National Institute of Environmental Health Sciences 

EMMMA: A Web-based System for Environmental 

Mercury Mapping, Modeling, and Analysis 

By Paul P. Hearn, Jr.1, Stephen P. Wente2, David I. Donato1, and John J. Aguinaldo3 

Report Series 2006-1086 

1

Geography Discipline, Eastern Geographic Science Center, U.S. Geological Survey, Reston, VA, USA; 2

Minnesota Water Science Center, U.S. Geological Survey, Mounds View, MN, USA; 3

Harris Technical Services Corporation, Falls Church, VA, USA 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

Gale A. Norton, Secretary 

U.S. Geological Survey 

P. Patrick Leahy, Acting Director 

U.S. Geological Survey, Reston, Virginia 2006 

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Hearn, Paul., Wente, Stephen, 2006, Title of the publication: OFR 2006-1086, page numbers 


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ii 
Contents 

Abstract................................................................................................................................................................................1
 

Background – Environmental Mercury as a Hazard to Human Health .................................................................................1
 

Mercury in Food Fish: Complexities of the Problem .............................................................................................................2
 

Increasing Research and Management Efficiency................................................................................................................3
 

Mercury Databases Available through EMMMA .................................................................................................................3
 

Model Predictions Available through EMMMA....................................................................................................................4
 

The National Descriptive Model for Mercury in Fish........................................................................................................4
 

Enhanced Analysis of Geographic and Temporal Trends .................................................................................................5
 

Development of Fish Consumption Advisories .................................................................................................................8
 

Cost Savings to Agencies that Monitor Fish-tissue Mercury .........................................................................................10
 

Using EMMMA ....................................................................................................................................................................10
 

The EMMMA Data Mapper.............................................................................................................................................10
 

The EMMMA Graphing Tool............................................................................................................................................12
 

Next Steps for EMMMA......................................................................................................................................................12
 

References ...........................................................................................................................................................................13
 

Figures 

Figure 1. Comparison of geographic trends in observed fish-tissue mercury concentrations (a) that 


appear to emphasize concentration differences between samples collected in North Carolina (north of 


red line in 1a) and South Carolina (south of red line in 1a) and model-standardized fish-tissue mercury 


concentrations (b) that appear to show concentration differences between physiographic zones of 


southeastern United States...................................................................................................................................................6 


Figure 2. Comparison of temporal trends identified from observed (a) and NDMMF standardized (b) 


fish-tissue mercury concentrations.......................................................................................................................................7 


Figure 3. Hypothetical fish consumption advisory developed using the predictions of the NDMMF and 


the EMMMA website............................................................................................................................................................9 


Figure 4. Many interactive mapping features are available through EMMMA’s data mapper (a) 


including point data display over topographic maps (b) and 1-meter-resolution aerial photography (c)..........................11 


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iv 

Abbreviations 

COALQUAL – National Coal Resources Data System Coal Quality Database 

EMMMA – Environmental Mercury Mapping, Modeling, and Analysis website (http://emmma.usgs.gov) 

MAS/MILS - Mineral Availability System/Mineral Industry Locator System 

MDN – Mercury Deposition Network 

NDMMF – National Descriptive Model of Mercury in Fish 

NEI – National Emission Inventory 

USGS – United States Geological Survey 

USEPA – United States Environmental Protection Agency 
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EMMMA: A Web-based System for Environmental 

Mercury Mapping, Modeling, and Analysis 

By Paul P. Hearn, Jr., Stephen P. Wente, David I. Donato, and John J. Aguinaldo 

Abstract 

Mercury in our environment – in our air, water, soil, and especially our food – poses significant 

hazards to human health, particularly for developing fetuses and young children. Because of the 

importance of this issue and the length of time it has been studied, large and complex data sets of 

mercury concentrations in various media and associated ancillary data have been generated by 

many Federal, State, Tribal, and local agencies. To facilitate efficient and effective use of these 

data in managing and mitigating human and wildlife exposure to mercury, the U.S. Geological 

Survey (USGS) and the National Institute of Environmental Health Sciences have developed a 

website for visualizing and studying the distribution of mercury in our environment. The 

Environmental Mercury Mapping, Modeling, and Analysis (EMMMA) website 

(http://emmma.usgs.gov) provides health and environmental researchers, managers, and other 

decision-makers the ability to: 

•	 Interactively view and access a nationwide collection of environmental mercury data (fish 

tissue, atmospheric emissions and deposition, stream sediments, soils, and coal) and mercuryrelated data (mine locations); 

•	 Interactively view and access predictions of the National Descriptive Model of Mercury in Fish 

(NDMMF) at 4,976 sites and 6,829 sampling events (events are unique combinations of site and 

sampling date) across the United States; and 

•	 Use interactive mapping and graphing capabilities to visualize spatial and temporal trends and 

study relationships between mercury and other variables. 

Background – Environmental Mercury as a Hazard to Human Health 

The world was shocked by the severity of the birth defects and community-wide illness seen 

in the 1950s at Minimata Bay, Japan, in the first documented case of dramatic human illness 

resulting from an ongoing industrial release of mercury and the accumulation of that mercury in 

food fish (Tsubaki and Irukayama 1977). Subsequent studies have revealed dramatic adverse 

effects on fetal brain development resulting from exposure to relatively low levels of 

methylmercury in utero (Gilbert and Grant-Webster 1995). Today, researchers and public-health 

officials at all levels of government recognize that the most significant human exposure to 

environmental mercury occurs through the consumption of fish, and that the populations at greatest 

risk from exposure to methylmercury include developing fetuses and young children (USEPA 

1997). 
2
 

Mercury is typically released into the atmosphere from both natural and anthropogenic 

sources in its less toxic inorganic form (Mason et al. 1994). Natural sources of mercury include 

relatively continuous inputs from weathering (slow breakdown over time) of rock and soil particles 

as well as specific and often dramatic events such as volcanic eruptions and wildfires. 

Anthropogenic sources include coal-fired power plants, mining, and various industrial activities. 

Depending on the inorganic forms of the mercury released, mercury may be dispersed over scales 

that range from local to global (USEPA 1997). Eventually though, this mercury is deposited back 

to the earth’s surface. As long as the airborne or deposited mercury remains in a dispersed (low 

concentration) inorganic form, there is little chance for significant adverse health impacts. 

However, inorganic mercury, which is deposited directly or washed by rainwater into aquatic 

environments, can be converted into the more-toxic organic form of mercury – methylmercury 

(Ridley et al. 1977). 

Although methylmercury typically occurs in very low concentrations in water, it 

accumulates into the lowest trophic levels of aquatic food chains (algae, bacteria, etc.) and 

subsequently biomagnifies as it passes through each step in the food chain (e.g., as insects eat 

algae, small fish eat insects, and so on). In this way large predator fish can have methylmercury 

concentrations that are a million times greater than the concentration in the water in which they live 

(Watras et al. 1994). It is the toxicity of methylmercury in conjunction with its ability to 

biomagnify through food chains that makes methylmercury in food fish such a potent health threat. 

Mercury in Food Fish: Complexities of the Problem 

Managing the health threat of human exposure to mercury is complex because exposure 

varies with many factors. The characteristics of the fish tissue consumed affect human and wildlife 

exposure because (1) different species at the same site accumulate different levels of mercury in 

their tissues (MacCrimmon et al. 1983; Suns et al. 1987; Cope et al. 1990; Kim and Burggraaf 

1999), (2) different tissues in the same fish accumulate different levels of mercury (Giblin and 

Massaro 1973; Boudou and Ribeyre 1983; Harrison et al. 1990), and (3) individual fish accumulate 

more mercury as they grow (Wiener and Spry 1996; Huckabee et al. 1979). Different water bodies 

produce fish of differing mercury concentrations due to the amount of mercury reaching the water 

body, the ability of the individual water body to convert inorganic mercury to methylmercury, and 

the mass and trophic levels of the organisms residing in the water body throughout which the 

methylmercury is distributed (Wiener et al. 2002). When a fish is caught also affects its tissue 

concentration because mercury levels change over time due to the interaction of natural 

environmental factors and human controls (such as regulations on emissions). Finally, the 

frequency of consumption and amount of fish consumed determine that person’s long-term 

methylmercury exposure (USEPA 2000). 

The abundant data on the distribution of mercury in various media and associated ancillary 

data should aid the management and mitigation of this health threat in a scientifically sound 

manner. However, major obstacles to using these data are that: the data are often stored at, and 

must be retrieved from, the agencies that originally collected the data; and the data are in various, 

and often incompatible, formats. Acquiring data, converting data sets into compatible formats, 

managing data, generating maps and graphs, and calibrating and interpreting models are timeconsuming activities that often require individuals with specialized skills. For researchers and 

agency project managers working on tight budgets, the cost of these activities alone may make 

many mercury-related projects infeasible. 
3
 

Increasing Research and Management Efficiency 

The EMMMA website was conceived as a tool for making mercury research and 

management more efficient by providing access to mercury and mercury-related data sets as well as 

the predictions of the NDMMF. For example, using EMMMA, researchers can use interactive 

mapping and graphing tools to formulate hypotheses, evaluate the availability of data to test 

hypotheses, and generate maps and graphs for discussing and refining hypotheses with colleagues. 

Although EMMMA does not help with all phases of the research and management process, the site 

does produce maps and graphs of sufficient quality for internal agency issue papers and proposals. 

For producing external proposal submissions, sophisticated research applications, and preparation 

of final reports, data can be downloaded from EMMMA and imported into the user’s software. 

Mercury Databases Available through EMMMA 

The EMMMA site provides access to a broad range of data related to the distribution of 

mercury in the environment. These data can also be displayed using EMMMA’s interactive 

mapping functions. (Approximately 79% of the fish-tissue mercury records have associated 

geographic coordinates and therefore can be displayed on maps. All records in the other data sets 

have complete sets of geographic coordinates.) Included in the EMMMA site are the following data 

sets: 

•	 Mercury in Fish Tissue – More than 45,000 fish-tissue mercury records were compiled from 

National, State and local agencies. To be suitable for use in the NDMMF, all records were 

required to have (1) a valid mercury concentration and species identifier; (2) a tissue type of 

whole fish, skin-on fillet, skin-off fillet, carcass, edible portion, eggs, liver, or viscera (samples 

identified only as ‘fillet’ were rejected); and (3) a length measurement that is appropriate for the 

sampled species. 

•	 Mercury Emissions – Data for point-source emissions of mercury (~24,000 sites) were 

extracted from the U.S. Environmental Protection Agency's (USEPA) National Emission 

Inventory (NEI) database (1999 NEI Point-Source Facility Summary). All NEI data and 

documentation are available at: http://www.epa.gov/ttn/chief/net/1999inventory.html 

•	 Mercury Deposition Network – The Mercury Deposition Network (MDN) is a network of 

monitoring stations included in the National Atmospheric Deposition Program 

(http://nadp.sws.uiuc.edu/mdn/). The EMMMA site contains a map file with locations of MDN 

monitoring sites and links to MDN data. 

•	 Mercury in Stream Sediments and Soils – Data for stream sediments (~33,000 samples) and 

soils (~11,000 samples) were extracted from the USGS National Geochemical Survey 

Database. The complete database is accessible at: http://tin.er.usgs.gov/geochem/. 

•	 Mercury and other Mines – Data for 1100 mercury mine locations were extracted from the 

USGS Mineral Availability System/Mineral Industry Locator System (MAS/MILS) database. 

The file available on the EMMMA site contains data from selected fields of five tables in the 

MAS/MILS database. A separate file containing all MAS/MILS mine locations (188,000 

records) is also included because mercury occurs as an accessory component in many ore 
4
 

deposits and has been widely used to refine gold ore. For more information see: 

http://tin.er.usgs.gov/mrds/ 

•	 Coal – Mercury data for ~7,200 samples of U.S. coals were extracted from USGS's National 

Coal Resources Data System Coal Quality Database (COALQUAL). For more information see: 

http://energy.er.usgs.gov/products/databases/CoalQual/intro.htm 

Model Predictions Available through EMMMA 

EMMMA has the capability to present the predictions of models in a variety of formats, and 

even allows comparison between a model’s predictions and the data to which the model was 

calibrated. Currently, EMMMA provides a web-enabled version of the NDMMF, described below, 

together with a variety of tools for on-the-fly generation of maps and graphs. This model is 

described in the next section and is used to illustrate many of the capabilities of EMMMA’s 

visualization tools in subsequent sections. However, EMMMA could be used to present the 

predictions of many other relevant models addressing the distribution and exposure effects of 

environmental toxic substances. In this sense, EMMMA should be thought of as infrastructure for 

providing access to, and visualization of, any model’s predictions and not as a set of tools that are 

tied to any particular model. 

The National Descriptive Model for Mercury in Fish 

The NDMMF is distinguished from other modeling approaches for estimating fish-tissue 

mercury concentrations in two major ways. First, the NDMMF is an empirically based, statistical 

model that is currently calibrated to, and validated by, a large data set of 45,605 measurements of 

fish-tissue mercury concentrations collected throughout the United States over the past 35 years 

(Wente 2004). Therefore, the NDMMF is very comprehensive in terms of its geographic breadth 

and temporal coverage as well as the diversity of sample characteristics (number of species, tissue 

types, and range of fish lengths) described by the model. Second, the NDMMF partitions or 

separates the variation in fish-tissue mercury concentrations in the calibration data set into 

concentration variation due to sample characteristics (species, tissue type, and length of fish) and 

spatiotemporal variation (concentration changes over location and time). This partitioning allows 

fish-tissue mercury concentrations to be validly estimated for combinations of fish characteristics 

that were not sampled at a particular site and time (sampling event) from the combinations of fish 

characteristics that were sampled. (Readers may view a detailed description of, and measures of 

accuracy for, the model at http://water.usgs.gov/pubs/sir/2004/5199/.) 

To understand why the NDMMF is useful for interpreting fish-tissue mercury concentration 

data, it is important to understand what information is sought from this data and the physical and 

financial constraints on sampling programs. The information sought is the answers to three basic 

questions: 

1.	 How do fish-tissue mercury concentrations vary spatially? Often this question is phrased as 

“which sites are most contaminated;” or “where should I or shouldn’t I fish?” 

2.	 How do fish-tissue mercury concentrations vary temporally? This information is useful for 

environmental regulatory purposes and for determining when fish consumption advisories 

should be strengthened or relaxed. 

3.	 Which fish (at a specific site and time) are safe to eat? This information is used for fish 

consumption advisories that provide consumers of wild-caught fish with advice for 

minimizing their exposure to toxic chemicals through the consumption of fish. 
5
 

Answering questions 1 and 2, which require comparison of fish-tissue mercury concentrations from 

fish of similar characteristics (same species, length, etc.) across sampling events, is impeded by the 

difficulty of obtaining fish with similar characteristics during those sampling events. Answering 

question 3, which requires estimation of fish-tissue mercury concentrations across broad range of 

fish characteristics at each sampling event, is impeded by constraints on the number of fish-tissue 

mercury measurements (typically, 3 to 8) that can be collected at each sampling event. 

Because the NDMMF can validly estimate fish-tissue mercury concentrations for a wide 

range of fish sample characteristics at each sampling event, this model can be used to answer all 

three questions at all sampling events. To answer questions 1 and 2, the model is used to predict a 

standardized (same species, tissue type and length) fish mercury concentration for each sampling 

event from the samples collected from each sampling event. Comparing standardized fish-tissue 

mercury concentrations for sampling events from a narrow range of time, but at different locations, 

depicts spatial variation in fish-tissue mercury concentrations. Similarly, comparing standardized 

fish-tissue mercury concentrations for sampling events from the same site, but on different dates, 

depicts the temporal trend at that site. To answer question 3, the model is used to predict fish tissue 

mercury concentrations across all of the fish characteristics (all species and full range of lengths) 

occurring at the site. Comparing these predicted fish-tissue mercury concentrations for a particular 

sampling event to established consumption advisory limits allows comprehensive advice on fish 

consumption to be given for a site, even for species, tissue types, and lengths of fish that were not 

sampled during the sampling event. Performing these tasks using the EMMMA website is 

illustrated in the following sections. 

Enhanced Analysis of Geographic and Temporal Trends 

Natural variations in fish-tissue mercury concentrations due to differences in species, tissue 

type, and length of fish may obscure significant spatial or temporal trends. By factoring out 

variation due to these variables (standardizing data), the NDMMF transforms the data into a form 

more suitable for spatial (geographic) and temporal analysis and for detection of trends that 

otherwise would not have been apparent. 

Figure 1 shows the utility of the NDMMF and EMMMA for detection of geographic trends 

by comparing observed and model standardized fish-tissue mercury data from southeastern United 

States using maps generated with EMMMA’s data mapper. Figure 1a shows the observed fishtissue mercury concentrations as reported. A cursory examination of these data shows what appears 

to be a large difference in fish-tissue mercury concentrations between the states of North and South 

Carolina. This difference likely reflects differences in the characteristics (species, tissue type, or 

fish length) of the fish selected for analysis in these two states rather than an environmental 

gradient in fish-tissue mercury concentrations. This hypothesis appears to be borne out in figure 1b, 

which shows the same data standardized to skin-off-fillet samples of 14-inch largemouth bass 

(Micropterus salmoides) across all of the sites shown in figure 1a using the NDMMF. 
6
 

 

> 1.5 

1.2 – 1.5 

0.9 – 1.2 

0.6 – 0.9 

0.3 – 0.6 

< 0.3 

PPM Hg 

b 

a 

Figure 1. Comparison of geographic trends in observed fish-tissue mercury 

concentrations (a) that appear to emphasize concentration differences between 

samples collected in North Carolina (north of red line in 1a) and South Carolina 

(south of red line in 1a) and model-standardized fish-tissue mercury concentrations 

(b) that appear to show concentration differences between physiographic zones of 

southeastern United States. 

The geographic trend of standardized fish-tissue mercury concentrations suggest a pattern 

related to physiographic zones. High concentrations appear to occur in the coastal plain (south and 

east of solid red line in 1b) and possibly along the line of contact between the Blue Ridge (north 

and west of the dashed red line in 1b) and Piedmont (between the dashed and solid red lines in 1b) 

physiographic zones. Relatively lower concentrations appear to occur within the Blue Ridge and 

Piedmont zones. Being able to identify geographic trends in fish-tissue mercury concentrations as 

depicted in Figure 2b has important implications for future research (e.g., do other portions of the 

coastal plain show similar concentrations, do populations that consume large quantities of fish from 

the coastal plain have elevated blood or hair mercury levels, etc.) and management issues (e.g., 

tailoring fish consumption advice to physiographic zones, regulating coastal plain mercury 

emitters) in this region. 
7
 

Similarly, Figure 2 shows the utility of the NDMMF and EMMMA for detection of 

temporal trends by comparing observed and model standardized fish-tissue mercury data from a 

site during a 26-year period from 1969 to 1995 using temporal trend graphs generated by 

EMMMA’s graphing tool. Figure 2a shows observed fish-tissue mercury concentrations. While 

five different species of fish were sampled, only two or three species were collected during any of 

the nine sampling events. Temporal trends in the observed concentrations are obscured by 

variations in the sample characteristics of fish species and length. Figure 2b shows the NDMMF 

predicted concentrations standardized to a 10-inch largemouth bass, analyzed as whole fish, for 

these same sampling events. Because the model-standardized predictions of the NDMMF do not 

reflect the concentration variation due to differences in sample characteristics, a temporal trend of 

decreasing concentration becomes readily observable. 

b 

a 

Figure 2. Comparison of temporal trends identified from observed (a) and NDMMF standardized (b) fish-tissue 

mercury concentrations. 

Analysis of fish-tissue mercury concentrations without due consideration of variation in 

sample characteristics can easily lead to erroneous conclusions. Consider, for example, what could 

happen if generally smaller fish are sampled at a particular lake one year and then generally larger 

fish are sampled at the same lake a few years later. Decision-makers might conclude that mercury 

available for bioaccumulation had increased even if it had in actuality not changed or decreased (a 

false positive detection of an increasing trend). Based on this erroneous conclusion, these decisionmakers might issue more restrictive consumption advice. More likely, however, is a scenario in 

which residual error about the trend in the observed data would lead decision makers to conclude 
8
 

that no trend can be detected at sites where an increasing or decreasing trend may actually exist (a 

false negative result). By decreasing the amount of residual variation left unexplained by a trend 

line (i.e., factoring out the variation due to differences in fish size or other sample characteristics), 

use of the NDMMF should reduce the occurrence of both false positive and negative conclusions. 

This same argument applies to the potential for misinterpreting geographic trends with the 

exception that comparison of fish from different sites typically will involve greater variation in 

sample characteristics than comparison of fish from the same site over time. Use of the NDMMF, 

therefore, should be of even greater benefit to detection of geographic trends than it is for detection 

of temporal trends. 

As a final point about trend detection, trends that are not evaluated can not be detected. 

Without a tool like EMMMA, the first step in detecting a geographic or temporal trend, creating a 

map or temporal trend graph, can be a time consuming process. By greatly reducing the effort 

involved in completing this first step and providing access to the standardized fish-tissue mercury 

concentration predictions of the NDMMF, EMMMA greatly increases the likelihood that trends 

will be identified and accurately evaluated. 

Development of Fish Consumption Advisories 

A key benefit of the NDMMF is its use as a decision-support tool for state and local 

fisheries managers charged with developing fish consumption advisories. This utility is 

demonstrated in Figure 3, which depicts both the observed and predicted fish-tissue mercury 

concentrations plotted over arbitrarily defined consumption advisory limits (these limits were 

arbitrarily chosen for demonstration purposes only and should not be construed as any kind of 

recommendation or endorsement of any particular consumption limits). For this sampling event, 

only seven fish-tissue mercury observations are available. Without using the predictions of the 

NDMMF, very little consumption advice could be given for this site. For smallmouth bass 

(Micropterus dolomieu), the responsible agency might recommend limited consumption for one 

size class (7 to 12 inches) and unlimited consumption for another size class (less than 7 inches). 

Additionally, general advice might be given for all sites encouraging fish consumers to avoid larger 

fish and possibly provide a list of species that tend to have lower fish-tissue mercury 

concentrations. 
9
 

Figure 3. Hypothetical fish consumption advisory developed using the predictions of the NDMMF and the 

EMMMA website. 

All of this same advice is conveyed in the NDMMF predictions, as well as useful 

information on the length of smallmouth bass that would be expected to fall into other fish 

consumption advice categories. Further, if the responsible agency wants to use the NDMMF 

predictions to estimate fish-tissue mercury concentrations for additional species that occur at the 

site, but were not sampled (Figure 3), predictions for bluegill (Lepomis macrochirus), walleye 

(Sander vitreus), northern pike (Esox lucius), and common carp (Cyprinus carpio) can be included 

as well. By including these additional species, consumers are not only warned of additional species 

and lengths of fish with high concentrations, but also can be shown which species are more likely 

to have low concentrations. Although there are legitimate concerns about issuing advice for 

species, tissue types and fish lengths that have not been sampled at a site, it is also valid to question 

whether enough advice would be given, in the absence of NDMMF predictions, for consumer to 

make intelligent decisions about the consumption of other species, tissue types, and fish sizes that 

were not sampled. 

One strategy that agencies might use to balance these concerns is to devote most of the 

samples to be taken at a site to those species, tissue types, and lengths of fish that are expected to 

have the highest mercury concentrations. Relatively fewer samples would be devoted to species, 

tissue types, and fish lengths expected to have lower mercury concentrations. Using this strategy, 

the consumption advice that most needs to be supported with samples would be, while the 

consumption advice that is likely to be less controversial would be supported mainly by the 

predictions of the NDMMF. In this way, agencies can issue more complete consumption advice 
10
 

without expanding their sampling programs and minimize the generation of controversial 

consumption advice. 

Because each agency that issues fish consumption advice may reasonably come to different 

conclusions about issuing advice based on the NDMMF predictions, EMMMA is designed to offer 

enough flexibility so an agency can use EMMMA no matter how much or how little the NDMMF 

predictions are used to generate that agency’s advice. Agency personnel that have suggestions for 

modifications to the fish consumption advisory graphing tool to aid agencies in developing fish 

consumption advisories are encouraged to contact the corresponding author. 

Cost Savings to Agencies that Monitor Fish-tissue Mercury 

Using the NDMMF, Federal, State, Tribal, and local agencies can also reduce the cost of 

their fish-tissue sampling programs. Without the NDMMF, it may be necessary to catch and 

process specimens across a range of sizes for each important sport or commercial species at each 

location to be able to estimate current fish-tissue mercury levels. With the NDMMF, however, a 

smaller set of samples may be adequate to estimate mercury levels at each site, even for species, 

tissue types, and lengths of fish not sampled. 

Using the NDMMF does not, of course, eliminate the need for ongoing sampling programs. 

The NDMMF does not predict fish-tissue mercury for any times and locations not sampled for fish. 

However, use of the NDMMF does allow a much wider range of species, tissue types, and fish 

lengths to be validly estimated from the samples that were collected. In this way, the agencies that 

sample fish for mercury can lower their analytical costs (by analyzing fewer samples) or sample a 

larger number of sites for the same cost (by spreading the same number of samples across more 

sites). 

Those users who wish to have their fish-tissue data modeled using the NDMMF and 

displayed on EMMMA will need to submit those data in a specific format and contact the 

corresponding author for instructions and a data template. Many of the state fish-tissue mercury 

data sets that appear in EMMMA were obtained from USEPA’s National Listing of Fish and 

Wildlife Advisories database. Many data records in this data set were missing key data fields 

deemed necessary for inclusion in EMMMA. Federal, State, Tribal and local agency personnel and 

other researchers who have fish-tissue mercury data that does not appear in the EMMMA data 

mapper or appears to be erroneous in the EMMMA data files or mapper are also encouraged to 

contact the corresponding author. 

Using EMMMA 

EMMMA is a relatively user friendly website which should not present difficulties to most 

users. Two features, the data mapper and graphing tools, are discussed in the following sections to 

better show the breadth of EMMMA’s capabilities. In the future, it is anticipated that both the data 

mapper and graphing tools will obtain additional capabilities beyond those described here. 

The EMMMA Data Mapper 

The data mapper is an online web mapping service that integrates mercury data with USGS 

maps, satellite images, aerial photographs, land-cover data, and other thematic data. Figure 4 uses 

data from the NEI to illustrate how the EMMMA data mapper displays data against a backdrop of 

USGS maps and images, and how the background images change with map scale. Figure 4a shows 

the distribution NEI data across the continental United States over a background of global land 

cover data and state boundaries. Figure 4b shows the location of an NEI emissions source, a coal­
11
 

 

fired power plant in eastern Tennessee, displayed against a USGS 1:100,000-scale topographic 

map. Zooming in further changes the map background to a larger-scale USGS 1:24,000-scale 

topographic map (not depicted). Zooming in even further changes the backdrop to a 1-meterresolution aerial photograph (Figure 4c). Notice that the data point from the NEI database now 

appears in front of the power station. Although the data submitted to the NEI represent mercury 

emitted from the station’s smoke stacks, the map shows the agreement between coordinates in the 

NEI database and the background images is quite good. However, it is important to realize that all 

spatial data contain some degree of error in location and users need to make sure that the spatial 

accuracy of the data set is appropriate for the scale of map being generated (especially, when 

working at large scales such as Figure 4c). 

a 

b 

c 

Figure 4. Many interactive mapping features are available through EMMMA’s data mapper (a) including point 

data display over topographic maps (b) and 1-meter-resolution aerial photography (c) 

Combining environmental mercury data with maps and other thematic data in a geographic 

context facilitates the recognition of geographic trends and the formulation of preliminary 

hypotheses (i.e., Figure 1b). Multiple backgrounds (land use, hydrography, etc.) are available at any 

scale so that point data can be compared with many types of background data. Further, multiple 

point data sets can be combined such as NEI data and standardized fish-tissue mercury 

concentrations over various backgrounds. Additionally at any scale, individual records or groups of 
12
 

records may be located, selected, and downloaded as a zipped .dbf file from the EMMMA data 

mapper. 

The EMMMA Graphing Tool 

The graphing tool currently works with fish-tissue mercury data only and produces two 

basic types of graphs, temporal trend (Figure 2) and sampling event (Figure 3) graphs. Through the 

graphing tool, temporal trend and sampling event graphs can be created that display observed or 

NDMMF predicted concentrations for one or more species, tissue types, and lengths of fish. 

Display of observed data is restricted to those species sampled from a specific site for temporal 

trend graphs or sampling event for sampling event graphs. Display of NDMMF predictions is 

restricted to those species and tissue types in the NDMMF calibration data set (currently, 428 

combinations of species and tissue type) for either type of graph. When creating graphs of 

NDMMF predictions, care should be taken to select species that are appropriate for the site or 

sampling event being graphed. Consumption advisory limits can be displayed as background as in 

Figure 4 for either type of graph. Only sampling event graphs allow observed and NDMMF 

predicted concentrations to be displayed on the same graph as in figure 3. 

Next Steps for EMMMA 

The approach taken in developing the EMMMA website for mercury is equally applicable 

to other environmental contaminants. It is true that specific contaminants are often unique with 

regard to their behavior in the environment, and require specific models and supporting data to 

characterize their distribution, transport, and human exposure vectors. Nonetheless, the general 

approach of Web-based integration of contaminant data with relevant thematic data, detailed maps, 

and online analytical tools is a flexible approach, and certainly one which can be customized to 

support studies of a wide range of contaminants. 
13
 

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