson TW (1998). Effects of prenatal and postnatal 

methylmercury exposure from fish consumption on neurodevelopment: outcomes at 66 

months of age in the Seychelles Child Development Study. Journal of the American 

Medical Association, 280(8):701-7.

Davidson, P.W., G. Myers, C.C. Cox,C.F. Shamlaye, D.O.Marsh, M.A.Tanner, M. Berlin, J. SloaneReeves, E. Chernichiari, , O. Choisy, A. Choi and T.W. Clarkson. 1995. Longitudinal 

neurodevelopment study of Seychellois children following in utero exposure to 

methylemrcury form maternal fish ingestion: outcomes at 19 and 29 months. 

NeuroToxicology 16:677-688.

Davidson, P.W., Strain, J.J., Myers, G.J., et al., (2008). Neurodevelopmental effects of maternal 

nutritional status and exposure to methylmercury from eating fish during pregnancy. 

NeuroToxicology 29, 767–775.

Dellinger, JA (2004). Exposure assessment and initial intervention regarding fish consumption of 

tribal members in the Upper Great Lakes Region in the United States. Environmental 

Research 95 (2004) p. 325-340.

Dominici F, Samet JM, Zeger SL (2000). Combining evidence on air pollution and daily mortality 

from the 20 largest US cities: a hierarchical modeling strategy. Journal of the Royal 

Statistical Society A, 163:263-284.
83

Doyon, J.-F., Schetagne, R. and Verdon, R., 1998. Different mercury bioaccumulation rates 

between sympatric populations of dwarf and normal lake whitefish (Coregonus 

clupeaformis) in the La Grande complex watershed, James Bay, Quebec. 

Biogeochemistry, 40: 203-216.

Drevnick, P.E., D.E. Canfield, P.R. Gorski, A.L.C. Shinneman, D.R. Engstrom, D.C.G. Muir, G.R. 

Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and J.T. Oris. 2007. 

Deposition and cycling of sulfur controls mercury accumulation in Isle Royale fish. 

Environmental Science and Technology 41(21):7266–7272.

Driscoll, C.T. et al., 1995. The role of dissolved organic carbon in the chemistry and 

bioavailability of mercury in remote Adirondack lakes. Water, Air, Soil Pollution, 80: 499-

508.

Dunstan, J.A., Simmer, K., Dixon, G., et al., (2006). Cognitive assessment of children at age 2.5 

years after maternal fish oil supplementation in pregnancy: a randomized controlled 

trial. Archives of Diseases in Childhood. Fetal Neonatal Ed; December 21, 2006.

Ebert, E., N. Harrington, K. Boyle, J. Knight, J. and R. Keenan. 1994. “Estimating Consumption of 

Freshwater Fish among Maine Anglers.” North American Journal of Fisheries 

Management 13:737-745.

EPA, 1997. U.S. Environmental Protection Agency, Volume I - General Factors Exposure Factors 

Handbook Update to Exposure Factors Handbook, EPA/600/8-89/043—May 1989, 

EPA/600/P-95/002Fa, August 1997.

Ericksen, J. A., Gustin, M. S., Schorran, D. E., Johnson, D. W., Lindberg, S. E., & Coleman, J. S. 

(2003). Accumulation of atmospheric mercury in forest foliage. Atmospheric 

Environment, 37(12), 1613-1622.

Evers, D.C., 2006. Status assessment and conservation plan for the common loon (Gavia immer) 

in North America. U.S. Fish and Wildlife Service, Hadley, MA, USA.

Evers, David C., Savoy, Lucas J., DeSorbo, Christopher R., Yates, David E., Hanson, William, 

Taylor, Kate M., Siegel, Lori S., Cooley, John H. Jr., Bank, Michael S., Major, Andrew, 

Munney, Kenneth, Mower, Barry F., Vogel, Harry S., Schoch, Nina, Pokras, Mark, 

Goodale, Morgan W., Fair, Jeff. Adverse effects from environmental mercury loads on 

breeding common loons. Ecotoxicology. 17:69-81, 2008.

Frederick, Peter, and Jayasena, Nilmini. Altered pairing behavior and reproductive success in 

white ibises exposed to environmentally relevant concentrations of methylmercury. 

Proceedings of The Royal Society B. doi: 10-1098, 2010.

Frescholtz, T. and Gustin, M.S., 2004. Soil and foliar mercury emission as a function of soil 

concentration. Water, Air, and Soil Pollution, 155: 223-237.
84

Gbundgo-Tugbawa and Driscoll, 1998. Application of the regional mercury cycling model 

(RMCM) to predict the fate and remediation of mercury in Onondaga Lake, New York. 

Water, Air, and Soil Pollution, 105: 417-426.

Gilmour, C.C. and Henry, E.A., 1991. Mercury methylation in aquatic systems affected by acid 

deposition. Environmental Pollution, 71: 131-169.

Gilmour, C.C. et al., 1998. Methylmercury concentrations and production rates across a trophic 

gradient in the northern Everglades. Biogeochemistry, 40: 327-345.

Gobas, F.A.P.C., Pasternak, J.P., Lien, K. and Duncan, R.K., 1998. Development and field 

validation of a multimedia exposure model for waste load allocation in aquatic 

ecosystems: application to 2,3,7,8-tetrachloro-p-dioxin and 2,3,7,8-

tetrachlorodibenzofuran in the Fraser River watershed. Environmental Science and 

Technology, 32: 2442-2449.

Gobas, F.A.P.C., Z’Graggen, M.N. and Zhang, X., 1995. Time response of the Lake Ontario 

ecosystem to virtual elimination of PCBs. Environmental Science and Technology, 29(8): 

2038-2046.

Gorissen, Leen, Snoeijs, Tinne, Van Duyse, Els, and Eens, Marcel. Heavy metal pollution affects 

dawn singing behavior in a small passerine bird. Oecologia. 145: 540-509, 2005.

Grandjean P, Weihe P, White R, Debes F, Araki S, Yokiyama K, Murata K, Sorensen N, Dahl R, 

Jorgensen P. Cognitive deficit in 7-year-old children with prenatal exposure to 

Methylmercury. Neurotoxicol Teratol . 1997. 19:(6)417-428. 

Grandjean, Phillippe, Esben Budtz-Jørgensen, Roberta F. White, Poul J. Jørgensen, Pal Weihe, 

Frodi Debes, and Niels Keding (1999). Methylmercury Exposure Biomarkers as Indicators 

of Neurotoxicity in Children Aged 7 Years. American Journal of Epidemiology. Vol. 150 

(3): 301-305.

Great Lakes Sport Fish Advisory Task Force. September 1993. Protocol for a Uniform Great 

Lakes Sport Fish Consumption Advisory.

Greenfield, B.K., Hrabik, T.R., Harvey, C.J. and Carpenter, S.R., 2001. Predicting mercury levels in 

yellow perch: use of water chemistry, trophic ecology, and spatial traits. Canadian 

Journal Fisheries and Aquatic Sciences, 58: 1419–1429.

Grieb, T.M. et al., 1990. Factors affecting mercury accumulation in fish in the upper Michigan 

peninsula. Environmental Toxicology and Chemistry, 9: 919-930.

Grosse, Scott D., Kurt V Krueger, Mercy Mvundura (2009). Economic Productivity by Age and 

Sex: 2007 Estimates for the United States. Medical Care: July 2009 - Volume 47 - Issue 

7_Supplement_1 - pp S94-S103, doi: 10.1097/MLR.0b013e31819c9571.
85

Gustin, M. et al., 2004. Application of controlled mesocosms for understanding mercury air-soilplant exchange. Environmental Science and Technology, 38: 6044-6050.

Hallinger, Kelly K., Cornell, Kerri L., Brasso, Rebecka L., and Cristol, Daniel A. Mercury exposure 

and survival in free-living tree swallows (Tachycineta bicolor). Ecotoxicology. Doi: 

10.1007/s10646-010-0554-4, 2010.

Harmon, S.M., J.K. King, J.B. Gladden, and L.A. Newman. 2007. Using sulfate-amended sediment 

slurry batch reactors to evaluate mercury methylation. Archives of Environmental 

Contamination and Toxicology 52:326–333.

Harris, R., 2007. Ecosystem Responses to Mercury: Indicators of Change. CFC Press ISBN: 

97808493888927.

Harris, R. and Hutchison, D., 2003. Factors Affecting the Predicted Response of Fish Mercury 

Concentrations to Changes in Mercury Loading. 1005521, Electric Power Research 

Institute, Palo Alto, CA. 

Harris, R., Gherini, S. and Hudson, R., 1996. Regional Mercury Cycling Model: A Model for 

Mercury Cycling in Lakes, R-MCM Version 1.0 Draft User Guide and Technical Reference, 

Electric Power Research Institute, Wisconsin Department of Natural Resources, 

Lafayette, California.

Harris., R. C., John W. M. Rudd, Marc Amyot, Christopher L. Babiarz, Ken G. Beaty, Paul J. 

Blanchfield, R. A. Bodaly, Brian A. Branfireun, Cynthia C. Gilmour, Jennifer A. Graydon, 

Andrew Heyes, Holger Hintelmann, James P. Hurley, Carol A. Kelly, David P. 

Krabbenhoft, Steve E. Lindberg, Robert P. Mason, Michael J. Paterson, Cheryl L. 

Podemski, Art Robinson, Ken A. Sandilands, George R. Southworth, Vincent L. St. Louis, 

and Michael T. TateRudd, J. W. M., Amyot M., et al., Whole-Ecosystem study Shows 

Rapid Fish-Mercury Response to Changes in Mercury Deposition. Proceedings of the 

National Academy of Sciences Early Edition, PNAS 2007 104 (42) pp. 16586-16591; 

(published ahead of print September 27, 2007).

Hastie, T.J., Tibshirani, R., (1993). Varying coefficient models. Journal of the Royal Statistical 

Society: Series B 58, 379–396.

Hawley, Dana M., Hallinger, Kelly K., Cristol, Daniel A. Compromised immune competence in 

free-living tree swallows exposed to mercury. Ecotoxicology. 18:499-503, 2009.

Helland, I.B., Smith, L., Saarem, K., et al., (2003). Maternal supplementation with very long 

chain n-3 fatty acids during pregnancy and lactation augments children’s IQ at 4 years of 

age. Pediatrics 111, E39–E44.
86

Hintelmann, H. and Evans, R.D., 1997. Application of stable isotopes in environmental tracer 

studies - measurement of monomethylmercury by isotope dilution ICP-MS and 

detection of species transformation. Fresenius Journal of Analytical Chemistry, 358: 378-

385.

Hintelmann, H. et al., 2002. Reactivity and mobility of new and old mercury deposition in a 

boreal forest ecosystem during the first year of the METAALICUS study. Environmental 

Science and Technology, 36: 5034-5040.

Hintelmann, H., Keppel-Jones, K. and Evans, R.D., 2000. Constants of mercury methylation and 

demethylation rates in sediments and comparison of tracer and ambient mercury 

availability. Environmental Toxicology and Chemistry, 19(9): 2204-2211.

Hoffman, David J., Henny, Charles J., Hill, Elwood F., Grover, Robert A., Kaiser, James L., 

Stebbins, Katherine R. Mercury and drought along the lower Carson River, Nevada: III. 

Effects on blood and organ biochemistry and histopathology of snowy egrets and blackcrowned night-herons on Lahontan Reservoir, 2002-2006. Journal of Toxicology and 

Environmental Health, Part A. 72: 20, 1223-1241, 2009.

Hrabik, T.R., and C.J. Watras. 2002. Recent declines in mercury concentration in a freshwater 

fishery: isolating the effects of de-acidification and decreased atmospheric mercury 

deposition in Little Rock Lake. Science of the Total Environment 297:229–237.

Hurley, J. et al., 1995. Influences of watershed characteristics on mercury levels in Wisconsin 

rivers. Environmental Science and Technology, 29(7): 1867-1875.

Hurley, J.P., Cowell, S.E., Shafer, M.M. and Hughes, P.E., 1998. Tributary loading of mercury to 

Lake Michigan: Importance of seasonal events and phase partitioning. Science of the 

Total Environment, 213: 129-137.

Hutchinson, J.W. LaBaugh, R.G. Sayre, and S.E. Schwarzbach. 2007. USGS goals for the coming 

decade. Science 318:200–201.

International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation 

of Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, 

and Exposures in the Glass Manufacturing Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 

1961. Poisoning by ethyl mercury toluene sulphonanilide. Br. J. Indust. Med. 

18(Oct.):303-308 (as cited in NRC 2000).

Jackson, T.A., 1998. Mercury in aquatic ecosystems. In: W.J. Langston and M.J. Bebianno 

(Editors), Metal Metabolism in Aquatic Environments. Chapman & Hall, London, pp. 77-

158.

Jakus, P., M. McGuinness, and A. Krupnick. 2002. “The Benefits and Costs of Fish Consumption 

Advisories for Mercury.” Discussion Paper 02-55. Washington, DC: Resources for the 

Future. 
87

Judge, M.P., Harel, O., Lammi-Keefe, C.J., et al., (2007). Maternal consumption of a 

docosahexaenoic acid-containing functional food during pregnancy: benefit for infant 

performance on problem-solving but not on recognition memory tasks at age 9 months. 

American Journal of Clinical Nutrition 85, 1572–1577.

Karlsson, T. and Skyllberg, U., 2003. Bonding of ppb levels of methyl mercury to reduced sulfur 

groups in soil organic matter. Environmental Science and Technology, 37: 4912-4918.

Kidd, K., Hesslein, R., Fudge, R. and Hallard, K., 1995. The influence of trophic level as measured 

by delta-N-15 on mercury concentrations in fresh-water organisms. Water, Air, and Soil 

Pollution, 80(1-4): 1011-1015.

et al.Kjellstrom, T., P. Kennedy, S. Wallis, A. Stewart, L. Friberg, B. Lind, P. Witherspoon, and C. 

Mantell. 1989. Physical and mental development of children with prenatal exposure to 

mercury from fish. Stage 2: Interviews and psychological tests at age 6. National 

Swedish Environmental Protection Board Report No. 3642.

Knights, D. C, Sunderland, E. M., Barber, M. C., Johnston J. M., and Ambrose, R. B., Application 

of Ecosystem-Scale Fate and Bioaccumulation Models to Predict Fish Mercury Response 

Times to Changes in Atmospheric Deposition. Environmental Toxicology and Chemistry, 

Vol 28, No. 4., pp. 881-893.

Lalonde, J., Amyot, M., Kraepiel, A. and Morel, F., 2001. Photooxidation of Hg(0) in artificial and 

natural waters. Environmental Science and Technology, 35: 1367-1372.

Landis, M. and Keeler, G., 2002. Atmospheric mercury deposition to Lake Michigan during the 

Lake Michigan mass balance study. Environmental Science and Technology, 36(21): 

4518-4524.

Landis, M.S., Lynam, M. and Stevens, R.K., 2004. The Monitoring and Modeling of Mercury 

Species in Support of Local Regional and Global Modeling. In: N. Pirrone and K.R. 

Mahaffey (Editors), Dynamics of Mercury Pollution on Regional and Global Scales. 

Kluwer Academic Publishers, New York, NY.

Laporte, J.-M., Andres, S. and Mason, R.P., 2002. Effect of ligands and other metals on the 

uptake of mercury and methylmercury across the gills and the intestine of the blue crab 

(Callinectes sapidus). Comparative Biochemistry and Physiology Part C, 131: 185-196.

Lawrence, A.L. and Mason, R.P., 2001. Factors controlling the bioaccumulation of mercury and 

methylmercury by the estuarine amphipod Leptocheirus plumulosus. Environmental 

Pollution, 111: 217-231.

Lawson, N.M. and Mason, R.P., 1998. Accumulation of mercury in estuarine food chains. 

Biogeochemistry, 40: 235-247.
88

Leaner, J.J. and Mason, R.P., 2002. Factors controlling the bioavailability of ingested 

methylmercury to channel catfish and atlantic sturgeon. Environmental Science and 

Technology, 36: 5124-5129.

Lin, C.J. and Pehkonen, S.O., 1999. The chemistry of atmospheric mercury: a review. 

Atmospheric Environment, 33: 2067-2079.

Lindberg, S.E. and Stratton, J.E., 1998. Atmospheric mercury speciation: concentrations and 

behavior of reactive gaseous mercury in ambient air. Environmental Science and 

Technology, 32(1): 49-57.

Lynch, Miranda L., Li-Shan Huang, Christopher Cox b, J.J. Strain, Gary J. Myers, Maxine P. 

Bonham, Conrad F. Shamlaye, Abbie Stokes-Riner, Julie M.W. Wallace, Emeir M. Duffy, 

Thomas W. Clarkson, Philip W. Davidson, (2010). Varying coefficient function models to 

explore interactions between maternal nutritional status and prenatal methylmercury 

toxicity in the Seychelles Child Development Nutrition Study. Environmental Research, 

doi:10.1016/j.envres.2010.09.005.

Marsh, D.O., T.W. Clarkson, C. Cox, et al. 1987. Fetal methylmercury poisoning: relationship 

between concentration in single strands of maternal-hair and child effects. Arch. Neurol. 

44:1017-1022. (as cited in EPA 2002 IRIS documentation.)

Mason, R.P. et al., 1999. Mercury in the Chesapeake Bay. Marine Chemistry, 65: 77–96.

Mason, R.P., Morel, F.M.M. and Hemond, H.F., 1995. The role of microorganisms in elemental 

mercury formation in natural waters. Water, Air, and Soil Pollution, 80: 775-787.

McIntrye, J.W., and Evers, D.C.,(eds)2000. Loons: old history and new finding. Proceedings of a 

Symposium from the 1997 meeting, American Ornithologists’ Union. North American 

Loon Fund, 15 August 1997, Holderness, NH, USA.

McIntyre, J.W., Barr, J.F. 1997Common Loon (Gavia immer) in: Pool A, Gill F (eds) The Birds of 

North America. Academy of Natural Sciences, Philadelphia, PA, 313

Mierle, G. and Ingram, R., 1991. The role of humic substance in the mobilization of mercury 

from watersheds. Water, Air, and Soil Pollution, 56: 349-357.

Mitro, Matthew G., Evers, David C., Meyer, Michael W., and Piper, Walter H. Common loon 

survival rates and mercury in New England and Wisconsin. Journal of Wildlife 

Management. 72(3): 665-673, 2008.

Morel, F., Kraepiel, A.M.L. and Amyot, M., 1998. The chemical cycle and bioaccumulation of 

mercury. Annual Reviews of Ecological Systems, 29: 543-566.
89

Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. “Effects of Commonly Used Cooking Practices 

on Total Mercury Concentration in Fish and Their Impact on Exposure Assessments.”

Journal of Exposure Analysis and Environmental Epidemiology 7(1):119-133.

Moya, J. 2004. Overview of fish consumption rates in the United States. Human and Ecological 

Risk Assessment: An International Journal 10, no. 6: 1195–1211. 

Munthe, J., Bodaly, R. A., Branfireun, B. A., Driscoll, C. T., Gilmour, C. C., Harris, R., et al. (2007). 

Recovery of Mercury-Contaminated Fisheries. Environmental Science & Technology, 

36(1), 33-44.

Myers GJ, Davidson PW, Cox, C, Shamlaye CF, Palumbo D, Cernichiari E, Sloane-Reeves J, 

Wilding GE, Kost J, Huang LS, Clarkson TW (2003). Prenatal methylmercury exposure 

from ocean fish consumption in the Seychelles child development study. Lancet, 

361:1686-1692.

Myers, M.D., M.A. Ayers, J.S. Baron, P.R. Beauchemin, K.T. Gallagher, M.B. Goldhaber, D.R.

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on 

the Toxicological Effects of Methylmercury, Board on Environmental Studies and 

Toxicology, Commission on Life Sciences, National Research Council. National Academy 

Press, Washington, DC.

Neisser U, Boodoo G, Bouchard TJ, et al. (1996). Intelligence: Knowns and unknowns. American 

Psychologist, 51:77-101.

NRC (2000). Toxicological Effects of Methylmercury. National Research Council. Washington, 

DC: National Academies Press.

Oken E., K.P. Kleinman, W.E. Berland, S.R. Simon, J.W. Rich-Edwards, and M.W. Gillman. 2003. 

“Decline in Fish Consumption Among Pregnant Women After a National Mercury 

Advisory.” Obstetrics and Gynecology 102(2):346-351.

Rasmussen, P.E., 1994. Current methods of estimating atmospheric mercury fluxes in remote 

areas. Environmental Science and Technology, 28(13): 2233-2241.

Ravichandran, M., 2004. Interactions between mercury and dissolved organic matter - a review. 

Chemosphere, 55: 319-331.

Rea, A. W., Lindberg, S. E., Scherbatskoy, T., & Keeler, G. J. (2002). Mercury Accumulation in 

Foliage over Time in Two Northern Mixed-Hardwood Forests. Water, Air, & Soil 

Pollution, 133(1), 49-67.

Rice GE, Hammitt JK, Evans JS. (2010). A probabilistic characterization of the health benefits of 

reducing methyl mercury intake in the United States. Environmental Science Technology. 

2010 Jul 1;44(13):5216-24
90

Rimmer, C. C., McFarland, K. P., Evers, D. C., Miller, E. K., Aubry, Y., Busby, D., et al. (2005). 

Mercury Concentrations in Bicknell’s Thrush and Other Insectivorous Passerines in 

Montane Forests of Northeastern North America. Ecotoxicology, 14(1), 223-240.

Rolfhus, K. et al., 2003. Distribution and fluxes of total and methylmercury in Lake Superior. 

Environmental Science and Technology, 37(5): 865-872.

Ryan, LM (2005). Effects of Prenatal Methylmercury on Childhood IQ: A Synthesis of Three 

Studies. Report to the U.S. Environmental Protection Agency.

Salkever, D. 1995. “Updated Estimates of Earnings Benefits from Reduced Lead Exposure of 

Children to Environmental Lead.” Environmental Research 70:1-6.

Schaefer, J. et al., 2004. Role of the bacterial organomercury lyase (MerB) in controlling 

methylmercury accumulation in mercury-contaminated natural waters. Environmental 

Science and Technology, 38: 4304-4311.

Scheuhammer, Anton M., Meyer Michael W., Sandheinrich, Mark B., and Murray, Michael W. 

Effects of environmental methylmercury on the health of wild birds, mammals, and fish. 

Ambio. Vol.36, No.1, 2007.

Scholtz, M.T., Heyst, B.J.V. and Schroeder, W.H., 2003. Modeling of mercury emissions from 

background soils. Science of the Total Environment, 304: 185-207.

Schroeder, W.H., Munthe, J. and Lindqvist, O., 1989. Cycling of mercury between water air and 

soil compartments of the environment. Water, Air, and Soil Pollution, 48: 337-347.

Schwartz, Joel (1994). Societal Benefits of Reducing Lead Exposure. Environmental Research 66, 

105-124.

Seigneur, C., Jayaraghavan, K., Lohman, K., Karamchandani, P. and Scott, C., 2004. Global 

Source Attribution for Mercury Deposition in the United States. Environmental Science 

and Technology, 38: 555-569.

Sellers, P., Kelly, C.A., Rudd, J.W.M. and MacHutchon, A.R., 1996. Photodegradation of 

methylmercury in lakes. Nature, 380: 694.

Sepulveda, Maria S., Frederick, Peter C., Spalding, Marilyn G., and Williams, Gary E. Jr. Mercury 

contamination in free-ranging great egret nestlings (Ardea albus) from southern Florida, 

USA. Environmental Toxicology and Chemistry. Vol. 18, No.5, 1999.

Shilling, Fraser, Aubrey White, Lucas Lippert, Mark Lubell (2010). Contaminated fish 

consumption in California’s Central Valley Delta. Environmental Research 110, p. 334-

344.
91

Siciliano, S., O’Driscoll, N. and Lean, D., 2002. Microbial reduction and oxidation of mercury in 

freshwater lakes. Environmental Science and Technology, 36(14): 3064-3068.

St. Louis, V. et al., 2001. Importance of the forest canopy to fluxes of methyl mercury and total 

mercury to boreal ecosystems. Environmental Science and Technology, 35: 3089-3098.

Stern, AH, Smith AE (2003). An assessment of the cord blood:maternal blood methylmercury 

ratio: Implications for risk assessment. Environmental Health Perspectives, 111:1465-

1470.

Strain, J.J., Davidson, P.W., Bonham, M.P., et al., (2008). Associations of maternal long-chain 

polyunsaturated fatty acids, methylmercury, and infant development in the Seychelles 

Child Development Nutrition Study. NeuroToxicology 29, 776–782.

Sunda, W.G. and Huntsman, S.A., 1998. Processes regulating cellular metal accumulation and 

physiological effects: Phytoplankton as model systems. Science of the Total 

Environment, 219: 165-181.

Sunderland, E.M. et al., 2004. Speciation and bioavailability of mercury in well-mixed estuarine 

sediments. Marine Chemistry, 90: 91-105.

Swartout, J., and G. Rice. 2000. “Uncertainty Analysis of the Estimated Ingestion Rates Used to 

Derive the Methylmercury Reference Dose.” Drug and Chemical Toxicology 23(1):293-

306. 11-41

Tseng, C.M., Amouroux, D., Abril, G. and Donard, O.F.X., 2001. Speciation of mercury in a fluid 

mud profile of a highly turbid macrotidal estuary (Gironde, France). Environmental 

Science and Technology, 35(13): 2627-2633.

U.S. Department of the Interior (DOI), Fish and Wildlife Service and U.S. Department of 

Commerce, Bureau of the Census. 1992. 1991 National Survey of Fishing, Hunting, and

Wildlife-Associated Recreation. Washington, DC: U.S. Government Printing Office.

U.S. Department of the Interior (DOI), Fish and Wildlife Service and U.S. Department of

Commerce, Bureau of the Census. 1997. 1996 National Survey of Fishing, Hunting, and

Wildlife-Associated Recreation. Washington, DC: U.S. Government Printing Office.

U.S. Department of the Interior (DOI), Fish and Wildlife Service and U.S. Department of

Commerce, Bureau of the Census. 2002. 2001 National Survey of Fishing, Hunting, and

Wildlife-Associated Recreation. Washington, DC: U.S. Government Printing Office.

U.S. Department of the Interior (DOI), Fish and Wildlife Service and U.S. Department of 

Commerce, Bureau of the Census. 2007. 2006 National Survey of Fishing, Hunting, and 

Wildlife-Associated Recreation. Washington, DC: U.S. Government Printing Office.
92

U.S. Environmental Protection Agency (EPA), 1997. Mercury Study Report to Congress. EPA452/R-97-005, Office of Air Quality Planning and Standards United States Environmental 

Protection Agency, Washington.

U.S. Environmental Protection Agency (EPA). 1997. Mercury Study Report to Congress. Volume 

V: Health Effects of Mercury and Mercury Compounds. EPA-452/R-97-007. U.S. EPA 

Office of Air Quality Planning and Standards, and Office of Research and Development.

U.S. Environmental Protection Agency (EPA) (1997). Volume I - General Factors Exposure 

Factors Handbook Update to Exposure Factors Handbook, EPA/600/8-89/043—May

1989, EPA/600/P-95/002Fa, August 1997.

U.S. Environmental Protection Agency (U.S. EPA). 1999. 1999 National Emission Inventory 

Documentation and Data—Final Version 3.0; Hazardous Air Pollutants Inventory—

FinalNEI Version 3; HAPS Summary Files. (12 December 

2006;www.epa.gov/ttn/chief/net/1999inventory.html)

U.S. Environmental Protection Agency (EPA), 2001a. Mercury Maps. A Quantitative Spatial Link 

Between Air Deposition and Fish Tissue. Final Report. EPA/823/R-01/009, USEPA, 

Washington, D.C.

U.S. Environmental Protection Agency (EPA), 2001b. Methylmercury fish tissue residue 

criterion, United States Environmental Protection Agency, Office of Water, 4304 EPA823-F-01-001, January 2001, 

www.epa.gov/waterscience/criteria/methylmercury/factsheet.html.

U.S. Environmental Protection Agency (EPA) (2002). Mercury Neurotoxicity Workshop Notes.

Washington, DC. November 4, 2002. 

http://www.epa.gov/ttn/ecas/regdata/Benefits/mercuryworkshop.pdf.

U.S. Environmental Protection Agency (EPA 2002). Mercury Neurotoxicity Workshop Notes; 

available at: www.epa.gov/ttn/ecas/benefits.html

U.S. Environmental Protection Agency (EPA). 2002 (date of most recent revision of on-line 

materials; website accessed January 2005). Integrated Risk Information System (IRIS). 

Methylmercury. U.S. EPA Office of Research and Development, National Center for 

Environmental Assessment. Oral RfD and inhalation RfC assessments last revised 

7/27/2001. Carcinogenicity assessment last revised 5/1/1995. Available online at 

http://www.epa.gov/iris/subst/0073.htm

U.S. Environmental Protection Agency (U.S. EPA). 2005. Regulatory Impact Analysis of the Final 

Clean Air Mercury Rule. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC., March; EPA report no. EPA-452/R-05-003. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/mercury_ria_final.pdf
93

U.S. Environmental Protection Agency (EPA) (2005). Regulatory Impact Analysis of the Clean Air 

Mercury Rule, Research Triangle Park, NC. June 2008. EPA-452/R-05-003.

U.S EPA (Environmental Protection Agency). 2008. Integrated Science Assessment (ISA) for 

Oxides of Nitrogen and Sulfur—Ecological Criteria (Final Report). EPA/600/R-08/082F. 

U.S. Environmental Protection Agency, National Center for Environmental AssessmentRTP Division, Office of Research and Development, Research Triangle Park, N.C. 

Available at http://cfpub.epa.gov/ncea/cfm/recorddisplay.cfm?deid+201485.

U.S. Environmental Protection Agency (EPA) (2008). Proposed Lead NAAQS Regulatory Impact 

Analysis Office of Air Quality Planning and Standards, Research Triangle Park, NC. June 

2008.

U.S. Environmental Protection Agency (EPA). 2008. Integrated Science Assessment (ISA) for 

Oxides of Nitrogen and Sulfur–Ecological Criteria (Final Report). EPA/600/R- 08/082F. 

U.S. Environmental Protection Agency, National Center for Environmental Assessment–

RTP Division, Office of Research and Development, Research Triangle Park, NC. Available 

at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485.

United States Department of Health and Human Services (DHHS), Centers for Disease Control 

and Prevention (CDC), National Center for Health Statistics (NCHS), Division of Vital 

Statistics, Natality public-use data 2003-2006, on CDC WONDER Online Database, March 

2009. Accessed at http://wonder.cdc.gov/natality-current.html on Nov 3, 2010 4:44:23 

PM

USDA Forest Service (1994). “National Survey on Recreation and the Environment: 1994-95.”

Washington DC: USDA Forest Service.

Wada, H. and Cristol, D.A. and McNabb, F.M.A. and Hopkins, W.A. (2009) Suppressed 

adrenocortical responses and thyroid hormone levels in birds near a mercurycontaminated river. Environmental Science & Technology 43(15), 6031-6038.

Wada., H., Yates, DE., Evers, DC., Taylor, RJ., Hopkins, WA. (2010) Tissue mercury 

concentrations and adrenocortical responses of female big brown bats (Eptesicus 

fuscus) near a contaminated river. Ecotoxicology. 19(7), 1277-1284.

Watras, C.J. and Bloom, N.S., 1992. Mercury and methylmercury in individual zooplankton: 

implications for bioaccumulation. Limnol. Oceanogr., 37: 1313-1318.

Watras, C.J. et al., 1998. Bioaccumulation of mercury in pelagic freshwater food webs. Science 

of the Total Environment, 219(2-3): 183-208.

Wechsler D (1991). WISC-III Manual. San Antonio: The Psychological Corporation.
94

West, P.C., J.M. Fly, R. Marans, F. Larkin, and D. Rosenblatt. May 1993. 1991-92 Michigan Sport 

Anglers Fish Consumption study. Prepared by the University of Michigan, School of 

Natural Resources for the Michigan Department of Natural Resources, Ann Arbor, MI.

Technical Report No. 6.

West, P.C., M.J. Fly, R. Marans, and F. Larkin. 1989. Michigan Sport Anglers Fish Consumption 

Survey. A report to the Michigan Toxic Substance Control Commission. Michigan 

Department of Management and Budget Contract No. 87-20141.

Woods & Poole Economics, Inc. 2008. Population by Single Year of Age CD. CD-ROM. Woods & 

Poole Economics, Inc.

Wren, C.D. and MacCrimmon, H.R., 1986. Comparative bioaccumulation of mercury in two 

adjacent freshwater ecosystems. Water Research, 6: 763-769.

Yates, David E., Mayack, David T., Munney, Kenneth, Evers David C., Major, Andrew, Kaur, 

Taranjit, and Taylor, Robert J. Mercury levels in mink (Mustela vison) and river otter 

(Lonra canadensis) from northeastern North America. Ecotoxicology. 14, 263-274, 2005.

Zhang, H. and Lindberg, S.E., 2001. Sunlight and Iron(III)-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 FRESHWATER 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 describes 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 from 

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 reported 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 multiday 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 your 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, from 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 the sample is white;

males comprise a higher percentage of the sample (62 percent) than females. More than half
4A-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 Anglersa

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 

the 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 the 

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 continuous 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 Freshwater 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 estimated 
4A-4

Table 4A-4. OLS Regression Results for Determinants of Reported Trip Travel Distance (Miles)

(1)

Full Sample (both singleand 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 all 

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 Midwest 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 positively 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 the Population Centroid Approach

Given the high significance of geographic area and family income across the regressions, 

nonparametric results (frequency distributions) were generated for four mutually exclusive 

subgroups of respondents and five travel distance categories. The results are reported in 

Table C-5. Respondents were categorized into the four following groups: 

 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 the 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 high-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 frequency estimates reported in Table C-5 for the 

full sample were used in the population centroid approach to weight exposures to mercury in 
4A-6

fish according to distance from the Census tract centroid, income levels in the tract, and 

whether the tract is predominantly 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%